Amputation levels in Egypt and the surrounding neighborhood require a state intervention to localize the manufacturing of prosthetic feet. Amputations are mainly due to chronic diseases, accidents, and hostilities’ casualties. The prosthetic foot type is traditionally classified according to the number of axial rotational movements, and is recently classified according to the energy activeness of the foot. The localization of this industry needs a preliminary survey of the domestic technological levels with respect to the foot type. Upon the results of this survey, the energy storage response foot has appealing metrics to proceed with its manufacturing. A prototype manufacturing chain is designed and a set of these feet with a certain commercial size of 27 is manufactured. Resin impregnation technology for carbon fiber composites is followed in this work. The feet are tested according to ISO 22,675. Based on the dimensional and mechanical results, a manufacturing value chain is proposed with the prospective resin transfer molding technology. This value chain will guarantee the required localization as well as the natural growth of this value chain with all related activities like accreditation of practices as well as manpower certification. Limb amputation significantly reduces the quality of life (QOL)2 particularly in case of lower limb amputation as it impedes amputees’ mobility3. Amputation levels, as shown in Figure 4, lower limb amputations represent almost 97% of amputations in the United States out of 1.7 million amputees’ population. This indicates the significance of lower limb problem to be handled. On the Egyptian local level, databases about amputation levels are not comprehensively updated. However, the trend of higher lower limb extremity, represented in the United States, can be expected to take place also in Egypt. A supportive argument for this expectation is the high percentage of Diabetes in Egypt where almost 0.5% of them suffer lower limb extremity5. Diabetes, with its consequences of vascular diseases, results in more than 90% of the lower limb extremity6. Out of the Trade map website about export and import data; the documented import bill of Prosthetics and Orthotics (P&O) costs Egypt at least 600 Million L.E. in 2020 based on trade map statistics for HS-code: 9021–10,31–39 up to 1 billion L.E. according to governmental figures7. A greater portion of P&O import bill is not documented because of the non-counted direct purchase of the amputees abroad. This results in an untrusted database about the amputation levels and the P&O parts. The Egyptian state declared its intention in tackling this problem as represented in the conference of “Different, We Are Able’’ which is held in Cairo 2018. The state started to merge the efforts of all related local authorities and individual experts into single consortium. The activities of this consortium are putting rules for providing high-level of medical services, integrating amputees in society, establishing a comprehensive database for people with physical disabilities, and following up the process of an integrated industrial complex to localize and transfer technology for manufacturing prosthetic limbs to overcome the market gap8. This process requires; from another point, building well-trained manpower resources, to have an accredited professional education program and to start local Research and Development R&D capacity to sustain the localization process of P&O industry. This orientation is ensured by funding scientific and applied projects from the Egyptian state as mentioned in the acknowledgment.Globally, thousands suffer every year from complications of various diseases, such as diabetes, circulatory and vascular disease, trauma, and cancer which could lead to limb amputations9 and substitute the missing limb10. During rehabilitation, temporary lower limb prosthetics are used to make amputees accustomed to walking and performing daily life activities safely. The prosthetics are designed to satisfy the functions of the lost limb namely, shock absorption, weight-bearing stability, and progression.In order to improve the amputee’s QOL with lower limb extremity; lower prosthetic limbs are designed to retrieve some movement functions11. It goes without saying that ankle foot manufacturing is a subset of the whole below knee set.More concern is given to the ankle foot and the below knee amputation due to their higher statistics12. These variables affect the prosthetic foot design and consequently the manufacturing technology adopted in Egypt.The required functions of the lost limb depend on the expected activities of the amputee. The prosthetic foot affects the posture, walking correctness, and the loading degree on the joints. Prosthetic foot takes different shapes depending on the severity of disability and functionality and therefore suitable designs and materials are chosen for each caseIn brief, the localization chances of lower limb prosthetic foot are studied in terms of the available technologies in Egypt. The factors affecting the status of the Egyptian manufacturing capabilities are discussed, namely the number of target amputees, definition of amputation levels, prosthetics’ design concepts, material selection, technology readiness level in Egypt for the different manufacturing alternatives. Consequently, a value chain, to control such an industry in the Egyptian state, is proposed.13. For low K-level amputees, solid-ankle-cushion-heel (SACH) foot is considered the most suitable option. SACH foot is the most basic type of prosthetic foot, consisting of a solid foot shaped block, usually made of wood, and combined with an aluminum pylon to join the foot to the socket. This type of prosthetic foot provides supporting function and basic mobility through a simple hinge to mimic the ankle joint motion in sagittal plane, see Fig. As a normal procedure in selecting a suitable prosthetic foot design, the potential level of amputees; according to their mobility and capability to use lower limb prosthesis, is assessed by a physician then amputees are assigned a K-level which has values of K0, K1, K2, K3, or K4. This classification determines the ability of amputees to safely utilize the prosthetic foot, where K0 represents amputees who do not have the ability to walk safely without assistance while K4 represents amputees who can use prosthesis effortlessly and perform dynamic activities14. ESR provides mobility and convenience for users with high K-levels as it is designed with elastic materials. These materials deform under loading, then stores potential energy that is later released in the gait cycle which allows the foot to return to its original shape15.However, SACH foot is not suitable for higher K-level amputees as it loses a large amount of energy during gait cycle, therefore a new design was created for high K-level amputees. Energy-storage-and-return (ESR) foot is the new design which started after the launching of the Seattle Foot16. Composites can improve the gait efficiency by cumulating, storing, and then releasing energy during the gait cycle which is essential in ESR design. The efficiency primarily depends on the prosthetic foot design as well as the composite parameters such as fiber selection, fiber form, type of combination, mass content, as well as the design of the prosthesis17.Composites are used normally as materials in fabrication of ESR. These composites are reinforced with either carbon or glass fibers. Compared to other materials, composites are characterized by superior strength to weight and exceptional biocompatibility18. Despite the ESR prosthetic foot being able to store and release mechanical energy, there is no net positive output work to help the amputee in forward progression. This ESR prosthetic foot does not have the ability to adapt to different terrain. The amputees with passive foot prostheses suffer and face difficulties during walking on slopes20. Also, the smooth roll-over shape of the human ankle–foot which was presented by Hansen21 affects the walking efficiency and performance. Hence, the prosthetic foot should have roll-over characteristics similar to human. Hansen also22 developed the third type of feet in the modern classification which is the bionic foot. It is the ankle–foot prosthesis, which is capable of automatically adapting to different walking surfaces and changing the ankle joint impedance from low to high throughout stance phase. The main problem of adding actuators to the ankle–foot prosthesis is the increasing of the total prosthetic weight which affects the amputee comfort.In brief, the design of the lower limb prosthetic has traditional and modern approaches. The traditional one goes back to 1980, where the foot types, as mentioned earlier, are classified upon the number of axes namely, single axis SACH, multi-axis, and dynamic response. This traditional classification comprises what is called conventional foot (CF) types. The modern classification is based on the energy timeline which divide the prosthetic foot into CF, ESR, and bionic footThe following paragraph will discuss which type of feet is more appealing to the planned industrial complex regarding the expected demand statistics, its economic burden and needed technology.23.Technology readiness level (TRL) is an agreed-upon method to assess the maturity of certain technology. It is a nine-level system as shown in Table 26, Table 28. In other words, keeping a low level of mimicking the natural foot meets the cost limitation of the low-income country amputee. However, it reduces the QOL and negatively affects the surrounding relatives and the whole community’s efficiency. Therefore, selecting a relatively higher degree technology would influence positively the QOL of the amputees and their relatives.The simple non-advanced technology in the low-income countries is the governing reason for selecting CF foot due to its relatively low price and simple maintenance. However, CF foot meets the needs of utmost K2 amputees. Getting the amputee feels more natural walking pattern (gait) requires mimicking the dynamics of an anatomical foot. The second type of feet in Table 29.The import bill of the prosthetic foot to Egypt is mainly attributed to the higher added value of advanced technology foot. This is attributed to the presence of manufacturing centers for CF foot in Egypt. Also, this is evidenced by the market report about growing market in the Middle East especially for advanced prosthetic feet30. The opportunity to automate part of this chain is highly potential by some technologies not available in Egypt like resin transfer molding RTM or Resin pre-impregnated Fabric PREPREG. RTM is more potential to be realized as it is not complicated technology involves the pumping of resin into a closed die filled with stacked carbon fiber fabric in the required foot preform as a one part or more according to the design, see \* MERGEFORMAT Fig. 31. The most advanced techniques are pressurized Resin Transfer Molding (RTM)32 and the use of pre-impregnated carbon fabrics with resin (PREPREG)33 to ensure good quality.TRL of ESR foot is promising. Except for the carbon fiber part, the manufacturing technologies of the foot components are available and mature in Egypt. The carbon foot part itself is processed manually. The chain of the processes comprises of fabric cutting, orientation, stacking, resin infusion, curing, trimming, and machining. The manufacturing process of ESR using composite material has gone through different phases. Starting from manual hand layup which is considered the simplest technique to produce layers of laminates in the composite as it is a low-cost tool and uses room temperature-cured resins. However, this technique is time-consuming and the composite is prone to air bubble formationAs mentioned previously, the aim of this work is to evaluate the current technologies available in Egypt to start localization of manufacturing the lower limb. This evaluation depends accordingly on the TRL of each type of the prosthetic feet prosthesis illustrated in Table 1. Figure Following a modular design would help in the industrialization of the P&O parts and in the definition of the required TRL that should be met. The prothesis consists of several componentsDesigning of the prosthetic foot where the foot breakdown consists of modular parts to help mass production, maintenance and interchangeability. The proposed design is checked by modeling regarding the foot endurance to the expected stresses;Selection of the manufacturing method with respect to the TRL in Egypt and the product complexity;Testing of the ESR foot;Developing a value chain regarding the results and discussion.The proposed methodology of localizing the prosthetic foot manufacturing in Egypt is as follows:To model static tests on the prosthetic foot, ANSYS workbench was used. ACP module was used to develop solid composite parts, the mechanical model module to develop a meshed platform for the static test, and finally static structural module to perform the static test using the parts developed in the other two modules. In the ACP module, the materials used for the parts are wet epoxy carbon woven and resin epoxy as built-in materials in the ANSYS engineering data sources. With the aid of the stack-up fabric layers from the manufacturing stage, a solid model was created using the oriented selection set and resin epoxy as its global drop-off material as shown in Fig. Parts created in the previous modules were transferred as solid bodies to the model section in this module. Since the parts were not pre-assembled on the CAD software, the parts were assembled in ANSYS Mechanical. Contacts and boundary conditions were applied to the parts to perform the static tests. For the contact between the lower parts and the upper part was estimated as a bonded connection. For the contact between the lower parts and the platform, it was estimated as a frictional surface with a coefficient of friction equal to 0.2. For the formulations, the frictional contact was formulated based on Augmented Lagrange while the bonded contact was formulated automatically by ANSYS. To mimic the conditions of the ISO static test, multiple constraints were applied to ensure the same behavior throughout the simulation. Initially, the holes for the pyramid were defined as fixed supports. Then, the platform was given a displacement of 20 mm in the vertical direction while keeping the side face from moving in the horizontal and transverse direction as shown in Fig. 2, Modulus of elasticity 200–588 GPa and tensile strength of 2800–5490 MPa. Upper and lower carbon fiber parts are prototyped using assisted vacuum hand layup as shown in Fig. Manufacturing of the prosthetic foot parts is carried out briefly as follows. Carbon fiber fabrics are used in manufacturing the upper- and lower-foot parts. They are of density of 200 g/mAfter foot manufacturing, static compression tests are carried out to check foot endurance and to validate modeling. By comparing the results obtained from testing the manufactured model using FEA and experimental testing as shown in Fig. The entire assembly of the prosthetic foot components took place after fabrication, and it went through mechanical testing following ISO22675 guidelines, as detailed in Table We confirm that all the tests in this work were not conducted on human subjects. Additionally, all methods were performed in accordance with the relevant guidelines and regulations.e.Well definition of the number of needed production lines to provide service. This will be based on the geographical distribution of amputees and the available trained/ educated manpower;f.Standardizing the health and insurance services for the people with disabilities in a proper management procedure;g.Best value-to-money or highest feasible QOL with affordable price.The value chain for the prosthetic foot in Egypt would be better to be designed and oriented not to be left for market driving forces. This objective is in agreement with the governmental policy. To build this value chain, the sub-objectives are targeted as following:Regarding the third point mentioned above and based on the TRL study; the supposed manufacturing line for the prosthetic foot is shown in Fig. 44.This value chain is still a global one as it depends on imported materials like carbon fiber. But the use of other alternative local materials like natural fiber will make the chain more self-sufficient. This suggested value chain focuses on the prosthetic foot composite part only but the other components in touch with the patient like the socket are not considered in this work. This leaves a chance for the current practices and the available prosthetists to adapt gradually with the new technologies as recommended inKnow-how transfer and localization of ESR Prosthetic Feet Manufacturing Processes in Egypt, as shown throughout this work, are essential to cover the community needs and comply with the state vision. The TRL of the product components shows promise for proceeding with the establishment and growth of this value chain.The study proves the possibility of prosthetic feet manufacturing using available material and manufacturing technologies. Available materials include carbon fiber fabrics, binding epoxy materials, metallic adapters and joints. Available technologies like VARTM technology for resin impregnation in fiber fabrics and conventional machining for metallic parts.Also testing of the prosthetic foot is an important part of the value chain. Testing is carried out according to ISO 22,675 to localize the product accreditation in the future.45. It is worthy noted that there are other prospective technologies for prosthetic feet manufacturing like 3d printing which can be followed.In the near future, prosthetic feet manufacturing will open the door to foster the use of many non-localized technologies in resin impregnation for engineering products of fabric composites. The impact of localizing these new technologies of resin impregnation is not limited to the medical rehabilitation, but it will disseminate to other versatile aspects such as automobile industry


Successful development of a drug candidate requires availability of robust methods that enable precise and quantitative assessment of the biological effects exerted by the molecule of interest. In case of DNA Damage Response inhibitors, the most proximal readout of their efficiency is the level of induced DNA damage, usually - DNA breaks. Here we review the methods that are currently used for the assessment of the level of DNA damage, with special attention to their specificity and sensitivity. We also discuss the most common problems and challenges related to the classic IF or IHC methods that indirectly report on the activation of DNA repair mechanisms as the downstream effects of occurrence of the DNA lesions. Finally, we highlight the advent of new tools, such as STRIDE, which have the potential to transform the landscape of DDR functional biomarkers. Cancer therapy based on inhibition of DNA repair pathways still seems a relatively new concept , yet canThe enormous leap in our understanding of the DDR in the last 20 years would not have been possible without the development of immunofluorescence (IF)- and immunohistochemistry (IHC)-based microscopy methods aimed at detecting players engaged in various DDR pathways. A classic example of such methods is the detection of γH2AX—a phosphorylated form of H2AX histone variant . PhosphoWhile undeniably the most popular, γH2AX is only one of many DDR-related markers that can be used for assessing the level of DNA damage. Within the ever-growing list of known DDR players RAD51, 53BP1, pRPA and pKAP1 gained the most interest in recent years . HoweverAlthough sometimes thought of as old-fashioned, the comet assay is currently one of the most popular methods used for DNA damage level measurements, especially in the genotoxicity testing field. The concept of the method, which combines single-cell electrophoresis with fluorescent microscopy is quite simple - negatively charged DNA migrates towards the anode when DNA breaks are present. The more DNA breaks there are, the farther the migration, which is then seen in the form of so-called ‘comet tails’ . While tin situ, which results in the attachment of a biotinylated linker that contains a barcode sequence. Genomic DNA is then extracted, captured with streptavidin beads and finally PCR amplified and sequenced. Improvements to the BLESS protocol have led to the introduction of BLISS, i-BLESS and sBLISS variants as well as other similar techniques, such as END-seq, DSB-capture, CNCC-seq and qDSB-Seq  methods to leave a footprint on the DNA damage detection field, in recent years we have seen an unexpected increase in the number of available methods for genome wide DNA breaks profiling at nuclear resolution. The breakthrough came with the BLESS technique introduced by the Crosetto group , in whicqDSB-Seq . Among oqDSB-Seq . The capDespite those very important advances, several limitations of the techniques remain, which makes it difficult to apply them on a regular basis in DNA repair related studies and drug development. There are important downsides from a technological perspective - artificial DSBs might be introduced during the fixation procedures, the minimal number of cells required for these assays to produce meaningful results is still high and they may not be suitable for all types of cells . Biologyin situ labelling and detection of single- or double-strand DNA breaks in any type of biological material. The method is based on enzymatic modification of free DNA ends by incorporation of modified nucleotides with subsequent PLA-based detection of these modifications. Two basic variants of the technology exist (sSTRIDE and dSTRIDE), which in most cases enable distinction between SSBs and DSBs by utilization of different enzymes . STRIDE in various ways can be considered superior to the existing methods of DNA damage detection. First, it is direct and sensitive—features that were not thought to be possibly combined in a single assay before. The lower limit of detection of STRIDE is one  single- or double-strand DNA break, which ensures an unprecedented sensitivity level of the assay. Second, the undeniable strength of the method is its specificity, which is achieved by incorporation of exogenous molecules (modified nucleotides) to the cells, minimizing non-specific binding of antibodies and further ensured by the principles of Proximity Ligation Assay (The critical pitfalls of existing IF-based methods for DNA damage detection are the lack of specificity, poor sensitivity and their reliance upon active DDR mechanisms. We have recently introduced STRIDE , a fluoron Assay . The latin situ modification of DNA ends is a step that is somewhat similar in STRIDE and DSBs profiling methods, these approaches also share some of the shortcomings. It is currently unknown what is the percentage of DNA breaks that are not enzymatically modified after cell fixation. The reasons for the lack of incorporation of modified nucleotides are at least twofold - these are chromatin compaction and overall steric hindrance around the DNA break and the presence of damaged, so called ‘dirty’ DNA ends, i.e., ends lacking a necessary 3′OH moiety. These problems are mitigated in the STRIDE protocol in sample preparation steps which allow to increase chromatin accessibility and convert damaged DNA ends into substrates appropriate for the enzymes used. The number of DNA breaks detected by STRIDE might be further underestimated when the lesions are clustered, leaving little space for the antibodies to bind or resulting in fluorescence foci located at a distance that is below the resolving power of standard wide-field or fluorescence confocal microscopy. The latter problem can be partially overcome by using super-resolution microscopy, but that would come with a trade-off in throughput and scalability. Also, it is important to note that while dSTRIDE is specific to DSBs, sSTRIDE is potentially capable of producing signals at 5′overhangs of a DSB, apart from DNA nicks and ssDNA gaps. Appropriate modification of the protocol, i.e., blunting of DNA ends before PolI enzymatic reaction can be applied to resolve this issue.As the in vivo pre-clinical and clinical studies and very often this test ends in a failure. The expectation for a robust PD biomarker to be able to accompany each stage of drug development is thus the most important and at the same time the most difficult to meet.General expectations of drug developers regarding PD biomarkers are usually quite similar. First, they should be as proximal as possible, i.e., informing about a process that is directly or very closely related to the target. Second, it is expected for a PD biomarker to immediately report on the effect of target modulation. Finally, there are technical requirements related to specificity, sensitivity and reproducibility of a particular assay. If a PD biomarker meets at least some of these criteria, the drug development process can be facilitated in many ways. In early-stage discovery it allows to speed up drug candidate screening, pick the most promising leads and often helps to discover the mechanism of action of the compounds. Some of the tools may also be found very useful in characterisation of the targets’ biology what can be applied at a very early stage of target validation, drug discovery and design. Yet the real value of any biomarker is verified when moving into The methods used for DNA damage level measurements discussed here  were rarin situ detection of DNA breaks provided by STRIDE overcomes most of the weaknesses of the existing methods (Direct and sensitive  methods . Finally methods . Prelimi methods  give hop


The widespread use of the lung ultrasound (LUS) has not been followed by the development of a comprehensive standardized tool for its reporting in the intensive care unit (ICU) which could be useful to promote consistency and reproducibility during clinical examination. This work aims to define the essential features to be included in a standardized reporting tool and provides a structured model form to fully express the diagnostic potential of LUS and facilitate intensivists in the use of a LUS in everyday clinical ICU examination. We conducted a modified Delphi process to build consensus on the items to be integrated in a standardized report form and on its structure. A committee of 19 critical care physicians from 19 participating ICUs in Italy was formed, including intensivists experienced in ultrasound from both teaching hospitals and referral hospitals, and internationally renowned experts on the LUS. The consensus for 31 statements out of 33 was reached at the third Delphi round. A structured model form was developed based on the approved statements. The development of a standardized model as a backbone to report a LUS may facilitate the guidelines’ application in clinical practice and increase inter-operator agreement. Further studies are needed to evaluate the effects of standardized reports in critically ill patients. The lung ultrasound (LUS) has emerged in the last decades as a highly sensitive and specific tool for the diagnosis of acute lung diseases both in Intensive Care Units (ICUs) and ordinary wards . LUS cliDue to the large number of findings to be considered while performing a LUS, standardized LUS recording and reporting are pivotal to promote consistency and reproducibility during a clinical examination.A standardized report could also improve the adherence to the published guidelines, as the type of report and its structure affect the quality of information reported and collected by physicians, and furthermore may increase inter-operator agreement . As endoTo our knowledge, a comprehensive standardized tool to report LUS examination in the ICU has not been validated yet. On the contrary, our group previously investigated differences and similarities of LUS reports in a network of ICUs in Italy  and founConsequently, this work aims to define the essential features to be included in a standardized reporting tool and provides a structured model form to fully express the diagnostic potential of the LUS and facilitate intensivists in its use in daily clinical examination.We conducted a modified Delphi process to build consensus on the items included in a standardized report form and on its structure. The modified Delphi technique is a well-known means to develop a consensus through subsequent rounds of anonymous evaluation of a list of statements, followed by the in-presence discussion of items . A commiThe 1st round used a structured anonymous multiple-choice questionnaire (MCQ) regarding the elements to be included in the LUS report . Answers were collected using the Google forms platform, and panelists were again able to express comments and suggestions in a free-text form.Results from the second Delphi round were collected and discussed at a new virtual meeting held on 21 October 2021. A total of 28 statements reached a consensus during the second round. The other five items not reaching a consensus were discussed and rephrased to improve clarity and increase agreement.At the end of the 2nd virtual meeting, the six modified statements were proposed for anonymous online voting through the Google survey platform. Four items reached a consensus (with an 80% threshold for consensus). In contrast, 2 items were excluded from the results.All the experts and clinicians involved and contributing to the Consensus Conference are listed in The process is displayed in The panelists reached a consensus on 31 statements out of 33 statements. All the statements are presented in n° 26: it is useful to report if the examination is performed in the emergency setting.n° 29: it is useful to report the type of probe.Specifically, the following two statements  did not reach the threshold for consensus:Several models using different thoracic LUS zones have been published throughout the last decade, often related to a single disease. Volpicelli investigated interstitial disease (pulmonary edema) and proposed a four-zone model investigating two anterior and two lateral zones per side, neglecting the posterior zones . SimilarIn critically ill patients, the interstitial syndrome is not the only encountered disease, and most pathological findings are localized in the posterior zones. Accordingly, Lichtenstein proposed a 3-zone model  , while VCompared to the 8-zone protocol and the 6-zone protocol, the use of a 12-zone protocol has the advantage of considering the posterior areas. Since the introduction of the Bedside Lung Ultrasound in emergency (BLUE) protocol , the 12-We assume that the potential disadvantage of lengthening the examination aiming at a thorough investigation of the lungs may be surpassed by the net advantage of detecting earlier those conditions otherwise overlooked by antero-lateral scanning protocols. We endeavor a more precise localization of findings by exploiting anatomical landmarks  to evaluate any further evolution of the condition, especially for the location of small subpleural consolidations or lung point in case of small pneumothorax.It is also preferable to report which zones have not been examined to make an exhaustive report: those zones that have been investigated but not described should be considered non-pathological.Lung sliding and lung pulse are cornerstones for the exclusion of pneumothorax and should be accurately investigated in every situation where this disease is clinically suspected.More frequently, when facing interstitial disease, describing pleural features as normal (regular) or pathological  may be crucial in differential diagnosis between infective/flogistic or cardiac/renal etiology. These characteristics should always be assessed and reported if abnormal.shred sign, an irregular deep border corresponding to small subpleural consolidations separated by the surrounded aerated parenchyma [Moreover, the pleura has been extensively investigated during the COVID-19 pandemic. Pleural features have been promptly recognized as highly suggestive if not pathognomonic of COVID-19 pneumonia , rangingrenchyma .Unlike a conventional ultrasound, a LUS is primarily based on artifacts generated by lung-air interface. Each artifact has been associated with a specific pulmonary condition but not all the signs have the same value and application in daily practice . Many LULung sliding, as an example, may effectively rule out pneumothorax with almost 100% negative predictive value  with lowWe endorse the reporting of LUS signs rather than profiles .A quantitative approach for the assessment of the severity of the disease is recommended. While multiple approaches have been proposed for the quantification of the loss of aeration , a quantThe LUS may detect pleural fluid from effusions larger than 20 mL, approaching the diagnostic performance of computed tomography. Historically, the LUS description of pleural effusion was semi-quantitative, described as minimal, mild, moderate, or severe. Another straightforward semi-quantitative approach is to report the extension of the effusion over the involved intercostal spaces, and its depth to calculate the volume of the effusion. Published formulas on effusion volume improve decision making on thoracic drainage. As reported by the statement n° 16, the position of the patient plays a key role not only in detecting the effusion but also in quantifying its volume  trunk elevation, the Eibenberger formula—patient supine). Robba et al. recently included the quantification of the effusion in a panel of basic US skills for intensivists .We suggest describing the consolidation in a semi-quantitatively manner as a precise quantification could be challenging especially for inexperienced users. The description of bronchograms  allows the differentiation between atelectasis and consolidation  and may The Lung Ultrasound score (LUSS) is a validated tool to assess regional and global lung aeration in ARDS  and enabAfter a systematic description of findings, we suggest reporting conclusions and recommendations in a dedicated free-text section. This free-text conclusions section minimizes the main limits of adopting a SR, such as non-reporting important information due to their exclusion from the standard model.We endorse including in the report the elective/emergency setting to concisely indicate signs of deterioration and to allow monitoring of its clinical evolution. Moreover, we suggest specifying any difficulty encountered during the investigation.Since the LUS is an artifact-based technique, technical factors might influence ultrasound findings, including the type of US device and the type of probe. Several studies aimed to determine the overlapping among results obtained using different probes . This coIt is noteworthy to consider how findings could change according to variations in ventilation; if neglected, this aspect could hamper a more precise quantification of aerated parenchyma under specific conditions and the assessment of the severity of illness and evolution over time.The LUS allows the quantitative estimation of lung recruitment using the LUS score , and thiI.C., M.G, R.S., S.F., E.G., S.M. generated the TUONO reporting tool, a structured model form for reporting a LUS which aims to summarize the features that were agreed on from the Modified Delphi process. The model form was distributed to the panel of experts for editing and approval before the manuscript submission.The aim of the model form was to facilitate the application of guidelines and the transmission of information between operators.The structured form is reported in the To our knowledge, this is the first consensus on LUS reporting, designed to define the elements to be included in a LUS report and to develop a reproducible model to facilitate the guidelines’ application in clinical practice and to increase inter-operator agreement. The LUS is a point-of-care widely available tool, gaining a significant role as a first-line evaluation during the COVID-19 pandemic, specifically in contexts where a CT scan was not available or scarce due to the high surge of patients ,33. The A limitation is the reduced number of ICUs involved in the project, which are part of a network of nonteaching centers and teaching hospitals in Europe (GiViITI network), dedicated to quality improvement in intensive care. Larger multicentric international studies are warranted to better assess the practices of reporting around the globe and compare the best options for LUS reporting. Another potential concern was related to the timeframe of this study; due to COVID-19 pandemic ebbs and flows, there was a 11-month-interval between round 1 and round 2. However, none of the experts withdrew their consensus.The main advantages of Delphi method are the opportunity for a large panel of experts to participate in a consensus process, the ability to focus on ideas rather than on individual opinions, and to define priorities. The modified Delphi process has the further advantage of face-to-face rounds to share and discuss available information while avoiding experts’ bias during the successive anonymous surveys.We presented the results of the first consensus on LUS reporting, designed to develop a reproducible model to facilitate the guidelines’ application in clinical practice and to increase inter-operator agreement. The panel proposed 31 items, to improve standardization in reporting and facilitate the LUS clinical practice, summarized in the proposal for a possible form. Further studies are needed to evaluate the effects of standardized reports in critically ill patients.


Climate change is having an increasingly rapid impact on ecosystems and particularly on the issue of water resources. The Internet of Things and communication technologies have now reached a level of maturity that allows sensors to be deployed more easily on sites to monitor them. The communicating node based on LoRaWAN technology presented in this article is open and allows the interfacing of numerous sensors for designing long-term environmental monitoring systems of isolated sites. The data integration in the cloud is ensured by a workflow driving the storage and indexing of data, allowing a simple and efficient use of the data for different users  through specific dashboards and extractions. This article presents this infrastructure through environmental monitoring use cases related to water resources. Climate change and human activities are having an increasing impact on ecosystems. It is crucial to monitor them in order to understand the interactions between the many factors that disturb the balance. In this context, the issue of water is paramount: how to improve and maintain its quality ? How canSensor networks and, more generally, the Internet of Things (IoT) make it possible to envisage long-term monitoring of these environments. Indeed, the progress made in electronics over the last few years in terms of integration, energy sobriety, and cost reduction promotes the large-scale deployment of electronic devices. In addition, telecommunication technologies now allow the transmission of data over long distances (a few kilometers), even in the absence of heavy infrastructure and power sources.Wireless sensor networks have revolutionized the field of environmental monitoring, allowing real-time data collection and analysis ,5,6, butThe latest research has focused on improving the accuracy and reliability of data collected through wireless sensor networks . One of Many experiments use commercial Arduino boards to make wireless nodes ,16,17. TThe main issue of our project is focused on the energy performance and longevity of the nodes. This issue is addressed in different ways in the literature. There are studies that focus on energy management at the node level through optimized operations of the node . There aWithin the framework of our project, we have designed a versatile, robust, and low-power node named Sensors open Lora node (SoLo).In order to demonstrate the relevance of our solution, four sites were instrumented where water is a major issue: (1) a mountain lake subject to the pressure of agricultural practices; (2) a river channel connected to an oxbow lake whose hydroecological responses to climate change must be understood; (3) a former uranium mine close to a watercourse that can have an impact on the environment, and finally (4) a farm that uses water for its production. The topology of a private LoRaWAN (Long Range Wide Area Network) network deployed on the ConnecSenS sites is given in A star network is built with a gateway at the center, which receives the data frame from nodes scattered around the site. The communication between the nodes and the gateway is performed using LoRaWAN, a Low Power Wide Area Network (LPWAN) networking protocol based on the RF LoRa modulation technology operating, in this case, at 868 MHz. This technology was developed in 2014 by the French start-up company Cycleo, and today it is managed by Semtech . It is bDue to the large variety of sites addressed by the present project and envisaged by future projects, as well as the number of different sensors deployed in the environmental sites, a generic LoRaWAN end node, named SoLo, has been developed. This node has been designed to allow data reading from a large variety of associated sensors, and it can be configured at the hardware and firmware levels to be adapted to the specific needs of measurements, autonomy, data processing, and data transmission. The hardware architecture of this node is given in The SoLo node is built around an STM32 microcontroller , which cThe SoLo node also integrates a small electronic card on which several sensors are implemented. These internal sensors can be activated or not. They measure temperature, air humidity, atmospheric pressure, luminosity, and acceleration. Several protocols of communication are implemented to communicate with external sensors: UART, SPI, SDI-12, I2C, analog and digital IO. These protocols correspond to a large variety of interfaces used by sensors. A slot implemented on the main electronic board allows the external sensors to be connected to the suitable input/output of the microcontroller.The electronic boards and the battery cell are protected by an IP67 package/box (151 cm × 125 cm × 60 cm). On the external sides of the box, four M12 connectors allowing the interfacing of several sensors, a reset button, an SMA connector for the LoRa antenna, and a USB waterproof connector to interface a PC with the node are present. All these external connectors allow operations in the fields without needing to open the node see .A main feature of the developed system is the possibility to easily configure its operations through a text file written on the SD card (“the configuration file” cited above). A list of the main parameters currently available is given in The energy autonomy of the node is a key issue in guaranteeing function for several months  without any human intervention. This autonomy depends mainly on the periodicity of the activation of the GPS module (mainly to synchronize the clock), the reading period of the sensors, and the delay between the LoRa transmissions. The SoLo node can thus be tuned to fit the supported application’s requirements accurately. It was validated on our sites: the nodes are still working in different places.The block diagram of the architecture of the firmware implemented inside the node SoLo is given in When the node initiates a LoRaWAN communication, an acknowledgment is returned by the server if it accepts the data to be sent. If the node does not receive this acknowledgment, it does not try again but stops communication until the next scheduled transmission time. Then, the previous data will be added to the new one within the limit of the payload. This mode of transmission saves energy when the conditions for the LoRa communication are not optimal. Still, it requires space in the payload to accumulate data in a single LoRaWAN frame.The data workflow infrastructure is composed of two main software modules: (1) the Network Management Module (NMM) and (2) the Data Management Module (DMM). The NMM focuses on LoRaWAN communication and architecture. LoRaWAN is one of the LPWAN technologies widely studied currently ,36,37 wiConcerning the data, this NMM converts LoRaWAN frames of SoLo nodes into files generally in JSON (JavaScript Object Notation) format. The generated data files are then processed by the DMM of the data center infrastructure. This DMM has multiple functionalities, and it can be called “hybrid” because it combines tools from different software families. For example, it integrates database management systems (DBMS) in the NoSQL model (storing JSON and using corresponding SQL (Structured Query Language) queries) and more recent search engine technologies. As a DBMS, we can mention PostgreSQL  with PosOther functionalities are related to data visualization with the availability of tools such as Grafana  and KibaIn order to achieve real-time data visualization and to address the most various cases of possible use, a near-real-time multi-pipeline architecture has been designed in a generic way. This architecture is then able to process data from various sensor deployments. Once the data JSON file is created or updated with a new measure, a lightweight shipper called Filebeat  forwardsThe routing of each measurement to its right index location is done using an index naming convention in which the experiment name , which largely contributes to research in lacustrine ecosystems in France.The Aydat lake is located in the French Massif Central  at 837 m above sea level. This natural lake was created by the damming of the Veyre river by a basaltic lava flow of 8551 ± 400 cal yr BP ago. It is a small dimictic lake with a total area of 60 hectares, a maximum depth of 15 m, and a small catchment area of 16,800 hectares. It is a eutrophic lake with recurrent cyanobacterial proliferation , especiaScientific objectivesSince the 20th century, anthropogenic activities have constantly increased, leading to a deterioration of water quality resources. The accumulation of nutrients, mostly coming from anthropic activities, led to a massive cyanobacteria bloom, which impoverished the ecosystem. The proliferation of cyanobacteria has a direct impact on ecosystem functioning and is also dangerous for human and animal health due to their capacity to produce cyanotoxins . The latIn order to reduce the cyanobacterial proliferation and restore the ecological functions of the lake, a wetland was created in 2012 . As thisSensors1–3: Aquatroll 200 data logger. This water level and water temperature probe is installed in the Veyre river, upstream and downstream of the lake. The water level measurement is based on a piezoresistive sensor, whereas the water temperature and the specific conductivity, the salinity, and the Total Dissolved Solids (TDS) are monitored using a balanced 4-electrode cell. The water discharge series can be computed using a rating curve calibrated with a gauging protocol.® Dissolved Oxygen Sensor, a temperature sensor, a turbidity sensor, chlorophyll-a sensor, a blue and green algae sensor, rhodamine sensor, and finally a pressure sensor for water depth measurement. The Hach LDO® Dissolved Oxygen Sensor provides a measure with Luminescent Dissolved Oxygen (LDO) technology. The Hydrolab conductivity sensor uses four graphite electrodes in an open-cell design to provide highly accurate and reliable data. The sensor measures specific conductance, salinity, total dissolved solids (TDS), and resistivity. The conductivity sensor uses four graphite electrodes designed to be compliant with the ISO 7027 Turbidity Measurement Standard. The Hydrolab temperature sensor is a variable resistance thermistor (316 stainless steel for corrosion resistance). Hydrolab sondes are available with integrated pressure sensors that provide depth measurements. Data acquisition of each parameter takes place every hour.4: Hydrolab HL7 multiparameter sonde. The multiparameter sonde comprises eight sensors, including an electrical conductivity sensor, a Hach LDO5: Temperature data logger. The HOBO data loggers record temperature with the high-frequency acquisition , located in the middle point of the Aydat lake, every 20 cm from the water surface to 3 m deep. The upstream river temperature (Veyre) is also monitored with eight HOBO data loggers regularly distributed (every 1 km) from the headwater to the river mouth.Three types of sensors are deployed on this site see :1–3: AquSite descriptionThe Allier River (France) is one of the last remaining European unregulated rivers with highly dynamic meandering sections. It flows 421 km north from its source located at 1485 m a.s.l. in the south part of the Massif Central  to its confluence with the Loire River at the Bec d’Allier. The hydrological and flood regime of the Allier River is considered close to natural or unregulated even though two dams (Poutès and Naussac) located in the upper basin partially affect the discharge . TherefoScientific objectivesRecent climate change has caused significant changes in the Allier River hydrology. More specifically, an analysis of discharge records shows an increase in both severity and duration of low flow during the summer period and a decline by 10% of higher peak flows ,54,55,56In this specific context, we designed a suitable monitoring system for the Auzon Oxbow site see  in orderSensorsThe ConnecSens monitoring system of the Auzon Oxbow constantly records three types of environmental measurements over the three studied plots of black poplar populations. (1) The water level and water temperature of each water source  with an Aquatroll 200 data logger; (2) the growth of three black poplar individuals per surveyed population using the PepiPiaf microdendrometric sensors ; (3) micSite descriptionThe former uranium mine of Roffin is located on the Gourgeat watershed in Lachaux . Underground mining began in 1946, and in 1948 a treatment plant was built where the first French industrial processes for extracting uranium from ore were tested. Several settling ponds were also created to collect the residues from the ore treatment. The absence of significant veins, the difficulties of extraction, and above all, the discovery in 1952 of the important Bois Noirs deposit, located about ten kilometers away, led to the abandonment of mining in 1956 and the closing down and dismantling of the plant in 1958. The concession was given up in 1976. All in all, this first French uranium mine operated for about ten years and produced a mere 30 tons of uranium.The more or less radioactive mine waste rock was left in place, and the highly radioactive ore processing residues  were abandoned in the old settling ponds. Over the years, vegetation has taken over the area without any significant human intervention since then, which is a unique case in France. Thus, the Roffin site is an in-situ laboratory (ZATU) for studying the medium- and long-term consequences of former uranium mines on ecosystems and the effects of low doses of radioactivity on living beings .Scientific objectivesThe objective of the instrumentation is to collect sufficient data to understand the transport and transfer of radionuclides in the different environmental compartments.The two associated vectors are air and water. For air, the parameter monitored is radon in order to determine the exposure of the ecosystem to radon gas and to understand the exhalation fluxes from the ground. For water, the aim is to monitor the entire watershed to obtain a hydrogeological model  and understand the exchanges between surface and groundwater. This model will be a major input for radionuclide transport simulations. The collection of a very large number of long-term data will allow us to obtain a reliable hydrogeological model and validate the results of the radionuclide transport simulation tools.SensorsThe monitoring system is based on four kinds of sensors: (a) the Aquatroll 200 for electrical conductivity, temperature, pressure, and water level; (b) rain gauges, (c) weather station; and (d) for radon gas, AERTT+©.The deployment of the sensor network on the site faces several communication difficulties: the topography (canyon), the vegetation, and the distance from the GSM network antennas that involves a distance between the wetland and the gateway of more than 300 m see .Site descriptionThe INRAE site of Montoldre is one of the real conditions deployment sites of the network and data infrastructure presented in the previous sections. The Montoldre farm is located in the department of Allier in the Auvergne-Rhône-Alpes region at 300 m above sea level. The main goal of this monitoring site is to study the water in an agricultural use case. The network is based on different SoLo nodes connected to a local gateway through the LoRaWAN communication protocol. One of the particularities of this deployment is using an Ethernet connection between the gateway and the LoRa Server instead of a mobile technology protocol found in other sites.Scientific objectivesIn the Montoldre monitoring site, an experiment was conducted for months with the acquisition of data related to air and soil elements. Water is studied as the essential element for the cultivation of agricultural plants. Sensors are also used in the context of this experimental farm to have information on soil conditions for machines or robots working in the fields. The performance of node SoLo is also tested on this site: robustness of the communication, autonomy, etc. see .Air temperature, humidity, and luminosity measurements are collected using the embedded sensors of the SoLo nodes. For the soil temperature and moisture, SoLo nodes are equipped with Truebner SMT-100 sensors ; see FigNode operating indicators are also monitored, such as battery level, RSSI , and SNR  for quality of transmission. For this experiment, SoLo nodes are located at different distances to test the impact of a LoRaWAN star topology network with heterogeneous ranges. Indeed, some nodes are localized at 50 m of the Montoldre LoRaWAN gateway, which is very close if we consider the LoRa capabilities. Other nodes are placed at 500 m of the gateway. The goal is not to reach a maximum transmission range but to study the possible impacts of spreading the nodes through different conditions at the level of an experimental farm.For data visualization, a dedicated interface was built for each data-collecting project with a possible mix of dashboards, mostly graphs and data tables. A collecting project can go from a group of SoLo nodes in the same or in different areas of a specific monitoring site. The following For example, this interface allows comparing measurements from different SoLo nodes equipped with the same sensors. Comparison between different areas or experimental deployment conditions can be made. Here, the graphs and tables are complementary. Graphs provide a “rapid look” at the measurements and their evolution over time. Tables allow focusing on a given date or period with visualization of the data values. Data visualization is done by time series generated by queries on quantities (variables). Strikingly, the simplicity of the queries allows all users to create their own graphs. Secure access through the use of a personal account  and the https (HyperText Transfer Protocol Secure) protocol enables data isolation, i.e., providing everyone with access only to the data they need and they are allowed to access. This is achieved with the combined use of group and organization with data sources in Grafana. An interesting point is potentially creating data sources for particular events by reindexing existing data reduced to defined variables, time intervals, or material. This reindexed source can then be deleted once the event is completed. Finally, although focusing on the real-time aspect, the dashboards can also be used for more sophisticated queries, such as cross-referencing or historical data. In this case, these graphs retrieved data from sources built upon PostgreSQL databases.The power consumption of the LoRa radio transceiver depends on the modulation parameters. Therefore, the channel frequency is set to 868 MHz, the bandwidth to 125 kHz, and the transmission power fixed to 25 mW. However, the spreading factor (7 to 12) can be selected through the data rate (5 to 0) parameter inside the configuration file;The power consumption of the internal sensors which the operator has activated;The power consumption of external sensors if the node itself provides the power;The period of data acquisition;The period of data transmission;The temperature of the battery cells has a significant impact on their discharge capacity .SoLo node battery voltage monitoring, through the LoRaWAN communication network, allows the detection of power failures and then triggers on-site maintenance. The battery life can also be estimated to anticipate the maintenance operations, but the modeling of the battery discharge must integrate several variables, such as:Results presented in  give somUsing a dedicated test bench, several discharge curves were obtained by connecting the battery to different values of resistors. For each, the initial slope and the battery lifetime have been measured. The results are plotted in In a wireless sensors network, the analysis of the Packet Loss Rate (PLR) is crucial to assess the reliability of the network. Several factors affect the PLR, such as the weakness of the radio signal, the radio interference, the transmission distance, and the radio screening introduced by vegetation, buildings, and topography. The LoRa technology, integrated into the ConnecSenS network, presents a feature that can help to increase the communication range. Depending on the value of the Data Rate (DR) parameter, the communication range between the nodes and the gateway can cover a more (DR = 0) or less (DR = 5) long distance. However, as shown in An analysis of the data collected on the field allowed us to extract the Packet Retransmitted Rate (PRR) and the PLR for each node. The PRR is the percentage of packets not received at their first sending. The PLR is the number of packets lost, computed as the total number of packets theoretically transmitted minus the number of received parquets out of the total number of packets theoretically transmitted. The results are presented in Depending on the nodes, the packet retransmission rate changes from 4% to 54%. The values presented in In This analysis shows that the transmit period and the DR parameter are interesting levers for reducing the packet loss rate. The optimal solution can be calculated theoretically if the size of the payload is known. If not, an experimental analysis can be carried out to find the best compromise.γ is the signal power loss coefficient (PLE). K is a constant governed by the operating frequency and power of the antenna: λ being the wavelength of the signal.The Received Signal Strength Indicator (RSSI) of the LoRaWAN can be seen as a function of the transmission frequency and antenna properties, but mainly the distance between transmitters and receivers. The most common path-loss model is the log-normal model, which is given by ,62:(1)D=Moreover, the received signal depends on many components, with visibility (Line-of-Sight or LOS) being the strongest. As far as we know, three LOS parameters may impact the signal attenuation: (1) whether the nodes are actually within the visibility range of the gateways; (2) the topographic configuration of the LOS; (3) the extent of the LOS masked by the forest cover. Based on the data collected from the four study sites, the objective is to assess the respective statistical effect of the logarithm of the Euclidian distance between nodes and gateways (logD) and the three LOS parameters listed below  over the signal strength (RSSI).The RSSI timeseries were collected from the four study sites using 16 SoLo nodes from 3 July 2021 to 14 November 2021 for the Auzon site , from 25 May 2021 to 20 December 2021 for the Aydat site , from 5 January 2021 to 20 April 2022 for the Montoldre site  and from 14 April 2021 to 23 May 2022 for the ZATU site (N = 2374 distributed over three nodes).Viewshed toolkit provided by the ArcGIS Pro software . Each node of the study site is then classified as IN or OUT of the viewshed of its gateway and stored in the VIEW bimodal variable of the dataset. The LOS_CONF parameter is derived from the LOS topographic profiles of each node which are extracted from the same RGE DEM using the Stack Profile tool provided by the ArcGIS Pro software . Each node is then classified into one of the three topographic configuration classes  based on the open or broken nature of the LOS and its topographic mask index, according to The LogD parameter is computed as the decimal logarithm of the Euclidian distance between each node and its associated gateway. Euclidean distances between nodes and gateways are derived from their geographic locations expressed in Lambert 93 cartographic projection and collected in the field using a Trimble R10 GPS/GNSS device with centimeter accuracy. The viewshed of the gateways of the four study sites was computed using Digital Elevation Model (DEM) with a ground resolution of 1 m  and the ggstatsplot R package [A linear regression model was fitted to the RSSI data as a function of the logarithm of the Euclidean distance (logD) between the nodes and the gateway in order to quantify the effect of distance on LoRa signal fading. The residuals  are then integrated into the dataset, and a random forest regression model is fitted to identify the visibility variables  that contribute the most to explaining the deviations from the standard lognormal path-loss model. Statistical comparison of RSSI between groups was also performed using the nonparametric Kruskal–Wallis test implemented in the  package .p-value < 2.2 × 10−16, residual standard error = 10.68 dBm). Indeed, the linear regression model predicts an average decrease of 19.15 dBm  for an increase of one logD unit.As expected, the RSSI data collected for the four study sites show a logarithmic degradation in signal strength with increasing geographical distance  between However, due to the high intra- and inter-node variance of the RSSI, the range of the linear regression residuals is extremely large. Some nodes exhibit highly platykurtic or even bimodal distributions of residuals . The staA random forest regression  was performed to assess the statistical contribution of the visibility variables  to the explanation of the distribution of the residuals. The random forest regression model poorly explains the distribution of RSSI residuals with a Mean Of Squared Residuals (MSE) of 57.87 dBm and only 49.23 of the variance explained. The variable importance measured by the random forest  points op-value < 2.2× 10−16, and see violin chart A partial dependence plot on VIEW clearly exhibits the deleterious effect on the signal strength of a location outside the gateway viewshed B. The paIn this work, we have proposed a multi-purpose communicating node to acquire and transmit environmental data for several sensors. The deployment on-site allowed us to validate the robustness and reliability of this solution in different water resource management use cases. This reliability has also been successfully tested on the Etna site for the monitoring of volcanic activity . The depThe proposed integration workflow based on free solutions has shown its relevance. The users appreciated the easy design of the dashboards allowing them to follow the activity of the sites and the status of the nodes (notably their battery level).The analysis of the different nodes on each site shows heterogeneity in the quality of service rendered in terms of energy consumption, transmission quality, etc. A major issue is predicting the operation of the network, which is particularly important for remote sites. We have shown that it is possible to have an estimate of the battery life after a few days of operation thanks to the interpretation of the first evolution points of consumption. On the other hand, it is more complicated to predict the transmission quality a priori. Still, we have shown that the LoRa signal strengths attenuation can be evaluated considering the distance and the visibility parameters. Future dedicated experiments will provide additional data to make this modeling more robust.Future work will also focus on deployments to other sites and on the issues of data quality: how to identify measurement drift and outliers and generate alerts accordingly? Smarter devices will also minimize data transmission and improve the battery lifetime.The firmware of the SoLo node is protected by the following IDDN certificate: IDDN1.FR2.0013.2000104.0005.S6.P7.20208.0009.1020010, 13 May 2020.


Deep learning, with continuous development, has achieved relatively good results in the field of left atrial segmentation, and numerous semi-supervised methods in this field have been implemented based on consistency regularization to obtain high-performance 3D models by training. However, most semi-supervised methods focus on inter-model consistency and ignore inter-model discrepancy. Therefore, we designed an improved double-teacher framework with discrepancy information. Herein, one teacher learns 2D information, another learns both 2D and 3D information, and the two models jointly guide the student model for learning. Simultaneously, we extract the isomorphic/heterogeneous discrepancy information between the predictions of the student and teacher model to optimize the whole framework. Unlike other semi-supervised methods based on 3D models, ours only uses 3D information to assist 2D models, and does not have a fully 3D model, thus addressing the large memory consumption and limited training data of 3D models to some extent. Our approach shows excellent performance on the left atrium (LA) dataset, similar to that of the best performing 3D semi-supervised methods available, compared to existing techniques. The ability to accurately segment the left atrial region from heart images plays a key role in the field of medical images. Deep learning has performed well at many tasks to find organs or lesions in medical images because of its efficient use of labeled data. However, the high performance of deep learning relies on a large amount of labeled data, which are difficult to obtain for medical images because accurate annotation can only be done by medical experts with specialized knowledge, which is costly and burdensome. Semi-supervised learning opens up a new way of thinking through its ability to exploit unlabeled data.Many semi-supervised methods based on consistency regularization have started to appear, with relatively good results. However, the lack of labeled data during training limits their ability to identify key features and hence their accuracy, which leads to bias in the prediction of unlabeled images. The models tend to have large discrepancies in the prediction of features that are difficult to identify. A model will improve the performance if these regions are focused. Therefore, to find these challenging regions and derive some useful features from them is the key to improving model performance. Discrepancy information has an important role in finding challenging regions. However, many semi-supervised methods based on consistency regularization exploit unlabeled data by reducing the discrepancy of prediction to make their predictions consistent, which causes them to exploit inter-model consistency, but not discrepancy. In addition, these methods do not focus on challenging areas. Our method exploits inter-model discrepancy to find these challenging regions and obtain some features from them that are harder to identify but improve model performance. However, finding these regions is not easy because they are usually irregularly shaped, scattered, and highly similar to surrounding regions. For this problem, some methods find these regions by uncertainty maps, and later discard them to improve performance. For example, Monte Carlo dropout  was usedIn summary, we design a double-teacher model to accomplish the task of left atrial image segmentation. Our mean-teacher framework encourages both student and teacher models to maintain consistent predictions under the same input with different perturbations, thus enabling them to learn from and optimize each other to obtain more information from unlabeled data. Accordingly, our framework can extract the isomorphic/heterogeneous discrepancy information between the predictions of the student and teacher models, and their combination can represent the uncertainty information between them to some extent. For more accurate extracted discrepancy information, our framework uses labeled images and their corresponding labels to optimize the final predictions of the 2D teacher model. We first supervise the extracted discrepancy information, using a loss function to ensure consistent predictions between the student and teacher models. We further use the extracted discrepancy information to improve the loss of consistency between the two teacher models, so that the model can focus on regions with high uncertainty. Our framework (ISO-MT) can then obtain some features from challenging regions in the images to improve the model prediction results. Our semi-supervised method obtained good results in experiments on the MICCAI 2018 Atrial Segmentation Challenge dataset, with similar performance to the latest 3D model-based semi-supervised framework.The main contributions of this paper are as follows. First, we adequately extract isomorphic discrepancy information between isomorphic models and heterogeneous discrepancy information between heterogeneous models, and optimize the final prediction of the 2D teacher model using the labeled images and their corresponding labels, thus indirectly optimizing the extracted two types of discrepancy information. Second, we use the extracted discrepancy information to indicate challenging regions, and focus on these by optimizing the inter-model consistency loss to obtain some hard-to-identify features. Third, the proposed methodological framework performs similarly to the state-of-the-art 3D model-based methodological framework in the semi-supervised left atrial segmentation task for the LA dataset.We describe work related to semi-supervised learning in the left atrial segmentation task and uncertainty.Semi-supervised learning can be categorized into traditional and deep learning methods. Traditional methods perform the segmentation task by manually designing features, mainly including clustering-based models. Portela et al.  proposedSemi-supervised methods have been applied successfully to left atrial image segmentation. Many of the more successful such methods are designed based on consistency regularization. Li et al.  developeWith the development of deep learning, uncertainty has gained wide attention. In addition to the prediction, reliability is needed. The uncertainty can detect the wrong prediction of the model and calculate the confidence of prediction results. Lakshminarayanan et al.  trained In our framework, the networks for the student model and 2D teacher model are constructed based on 2D Unet . To fullFor a semi-supervised segmentation task, the data used for training usually have two components, labeled data The prediction results of the teacher model in the mean teacher model need to be of higher quality than the student model to provide better guidance to students. Recent studies ,29 have er model . The ste2. The average predicted probability A of the student model for the target region of this batch of labeled images is calculated as3. A is the average predicted probability of the target region of this batch of labeled images by the student model. Since the data input into the framework come from the same dataset each time, the features of the targets have some similarity. Each time a batch of data is input into the framework, the average predicted probability A is obtained, which indicates the lowest probability of the pixel that is most probably the target predicted by the student model in this batch of input labeled images. This can guide the 2D teacher model to optimize its own prediction. In the prediction results of the 2D teacher model for the same batch of unlabeled data, when the predicted probability is greater than or equal to A, then the pixel is most probably the target, and when the predicted probability is less than A, the pixel is most probably the background, and the prediction results of the 2D teacher model are optimized in this way. For the unlabeled data, we want the segmentation results of the student model and the two teacher models to be consistent and valid. To achieve consistency between the student and the two teacher models, we first extract the discrepancy information  between the last output of the student model, the output of the last upsampling layer, and the output of the penultimate upsampling layer and the last output of the two teacher models, and combine the obtained discrepancy information to obtain discrepancy information D. Then, we use all-zero-valued images and D to compute our discrepancy loss to constrain the consistency between the student and teacher models and to motivate them to learn from each other. Additionally, during the calculation of D1, the predictions of the 2D teacher model are optimized by A, as follows.For the student and 2D teacher models, the discrepancy information Then, the discrepancy information Combining It is straightforward to calculate the discrepancy information The final discrepancy information isBased on the obtained discrepancy information D, the discrepancy loss is calculated asThe outputs of The loss of consistency between the student model and the teacher model that can handle both 2D and 3D information is calculated asWe combine We describe the following aspects: Data set and data pre-processing; Experimental details; Evaluation Metrics; Comparison with other methods; Ablation experiments; Relationship between mean discrepancy rate and mean dice; Exploring the importance of uncertainty derived from discrepant information; Exploring the importance of teacher models that can handle both 2D and 3D information; and Clinical applications.We evaluated our semi-supervised segmentation method on the dataset from the 2018 Left Atrial Segmentation Challenge , consistOur framework was implemented in PyTorch using an NVIDIA 3060 GPU. For the optimization update of the network parameters, we used an SGD optimizer , and the learning rate of the SGD optimizer was set to 0.02. To obtain the best performance, we optimized the framework for about 5000 iterations. The batch size was 8, and each batch consisted of four labeled 2D image slices and four unlabeled 2D image slices. The processed 3D slices corresponding to the four unlabeled 2D image slices were also fed into the frame, which accepted 2D image slices of size 128 × 128. The decay coefficient We used Dice, Jaccard, mean surface distance (ASD), and 95% Hausdorff distance (95hd) to evaluate performance on this left atrial dataset. These are defined asThis dataset has two popular semi-supervised experimental setups: 10% labeled data and 90% unlabeled data, and 20% labeled data and 80% unlabeled data. We performed experimental validation under both. We performed ablation experiments to validate each part of our method, with results as shown in To describe the size of the discrepancy, we use the discrepancy rate Dr, i.e., the ratio of the pixel sum calculated by the student and teacher model, This can accurately reflect the magnitude of the discrepancy information. During the model training process, discrepancy information can be generated for each iteration, and the discrepancy rate can be calculated. We used Dr to further compute the average discrepancy rate for each iteration of the training process, and calculated the average Dice of the corresponding model on the test set. It can be seen from For the first image, the challenging regions are a separate smaller target region at the top and the edge region of the large target, both of which are often difficult areas for image segmentation. This is easy to understand because features near the target boundary and in the smaller target region are more difficult to identify and distinguish. This is also true for the second and third images, where the challenging regions are concentrated at the target boundary and in small target regions. As can be seen from the third column, the uncertainty visualization map obtained by calculating the discrepancy between the prediction results of the student and teacher models can more accurately target the challenging regions, and the high-uncertainty regions correspond to the challenging regions. It can also be seen that for the three images, the prediction results of the teacher model are better than those of the student model, which are closer to the labels. This implies that the teacher model can indeed serve as a guide for the student model in some cases. As can be seen from the above, the uncertainty calculated from the discrepancy between the models can roughly reflect the uncertainty of the images, and is meaningful in roughly locating challenging regions. Since this uncertainty is obtained by calculating the discrepancy between models, it is called discrepancy information; this information helps improve the performance of the overall framework.As shown in Our technique achieves the task of left atrial segmentation and can be used in clinical situations to assist in the treatment of atrial fibrillation, a common cardiac arrhythmia, which refers to the irregular beating of the heart. Surgical treatment of the left atrium can reduce or eliminate AF by preventing the propagation of abnormal electrical signals within the atria. Our technology can be used to segment the left atrium from the patient’s heart image before its surgical treatment, to more precisely determine its location, and thus assist the physician. The steps are as follows: The doctor sends the 3D image of the patient’s heart into our program, whose preprocessing part slices it into some 2D slices that meet its size requirements. The processed 2D slices are fed into our test model to obtain the segmentation results, and the program combines all the slices to form a 3D segmentation result, which is output to the physician for viewing, to assist in locating the left atrium.In this paper, we proposed a semi-supervised left atrial segmentation framework (ISO-MT) based on a 2D model. The key idea of our framework was to extract the isomorphic discrepancy information between the prediction results of the student model and 2D teacher model for unlabeled images as well as the heterogeneous discrepancy information between the prediction results of the student model and the teacher model that can process both 2D and 3D information. Isomorphic discrepancy and heterogeneous discrepancy information could be combined as uncertainty information to optimize the consistency between the student model and the two teacher models’ loss, which could better focus the optimized consistency loss on the challenging regions in the unlabeled images; thus, some useful features could be captured from these regions to optimize our method framework. Our (ISO-MT) framework performed similarly to state-of-the-art 3D semi-supervised methods on the left atrial dataset. Our framework was based on a 2D model, so it ran faster, consumed less memory, and obtained relatively more training data for the same number of training images. Our method had some clinical value, was relevant to the diagnosis and treatment of atrial fibrillation, and could assist in the localization of the left atrium, reducing the burden on physicians. Because our method is computerized, there will always be errors. Hence, it cannot achieve 100% accuracy or completely replace the physician in the task of localizing the left atrium.