Wireless mesh networks (WMNs) have emerged as a scalable, reliable, and agile wireless network that supports many types of innovative technologies such as the Internet of Things (IoT), Wireless Sensor Networks (WSN), and Internet of Vehicles (IoV). Due to the limited number of orthogonal channels, interference between channels adversely affects the fair distribution of bandwidth among mesh clients, causing node starvation in terms of insufficient bandwidth distribution, which impedes the adoption of WMN as an efficient access technology. Therefore, a fair channel assignment is crucial for the mesh clients to utilize the available resources. However, the node starvation problem due to unfair channel distribution has been vastly overlooked during channel assignment by the extant research. Instead, existing channel assignment algorithms equally distribute the interference reduction on the links to achieve fairness which neither guarantees a fair distribution of the network bandwidth nor eliminates node starvation. In addition, the metaheuristic-based solutions such as genetic algorithm, which is commonly used for WMN, use randomness in creating initial population and selecting the new generation usually leading the search to local minima. To this end, this study proposes a Fairness-Oriented Semichaotic Genetic Algorithm-Based Channel Assignment Technique (FA-SCGA-CAA) to solve node starvation problem in wireless mesh networks. FA-SCGA-CAA maximizes link fairness while minimizing link interference using a genetic algorithm (GA) with a novel nonlinear fairness-oriented fitness function. The primary chromosome with powerful genes is created based on multicriterion links ranking channel assignment algorithm. Such a chromosome was used with a proposed semichaotic technique to create a strong population that directs the search towards the global minima effectively and efficiently. The proposed semichaotic technique was also used during the mutation and parent selection of the new genes. Extensive experiments were conducted to evaluate the proposed algorithm. A comparison with related work shows that the proposed FA-SCGA-CAA reduced the potential node starvation by 22% and improved network capacity utilization by 23%. It can be concluded that the proposed FA-SCGA-CAA is reliable to maintain high node-level fairness while maximizing the utilization of the network resources, which is the ultimate goal of many wireless networks. Wireless mesh networks (WMNs) enable flexible, robust connectivity and are a means to various applications, such as healthcare, smart grids, Internet of Things (IoT), Internet of Vehicles (IoV), and intelligent transportation systems , 2. The Channel assignment algorithms play an important role in improving the connectivity, throughput, and capacity of WMNs. These algorithms aim at finding an optimal distribution of the channels among the colocated links to maximize the utilization of network resources and reduce interference, that is, a channel assignment algorithm that tries to improve the bandwidth utilization by reducing the interference and efficiently utilizing the frequency spectrum , 4. The The node starvation problem occurs when the surrounding links are unable to support the required bandwidth of the adjacent clients due to link interference. Over the last decade, many solutions have been suggested to address various channel assignment problems , 32–40. The Fairness-Oriented Semichaotic Genetic Algorithm-Based Channel Assignment Algorithm (FA-SCGA-CAA) is proposed to maximize link fairness while minimizing link interference.A semichaotic genetic-based technique is proposed to create a diverse population with informative features that converge at the best solution and avoid being trapped in the local minima. The semichaotic technique is proposed to address two main issues of genetic algorithms, which are to speed up the convergence process of the algorithm and to increase the diversity of the searched solutions to find the best feasible solution.The problem of fair channel assignment is formulated as an optimization problem that entails a fitness function that combines several factors representing the network topology, link capacity, and required bandwidth/throughput to minimize link interference while maximizing link fairness.A new nonlinear fitness function is proposed to integrate both interference and fairness in one fitness function for minimizing link interference while maximizing link fairness that is directly reflected in improving client fairness.To this end, this paper proposes a Fairness-Oriented Semichaotic Genetic Algorithm-Based Channel Assignment Technique (FA-SCGA-CAA) that addresses the issue of node starvation through fair (equitable) channel distribution among mesh nodes in the WMN. The channel assignment problem has been formulated as an optimization problem with two objectives, minimizing the interference and maximizing node fairness. Because interference and fairness are not linearly correlated, this study introduces a new nonlinear fitness function that aims at minimizing the interference while maximizing the bandwidth utilization to ensure fair distribution of the nonoverlapping channels and guarantee the required bandwidth for each mesh client. FA-SCGA-CAA optimizes fairness based on multiple criterion using a modified version of the genetic algorithm (GA). The modification includes proposing a semichaotic technique for creating the primary chromosome with powerful genes. Such a chromosome was used to create a strong population that directs the search towards the global minima effectively and efficiently. The outcome is a nonlinear fairness-oriented fitness function that aims at maximizing the link fairness while minimizing the link interference. The contribution of this paper is four-fold:The channel assignment problem in a multiradio wireless mesh network has been the subject of many recent studies –38, 47. Fairness is defined by many researchers as the equal distribution of the resources among equal nodes , 43. ThiIn their study, Qu et al.  proposedBakhshi and Khorsandi  used intGhaleb et al.  proposedIn their study, Liu et al.  proposedTo sum up, many recent studies investigated fair channel assignment solutions. However, there are two main drawbacks of the extant research, which can be described as follows. First, most of those studies simply minimize the total interference in the network to achieve effective utilization of network resources. Such solutions lead to fair starvation of nodes and links which is not the goal of fairness in channel assignment. The common hypothesis among these solutions states is that reducing the total interference can improve network performance, which directly achieves fairness among mesh nodes. This hypothesis is inaccurate because reducing the total interference does not necessarily ensure equal distribution of channels in the network. In addition, fairness was represented in terms of total power level, total interference, or total throughput of the network. Moreover, the extant studies aimed at equal distribution of network resources (such as the bandwidth) among nodes, which does not necessarily lead to fairness and thus does not prevent node starvation problem. The equitable distribution of the resources among all nodes is the ultimate goal of a fair channel assignment algorithm to resolve the node starvation problem. That is, equal nodes only should receive equal resources. The resources should be distributed based on the requirements, which are not necessarily equal. The second drawback of the existing solutions is the use of heuristic methods to assign the channels. Unfortunately, heuristic techniques are not appropriate solutions as they are scenario-dependent and thus cannot be generalized. Metaheuristics techniques on the other hand such as genetic algorithms seem to be promising to approach channel assignment algorithm in WMN due to the smaller search scope as it searches in a controlled population sample. However, randomly generating the population samples may lead to a premature solution since the search can be directed to local minima. This study addresses this issue by proposing a fair channel assignment algorithm with more granularity. Links and nodes fairness have been represented based on the impact of interference on their expected bandwidth. Thus, a fairness-oriented fitness function was proposed and integrated into the genetic algorithm-based channel assignment, which aims at maximizing node and link fairness, while minimizing interference.In this study, the problem of channel assignment is formulated as an optimization task that maximizes link fairness to avoid node starvation problems. Thus, we have proposed a semichaotic genetic algorithm to solve such a problem. The proposed algorithm aims at finding the most effective solution that achieves node fairness and addresses the link starvation problem. The genetic algorithm (GA) is a metaheuristic algorithm inspired by the evolution theory and natural selection. GAs are adaptive search techniques that can find the optimal global solution by manipulating and recursively generating a new population of solutions from an initial population space . GA is uAs shown in The wireless mesh network (WMN) is represented by a graph that can represent the interconnection of all nodes connected in the network see . The graForming the initial population is an important step in genetic algorithms directly affecting the quality of the results. There are two approaches for the initialization, namely heuristic and random. The heuristic approach may direct the genetic algorithm to fast convergence to a local optimum, while the random-based approach may slow down the convergence. Therefore, in this study, a semichaotic-based population formation technique is proposed. In this technique, first, the primary chromosome which constructs the entire population has been created using a multicriterion channel assignment algorithm. The primary chromosome was created using strong genes so that the entire population inherits those powerful genes. The multicriterion channel assignment algorithm is a heuristic algorithm that uses multiple criteria derived from network topology and the expected traffic patterns used in the proposed channel assignment algorithm. As shown in fchl, the mesh network is represented as a conflicting graph where the vertices are the links, and the edge is the shortest distance between any two nodes from adjacent links. The link graph represents the mesh network, which is used for computing the nodes and links ranking. The interference graph is used to obtain the interfered radios which are used to form the conflicting graph. The conflicting graph is used to obtain the list of the interfered links during channel assignment. It is also used to calculate the expected bandwidth that the link can support after channel assignment. Thus, the conflicting graph is used to evaluate the effectiveness of the generated solutions. It is worth noting that the channel assignment algorithm in this stage uses a threshold for the acceptable interference for assigning a channel to a link. If the resulting interference is not accepted, the algorithm assigns a common channel as there is no other option in this case. That is, the resulting bandwidth of the link after channel assignment should not be less than the bandwidth if a common channel was used. The channel assignment algorithm continues to assign the channels until all links in the network obtain appropriate channels. Upon the completion of this stage, the primary chromosome is obtained and the initial population can be created.The least interfering channel is calculated in every iteration of the channel assignment. To estimate the expected interference The semichaotic technique is used to create the initial population from the primary chromosome. The weak genes (the link after the channel is assigned) with high interferences are replaced randomly from the available channels. Meanwhile, the strong genes (the links with nonoverlapping channels) are fixed. In doing so, two main advantages are obtained improving the metaheuristic approaches, namely the genetic algorithms for approaching the NP-Hard problems: the speed of the convergence and the effective solutions that are obtained. Because the semichaotic technique randomizes a portion of gene space in the chromosome, the search scope is small, and thus fast convergence is obtained as well as the good quality of the obtained solution.lfi is the fairness of the link i. Link fairness lf can be defined as a function of the link data rate as follows:After preparing the initial population using the semichaotic technique, the set of chromosomes in the initial population is evaluated. This required a fitness (objective) function in the genetic algorithm. The fitness function is used to evaluate the solution domain. As stated previously, the channel assignment aims to maximize the link fairness while minimizing the link interference to maintain a suitable link data rate for the mesh clients to address node starvation problems. The objective function of the optimization is written as follows:The data rate needs to be maximized to achieve higher fairness. The Actual_Link_Data_Rate is the expected data rate of the link after channel assignment while the Require Lin Dat Rate is the minimum accepted data rate of the link. In other words, the following condition should be satisfied:The Required_Link_Data_Rate can be calculated based on the sum of the required data rate of expected mesh clients or routers. The Required_Link_Data_Rate of each link in the network is assumed to be known. This is a reasonable assumption as the number of clients that will be connected to the mesh routers can be controlled according to the available resources. The data rate is indirectly correlated with the interference; i.e. if the interference is high, the data rate is low and vice versa. The actual data rate of a link can be calculated using Shannon–Hartley theorem which states that the data rate depends on the channel bandwidth and the signal-to-noise ratio, as in the following:Here, BW is the bandwidth of the channel, SNR is the signal-to-noise ratio, and capacity is the capacity of the channel in bits per second. To calculate the SNR, we first calculate the noise as the difference between the power of the transmitted signals and the power of the received signal.Thus, the signal-to-noise ratio (SNR) can be calculated as follows:SNR is usually expressed in decibels (dB) as follows:ll be the link_length, and TSS is the transmission signal strength in dB m. The received signal strength RSSin dB m  can be derived as follows: RSS =TSS – 10∗n∗  log10(ll),As the bandwidth can be known from the channel characteristics and SNR can be calculated as in the above equation, the data rate can be calculated. Practically the strength of the received signal is measured using a hardware sensor, given that the transmission power is known; thus, the expected or the actual data rate after channel assignment can be estimated. However, for the sake of this study, the link data rate has been estimated using a formula that is derived from the Shannon-Hartley theorem and the path loss assuming free-space path loss. Let li):As the interference influences the data rate, the interference on each length is the total overlapping of the link interference index. Thus,  can be rli) can be estimated in terms of channel overlabing ratio ov as follows:Consequently, using this formula, the data rate can be estimated numerically. By minimizing the link interference index, the SNR will be higher, which increases the data rate as well. The link interference index (li) in the Jain's index equation which was presented in Fairness-Aware Parent Selection. Based on the fitness function presented in  is fixed in every generation.where (2)Crossover. In the crossover process, the children are created from two parental chromosomes that were randomly selected from the previous step. The genes of the new child are mixed with the genes of the parents. The crossover process tends to pull the population towards the local maxima (the highest fairness in the population). For every two pairs, the powerful genes (high fairness link) are fixed, and the weak genes (low fairness link) are removed.(3)Mutation. In the mutation step, the genes with low fairness are replaced randomly while the genes with high fairness are fixed. The mutation process aims at increasing the diversity and preventing the algorithm from being trapped in local minima.This stage consists of three steps as follows:G with Nodes Positions, Available Channels ch, Fairness Tolerance Rate τ, and the maximum iteration max_i as input. Then, it returns the best solutions in the chromosome pg(n) which contains the links and the associated channels with the highest fairness index and lowest interference channels. As discussed in In this section, the performance of the proposed channel assignment algorithm is evaluated and compared with related work. The baseline algorithm starts assigning channels to nodes arranged in descending order based on the number of hops to the gateway , 21. Thin denotes the number of links and xi is the link fairness assigned to the ith link as calculated in (f(X) can be 1 when xi value is equal for all. This indicates that every link has got fair channel allocation. The network throughput is the sum of the capacity of all links in the network [l is the link between nodes i and j, interferencel is the interference on the link li, j), the fractional network interference (FNI), per-link capacity, per-link fairness index, and per-link interference index. NC is the total concurrent transmission in the network after the channel assignment algorithm takes place, while FNI is the ratio between network interference and the total conflicting links in the network. FNI is also defined as the number of conflicts that remain after channel assignment relative to the number of conflicts in a single channel network. It is the remaining ratio of interference after applying the channel assignment algorithm. Jain's index  has beenfX=∑i=1nx network , 55, 58.To analyse the impact of interference between the links, different scenarios were simulated, each of which has a different number of links, namely 5, 16, 36, 46, 58, 78, 119, and 126. With each scenario, many random topologies were generated and tested. All parameters used in this experiment were chosen according to the common practice in the field of channel assignment algorithms. The interference model was used to estimate the interference between the links as follows. Two links interfere with each other if they have been set to the same channel and the distance between any two nodes that form the links is smaller than the interference range (514 m). The number of links was created autonomously to preserve network connectivity. That is, the links are created if the distance between the nodes is smaller than the communication range (252 m). For the generalization, in the simulation, the number of nodes, links, and their position have been randomly selected considering the connectivity in mind during node creation. Two versions of the proposed algorithm were evaluated, namely the FA-SCGA-CAA and SCGA CAA. FA-SCGA-CAA is the semichaotic genetic algorithm with a fairness-oriented fitness function which is the main contribution of this paper. Meanwhile, SCGA-CAA is the semichaotic genetic algorithm without a fairness-oriented fitness function. The fitness function used is the interference-based fitness function. In other words, FA-SCGA-CAA aims at maximizing the fairness among nodes while minimizing the link interference, and SCGA-CAA aims at minimizing the sum of link interference.The proposed FA-SCGA-CAA and SCGA-CAA algorithms have been evaluated by comparing them with Multicriterion Link Ranking CAA (MCLR-CAA)  and the x-axis represents the average number of links in the studied network scenarios, while the y-axis represents the corresponding average network capacity. The network capacity is represented by the ratio of the concurrent connection in the network.Extensive simulations were conducted to evaluate the performance of the proposed algorithms and techniques (FA-SCGA-CAA and SCGA-CAA). The results of the proposed algorithm were compared to the multicriterion link ranking-based channel assignment algorithm MCLR-CAA  and the As shown in x-axis represents the average number of links in 9 scenarios, while the y-axis represents the corresponding average link capacity.From x-axis in y-axis represents the corresponding average link interference.As shown in x-axis represents the average number of links in the 9 studied scenarios, while the y-axis represents the fractional network interference (FNI).From x-axis in y-axis represents average link fairness.From x-axis represents the performance measures, namely, the average of the network capacity in terms of concurrent connection, the average of potential starvation nodes in terms of the ratio of the conflicting channels or the fractional network interference (FNI), the average link fairness index, and network interference index. The y-axis represents the average value of the corresponding performance measures.From Figures 5As shown in This section presents the discussion of the results presented in the previous section. The study aims at designing and developing a Fairness-Oriented Semichaotic Genetic Algorithm-Based Channel Assignment Technique (FA-SCGA-CAA) that addresses the node starvation problems, through fair (equitable) channel distribution among mesh nodes in the WMN. A fair distribution of channel assignment should not end up with a node starvation problem. However, existing channel assignment schemes suffer two main drawbacks as follows. Firstly, existing solutions focus on minimizing the total interference to achieve effective utilization of network resources. However, such a solution leads to a node starvation problem where the throughput of some links drops lower than the minimum required throughput. That is, reducing the total interference does not necessarily ensure equal distribution of channels in the network. As shown in The second drawback of the existing GA-based solutions , 39, 41 6O(gnm), where g is the number of generations (number of the iterations needed until convergence), n is the number of links (number of genes), and m is the population size. The time complexity of the MCLR-CAA isO(n), where n is the number of links in the network. Although the MCLR-CAA is more efficient in terms of time complexity than the metaheuristic algorithms, it has lower performance compared with the proposed model in terms of network capacity, interference, and fairness which are essential for WMNs. That is, in WMN the effectiveness of the channel assignment algorithms in terms of improving network capacity by reducing the interference among the channels while achieving fairness is more important. However, the efficiency of the proposed FA-SCGA-CAA and SCGA-CAA algorithms evaluated in terms of the number of iterations is needed until finding the best solution. x-axis represents the average number of links in the 9 studied scenarios while the y-axis represents the average iterations until convergence.Besides improving the effectiveness of the network, the proposed FA-SCGA-CAA and SCGA-CAA algorithms improve the efficiency of the channel assignments in terms of fast convergence to find the best solution. Furthermore, the cost in terms of time complexity of the studied metaheuristic algorithms , 39, 41 From In this paper, the Fairness-Oriented Semichaotic Genetic Algorithm-Based Channel Assignment Technique (FA-SCGA-CAA) for wireless mesh network was proposed. The node starvation problem that was overlooked by the extant studies due to unfair channel assignment has been addressed in this study. The unfair distribution of the network resources of the existing solution has been attributed to two main drawbacks. Firstly, the equal distribution of the interference on the network does not lead to a fair distribution of network resources. Second, randomly generating the population samples in the genetic algorithm-based solutions leads to a premature solution where the search is usually directed to local minima. These two limitations have caused unfair distribution of the channels and thus node starvation problem in WMN. To achieve node fairness, a semichaotic genetic algorithm-based technique was proposed to create a diverse population with informative features that converge to the best solution and avoid being trapped in the local minima. The channel assignment problem was formulated as an optimization problem with a fairness-aware fitness function. The fairness-oriented fitness function combines several factors that represent the network topology, load status, and required bandwidth/throughput in one function. The fairness was defined on node level to address the node starvation problem. Extensive experimental evaluations were conducted to measure the performance of the proposed technique and compare it with existing solutions. The results showed that the proposed algorithm outperformed existing solutions in terms of improving the link fairness and utilization of network capacity while reducing the interference and the potential number of node starvations. It can be concluded that the proposed FA-SCGA-CAA is reliable to maintain high node-level fairness while maximizing the utilization of the network resources, which is the ultimate goal of many wireless networks. Similar to any genetic-based algorithm, the proposed algorithm has two main drawbacks as follows. The first issue is that setting up the number of iterations is scenario specific. Therefore, it is difficult to select a fixed number of iterations for generalization. The second issue is on the selection of the convergence threshold. These issues contributed to the overall link fairness. We are currently working on addressing those two issues and the new findings will be the subject of our next publication.


Background: While performing surgeries in the OR, surgeons and assistants often need to access several information regarding surgical planning and/or procedures related to the surgery itself, or the accessory equipment to perform certain operations. The accessibility of this information often relies on the physical presence of technical and medical specialists in the OR, which is increasingly difficult due to the number of limitations imposed by the COVID emergency to avoid overcrowded environments or external personnel. Here, we analyze several scenarios where we equipped OR personnel with augmented reality (AR) glasses, allowing a remote specialist to guide OR operations through voice and ad-hoc visuals, superimposed to the field of view of the operator wearing them.Methods: This study is a preliminary case series of prospective collected data about the use of AR-assistance in spine surgery from January to July 2020. The technology has been used on a cohort of 12 patients affected by degenerative lumbar spine disease with lumbar sciatica co-morbidities. Surgeons and OR specialists were equipped with AR devices, customized with P2P videoconference commercial apps, or customized holographic apps. The devices were tested during surgeries for lumbar arthrodesis in a multicenter experience involving author's Institutions.Findings: A total number of 12 lumbar arthrodesis have been performed while using the described AR technology, with application spanning from telementoring (3), teaching (2), surgical planning superimposition and interaction with the hologram using a custom application for Microsoft hololens (1). Surgeons wearing the AR goggles reported a positive feedback as for the ergonomy, wearability and comfort during the procedure; being able to visualize a 3D reconstruction during surgery was perceived as a straightforward benefit, allowing to speed-up procedures, thus limiting post-operational complications. The possibility of remotely interacting with a specialist on the glasses was a potent added value during COVID emergency, due to limited access of non-resident personnel in the OR.Interpretation: By allowing surgeons to overlay digital medical content on actual surroundings, augmented reality surgery can be exploited easily in multiple scenarios by adapting commercially available or custom-made apps to several use cases. The possibility to observe directly the operatory theater through the eyes of the surgeon might be a game-changer, giving the chance to unexperienced surgeons to be virtually at the site of the operation, or allowing a remote experienced operator to guide wisely the unexperienced surgeon during a procedure. The challenges of learning, planning and performing procedures in spine surgery have been enriched by the recent development of new technological tools and instrumentations, able to assist surgeons and reducing surgical invasiveness  but maintaining a valuable profile of safety –5. One oWhile performing surgeries in the operating room (OR), surgeons and assistants often need to access several information regarding surgical planning and/or procedures related to the surgery itself, or the accessory equipment to perform a wide spectrum of operations . FurtherInteractions with such digital objects were something considered science fiction, as seen in movies projecting us in the future; nevertheless, recently Microsoft implemented this technology with the “hololens,” an AR visor with a tracking system able to recognize hand motion thus allowing interactions with holograms. Most likely this technology will access the general consumer market within the next 10 years. Our group has already large experience with mixed reality, having worked on one of the first large-scale setups for AR interactions “CAVE” , which wGenerating 3D models from medical images does not imply similar challenges compared to electron micrographs –12 , we took advantage of four different state of the art AR goggles: the Epson BT-300 and BT-350, both allowing HD projection, with a 5 MPx camera on board, and the Vuzix Blade, also allowing HD projection . The latTeamViewer pilot is a cross platform remote assistance software that was developed and enhanced for the purpose of exploiting AR and AI features combined . Key perAnother in-house Unity-based software tool customized specifically for the HoloLens goggles is the Holosurgery app. This piece of software holds features that enable more convenient input methods such as hand gestures and voice commands. Summoning optimized pre-processed imaging data such as 3D models of a patient's spine is achieved with simple key vocal inputs, e.g., “Show 3D Model.” In addition, the 3D model is manipulated using hands and fingers motions and that achieves re-scaling, movement, and rotating of the 3D model. There is also more complex geometry operations such arbitrarily clipping planes which neatly visualizes a clipped region of interest within the displayed 3D model.All three state of the art software tools along with the implemented AR goggles empowers the OR staff to perform normally in non-normal and challenging times similarly in the case of global pandemics.n = 5 expert who have used to devices in the OR.System Usability Scale is an industry standard used to give a gross but reliable evaluation of the usability of a product. It is a questionnaire that can be customized to a certain extent, based on individual needs. Each answer requires an answer on a scale from 1 (strongly disagree) to 5 (strongly agree). A 9 questions questionnaire reported in AR goggles allowed to stream videos and transmit still images from the surgical field to different specialists . ProcessSurgeons had the possibility to get a live visualization of the CT reconstruction and of the planned trajectories  7) of t of t7) oThe ability to obtain an ergonomic live-sharing of surgeons view, together with the possibility to overlay images or videos offered the opportunity to involve a group of young residents and medical students for a remote step-by-step interactive learning of the surgical procedure , 3.A total number of 12 lumbar arthrodesis have been performed while using the described AR technology. Five cases of Lateral Lumbar Interbody Fusion (LLIF) and 7 Transforaminal Lumbar Interbody Fusion (TLIF) were performed with posteriore screwing through Standard  or Cortical Bone Trajectory . Intraoperative neuromonitoring was used in all the cases. Neuronavigation was used in two TLIF procedures.In three cases  surgical procedures have been shared through enhanced videoconferences among three experienced surgeons. The surgeon in the OR discussed the case while showing the screw entry-point and the trajectory, with the aid of the fluoroscopy and neuromonitoring. In the LLIF case, the discussion about the procedure involved the lateral positioning, the trans-psoas approach and the cage placement. In seven cases (5 LLIF and 2 TLIF procedures) the positioning of neuromonitoring electrodes on patients skin and the wires connection to the central monitoring platform and display was made by surgeons wearing AR goggles with the remote assistance from specialized technicians. Similarly, the remote assistance allowed the surgeon to set neuronavigation in two cases. In all the procedures, companies ensured a live support for nurses assisting surgeons with regard to the devices and surgical instrumentations needed.In three cases AR goggles allowed the surgeon to access to the surgical planning of patients that underwent CBT fixation in real time while maintaining the view on the operator field.In two cases  a group of four residents belonging to their first year of the Residency program and two medical students got access to the procedure with a remote connection, with the possibility to interact with the surgeons. Surgeries were performed in a step-by-step manner.No complications potentially linked to the use of AR were registered, such as malfunction of the neuromonitoring and of the neuronavigation system, or infections. Surgeons reported a positive feedback as for the ergonomy, wearability and comfort during the procedure, as confirmed by the results shown on the graphs in AR represents the possibility to create a useful and real-time interaction between multiple environments and/or images/videos of interest . AR systIn this case series a simple, ergonomic and successful use of AR goggles is presented. In addition, the unfortunate conjunction with the COVID-19 pandemic has led to the chance of facing physical restrictions adding further applications of this technology.These tools, indeed, allow surgeons to view images and use apps anywhere and anytime they like overlaying digital content on their real field of view. Moreover, images, videos or screens shared by other devices could be watched on these see-through lenses, through an enhanced videoconference app . ConsideAnother important advantage offered by this AR system, is the real-time visual feedback of the operative field that allows the mentor to provide a better coaching, as reported in other previous papers , 20. DavThese examples could represent a starting point to better investigate the potential development of AR for the teaching/supervision of surgical techniques, reducing the need for physical presence of experienced surgeon and consequently its related constraints on time and budgets.The remote mentoring could also be considered to coordinate the setting up of the operative room for newer procedures or to help surgeons with the use of new instrumentations, even when specialist consultants could not physically enter the operative room. It is well-known that many traditional neurosurgical procedures often required the use of intraoperative neuromonitoring (IONM) in order to guarantee the best result, both in terms of extent of resection and neurological safeguarding. With the advent of new emerging minimal invasive techniques for spinal degenerative disease , this need has spread further. Consequently, the great spread of the use of these techniques has increased the need for IONM , 22, witFinally, and as already mentioned, although the remote mentoring and specialist counseling with the AR seemed, until few months ago, only a window on the future of the operative rooms and surgical activities, the recent dramatic experience of lockdown due to COVID-19 pandemic spread has changed the perspective, making it an everyday tool for the OR.Another important application of AR for surgery is represented by its role in surgical planning. In this series CBT planning was visualized by the surgeon while maintaining the view on the surgical field and obtaining a real time feedback of the planned screw entry points. During the past few decades, several tools have been developed to improve pre-operative surgical planning both for spine and cranial surgery , 23.The 3D printing era brought most surgical fields to an advanced new level, where even minimal differences from standard anatomy are detected, helping surgeons during the pre-operative planning and during the procedure, and then, leading to a customized surgical management. Nowadays, the so-called image guidance surgery is widely used in different surgical specialties  but recently, due to reached high accuracy, have been widely implemented in neurosurgery for cranial, spinal and skull base procedures . Penner To this end, systems incorporating haptic feedback for realistic rendering of contact forces experienced during the interaction with tissues are considered of fundamental importance for speeding up the learning curve . On the For these tasks, haptic rendering is required to provide realistic forces and torques created by the complex interactions between the surgical tools and tissues involving tool penetration, tissue removal, rotational speed and vibrations . The accApart of these considerations, the proposed system can be used for gathering data related to surgical tool trajectories that can be used for fitting haptic models describing the tool-tissue dynamics that can be derived through contact models  or more Masciatelli et al. and Cabrilo et al. firstly described the application of AR in neurovascular surgery showing optimized workflow by providing essential anatomical information , 36. In Resident training in surgical specialties is based on the apprenticeship model developed by Dr. William Halsted in 1980s and the training paradigm of “see one, do one, teach one” have been the pivotal concept until nowadays . DiffereThe operating theater has been the main classroom for many surgeons and is well-known that acquisition of surgical skills requires repeated occasions for hands-on practice. However, the limited number of people that can access to the OR and the large number of residents that need to learn surgical procedures often represent an issue, especially for small surgical centers and less developed countries.Thus, the application of new technologies to increase residents' exposure to surgical procedures could play a key role for the learning curve. Thanks to its integrated camera and the previous described videoconference function, the EPSON glasses gives to the surgeon the possibility to record all the procedure and to create a live streaming that could be shared with residents and medical students, reducing the need for physical presence in the operative room. Moreover, the possibility to watch the pre-operative planning and reconstructed 3D models superimposed on the surgical field through the EPSON glasses, provide a double advantage; on one hand, indeed, surgeon has the possibility to see the model without taking eyes off of the operative field, while on the other hand, the simultaneous view of the real surgical field and of the 3D model could improve and speed up the residents' learning process.Henssen et al. reported interesting results with their experience with AR comparing two different methods to study neuroanatomy; the classic method of studying cross sections of the brain and the one based on an AR-based neuroanatomy learning app . Hence, Understanding the true usability of the system, in order to assess whether it is not merely a technical exercise but rather a potential “everyday use tool” was key to us. In order to quantify how specialists perceived the use of the devices in the OR, they filled a SUS questionnaire  and rateTherefore, providing a precise and reliable 3D virtual and interactive environment, AR may become an extremely valuable tool for education of neurosurgical procedures, due to their intricate and complex nature.fabio.cofano@gmail.com.The data analyzed in this study is subject to the following licenses/restrictions: Ethical. Requests to access these datasets should be directed to Fabio Cofano, Ethical review and approval was not required for the study on human participants in accordance with the local legislation and institutional requirements. The patients/participants provided their written informed consent to participate in this study.CC wrote the first proof of the paper, with inputs from LD and MC. FC retrieved all the relevant literature, put together all qualitative/quantitative contributions from all authors and other users, and wrote the whole manuscript. MA contributed with a critical review of the manuscript and significant inputs during the rebuttal phase. GD, AL, MB, NM, FZ, and NZ tested the AR setup on field and gave their feedbacks to the technical team. DB was responsible for the technical support of the system and coordinated the work on the Unity code for the Microsoft hololens setup, together with MC. LD and DG coordinated the operations in OR with the AR setup. DG and CC coordinated the study. All authors contributed to the article and approved the submitted version.LD is the owner of the company LD Consulting. The remaining 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.


Pressure injury (PI) is a major problem for patients that are bound to a wheelchair or bed, such as seniors or people with spinal cord injuries. This condition can be life threatening in its later stages. It can be very costly to the healthcare system as well. Fortunately with proper monitoring and assessment, PI development can be prevented. The major factor that causes PI is prolonged interface pressure between the body and the support surface. A possible solution to reduce the chance of developing PI is changing the patient’s in-bed pose at appropriate times. Monitoring in-bed pressure can help healthcare providers to locate high-pressure areas, and remove or minimize pressure on those regions. The current clinical method of interface pressure monitoring is limited by periodic snapshot assessments, without longitudinal measurements and analysis. In this paper we propose a pressure signal analysis pipeline to automatically eliminate external artefacts from pressure data, estimate a person’s pose, and locate and track high-risk regions over time so that necessary attention can be provided. Pressure injury (PI) development is a chronic condition caused by the blockage of the blood flow due to prolonged interface pressure between skin and the supporting surface such as a bed or wheelchair. Seniors populations and patients with spinal cord injuries are the more high-risk groups to develop PI. As  mentionemonitoring mattress is another possible solution. As a previous study suggests [A possible solution to prevent the development of pressure injury is the use of a pressure mattress. The idea is to reduce the blood blockage by distributing the pressure more evenly over the body or changing the patient’s pose regularly. A pressure suggests , pressurA number of techniques for automatic human pose estimation from optical images and videos have been proposed in recent years with the help of artificial neural networks (ANN). Pose estimation networks such as OpenPose , HourglaWe introduce a robust algorithm to separate the occlusion imposed by external objects from the body pressure signals.Detect body part for high-risk regions including head, sacrum, shoulders, and feet, directly from the spatial pressure distribution. Unlike previous works, we do not need any model fitting or multi-modality pose estimation networks.Track the high-risk regions and the corresponding pressure values over time. This is an essential step to formulate PI development as the major factors causing PI are (a) the surface pressure, (b) how long the pressure is exerted, and (c) the skin resistance to the building pressure.We propose a novel method to remove external objects from the pressure signals, automatically detect the in-bed body postures, detect and label high-risk body regions, and track them over time. As our experiments show, the pressure signals generated by the body and the external objects have different statistical characteristics. We use these characteristics to clean the pressure signals and eliminate the external objects from the body silhouette. To the best of our knowledge, this is the first work that removes external objects from pressure signals. Related works assume that the body silhouettes are fully or noticeably detectable in the pressure signal maps. We train various classifiers on the histogram of oriented gradient (HOG) features of gradient vector field (GVF) of the pressure signals to classify the in-bed poses into back, right lateral, and left lateral. Finally, we detect and track the high-risk regions for each pose over time. The high-risk regions are head, shoulders, sacrum, and feet. Our work has a number of major contributions:The remainder of this paper is organized as follows: Pressure data analysis for in-bed pose estimation has been studied in the literature for patient monitoring, PI prevention, and sleep analysis. Researchers used edge and contour information, skeleton mapping, and pose estimation networks to detect in-bed postures, limb detection, and pose estimation.Conventional in-bed pose estimation methods from pressure data are based on feature extraction and a learning-based classifier. Grimm et al.  proposedAn artificial neural network (ANN), used to estimate in-bed postures from pressure data, was recently proposed in the literature ,21,22,23In this paper, we propose a novel method to clean and enhance the pressure signal, automatically estimate the in-bed body posture, and finally detect the high-risk body parts and track them over time. We use the statistical characteristics of the pressure signals  to remove the external objects and enhance the signals. HOG features are extracted from the GVF of the pressure signals and SVM and RF classifiers are used to classify the in-bed postures. The postures were classified into back, right lateral, and left lateral as they are the most relevant in-bed postures for the elderly and patients with spinal cord injuries. Finally, the high-risk body regions are detected and tracked over time. These regions are head, shoulders, sacrum, and feet. The captured pressure data from the pressure mattresses are usually noisy and of low resolution. More specifically, the existence of the external objects, such as pillows or wedges, on the mattress can affect the accurate pressure recording. Pillows and wedges are used to provide extra support for the patients. Usually these external objects can change the pressure signal distribution, the body silhouette, and make the pose estimation and body part detection and tracking quite challenging. Further, eliminating the external objects from the pressure signals is quite challenging as their corresponding effects will be different depending on the patient’s body structure, location, and characteristics of the external objects. To the best of our knowledge this is the first work that removes the external object from the pressure signals based on trend analysis of human body pressure distribution and external object pressure distribution. More details on the external object removal and signal enhancement are presented in The intuition behind our external object removal approach is that body pressure distribution shows very distinctive and different characteristics comparing to the external object pressure distribution. As k) over a window of size N. Given a window determined by The Savitsky–Golay filter is defined as a weighted moving average where weights are polynomial functions of specific degree S′[nEquation .(4)∂ε∂aThe above equation can be represented in matrix form as:k and M.The matrix r is the the number of the pressure sensors in the horizontal direction and In this work a polynomial function of a low degree  posture detection based on feature descriptors, such as ,12,14,15As we explained in , the higx and y coordinates, respectively. The gradient field is shown in The gradient is given in Equation . Note thWe use relative location information and morphological operations to remove small detected regions such as hands and elbows which are not at higher risks of developing PI.We use a simple spatial relation approach to label the detected regions automatically and accurately. The intuition is that for monitoring patients who cannot move effectively on the mattress because of physical disabilities, we can assume that body position and orientation will remain unchanged. We used the PmatData , which iSome results of the proposed external object removal approach for two different scenarios  are shown in We ran our experiment on a PC with Intel Core i5 processor and 8 GB of memory. We tried different classifiers, including support vector machine (SVM), K-nearest neighbour . Developing PI depends not only on the interface pressure, but also on the time that the body parts are under pressure. Some body parts, including head, shoulders, sacrum, and feet, are more prone to PI development. One of the challenges in analysing pressure signals captured from the pressure mattresses is the existence of external objects such as pillows and wedges. We proposed a novel method to separate the pressure signal caused by external objects from the body’s pressure signal and automatically detect and track the high-risk regions from the processed cleaned and enhanced pressure data. Our experimental results show that this signal cleaning and enhancement is a crucial first step to accurately estimate the in-bed body pose and detect and track body parts over time. However, the proposed external object removal method requires the distinguishable trend of the main signal and existence of numerous external objects on the mattress will affect the efficiency of the proposed approach. Further, the experimental results show that the proposed pipeline is more accurate in body pose estimation compared to the state-of-the-art techniques. Tracking body parts over time is a crucial first step in analysing and predicting the chance of PI development. In the future we will consider other factors that are important in developing PI, such as skin resistance and blood oxygen level. We will develop a computational model to automatically capture the effect of all of these factors and incorporate it with our body part detection and tracking algorithm to better estimate the chance of PI development for each individual.


With recent advancements in machine learning, especially in deep learning, the prediction of eye diseases based on fundus photography using deep convolutional neural networks (DCNNs) has attracted great attention. However, studies focusing on identifying the right disease among several candidates, which is a better approximation of clinical diagnosis in practice comparing with the case that aims to distinguish one particular eye disease from normal controls, are limited. The performance of existing algorithms for multi-class classification of fundus images is at most mediocre. Moreover, in many studies consisting of different eye diseases, labeled images are quite limited mainly due to privacy concern of patients. In this case, it is infeasible to train huge DCNNs, which usually have millions of parameters. To address these challenges, we propose to utilize a lightweight deep learning architecture called MobileNetV2 and transfer learning to distinguish four common eye diseases, including Glaucoma, Maculopathy, Pathological Myopia, and Retinitis Pigmentosa, from normal controls using a small training data. We also apply a visualization approach to highlight the loci that are most related to the disease labels to make the model more explainable. The highlighted area chosen by the algorithm itself may give some hints for further fundus image studies. Our experimental results show that our system achieves an average accuracy of 96.2%, sensitivity of 90.4%, and specificity of 97.6% on the test data via five independent runs, and outperforms two other deep learning-based algorithms both in terms of accuracy and efficiency. Fundus photography is a popular non-invasive method used to inspect anomalies associated with diseases of eyes and their progression, and has been widely utilized across the world for its comprehensiveness and convenience. Analysis of fundus images by ophthalmologists or optometrists is a labor-intensive task. With the ever-increasing quantity of fundus images as well as other types of images such as optical coherence tomography (OCT), there is a great shortage of human experts to interpret the available images, which results in delays of diagnoses and treatments . In addiRecent development of machine learning methods for fundus image analysis is mostly focused on detection of one particular eye disease, and the problem is commonly formulated as a binary classification problem with the goal to separate disease images from normal images. The widely used approaches are mainly convolutional neural networks (CNN) with different network structures. For example, Dong et al.  built a http://dbgap.ncbi.nlm.nih.gov/, accessed on 19 October 2021). Multi-disease classification is even more challenging when the training dataset is small. Choi et al. [Studying eye diseases one at a time based on fundus images is quite limited because fundus images can reveal many different conditions. However, the multi-disease classification problem based on fundus images is much harder. Research in this direction is very limited so far, and new methods are in great need given the inferior results from existing methods. For example, in an attempt to recover different sub-classes of AMD using ensemble learning based on six different convolutional neural networks, the study by Grassmann et al.  only obtIn this paper, we address the challenges of the multi-disease classification problem using small training data of fundus images, by taking advantage of some most recent breakthroughs in machine learning. The deep learning method of our primary focus is called MobileNetV2 , a lightThe rest of the paper is organized as follows. We introduce the model structure in In this section, we first introduce the network structure of MobileNetV2 and the training details. In combination with transfer learning, the system explicitly addresses the challenges of the multi-class classification problem with small training data. We also explain the visualization algorithm and discuss the two other approaches, InceptionV3 and AlexNet, in this section.r et al. ), followr et al. ).The design of MobileNetV2 focuses on the reduction in the size of the network and at the same time, it tries to maintain prediction accuracy. It has a lower parameter/layer ratio. The number of parameters in the deep separable convolution of each block can reduce to around 30% of the normal convolution operations. The residual connection in MobileNetV2 can also solve the gradient vanishing problem, which in turn improves prediction accuracy. Furthermore, the inverted residual of MobileNetV2 allows the dimension in each block to follow the narrow-wide-narrow  pattern, which achieves better memory utilization according to the original paper .We implemented the MobileNetV2  feature To better understand and locate areas that contribute the most to the classification results, we utilize the gradient-weighted Class Activation Mapping (grad-CAM)  visualizBy unfreezing the parameters in the last several layers during the transfer learning and fine tuning, the weights learned are calculated based on our own image data. This allows the establishment of connections between the feature map of the last convolutional layer and the fundus images, which enables grad-CAM to actually visualize the important areas of the input fundus images. To actually visualize the areas, a new weight for each feature map is calculated based on the gradients to the predicted class and a heat map is computed by summing the weights from all feature maps of the last convolutional layer. Through upsampling, the generated heat map is the same size as the original image, and the location that contributes to the predicted label can be highlighted.As a comparison, we also include two additional deep learning models in our study: AlexNet and InceptionV3. AlexNet was one of the earliest DCNNs that was very successful for image classification in the ImageNet contest . It consFor both AlexNet and InceptionV3, we followed the network configuration and training details outlined in the two papers ,14. Morehttps://www.kaggle.com/linchundan/fundusimage1000/, accessed on 19 October 2021). We used a subset of the dataset in this study, consisting of four diseases and the normal eye images, which was verified by retinal specialists. The entire dataset contains only 250 fundus images from five classes . We separated the data into training (80%) and testing (20%) for each category, thus the training set contains 202 samples and the remaining 48 samples are for testing. The resolution of the original images is very high (around 2000 × 3000). They are reshaped to the same size of 224 × 224 to fit the pre-trained networks. The color information from the original data is maintained.The medical images were obtained from a public dataset on Kaggle , the p-values of Levene’s test are in the range of 0.79 to 0.81. Therefore we cannot reject the null hypotheses that have equal variance, and the t-test can be used to compare the means. MobileNetV2 performs significantly better than InceptionV3 and AlexNet  and testing (20%). Each model was trained on the training dataset and results of the test dataset are shown in  AlexNet , with a In clinical diagnosis of eye diseases based on fundus images, different regions and different characteristics/features are examined and used by ophthalmologists or optometrists. For example, it is commonly known that the cup-to-disc ratio at the optic disc is used to identify glaucoma. On the other hand, Fuchs spot, tigroid fundus, lacquer cracks , and a tilted disc can be found in fundus images with pathological myopia. As we mentioned earlier, by taking advantage of the grad-CAM method, we can create a heatmap and visualize the important areas on each image that our method utilizes to classify different diseases. Doing so is a first step towards the ultimate goal to build an explainable AI-based diagnosis system. Therefore, for all the test images, we show the original images and their heatmaps side by side in Overall, for the majority of the images for all four diseases B–E, the We also tested our algorithm as a binary classifier on the same dataset, which goal was to differentiate each individual disease from normal controls. For each of the diseases, we coupled the images with the control samples and separated data into training (80%) and testing (20%). As expected, the problem became easier and the algorithm learned very quickly in a few epochs . For botIn this paper, we study the problem of retinal disease diagnosis based on fundus images using AI and machine learning algorithms. Our goal is to correctly label each image with one of several diseases. One of the challenges in the analysis is the limited number of training images. We propose to utilize the transfer learning technique and take advantage of the previously trained deep learning model MobileNetV2. Our results on a small dataset with 250 fundus images and five different labels show that MobileNetV2 was able to correctly predict most of the images and achieved an average accuracy of 96.2%. It outperformed two other alternative deep learning models, AlexNet and InceptionV3, both in terms of accuracy and efficiency. Our study shows the capacity of deep learning in the field of fundus image diagnosis, and transfer learning makes training on a small dataset a reality. The fine-tuning step further improves the performance of the model.In an attempt to overcome the drawbacks of black box AI models, we add the explainable learning step by highlighting the areas on the images that are important to the classification results, based on the learning process. The overall results based on the visualization show some promising progress and the areas of focus are mostly aligned with the disease labels. At the same time, we observe that for some images, the highlighted areas are different from where they are supposed to be based on the opinions of human experts. This kind of gap seems inevitable given the small dataset. As a future direction, we will investigate possible ways to “guide” the algorithms to focus on important areas or features. One possible direction is to incorporate prior knowledge into deep learning models, a topic that has recently drawn attention from the deep learning community . AlthougWhen MobileNetV2, as well as many other deep learning models, were trained on the ImageNet dataset, they normally resized their input images to the size of 224 × 224. The network structures and parameters were optimized based on the input size. In this study, the original images have very high resolutions . To fit the data into the pre-trained models, we simply downsampled the images. Doing so obviously loses some information retained in the high resolution images. In theory, the models can handle images of different sizes if the models are trained from scratch and if sufficient computational resources are available. For transfer learning, although one can possibly change the dimension of the input tensor to accommodate images of different sizes in practice, the use of such an strategy has certain limitations. In models with fully connected layers, the weights may not be transferable. In addition, when the images are too small, they may not provide enough data through many pooling layers. When the images are too large, the network architectures may not have enough number of layers to learn reliable and discriminative filters. In either case, fine-tuning, or even modification of the network structures are needed. This essentially is a trade off between computational costs and prediction accuracy. Our experiments show that downsampling the images beforehand produced quite robust results with high accuracy. Nevertheless, we will explore the impact of different options on prediction accuracy in the future.Fundus photography allows clinicians to diagnosis many eye diseases and conditions, and to monitor their progression. In theory, the proposed model can be extended easily to incorporate many more different conditions/labels. Further experiments are needed to assess how well the model will perform with more images of different disease classes added. In this study, each image is labeled with one disease. In practice, one patient might have more than one eye disease. The computational problem is called multi-label learning, and it is generally harder than the multi-class learning proposed here. Furthermore, images with multiple labels  are generally hard to obtain. Nevertheless, this topic can be considered for future studies.This study only consists of monoscopic fundus images. Fundus cameras have the ability to stereoscopically image the central and peripheral retina by sequentially taking two images. It would be interesting to extend the system to incorporate stereoscopical images and compare the performance using these two different types of images. This will be one of our future research directions. Furthermore, clinicians rarely make a diagnosis purely based on fundus images. Instead, multiple evidences are usually considered when making a decision . In the future, we can take advantage of multi-module learning by constructing models that can take different types of features as input to better capture the information clinicians use in real clinical settings.


Artificial Intelligence (AI) is the domain of computer science that focuses on the development of machines that operate like humans. In the field of AI, medical disease detection is an instantly growing domain of research. In the past years, numerous endeavours have been made for the improvements of medical disease detection, because the errors and problems in medical disease detection cause serious wrong medical treatment. Meta-heuristic techniques have been frequently utilized for the detection of medical diseases and promise better accuracy of perception and prediction of diseases in the domain of biomedical. Particle Swarm Optimization (PSO) is a swarm-based intelligent stochastic search technique encouraged from the intrinsic manner of bee swarm during the searching of their food source. Consequently, for the versatility of numerical experimentation, PSO has been mostly applied to address the diverse kinds of optimization problems. However, the PSO techniques are frequently adopted for the detection of diseases but there is still a gap in the comparative survey. This paper presents an insight into the diagnosis of medical diseases in health care using various PSO approaches. This study presents to deliver a systematic literature review of current PSO approaches for knowledge discovery in the field of disease detection. The systematic analysis discloses the potential research areas of PSO strategies as well as the research gaps, although, the main goal is to provide the directions for future enhancement and development in this area. This paper gives a systematic survey of this conceptual model for the advanced research, which has been explored in the specified literature to date. This review comprehends the fundamental concepts, theoretical foundations, and conventional application fields. It is predicted that our study will be beneficial for the researchers to review the PSO algorithms in-depth for disease detection. Several challenges that can be undertaken to move the field forward are discussed according to the current state of the PSO strategies in health care. The application of computational intelligence for diagnosis of medical diseases has become a new trend in recent years. Numerous methods of medical disease diagnosis can be grouped as intelligent data classification tasks. In terms of the total number of groups that are continuously distributed, the classification techniques may are divided into two categories. The first classification distribution is Binary Classification (Two-class task), which differentiates the data solely between two classes. The second classification is Multi-Classification (multi-class task), which distinguishes data from more than two classes , 2. SeveExcluding the other standard classification complexities, the classifications of medical datasets furthermore are employed in disease detection. Consequently, doctors or patients must not only observe the classification findings that have been evaluated but also be familiar with the symptoms that have been used for the classification purpose. Linear programming  models aMany researchers in the literature have used PSO state-of-the-art algorithm to solve this problem by embedding various other approaches such as a random forest with PSO  and K-neThe structure of this SLR is organized in the following way: A primary study related to the utilization of PSO for medical disease detection is presented in For diagnosis the Alzheimer's disease (AD), a novel method is introduced in  that is A novel PSO and Artificial Neural Network (PSO-ANN) based model is developed for diagnosing dengue fever at an early stage . In the A novel machine learning model is developed for the detection of AD from brain MRI . At firsAn extended ANN approach termed Optimized Artificial Neural Network (OANN) is proposed and implemented on medical datasets to diagnose heart disease . For redTwo novel modified Boolean PSO are introduced named Improved Velocity Bounded BoPSO (IVbBoPSO) and Velocity Bounded BoPSO (IVbBoPSO) to figure out the feature selection challenges, while diagnosing kidney and liver cancer . In modiIn , a RandoA novel approach Block-Based Neural Network (BBNN) using PSO is introduced for the classification of Electrocardiogram signals (ECG) . The PSOThis research applied the systematic review methodology of Brereton et al. , which pThe planning phase discussed the scientific research questions definition, identification of databases, definition of keywords, searching techniques, standards for include & exclude, and quality of article –20. ThusRQ1: What is the brief overview of current research?RQ2: What are the extensively applied approaches?RQ3: How the work is distributed according to time division?RQ4: Which research papers are frequently cited?RQ5: What are the diseases and algorithms are used?RQ6: What are the research possibilities?Generally, include, exclude and quality standards are defined next to the explanation of research questions . TherefoDigital Library of IEEEElectronic Library ACMGoogle ScholarsSpringer LinkElsevier Science DirectSemantic ScholarsEmeraldThe goal of this process may be to examine the judgments related to the type of reconsiderations that should be studied in this research, which are applied to the sub-categories of primary studies for directing the selection criteria , 22. As ((“Particle Swam Optimization” OR“PSO” OR “Swarm Intelligence” OR “Bio-inspired PSO Algorithm” OR “Meta-Heuristic Algorithm PSO” OR “Nature-inspired Algorithm PSO” OR “Evolutionary Computing Algorithm PSO”) AND ).Finally, the Boolean recovery approach was used to search literature from the aforementioned databases. Fundamentally, it splits the search space and defines the sub-category of the document, as stated in the criteria of consultation . In our This phase implies five steps: (1) implements the search in the preferred database; (2) correlate the search results for excluding the duplicated articles; (3) apply the include, exclude, and quality standards; (4) assessment of all articles that accepted in the preliminary study; (5) data formation , 18.However, to purify the search and discard the papers that were irreverent according to the scope of this review, a precise investigation was employed on the titles, keywords, and abstracts with respect to the exclude standards . It exclFinally, a formation (synthesis) was performed to research under the aforementioned quality criteria. As a result, 11 of the 54 publications were eliminated due to quality issues, leaving a final collection of 43 publications with important information on Particle Swarm Optimization for medical disease diagnosis.The results of SLR are discussed in this section. Thereby, each sub-section will demonstrate the challenges, which are addressed at the start of this research.Heart DiseaseDental DiseaseLymphatic DiseasesCeliac Disease (CD)Liver DiseaseCancer DiseaseBrain DiseaseHepatitis DiseaseDiabetesOthersThe selected literature for this SLR is illustrated in The objective of this study is to find out the PSO techniques that are used to detect the below-mentioned categorized diseases. The concerned topic of diseases and the research papers which are used in the particular diseases are displayed in A cloud of words is illustrated in The evaluation related to the Distribution of selected literature with respect to the time division is presented in The total citations of elected top 12 papers from literature are identified as 1193 on the topic of PSO techniques and medical diseases, which can be review in In this question, we describe a concise explanation of medical diseases and the variants of PSO that are utilized for disease detection in the selected articles for this SLR. The comparative description of medical diseases and variants of PSO can be seen in This domain further has numerous branches that need to be investigated. Hence, in PSO has been widely adopted to solve real-world nonlinear complex optimization problems in various areas. This study is shown a systematic review of existing studies on the standard PSO and its variants to diagnose the medical diseases for health care. Researchers have been suggested various PSO variants for medical disease diagnosis in health care, although, PSO still requires an extreme inspection to enhance its performance. The paper is giving detail on different medical diseases that have been utilized in numerous PSO approaches for solving medical disease detection in health care. We tried to give a systematic survey of various medical diseases and analyzed each PSO technique separately. In order to perform the systematic survey, the gaps in the literature are figured out and converted into six research questions. For the next stage, the research questions are taken into account for analysis, where the following points are briefly explained: the utilization of PSO and its variants in diseases detection, the worth of selected articles, the time division of published articles, generally encountered medical diseases, maximum applied PSO variants for disease detection. With the proper rate of growth in the research area, it is expected that additional work should be achieved in the future. The findings of this systematic survey depict that many researchers incorporated PSO and its variants to detect Heart disease, Cancer disease, Brain disease, Hepatitis disease, and Diabetes. Furthermore, the analytical results of the systematic survey illustrate that various scientists and researchers frequently targeted the Standard PSO and Improved versions of PSO for disease diagnosis. As the future direction, the researchers can utilize the improved versions of Neural Networks with PSO, as well as, can use the diverse hybridized versions of PSO for disease diagnosis. We anticipated that this survey will draw more attention to these problems and the substantial research will provide basic insight into how PSO mutation strategies enhance the performance of standard PSO in the health care domain. We are confident that such knowledge will encourage the PSO researchers to gain better awareness about a particular PSO, to enhance it, or to devise a new one.