The field of alcohol intoxication sensing is over 100 years old, spanning the fields of medicine, chemistry, and computer science, aiming to produce the most effective and accurate methods of quantifying intoxication levels. This review presents the development and the current state of alcohol intoxication quantifying devices and techniques, separated into six major categories: estimates, breath alcohol devices, bodily fluid testing, transdermal sensors, mathematical algorithms, and optical techniques. Each of these categories was researched by analyzing their respective performances and drawbacks. We found that the major developments in monitoring ethanol intoxication levels aim at noninvasive transdermal/optical methods for personal monitoring. Many of the “categories” of ethanol intoxication systems overlap with each other with to a varying extent, hence the division of categories is based only on the principal operation of the techniques described in this review. In summary, the gold-standard method for measuring blood ethanol levels is through gas chromatography. Early estimation methods based on mathematical equations are largely popular in forensic fields. Breath alcohol devices are the most common type of alcohol sensors on the market and are generally implemented in law enforcement. Transdermal sensors vary largely in their sensing methodologies, but they mostly follow the principle of electrical sensing or enzymatic reaction rate. Optical devices and methodologies perform well, with some cases outperforming breath alcohol devices in terms of the precision of measurement. Other estimation algorithms consider multimodal approaches and should not be considered alcohol sensing devices, but rather as prospective measurement of the intoxication influence. This review found 38 unique technologies and techniques for measuring alcohol intoxication, which is testament to the acute interest in the innovation of noninvasive technologies for assessing intoxication. Ethanol consumption is a major component of social life in the Western world. Ranging from casual drinking or as a part of celebration to more extreme binge drinking or alcohol dependence/alcoholism, often referred to as alcohol use disorder (AUD) , alcoholAlcohol intoxication is a standardized metric denoted by blood alcohol concentration (BAC) only and not the effect it has on an individual, thus not accounting for tolerance resulting from regular exposure to ethanol. Although similar symptoms of intoxication can be seen amongst individuals, the influence of alcohol tolerance remains a poorly explored phenomenon in the context of the wider population. The BAC level corresponds to the weight of ethanol in milligrams per 100 mL of blood. The level of intoxication is positively correlated with the amount of ethanol in the bloodstream, with the high end of ethanol intoxication at 0.5% (500 mg/dL) and with levels as low as 0.35% (350 mg/dL) being linked to death or serious harm to the individual or those around them . RegularShort-term influences of alcohol intoxication, however, do not carry such damaging consequences, yet they are not without harm. Acute intoxication can have damaging effects on people diagnosed with cancer or currently taking antibiotics. The reaction of ethanol in the liver can trigger inflammation and damage the liver of the user. Other cases where acute consumption poses a risk of damage is particularly seen amongst people who are suicidal, increasing the risk of taking their life. . EthanolWith so many problems associated with alcohol consumption, methods of estimating alcohol intoxication were reported in medical literature as early as 1920 by Widmark . With fuCurrently, sensing alcohol intoxication techniques include devices of various natures that take advantage of machine learning, optical spectroscopy, and biochemical sensing methods. This review aims to comprehensibly explore all available literature on quantitative and qualitative assessment of alcohol intoxication, as well as observing the evolution of approaches and methodologies for collecting these measurements and exploring the current gaps in the field of in vivo sensing of alcohol intoxication.Intoxication can be defined as loss of control over actions or behavior changes under the influence of a drug. Intoxication due to ethanol can be divided into three main parts: initial take-up, the peak, and the decay stage. This can be illustrated by studies performed on human volunteers to investigate the changes of alcohol in their blood over time . The iniConsidering the uptake of ethanol, this period is characteristic of euphoric behavior, including laughter, social inhibition, and generally increased well-being due to the release of hormones, such as serotonin. At the peak of intoxication, these effects begin to slowly fade away, due to decreasing levels of ethanol in the body. The roll-off stage is associated with increased tiredness and depression . The priAlcohol affects a number of systems in the body, resulting in an intoxicated state. As mentioned previously, these effects manifest themselves in bodily organs, such as the heart, lungs, liver, and brain. However, these effects are short-lived and fade away after time. On the other hand, long-term consumption of excessive amounts of alcohol can contribute to a multitude of diseases, both physical and mental. Amongst them are the mental illness associated with dependence or addiction to alcohol. The root causes of these diseases are mostly unexplored in terms of explaining the susceptibility to developing an alcohol addiction . Some reThe literature review of ethanol intoxication sensors yielded several results encompassing different aspects of alcohol intoxication, i.e., behavioral, physiological, and chemical changes in the individual’s body. All the methods were categorized into six main sections: pharmacokinetic estimates, breath-sample testing, bodily fluids, physiological changes, transdermal, and optical spectroscopy. The findings of the review and all the devices and techniques considered are summarized in As seen from The earliest methods of quantifying the level of alcohol in blood were introduced by Erik Widmark in 1918  by a metβ), distribution of water in the body (r), and the consumption start time. This correlation is outlined in Equation (1). It is key to note that the values of constants β and r are subject to variation across individuals, most noticeably between sexes [The equation introduced by Widmark and Tandberg in 1924  aimed toen sexes .This method of calculating the estimated level of BAC has been at the forefront of forensic examinations and contributed significantly to the field of ethanol toxicology . WidmarkEBAC equation provided a quick and easy to use method for determining ethanol levels in the body; however it has several shortcomings, mainly those associated with the values of β and r. Since these values vary considerably among individuals, the values used for generic applications of EBAC calculations do not often account for this natural variance. For this reason, Widmark developed variants of this equation to account for natural variations between men and women, thereby measuring the uncertainty of the result. As a result, the uncertainty of the EBAC is around ± 20% [β and r, as well as other parameters in the EBAC equation.For its time, the nd ± 20% . This hiβ is a subject of large variation and thus not truly representative of the general population. As a result, further work in this area aimed at relating levels of ethanol in the blood to those in air exhaled after the consumption of alcohol.Although significant in defining the metabolism of ethanol, the value of The Widmark equation is a very useful tool for estimation of blood alcohol levels in the absence of testing equipment.The estimations consider the sex and the elimination rate of ethanol, accounting for some of the most important factors in ethanol metabolism.The Widmark flask is the first proven method of measuring ethanol levels in the blood.The Widmark flask requires blood samples, making it an invasive procedure.The Widmark equation is not completely representative of true intoxication levels.The Widmark equation requires use of tables and knowledge of time consumption.The earliest mentions relating BAC to breath alcohol concentration (BrAC) date back to 1874, by Baldwin , with obThe scope of development of breath alcohol devices is broad and encompasses many areas of chemical analysis to determine the level of ethanol, subsequently giving rise to various devices measuring BrAC. Regardless of the variations in the devices used to measure BrAC, one feature remains unchanged: the method of calibration. Most BrAC devices consider a calibrating ratio of 2100:1 for the alveolar air and ethanol volume, such that 2100 mL of alveolar air is equivalent to 1 mL of ethanol in a breath sample . This apBrAC devices may be divided into four main categories, based on the ethanol detection principle they employ. Photovoltaic assays measure the color change of a liquid experiencing a redox reaction, usually from red/orange to green, when ethanol is detected. The degree of color change is measured and translated into an output of BrAC level and converted into BAC . Infrare2 exhaled by means of infrared spectroscopy (IRS). This feature aims to minimize false positives originating from spillage of ethanol-containing beverages or cleaning products. This technique is designed to be implemented in a vehicle, where each of the passengers can be monitored passively throughout the duration of a car ride. This implementation, however, shares many of the same shortcomings and sources of error as any other traditional BrAC devices [An innovative sensor developed by Ljungblad et al.  proposes devices . The senIn general, BrAC devices are convenient and effective for estimating BAC by considering an associated measure of BrAC. It is key to note that BrAC devices are prone to erroneous readings and can only be used to estimate the level of ethanol in a subject’s blood. Besides the error associated with the calibration of these devices, the environmental conditions under which the devices operate must also be considered, specifically the temperature, as demonstrated by Legge . Legge aProven to be effective in law enforcement, prevention of DUINonintrusive procedureResults generated within seconds of the readingPassive measurements of an enclosed space possiblePossibility to incorporate BrAC into vehicles, for example, the Interlock systemPortable, small form factor, light, battery powered.A large selection of devices available in retail outlets, many of varying qualityWell-performing breathalyzers are very expensive and require laboratory calibration.BrAC devices apply an outdated conversion ratio of 2100:1, which has been proven to vary between sex and lung size.In cases of more accurate BrAC devices, such as the Intoxilyzer. the form factor is increased and requires a permanent allocation.Testing requires replaceable parts.Amongst alcohol testing techniques, the bodily fluids of blood, urine, and saliva yield large variations, both in technique implementations and the results obtained by each method. Hence, each finds its application in a different field of testing.Bodily fluid testing concerns the measurement of ethanol concentration in the blood, urine, and saliva by measuring it directly from the blood itself as the gold standard of such measurements. In order to determine the concentration of ethanol in the sample, the most common technique used is gas chromatography (GC) or headspace GC, which measure the presence of exact levels of ethanol in a sample through separation of components in a gas state . These tIt is key to note that blood samples must be handled with care and in many cases adhere to rigorous standards of storage and handling . These iBlood ethanol testing also comes with its own array of strengths and shortcomings, which determine its viability in ethanol testing. As the most direct method of measuring ethanol levels, the results obtained are considered highly accurate and representative; however, one must consider the site of blood-sample collection, since arterial blood has higher concentrations of ethanol, but venous blood samples are easier to acquire . Blood tBesides blood testing, BAC levels can also be estimated from urine samples. However, this method comes with an entire range of shortcomings and is thus not useful for accurate determination of ethanol levels in the subject’s bloodstream. The greatest error associated with urine ethanol testing comes from the behavior of ethanol in the body, more specifically the changes in ethanol level during the absorption and elimination stages . During Urine ethanol testing techniques include those used for blood-sample testing, such as gas chromatography. However, alternatives exist, such as the use of EtG tests , determiBesides the use of gas chromatography, urine samples may also be tested using headspace gas chromatography , due to Another alternative bodily fluid that can be used to estimate BAC is saliva. Since ethanol is highly soluble in water, its level can be estimated from saliva samples; however, this technique also comes with many shortcomings. Similar to blood and urine sampling, the techniques used in measurement of saliva alcohol concentration (SAC) include gas chromatography and enzymatic oxidation of ethanol through alcohol dehydrogenase. SAC tests find most of their applications in testing trauma patients in emergency rooms . In ordeBodily fluid testing has not been limited to chemical color changes or analytical measurements of mixture composition. Recently developed by Mishra et al.  a salivaAmongst some of the less popular methods of alcohol testing in bodily fluids includes testing of the breast milk. The aim of testing alcohol content in breast milk is to inform the mother of an infant if their breast milk is fit for consumption. It has been shown that infants consume less of their mother’s milk following an intake of alcohol by their mother . ReductiBodily fluid testing is an area of ethanol testing that produces mostly mixed results, depending on the fluid used. By far, the most accurate method of ethanol intoxication is blood testing, which is considered the gold standard of ethanol testing. On the other hand, urine provides a good enough indication of the consumption habits of alcohol, and saliva samples give good estimates of BAC with a correlation between 0.879 and 0.957 when tested against serum alcohol level , yet stiBlood headspace GC is the most accurate method of measuring ethanol levels in the blood—Gold standardSmall sample volume required for testingStorage of samples allows for delayed analysis without compromising the sample qualitySample kits for urine, saliva, and breast milk are readily available in retail outlets at an affordable priceUrine and breast-milk tests yield representative results in a short time (1.5–2 h)The patient needs to be conscious to collect the samples.Blood testing is an invasive procedure, risking infectionVery expensive equipment, specifically the GCThe accuracy of saliva and breast-milk samples is questionable in terms of real-time ethanol levelsAlcohol detected in urine samples represents the average exposure to ethanol during the time of urine collection in the bladder.High consumption of sugar can result in false-positive readings in urine samples, due to fermentation of the sugar in the bladder.Besides the level of alcohol in breath, blood, urine, and saliva, ethanol intoxication also manifests as a change in physiological parameters and behavior. More recent developments in machine learning and image analysis have allowed for techniques to estimate the level of intoxication based on changes associated with common symptoms of alcohol intoxication. Such changes found for this review concern changes to photoplethysmography (PPG) signal, body temperature, eye position, etc. These methods provide a new outlook on measuring alcohol intoxication by not only considering the level of ethanol in the bloodstream but the degree to which a person is affected, giving rise to the true definition of intoxication as changes in behavior, regardless of the amount of alcohol ingested.One emerging field of quantifying alcohol intoxication through physiological means is the analysis of the PPG signal, more specifically the diastolic and systolic patterns of a PPG signal, as shown by Chen . Their pDevelopment in the field of PPG analysis for intoxication levels has even been examined in comparison to ECG by Wang et al. . Their sPPG signal analysis for detection of intoxication has already been implemented into a “smart-steering” system developed by Rachakonda et al. . The sysClearly, PPG signal analysis offers a convenient way of establishing if an individual is intoxicated. These algorithms can be implemented into already well-established platforms of PPG data acquisition, especially wearable smart devices. Without a large enough calibration set, or special conditions implemented into the algorithms, individuals with irregular heart action could fall victim to false positives if this system was to be more broadly implemented. Association of changes in the operation of the cardiac muscle can be of great guidance for the degree of intoxication influence ethanol has on an individual. These can vary based on the regularity of consumption and tolerance.The effects of alcohol intoxication are not strictly limited to the changes in the behavior of the heart but also of other parts of the body. Recent research conducted by Kubieck et al.  suggest Their work concluded that the multiregional segmentation is capable of distinguishing changes in the temperature of the nose and the forehead, associated with alcohol consumption. Their results showed a noticeable change of thermal region in each of the facial regions investigated, suggesting a link between facial temperature distribution and gradual alcohol intake, particularly useful for continuous monitoring and prevention of DUI. It is important to note that it is only an early stage of method development, only pointing at areas of consideration for IR image intoxication measure, and further work on the classification of intoxication level is required.Similar efforts in passive driver monitoring are also being implemented by large automobile manufacturers such as Volvo , proposiSuch implementations of image analysis both have a potential to reduce the number of incidents caused by drunk drivers, however it is susceptible to possible exploits such as covering of the cameras or false negatives from individuals with higher-than-average tolerance to alcohol intoxication .Additionally, the influence of ethanol consumption impacts the impedance of the skin, as demonstrated by Chaplik et al.  in theirBioimpedance measurement of the skin provides a unique application of estimating ethanol intoxication; as bioimpedance can be measured with capacitive and induction sensors ,76, suchAlthough not an estimation algorithm, another method of measuring intoxication and/or expose to ethanol is to investigate the chemistry of the hair and nails . As the It is key to note that amongst all the categories of ethanol intoxication sensing devices, those employing changes in the physiological states of the individual, such as PPG data analysis of skin impedance, their results can only be correlated due to the extensive use of machine learning methodologies, allowing for a variety of characterizations and categorizations from the provided data sets. If given the known parameter changes associated with intoxication, systems trained using these models expand their own library of data and hence become progressively more accurate in their measurements. Given the broad impact ethanol has on the human body, systems encompassing multiple factors as an indication of intoxication can only be established with machine-learning methods. Out of all these methodologies, it may be very soon at that advancements in big data and data aggregation will be capable of determining if a mobile phone user might be intoxicated. However, these applications of machine learning are a subject to personal data privacy laws and hence not likely to be implemented any time soon.In conclusion, modern estimation methods spam a wide variety of fields, each of them with their own respective merits and shortcomings. It is key to note for these methods that they aim to correlate changes in behavior, physiological factors to alcohol intoxication, both short and long term, yet they are largely impractical for most applications of alcohol testing, whether it would be for roadside testing or personal monitoring. However, these methods provide an important insight into the nature and behavior of alcohol in the body, potentially providing explanations for topics of tolerance and addiction still not fully understood.Behavior analysis allows for classification of intoxicated actions or tick, independently of a sensing elementNoninvasive method of measurementPossibility of incorporating into wearable devices equipped with PPG sensorsPossibility of incorporating these systems into vehicles.Many of the methods only serve as estimates tracking another quantity changing with intoxication, the impact of intoxication on individual is not consideredA room for error from false positives originating from general behaviorSkin impedance shows low correlation results, with only strong correlation being impossible to incorporate into everyday life.Besides considering testing the behavioral and physiological changes that occur as a result of alcohol intoxication, the metabolic rate of ethanol can also be used as an indicator to the level of intoxication experienced by an individual. A series of sensory devices have been developed over the last two decades to allow monitoring of the rate of expulsion of ethanol through the skin, by measuring the contents of the sweat through wearable or standalone devices. Wearable devices have received a lot of attention over recent decades with several review papers published covering different devices available on the market as well as those in laboratory testing. The findings of these papers were included in this section as well as the addition of other techniques for transdermal alcohol intoxication measurement.p > 0.001) for a variety of doses, showing that early implementation of transdermal alcohol sensors have been successful in determining the quantity of alcohol in the blood.The review by Swift et al.  in 1992 The review of wearable devices for alcohol use disorder by Davis-Martin et al.  in 2021 Within the selection of the above devices, one device that is strictly distinctive is the iontophoretic biosensing system . It is uIt is key to note that the review considered many works including those concerning the level of alcohol in tissue, but also those focusing on the physical and mental influences of alcohol use, to control (AUD). The review however is limited only to electrochemical wearable devices for measuring intoxication levels. This review considered two alternative techniques for measuring tissue alcohol concentration (TAC), using Enzymatic Biosensors and Biosniffers.Developed by Lansdorp et al.  an EnzymDevelopment in transdermal sensors is not exclusively limited to wearable devices, but also to standalone devices. A Biosniffer device developed by Arakawa et al.  based onDeveloped by Selvam et al. , a wearaIn consideration of the use of electrochemical sensors to measure ethanol metabolites, much attention must be given to the natural occurrence of the metabolite and any possible interference from associated by-products. Regardless of these interferences, the sensor presented in this paper achieved a linear relationship between the amount of EtG measured and the impedance of the electrodes, with r2 = 0.97.Conclusively, the field of active wearable sensors is expanding. However, a distinction must be made regarding the nature of measurement. The developed sensors do not have the ability to quantify the level of intoxication, but only as an indicator of the presence of one of the metabolites of ethanol, not the BAC of a subject.Alternative methods to measuring alcohol levels in the tissue are not limited to analyzing the sweat components, but can also be more active in their operation. An example of this was presented by Venugopal et al.  with a dMore recent developments in the field of analyte sensing in ISF, implement electrochemical sensing methods, so much so, they are capable of monitoring multiple analytes simultaneously. Tehrani et al. , presentThe reviews concerning the current state of transdermal technology for measurement of alcohol intoxication include those by Fairbairn et al. , categorIn conclusion, transdermal sensors have been at the forefront of innovation in the field of noninvasive alcohol intoxication sensing, strongly supported by the number of review papers already published on this topic. Transdermal sensors provide a unique outlook on the measurement of alcohol intoxication by their incorporation into wearable systems linked with smartphones, increasing both the number of sensors available to measure intoxication but also the computing power for processing of all the data points gather from those sensors. However, both electrochemical sensors and enzymatic biosensors suffer from the wear of materials over time impacting the operation of the sensors, hence increasing the error in the measurements it gathers. This is partially mitigated by the enzymatic biosensors which incorporate a replaceable cartridge; however, this increases the engagement of the user, and introduces the possibility of human error, through damage or incorrect placement of the cartridge. Moreover, transdermal sensors for ethanol sensing can be susceptible to false positives from environmental factors, such as alcohol-containing cleaning fluids or ethanol in the air. The next generation of alcohol sensing devices should aim to move away from using systems that are directly exposed to a chemical process and instead focus on more passive forms of measurement, such as the use of light spectroscopy.Small form factors, and portable for most of the devicesNoninvasive method, sensing ethanol in the sweatMonitoring the patients over long periods of time, establishing intoxication profilesLarge potential for personal monitoring and intoxication studies applicationsIncorporated with wireless communications.Most of the devices available are experimental and conceptual devices, except SCRAM braceletCannot isolate the value of BAC at a specific point in time, average over time onlyDelay between absorption in the blood and release of ethanol in the sweatMust be worn continuously/over long periods of time to establish readingsEnzymatic based sensors require cartridge replacement.The transdermal sensors described in the above section focused on the ability of the sensor to capture the release of ethanol or ethanol related compounds to estimate the BAC. These methods, however, are susceptible to the variance between individuals, both the metabolic rate of ethanol elimination and in the variance of individual’s physiology such as the rate of sweat release. Optical spectroscopy aims at eliminating the bias caused by individuals’ ability to excrete sweat, by considering not the release of gases and liquids by the skin tissue, but rather investigating the state of the tissue itself. It has been shown that optical spectroscopy can be applied to measure the concentration of an analyte to a high precision, both in transmission and diffuse reflectance modes . This isConsequently, the field of using optical spectroscopy has grown over to investigating the tissue spectroscopy changes associated with alcohol consumption, not by estimating BAC via metabolic rates of ethanol and sweat release, but rather directly by measuring its concentration from tissue, through analysis of the detected light intensities, spectra shift or time delays.Since ethanol is highly miscible in water and its presence in tissue can be clearly detected in the dermal layer of the skin, consisting of roughly 65–70% of water; therefore, the probability of finding ethanol in the tissue post consumption is very likely. Most of the problems associated with optical spectroscopy of biological analytes focuses on the selectivity (differentiate between compounds in a mixture) and the specificity (assessment of a particular compound in a mixture) of the device/model implemented.This review focuses on a particular optical technique, of using near-infrared spectroscopy (NIRS) to detect the presence of ethanol. Besides NIRS, other optical techniques will also be evaluated.The most notable devices for measuring TAC, are the TruTouch AlcoSense devices, including TT1100 and TT2500 , as the In the US patent for TT2500  the systWen-fei et al. , also prAlternative to NIRS techniques, a method developed by Guo et al. , used waAlongside the developments in the broad field of spectroscopy, a suggested system by Yamakoshi et al.  approachIn more recent times, Rockley Photonics have been promoting their VitalSpex biosensing platform. The parameters measured by this system include heart rate variability, respiration rate, pulse oximetry, blood pressure, core temperature, hydration. These parameters are included in the Baseline version of the VitalSpex. The extension of this platform under the name VitalSpex Pro, include additional sensing parameters, including blood alcohol content, blood lactate and glucose indicator. The expected date for this platform to become available will be some time in 2023 .Unlike transdermal sensors, optical spectroscopy sensors have not seen as much interest as electrochemical or enzymatic sensors, with only a handful of published works and even fewer devices available on the market. This presents a question regarding the current state and the future of in-vivo alcohol sensing technology. It is key to note that there have been no optical spectroscopy wearable devices developed for measuring ethanol intoxication, as far as this review is concerned, presenting a unique gap for future research and development in the field. Other developments in the field of fiber optical sensors in alcohol have already been summarized by Memon et al. and by Pathak et al. in their respective reviews ,109. TheComplete noninvasive blood alcohol measurementNo requirement for regular component replacementVery fast acquisition timePotential for simultaneous analysis of other analytesNIR radiation is unaffected by skin pigmentationWearable device implementations.Mostly large devices, with high power requirementsVery expensive to develop/purchase, specifically for TT2500Highly susceptible to error due to difference in skin thickness (path length)Reflectance spectroscopy is significantly less accurate than transmittance spectroscopy, hence wearable implementations are difficult.The field of measuring ethanol blood concentration encompasses a wide range of techniques, including pharmaceutical, electrochemical, enzymatic, optical, and in certain cases evaluating changes to physiological signals, such as the PPG and dynamic NIR spectroscopy to establish patterns associated with alcohol intoxication. Considerable attention is given to the application of BAC testing for prevention of DUI incidents, especially shown by the BrAC and NIRS devices . Since vElectrochemical and enzymatic sensors are the most diverse range of innovations in the field, with a wide range of techniques, both for signal acquisition and processing. The results of this literature review also indicate their main application in long-term monitoring of individuals either in law enforcement for individuals who have a history of alcohol-related crimes or in alcohol intoxication related studies, where it is not feasible to regularly take blood or breath samples from an individual. Transdermal sensors are also particularly useful in cases where the cooperation of a subject is not possible, such that gaining a breath or blood sample is not possible. However, those sensors as previously discussed are only as good as the pharmacokinetic model on which they are based, and any variance from the model will increase the error of measurement. Transdermal sensors also present a unique opportunity to introduce personal alcohol testing by means of wearable technology. While the instrumentation required for electrochemical/enzymatic in ethanol testing can be miniaturized, the use of chemical/enzymatic sensors presents a unique shortcoming regarding the operating life of such sensors. Since they depend on either enzymatic reaction rate or deposition of by-products on electrodes the wear experienced by these components will impact their operation over longer periods of time. This can only be resolved by regularly replacing the sensing component of the device, increasing the cost to the consumer and labor manufacturing overheads.A cheaper alternative to wearable electrochemical/enzymatic devices are the strip tests available for bodily fluid testing. These are inexpensive and simple to use; however, they are by no means an accurate method of measurement of BAC levels, but rather indicators of the presence of alcohol over a specific period of time. Although bodily fluid alcohol testing is far more advanced in the forensic field of study , by meanA narrow field of measuring physiological changes in the body included the systems such as the one implemented by Volvo to monitor the facial expression and the gaze for accident prevention and the measurement of changes in IR imaging of the subject’s face, gave an indication as to what changes occur in human behavior when they are under the influence. Such systems carry a unique advantage, as they are passively measuring the effects of intoxication, and can act when the effects of intoxication put lives at risk. However, as with any of the devices and the techniques reviewed in this paper, there are shortcomings. Implementation of such a system in the next generation of cars, would be challenging, and thus require a lot of capital investment. The system must also be fault proof, such that a person who just simply “looks” tiered is not recognized as a false positive. It is also important to remember that these systems do not measure the level of ethanol in a subject, instead they consider changes in the face position or surface skin temperature, and therefore the true estimation of BAC by these methods is unreliable and highly dependent on the data collected for calibration or the training of the model. However, the innovation of the automotive industry with the aim to limit the number of incidents caused by DUI is to be applauded, yet the application of a correct method is also a question worthy of exploring.2 sensor, a method like BrAC devices. Unlike the one suggested by Volvo or the thermal imaging comparison measure the presence of ethanol. But as for any BrAC device the conversion rate of 2100:1 for the BAC estimation combined with all the uncertainties of breath measurement itself demean the accuracy of these devices. Moreover, the question should also be raised about the practicality of measuring BrAC passively in a closed car, with multiple subjects where the driver may not necessarily be intoxicated but one of the passengers can possibly exhale ethanol and set off a false positive.This has subsequently increased in the interest of monitoring intoxication of drivers in the car, by devices such as the one developed by Autoliv  to measure the exhaled air ethanol content and traces the origin of the ethanol through a COTherefore, a more feasible application of BrAC devices lies with the application of a breath alcohol device, where a subject has to blow into a tube, and the breath sample can be analyzed with any interference from other passengers in the vehicle. The accuracy of the BrAC devices has been proven to be effective at estimating BAC of a subject to a relatively high accuracy, yet not specific enough compared to gas chromatography. BrAC devices also depend highly on the cooperation of the subject, and the results of a BrAC test can be manipulated by controlled ventilation before the test is undertaken. However, as a deterrent factor for DUI, breath alcohol devices help to prevent many accidents every year, but only at one point in time. Implementations of BrAC devices exist, where they are linked to the ingestion system and require a driver to blow into a BrAC device before the vehicle can be engaged . This, hFor any of the physiological devices/techniques, to find an appropriate implementation would need to link the system to an alcohol sensor, to justify the changes in the physiological factor that is measured. Without the establishment of ethanol consumption, these systems are only making estimates about the level of intoxication. Therefore, any of these implementations must be combined with a sensor for ethanol level, either the tissue or breath. However, physiological changes devices and techniques are useful at pointing out the outlining of the changes that may be associated with ethanol consumption. Therefore, these sensors would find a far more applicable implementation in determining the physical state of an individual to assess whether they are physically impaired by ethanol, as the variance in ethanol metabolism and tolerance is high. These devices would be more appropriate in determining the physical impact of alcohol intoxication and to determine the state of the person, rather than trying to establish an estimate of BAC, which would reach a bottleneck for subjects consuming high doses of alcohol.Ultimately, all techniques reviewed in this paper show that the effects of ethanol intoxication can be attributed to many changes in physiological behaviors, yet the results they obtain for some of the techniques are mostly insignificant or are not feasible for everyday applications. These reasons may include the time required for measurements or the principle of measurement that may be faulty by not considering other compounds which originate or increase from their normal concentration during an intoxication period. In the case of devices which measure physiological changes, the measurement is only noted as a change of a physiological factor, which may not be an exclusive change for ethanol intoxication. These types of devices and techniques are most likely not going to find any application for real life intoxication testing, since the knowledge of intoxication is given in all of them, and they are not well adapted for recognition of false positives, such as tiredness or use of medication with similar effects as ethanol. However, one must also consider the fact that none of the devices and techniques reviewed in this paper considered the use of neural networks or any other deep-learning algorithms for classification of data, A system that would encompass all three of these fields would be the new competitor in the field of ethanol intoxication testing.Considering the scope of the field of alcohol intoxication sensing, it is clear to see that the market is heavily dominated by BrAC devices, this is particularly evident in the implementations of in-vehicle passive and active alcohol detecting systems. In fact most of the developments in the field of noninvasive ethanol sensing is aimed at implementing alcohol/intoxication state in cars, with an objective of reducing the fatality rate by drunk driving. Systems which lock the driver out of their vehicle, could be of great assistance in assisting roadside safety, however they would not be amongst the desired features in a car by the user. Potential fear of false positives could result in driver being locked out of their vehicles with no direct recourse. This in turn can lead to many problems for car manufacturers, in term of customer relations. Also implementing these systems into all modern cars would require a very long time, especially if older vehicles will need to be equipped with these systems. These in vehicle intoxication sensing systems, although good in nature will most likely not be appreciated by drivers and will be very expensive to implement. Alternatively, the most likely future for this system would lie in the heavy machinery and construction, replacing the already existing breath-based interlock systems.By far the brightest future would be seen for the new generation of wearable sensors, both optical and transdermal sensors offer a large variety of methods for measuring intoxication levels, alongside other analytes such as glucose and lactate. This makes wearable devices one of the most prominent future technologies in the field of intoxication sensing. With wearable implementations of intoxication sensors, the application of such systems would be seen from personal monitoring, research of intoxication, addiction treatment or monitoring high risk individuals. Wearable devices such as SCRAM are already in use by the law enforcement for monitor high-risk individuals; however, the form factor of the device makes it very intimidating, instead smaller version of these devices could be fitted on the wrist with the same or even improved accuracy.Nonetheless, intoxication is not the same as blood alcohol level. When considering the estimation of the physical state of an individual, factors such as tolerance play an important role in determining the degree to which an intoxicating substance has affected them. Therefore, monitoring of multiple parameters, such as PPG and behavior changes, temperature changes can play a vital role in establishing the true image of intoxication. Combined, these parameters could be used to correlate specific conditions/diets/moods to intoxication state and the rate of ethanol elimination. Hence, the use of multiple modalities and implantations of machine learning algorithms can pave the new wave of intoxication state interpretation and maybe shed some new light on the mechanism of tolerance.Amongst the up-and-coming technologies nanomaterials have a very high potential to be the future of biosensing application. By synthesizing strings made from specific compounds, the properties of which correspond to those of the analyte, a response triggered by their interaction can be interpreted as an indicator of the analyte’s presence. These developed sensing techniques have very high selectivity and hence are highly unlikely to produce false positives. The future of VOC sensing is very bright and full of potential innovations. These developments are described by many researchers in the commonly available literature mostly through the use of semiconductor materials ,115,116.In retrospect, the field of intoxication technology is continuously progressing and innovating new methods of detecting and quantifying ethanol concentration in the blood, where it is through analysis of chemical interactions or physiological changes in the person’s body. Further research in the field of sensing will benefit the development of treatment methods and one day will be available in all wearable devices.This review of the literature on ethanol testing identified several different techniques for measuring and estimating the BAC level through numerous means, including metabolic rate estimation, breath sample analysis, physiological changes, transdermal sensors and optical spectroscopy. Although many of these techniques claim to accurately and precisely measure the BAC levels, they are far from the accuracy achieved by gas chromatography–mass spectrometry. Regardless, many of the techniques outlined in this paper showed promising and impressive results for the methods they use to estimate BAC. As shown by most of the reviewed literature, the main focus of alcohol intoxication sensing is related to incidents caused by DUI ad preventing their occurrence. A large branch of alcohol intoxication sensors focuses on transdermal techniques, analyzing the presence of ethanol or ethanol-related compounds in sweat. These devices are compact and many of them implement a wearable application, such as a smart sensor linked to a smartphone. The problem associated with these devices is the question of their reliability after regular use, due to the use of deteriorating components that may give inaccurate readings if not maintained appropriately.Although the field of alcohol intoxication is broad, it still has not been established as a social habit to measure personal intoxication during a consumption episode. This mostly arises from the impracticality of the handheld devices, or the above outlined shortcomings of electrochemical/enzymatic biosensors. This review thus concludes that the field of alcohol intoxication measurement has not yet been introduced into the consumer domain, and current technology still has not implemented a system for using NIRS as a smart wearable device as a possible area for further innovation in this field. In addition, the measure of intoxication is not strictly limited to the amount of ethanol in the bloodstream but is also dependent on individual’s tolerance to the intoxicating effects of ethanol. Such impacts can be observed in variations such as changes in PPG morphology or face heat signatures Therefore, a combination of measurements from both the concentration of ethanol in the blood and the changes in physiological signals may be a better indicator as to the state of an individual. A device attempting this should consider individual such factors as weight, sex, and the metabolic rate of ethanol breakdown, as outlined by Widmark. By also considering recent interest in the role of other metabolites of ethanol, namely acetaldehyde and acetic acid, it is possible that the next generation of intoxication sensors will increase the number of factors. As the field of intoxication is vast and ethanol intoxication remains an intensively studied research area, so do the methods and tests used for measuring and quantifying intoxication.


The global death toll from lack of access to basic surgical care is three times as much as for tuberculosis, HIV and malaria combined. Patients dying of curable neurosurgical conditions solely because of inadequacy or absence of neurosurgical infrastructure is an issue deserving immediate attention and action. Global neurosurgery is an important step forward in this regard, under which different models of collaboration between HICs and LMICs aim to increase both the number of neurosurgeons as well the quality of neurosurgical care available in these countries through arranging surgical camps, providing neurosurgical training and education, and restructuring the health system in these countries in order to create an environment conducive to the provision of the highest form of neurosurgical care. Despite the many challenges faced by LMICs in furthering neurosurgery programs such as poor resource allocation, brain drain, turbulent socioeconomic conditions, limited training facilities, and population explosion, data now being reported from LMICs the world over, exemplifies the immense positive impact that collaborations have had over the last few decades in improving neurosurgical capacity and infrastructure. So far, conventional methods of collaboration  have been effective in progressively bringing about the desired change in LMICs. However, these methods have been limited by a finite funding, pushing the global neurosurgical community to look for alternatives such as online curricula, task shifting and sharing, and long distance mentor-mentee relationships. In this review, we aim to provide an update on the current state of neurosurgical collaborations and identify the barriers in the way of collaborations and what alternative models of collaboration might be used to overcome them.. The advancements in neurosurgical care have not been able to find a way to lower middle-income countries (LMICs) and lower income countries (LICs). The equitable distribution of surgical expertise and surgical infrastructure continues to be a problem even today. Surgery, by default, has long been treated as the neglected stepchild of all medical sciences, with an estimated 2 billion of the world population without adequate surgical care available . NeurosuHowever, all is not lost for neurosurgery in lower income countries. The concept of Global neurosurgery was conceived to offset this disparity in the 1960s as surgical camps organized in Africa under the umbrella of Foundation for International Education in Neurological Surgery (FIENS) which spearheaded the development of neurosurgical capacity in Africa where neurosurgery was practically nonexistent up until that time ,4. GlobaAs surgical collaborations continue to grow around the world, there is a need to update and synthesize our knowledge of collaborations in the past and use them to guide our future approach to the topic. Our paper summarizes the past and present of neurosurgical collaborations, what has changed with the advent of newer educational tools, what models may be of use in expanding the current programs and ensuring sustainability, what are the barriers to it and what should be the way forward.Almeida et al. scrutinized the methods employed at the University of Toronto for dissemination of neurosurgical education to LMICs and proposed models of collaboration . All of 1.Training programs allow neurosurgeons from LMICs to visit well developed and well reputed neurosurgery programs in HICs and benefit not only from the patient load but also from the opportunity to work at well reputed programs and establish lifelong tele-collaborations with mentors of neurosurgery ,11. Also2.Traditionally, neurosurgical missions have meant a team of neurosurgeons from the developed world ‘visiting’ the developing countries in order to cater to the consultation and surgical needs of the population. However, these missions have now evolved to include education and training of neurosurgeons in LMICs as an integral part of building global neurosurgical capacity . Collabo3.Mentor mentee relationships are a norm in medical practice. It is extremely common for such relationships to be established as a result of both of the above models and even remotely without them. It is, in fact, one of the most employed tools in the modern age of internet and allows the possibility of ‘tele-collaborations’, whereby well-trained mentor neurosurgeons from HICs can remotely assist in surgical procedures and management plans for rare case presentations via real time audiovisual tools ,16. This4.The rise of the internet has marked the advent of a new era for neurosurgical collaboration. Despite not being utilized up to its fullest potential yet, online education may be the future of neurosurgery for a number of reasons. For instance, it offsets the problems encountered due to a lack of funding for collaboration ventures without compromising the quality of education, as has been evidenced by extremely favorable results in other fields of medical sciences ,18. Howe5.Owing to a persistent workforce deficit in LMICs despite a multifaceted approach to neurosurgical capacity building, there has been an increasing interest in the delegation of neurosurgical tasks to non-neurosurgeons such as general surgeons and neuro physicians in critical care settings . While tOne of the most outstanding examples of the benefits yielded by collaboration in the field of neurosurgery is provided by the Rhoton labs established by Dr. Albert L. Rhoton in 1975 at the University of Florida . The RhoIn fact, the development of Neurosurgery as a distinct field in the United States is in itself a testimony to the importance of collaboration. The art of medicine is not just conveyed by textbooks and lectures, but in person through mentorship. Thus, Harvey Cushing, the Father of Neurosurgery, laid the foundations of neurosurgery as a distinct field by not only establishing and perfecting various neurosurgical techniques, but also training and mentoring many neurosurgeons over the span of his career, who went on to establish neurosurgery departments in various institutions all over the US . This efIt can be concluded from this discussion that collaborative models, though hard to establish effectively, result in tremendous efficiency. They owe their success to the generation of a diverse and inclusive environment that allows the free flow of innovative ideas, while providing the mentorship and facilities to polish and develop those ideas. Thus, collaboration not only among neurosurgeons from diverse regions but diverse fields is essential to further improving the discipline.The advancements in science and technology have changed the landscape and challenged the relevance of traditional collaboration models. Collaboration among different fields of medical sciences via online curricula is now not only feasible but also extremely productive and hence is now done very frequently . Social Studies on the use of Twitter and Instagram in neurosurgery have consistently revealed that these social media platforms can act as a communication platform for connecting not only neurosurgeons, trainees and medical students across the globe, but also patients across borders, allowing for remote consultation and taking load off of the already struggling neurosurgery programs in LMICs ,35. SimiAs is evident from our detailed discussion on the topic, cross-border collaborations and partnerships in neurosurgery between LMICs and HICs have been crucial in the advancement or even establishment of neurosurgery departments in LMICs. However, the state of neurosurgery worldwide still leaves much to be desired. For instance, in contrast to a ratio of 1 neurosurgeon for 100,000 population in HICs, LMICs have a ratio of 1 neurosurgeon for a population of 6400,000 , but alsWith internet based education on the rise, an online model of education has been adopted by the Surgical Neurology International (SNI) Digital- Baghdad neurosurgery educational series, a joint effort between the United States and Iraq to discuss, debate and scrutinize the unique and complicated experiences in neurosurgery together in an online meeting format and provide a symbiotic educational experience to people from different parts of the world . A step Apart from lacking research capacity, neurosurgery residents and medical students from LMICs also lack access to specialized training fellowship programs and by extension, newer advances in the field of neurosurgery. To address this issue, The Global Neurotrauma Fellowship (GNTF) Program was initiated in 2015 by Barrow Neurological institute, USA . It is aConventional methods of neurosurgical camps and workshops are also being employed in the modern world for countries determined to be seriously lacking in the neurosurgery skillset, especially in the context of African countries but also pertaining to other developing nations. Papua New Guinea (PNG) is one such developing Pacific nation where neurosurgical services are still close to none. Australian neurosurgeons, therefore, visit twice a year in order to extend the range of surgery and work on capacity building by teaching the skills to local surgeons and nurses . MyanmarCollaboration in neurosurgery has birthed several success stories around the world. For example, as a result of the work of Dr. Roger Härtl in Tanzania under the patronage of Weill Cornell Brain and Spine Center, the number of neurosurgeons in Tanzania has soberly increased from 2 to 8, and residents from 0 to 5. There has been a subsequent increase in the number of research papers and surgical procedures for basic life threatening conditions such as hydrocephalus, brain tumor, and cervical spine injury ,51. A moCollaborative efforts have also borne fruit in the form of a pediatric brain disorder program in Ukraine, the Ukraine Pediatric fellowship program (UPFP), by the Department of Neurosurgery at the Sick Kids Hospital in Toronto . With siDr. Jawahar Dar from India established the first neurosurgery department in Kenya as early as 1974, followed by the establishment of a second department in 2007 by Dr. Florentius Koech, a locally trained neurosurgeon who had pursued his neurosurgery fellowship in Australia . Dr. KoeTapping the hidden potential in LMICs is no short of a breakthrough when it comes to cross-border neurosurgical collaboration. Allowing promising neurosurgeons from LMICs to experience firsthand state of the art neurosurgical care in HICs, become familiar with the novel research and technological advancements in the field, and connect with the giants bringing about these advancements, inculcates in them a deep-rooted desire to both set into motion and sustain similar practices in their home country on their return, to whatever extent is allowed by the resource strained health system of LMICs. Despite the various breakthroughs as a direct result of collaboration, as Dr. Rhoton would say, there is no finishing line. There is still much work to be done to ensure equality and/or equity in neurosurgical care across the world. Traditional collaboration, however, is unfortunately becoming harder given the barriers explained in the subsequent section.1.Financial constraints impose a limit on both the number and magnitude of collaborative projects, implying that the efficacy of such undertakings will always be less than if they were to be reproduced in a model with ample funding. Government funding for these projects is often non-existent, leaving the involved surgeons to bear the cost themselves or depend upon donations. This decreases the likelihood of longitudinal collaborations that are more effective in producing sustainable programs in LMICs .2.The surgical training model of neurosurgical collaboration between HICs and LMICs is associated with significant brain drain i.e. the promise of political stability, improved quality of life, better opportunities in research and career advancement, greater job satisfaction and abundant resources are often enough incentives for visiting doctors from resource-strained areas to choose to settle in HICs. While this collaboration model can further add to the deficit of neurosurgeons in the source countries, it should be viewed as an opportunity to improve healthcare as a whole in these source countries to combat brain drain –61.3.Three major themes pertaining to barriers to participation in global surgery academic collaborations have been identified as individual, community, and system barriers. Individual barriers encompass loss of income, family commitments, perceived risk to their well-being when traveling to politically unstable LMICs, and the choice of junior faculty to prioritize advancing their own career over involvement in such projects. Community barriers like lack of colleague support and early exposure to global surgery and system barriers like lack of time, institutional support, and funding also hinder participation. Another common theme is a greater sense of responsibility towards patients from their own country who require their attention and surgical care, which limits both the desire and the time to help out patients in foreign countries .There is currently no standard metric to assess the outcomes of ongoing and past collaborative efforts between different countries in the field of neurosurgery. This makes it incredibly hard to call for greater resource allocation to further these collaborative efforts when impact of the previous collaborations cannot be accurately gauged ,64.The advancement of neurosurgery in LMICs demonstrates the incredible impact that international neurosurgical collaborations have had in neurosurgical capacity and acumen building, especially in Africa. However, the challenges in providing ‘neurosurgical care for all’ persist owing to a poor infrastructure and lack of equipment, limited funding, brain drain, and population overload. Future collaborative efforts must focus on cost effective, self-replicative, and most importantly, sustainable models of collaboration along with the ongoing traditional collaborations.


Image registration is one of the most important image processing tools enabling recognition, classification, detection and other analysis tasks. Registration methods are used to solve a large variety of real-world problems, including remote sensing, computer vision, geophysics, medical image analysis, surveillance, and so on. In the last few years, nature-inspired algorithms and metaheuristics have been successfully used to address the image registration problem, becoming a solid alternative for direct optimization methods. The aim of this paper is to investigate and summarize a series of state-of-the-art works reporting evolutionary-based registration methods. The papers were selected using the PRISMA 2020 method. The reported algorithms are reviewed and compared in terms of evolutionary components, fitness function, image similarity measures and algorithm accuracy indexes used in the alignment process. The image registration process  encompasses a set of operations performed on pairs of images with the purpose of preparing them for further use in various applications, hence the general view of image registration as a preprocessing step. The involved images come from the same sensor or different sensors (in the same spectrum or different spectrums) and are captured at different moments in time, from different angles. Generally, they represent the same subject but are different in some way. The purpose of this kind of processing varies, but it usually involves applying a transformation on an image to make it align or match another. The goal can be enriching the informational content in an image by superimposing other images, stitching images together to create a larger image, the recreation of a 3D model, the detection and tracking of objects etc. The two images involved are usually called object  and scene .Two main approaches are used: feature registration and intensity registration. Feature registration means that some elements  are detected in both images that go through the registration process, and a transformation is applied on the scene image such that selected elements match their correspondents in the object image. The features can be contours or points (landmarks), and they can be manually marked and paired or automatically detected. Manual marking is done by experts and obviously takes a lot more time and is prone to errors. The availability of experts can also be a problem. With the development of new algorithms and computer power, automated detection of features solves the problems raised by the need of human intervention. Feature registration requires less processing power since it uses few pixels in the image to compute the required transformation. In order to assess how well the images are aligned, various indicators were developed.The increase in computer power allowed researchers to turn their attention towards intensity registration, which uses all the pixels in the images during the registration process. Since all pixels must be matched, the amount of computations is much higher, but more elements in the images are aligned. Specific indicators that take into account all the pixels were developed to assess the quality of the results.Most digital images are 2D, so registration efforts naturally went into computing the parameters for a 2D transformation. Still, not everything is 2D. There are also 3D objects and efforts to compute 3D transformation to match such objects. The type of transformation can be rigid , where all the pixels go through the same transformation, or free-form deformation.The transformation parameters are vectors part of a large solution space, with infinite points . Total exploration of such a space is impossible (it would require an infinite time), so researchers turned to stochastic algorithms that draw inspiration from biology. From the initial evolutionary algorithms (EAs) and genetic algorithms (GAs), a plethora of evolutionary algorithms of biological/nature inspiration were developed  ). Variat-identification of the most recent research papers in the field of evolutionary image registration, published in the last 5 years and indexed in major databases, available either openly from the publisher or the authors, or through the research network of which our university is a member;-focus on successful use of evolutionary algorithms for image registration;-identification of fields of application where research efforts are focused;-main ideas of each reported research are summarized giving a clear view of the approach;-identification and comparative analysis of the main (dis)similarity measures used to register images and performance indicators used to assess algorithms’ results and to compare them between algorithms;-comparative analysis of main elements of evolutionary approaches: basic algorithm and algorithm class and fitness functions.This is what drove us to find the current status and trends in solving image registration tasks using evolutionary approaches. We found that, as expected, evolutionary image registration is a very dynamic field, where advancements are supported by the rapid technological development. The main contributions of this paper are:The rest of this paper is organized as follows: The review was conducted following the guidelines of the revised Preferred Reporting Items for Systematic Reviews and Meta-Analyses (PRISMA) . The tarThree searches were performed on the Web of Science site, using their own search engine . The firA search was performed on the Scopus database using the keywords “evolutionary AND image AND registration”, yielding 70 records. Of these, 29 were duplicates of records already found in WoS. Twenty-five of the remaining articles were not relevant for the purpose of this review. Eleven more records pointed to articles that were not accessible (two of them because of the language in which they are written). One record was removed because it was a review. Four articles remained to be reviewed in this work.The search on IEEE Xplore database using the criterion ‘(“All Metadata”:evolutionary) AND (“All Metadata”:image) AND (“All Metadata”:registration)’ yielded 20 records. Only 8 were new (not previously found in WoS or Scopus). Three of these articles were not relevant to the purpose of this review. Four more were not accessible to us. The 1 remaining article is included in this review.The search on the Springer database was performed using the ‘“evolutionary” AND “image registration”’ search criterion and limited to full text accessible items. A total of 162 results were returned. After analyzing the titles, abstracts and the full text (where needed), only 4 of these records were retained as relevant articles not previously found in WoS or Scopus databases. The remaining results were either previously encountered (3 results) or not relevant .In this section, the selected articles are briefly reviewed, mentioning the key elements and the idea underlying the proposed approaches. The articles are grouped considering the class of base algorithms used in the respective approaches.Articles reviewed in this section use approaches based on the genetic algorithm (GA).Pirpinia et al., 2019: In , an evolNakane et al., 2022: In , evolutiArticles reviewed in this section use approaches based on evolutionary strategies and various kinds of swarm intelligence algorithms.Keikhosravi et al., 2020: In , the autVidal et al., 2022: In , the CMABermejo et al., 2018: In ,20 algorR) [Wu et al., 2019: In , the autR)  and add Zhang et al., 2022: In , the autBejinariu and Costin, 2018: In , the autChen et al., 2018: In , the autSaxenna and Pohit, 2022: In , the autChen et al., 2022: In , the autShen et al., 2020: In , the autHu et al., 2020: In , the autLiu et al., 2018: Manual work on the registration of images of various resolutions and captured by different types of sensors  is time consuming and prone to errors. Additionally, traditional registration algorithms focus on affine transformations, excluding non-affine distortions often induced by various factors in satellite images. In , the autWang and Yu, 2022: In , the autBanharnsakun, 2018: In , the autCocianu et al., 2018–2022: In a series of articles ,69,70,71In the next proposal,  . The choice obviously depends on the type of registration: intensity registration  or feature registration (which uses only some pixels). There are algorithms that use only some pixels to compute the registration parameters and all the pixels to estimate the (dis)similarity of the images. The most used classes of indicators are listed in In addition to the standard SSIM and SNR class of measures used in image processing, various similarity/dissimilarity measures have been used either to define the fitness function or to assess the performance of the obtained algorithm, each with its own strengths and weaknesses.In feature-based methods, the similarity functions usually measure the distance between corresponding features . For insIntensity-based approaches are usually developed using measures that can capture the resemblance of the intensity values in images depending on what kind of relationship is established between their intensity distributions (linear or non-linear) . ED is tIn case of multi-modal registration, the relationship between the intensity values in the images is usually non-linear. Metrics based on information theory, such as mutual information (MI), are better suited for this scenario. Shannon mutual information is widely used in image registration, but it is sensitive to noise. To reduce the influence of outliers, one can use similarity measures based on Tsallis entropy instead. The metric MI can be normalized by the joint entropy ,116. TheFinally, in case of binary images, one can represent data using vectors of object pixels instead of intensity matrices. For this type of dataset, simpler and more efficient similarity measures, for instance the DICE coefficient, can be used.All evolutionary algorithms make use of a fitness function that evaluates the quality of each and every candidate solution. The fitness is used, depending on the type of algorithm, to select parents for the creation of new individuals and survivors for the next generation. In image registration problems, the fitness is usually based on the similarity metric used by the algorithm, often being one such indicator. Some studies ran experiments using several fitness functions to compare the results and decide which one is more suited for each task. There is a large variety of problems, using many different algorithms and (dis)similarity measures, making it impossible to name only one fitness function as a general recommendation. The fitness functions are based on the (dis)similarity indicators and aim to maximize either one such indicator or minimize in case of dissimilarity. For binary representations, simpler similarity measures are indicated, such as DICE. For more complex images, informational-based indicators, for instance NMI, are in most cases better suited. In cases where pairs of feature points are used for registration, Euclidean distance and CC are commonly used choices.The performance of algorithms must be compared in order decide if a proposal is an improvement or to decide which algorithm is better suited for a specific task. Various performance indicators can be used for this purpose. Usually, the criteria used are related to robustness/success rate (correct registration was achieved or not), time consumed (either clock time or number of iterations) and accuracy of registration (various error measures). A large variety of performance indicators are used, demonstrating the preoccupation of researchers to thoroughly analyze the algorithms and their applicability to real registration problems.Throughout the reviewed articles, there is a large variety of algorithms, approaches and applications. All authors report improved performance compared to basic and classical/state-of-the-art algorithms. Performances are measured in various ways, using various indicators, depending on the field of application and the purpose of the research. Many researchers report the results in terms of success rate. In some cases, the success rate is defined as the recognition capability, the algorithms proposed in ,105,112 As a conclusion regarding the performance indicators, besides standard image processing measures , the specifics of stochastic methods for image registration impose the use of recognition rate, success rate and average Euclidean distance between pairs of feature points. The recognition/success rates are closely related to the values of similarity measures used to define the fitness functions, as success means achieving a similarity measure above a predefined threshold. This is why normalized similarity measures are recommended: the optimum value is known in advance, and a threshold can be defined.The goal of this review is to find out what are the current trends in regards to the use of evolutionary algorithms in solving image registration problems. We set out to find what are the latest developments and hot areas regarding the fields of application, type of algorithms, image (dis)similarity measures and fitness functions. Additionally, we studied what performance indicators are considered when comparing various algorithms. We wanted to find out what are the recent advances and promising research directions for the future. In our opinion, this review is a very good tool for other researchers that enter this field and pursue the development of computer vision components and systems.We found that, as expected, evolutionary image registration is a very dynamic field, where advancements are supported by the rapid technological development. Population-based evolutionary research is resource intensive, requiring lots of computations, especially for the fitness function. A huge number of fitness function evaluations is the main consumer of time and obstacle in achieving a fast and accurate registration. Technological development mitigates this problem with ever faster processors with multiple cores and the possibility of parallelization. This also permits more tests to be carried out, both to compare algorithms on a specific problem and/or data set and also to compare the behavior of a specific algorithm and combination of elements on multiple data sets and problem variations. The decreasing cost of fast computers also allows more and more researchers to enter this field without limitations regarding access to computer power.The reviewed articles demonstrated that new algorithms and algorithm variants are continuously proposed and explored. Algorithm components, such as variation operators, are also developed, either as new operators or adapted to the specific problem. Operators are tested in new combinations, sometimes surprising ones. The main target of the research regarding variation operators is to find a better balance between exploration of the search space and exploitation of good individuals. Exploration is intended to look for good solutions in all areas of the search space, which is especially important for multimodal functions. Exploitation is intended to refine good individuals and find the most accurate solution possible.The availability of computing power also supports the move towards intensity-based registration. While feature-based registration is concerned with only some elements of the images processed, intensity-based registration works on all pixels in the image. Obviously, a huge amount of calculations is required to determine the similarity of two images by involving all the pixels, and computer power was a powerful limitation. Now, this barrier is falling and the path for exploration is open. New indicators are developed and classical ones are adapted, either to make them more useful or easier to use. From the multitude of similarity indicators developed, the reviewed articles show a preference for the ones based on mutual information.Tests carried out on various algorithms showed that while improvements are achieved in robustness, accuracy and time consumed, there are variations regarding the performance between datasets. A universally appliable algorithm combination is yet to be found, so the path is open for further exploration.We conclude that this review is very useful for all researchers that delve into the field of evolutionary image registration because it surveys in detail the main approaches of all recently published and indexed research papers. It points specialists to hot areas and also very promising research directions. Additionally, it presents all the elements that must be used to develop fast and accurate real-world applications in various fields that take advantage of image registration capabilities.


Ethiopia has a large population of small ruminants (sheep and goats) which are mostly kept in traditional subsistence production systems that are poorly described. Understanding these different systems, their population structure, biomass, production, and economic value is essential for further analysis and effective policy making. The objective of this study was to quantify these parameters for small ruminant production systems in Ethiopia to use them as a basis for analysis of disease burden within the Global Burden of Animal Diseases program. Population structure and trends of small ruminants were analyzed using data from ten annual national agriculture surveys. A stochastic herd model was used to simulate the small ruminant population, biomass, and economic value. The model was parameterised stochastically using data from statistical databases and the literature, and sensitivity analysis of main model outputs to the stochastic inputs was done. Small ruminants are held across the country mainly managed under two major production systems: the crop-livestock mixed system and the pastoral system. The small ruminant population has grown in the past 10 years with an average annual growth rate of 4.6% for sheep and 6.7% for goats. The national average small ruminant population for 2021 was projected at 96.4 (range 95.3–97.7) million heads and the mean stock biomass was about 2,129  million kilograms. The monetary value of the small ruminant population was estimated at USD 5,953  million. The annual monetary value of small ruminant production outputs was estimated at USD 1,969  million. Although the small ruminant population is large and rapidly growing, contributing about 2% of national annual GDP, the sub-sector is characterized by low productivity, low offtake rates, and a limited range of production outputs with no signs of intensification. Efforts should be made to reduce small ruminant mortality, improve fertility, and better utilize products such as milk to improve the livelihoods of rural households and to benefit the national economy. The approaches developed in this study can be replicated in other systems and countries to reveal trends in the size and value of livestock systems, providing a better understanding of its economic importance and performance. Ethiopia has one of the largest populations of small ruminants (42.9 million sheep and 52.5 million goats) in the world . Based oSmall ruminants play an important role in food security for livestock keeping households , with smDespite a large population, small ruminant production in Ethiopia is largely based on traditional subsistence production with limited commercialization and modernization. Both production offtake rates and production quantities are very low . ReproduTechnical analysis of problems in existing production systems and potential interventions such as feed, genetics and health are needed to bring sustainable improvements in the sector. This requires an understanding of the systems, including its population structure, biomass, production, and economic value. However, this understanding is limited in the small ruminant livestock sector in Ethiopia.The Global Burden of Animal Diseases (GBADs)Small ruminant production was classified and mapped into different systems and subsystems to create uniform analytical units. First, sheep and goats were allocated to pre-existing agro-ecological systems based on Shapiro et al.  and Sereensetenset growing area” when the intensity of enset production in the zone had an enset tree to small ruminant ratio of ≥ 1  were further classified into subclasses according to feeding system practiced and household livestock composition. In this subclassification, the CLM system was divided into two sub-systems based on feeding system ; small ro of ≥ 1 . In enset system . The pashttp://qgis.osgeo.org).The production systems mapping was done using QGIS 3.18 GIS software  was analyzed using data from the CSA annual agricultural sample surveysadj is the adjusted population for a previous year, P is the population in a previous year and PrexcZP is the average proportion of the population of previously excluded zones in the latest two years where all zones were included.Where, PSpatial distributions of the small ruminant population were mapped using zone-level number of small ruminants per capita , calculated as small ruminant population divided by human population, with human population data obtained from CSA . Flock sBiomass, production, and economic value of sheep and goats in the different production systems were simulated using the DYNMOD herd model . DYNMOD This herd model was used to simulate and estimate the small ruminant population, liveweight biomass in kilograms (kgs), and production outputs and their financial values for the year 2021. These are calculated by multiplying the average parameter values  in a particular sex-age category by their corresponding numbers in those sex-age categories. While the model directly calculates monetary value of the stock biomass and primary production outputs such as stock variation and live animal offtakes, in our study the monetary value of secondary outputs such as milk, manure and skins were calculated manually by multiplying the physical quantities of these outputs from the model by their corresponding prices.The herd model uses several input parameters such as initial population size and sex-age structure, demography parameters (reproduction and death rates), production parameters , and output prices , etc. Most parameter inputs such as parturition rate, twinning rate, death rate, liveweight, and animal prices were inputted in the form of probability distributions to capture uncertainties in the input data. Consequently, the model outputs also take the form of probability distributions reflecting their variability and uncertainty. This allowed a sensitivity analysis to assess how individual input parameters influenced the output. The model was stochastically simulated for 100,000 iterations by Latin Hypercube sampling using @ risk software version 8.2 . A sensitivity analysis was conducted for major outputs such as population size, total stock biomass, production outputs and financial value of the total stock of small ruminants. The change in output statistics method was used for the sensitivity analysis.Small ruminant populations were categorized into six sex-age categories for the model. The age groups were made to align roughly with important life stages; juveniles , subadults , and adults of reproductive age .Reproductive performance is defined by parturition and prolificacy rates. Parturition rate is the number of parturitions per year divided by the number of females of reproductive age present in that year in the population. Prolificacy rate (average litter size) is the average number of offspring born alive per parturition. Parturition rate was derived from the proportion of births to female adults reported in CSA  divided The death rate corresponds to the proportion that die over the duration of the entire age class if age class length was ≤ 12 months , or proportion that die over a 1-year period where the age class length was >12 months . Death pOfftake refers to the net animal offtake, i.e., the animals leaving the flock minus animals entering the flock divided by the total number of animals in the flock. The animals leaving the flock consist of animals sold, slaughtered, or given as gifts, and animals entering the flock consist of purchases and gifts received. The offtake rates correspond to offtake over the duration of the entire age class if the age class length is <12 months  or offtake over a 1-year period if the age class length is equal or >12 months . It was Liveweight for different species and sex-age categories were compiled from the literature . A trianThe price of a live animal was taken from livestock market information system database of the Ministry of Trade and Industry of Ethiopia from for year 2021*0.49 liter. Parameters related to milk production are given in The milk production parameters required by the model were lactation yield per day and lactation length in days. Milk production was considered only from goats as sheep are rarely used for milk production and were not covered in the CSA surveys. Even for goats only 14% of female adult goats were used for milk production  million heads and the mean stock biomass as 2,129  million kgs which is equivalent to 8.52 million TLU (where 1 TLU = 250 kg liveweight). The monetary value of the total small ruminant stock was estimated at 5,953  million USD. The total population, stock biomass, and monetary value of small ruminants by species and production system are summarized in Net offtake rates were generally higher in goats (27%) than sheep (18%) and higher in the CLM system than the pastoral system. The monetary value of small ruminant primary production output in terms of increase in stock value and net animal offtake for the whole country in the year 2021 was estimated at USD 1,735  million. This value by species and production system is presented in In all production systems and both species, except goats in the pastoral system, the population size is strongly influenced by (most sensitive to) lamb/kid mortality  the small ruminant population has grown substantially, driven by increases in the number of holdings/flocks rather than flock sizes. This indicates the slow or absence of structural change in the sector toward intensification and commercialization, which should have been manifested by incremental growth in individual flock size and a decrease in the number of holdings.Similarly, Bachewe et al.  found anWhile the average small ruminant flock size was small overall, it was larger in the pastoral system than in the CLM system. In line with this, the small ruminant count per capita was higher in pastoral areas. However, high sheep number per capita and high goat number per capita were also observed in the highland zones of the Amhara region and western parts of Tigray region, respectively, in the CLM system. The high count per capita could be an indication of the importance of small ruminants in those areas, which can be prioritized for small ruminant development interventions. It may also reflect the ecological burden from overgrazing of livestock in these areas.The estimated small ruminant biomass accounts for about 13% of the total livestock biomass of the country and is only second to cattle. This estimate provides insight into the importance of the sector in terms of the resource base and economic contribution. Moreover, the biomass can also be used as a denominator for various livestock related analyses and between species comparisons, such as feed requirements, greenhouse gas emissions and other environmental impacts such as deforestation and land degradation, amongst others.The herd model predicted net offtakes were higher in the CLM system than pastoral system and for goats than sheep. The offtakes would have been expected to be higher in the pastoral system as livestock production is the main livelihood in this system, but that was not the case. This could partly be due to higher mortality which reaches up to a staggering 45% in lambs/kids and 22% in adults in the pastoral as compared to the CLM system . The netThe estimated total economic output from small ruminants is a significant contribution to the national economy which accounts for 2% of USD110 billion GDP estimated for Ethiopia in the year 2021The data used in this analysis have some limitations. The main issue was that available data were mostly crude and not disaggregated by species, sex, or age. When reported by age category, age categories were not uniform. Lamb/kid mortalities were reported for different age lengths. This problem has been also noted in the wider livestock mortality literature globally . FurtherStochastic simulation and sensitivity analysis were undertaken to reflect and investigate the uncertainty in the data. The demographic outputs such as population number were mainly influenced by juvenile mortality. Adult mortality was derived from juvenile mortality, and better mortality data for different age groups would improve the reliability of the demographic outputs. The other main outputs such as stock biomass, production and monetary values were most sensitive to the weight of adult animals, especially female adults. Female adults were particularly important because they constitute the major proportion of the flocks.Estimates for live weights in the literature varied greatly and this was reflected in the model inputs. One reason for this wide range could be the variability of weight for different local breeds. For example, the 6-month weight of Menz sheep breed is about two-thirds to that of Bonga sheep breed . While tVarious data sources were used, such as the government Central Statistical Agency's (CSA) annual agricultural sample survey and both peer reviewed and gray literature. However, the analysis was heavily based on the CSA data. The CSA data collection is excellent in that it covers the entire country geographically, conducted regularly, and has a good level of granularity in terms of disaggregation by sex, age, and purpose. However, the age disaggregation is limited only to the total population number and is not provided for other important demographic parameters like birth, mortality, and offtake rates. A major weakness of the CSA data is that it is limited to rural holdings. While the commercial sector is a growing sector, no data has been collected or reported for this sector. Missing or inconsistent data was occasionally a problem. In the future, addressing these weaknesses in CSA data will further increase its value for informing national policy in livestock development.Ethiopia has a large and rapidly growing small ruminant populations, especially goats. However, the sectors are failing to specialize and intensify. This is needed if output and efficiency are to improve. Despite the high number of small ruminants per capita in the pastoral system, the offtake rates are lower with very high kid mortality up to 40%). Efforts to identify and address this high mortality are needed as this will result in big increases in offtakes and productivity, and thus livelihoods. Small ruminants are underutilized for milk production. Harnessing the large goat population for milk production has the potential to improve nutrition and food security in poor small ruminant keeping households, which is needed given the high levels of undernutrition in rural Ethiopia. Small ruminants constitute about 13% of the livestock biomass in the country and, despite the low productivity, still contribute a sizeable proportion (2%) of annual national GDP. With sound policies and effective implementation this contribution can be greatly enhanced whilst minimizing environmental impact. These findings, and those of follow on analyses on disease burden, are needed to guide future strategies for sustainable growth of the sector.The original contributions presented in the study are included in the article/Ethical review and approval was not required for the animal study because the study was based on secondary data.WJ designed the study, collected and analyzed data, and wrote the draft manuscript. YL analyzed data. WA collect data. DM and PS wrote part of the manuscript. JR conceived the idea and designed the study. TK-J designed the study, led the study, and wrote the manuscript. All authors contributed to manuscript revision, read, and approved the submitted version.This study was supported by Global Burden of Animals Diseases Program funded by the Bill and Melinda Gates Foundation, Seattle, WA, and Foreign, Commonwealth and Development Office of UK government [Grant number NV 005366) and International Livestock Research Institute funded by CRP livestock of Consultative Group on International Agricultural Research.The authors declare that the research was conducted in the absence of any commercial or financial relationships that could be construed as a potential conflict of interest.All claims expressed in this article are solely those of the authors and do not necessarily represent those of their affiliated organizations, or those of the publisher, the editors and the reviewers. Any product that may be evaluated in this article, or claim that may be made by its manufacturer, is not guaranteed or endorsed by the publisher.