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Background: Annotated data are foundational to applications of supervised machine learning. However, there seems to be a lack of common language used in the field of surgical data science. The aim of this study is to review the process of annotation and semantics used in the creation of SPM for minimally invasive surgery videos., Methods: For this systematic review, we reviewed articles indexed in the MEDLINE database from January 2000 until March 2022. We selected articles using surgical video annotations to describe a surgical process model in the field of minimally invasive surgery. We excluded studies focusing on instrument detection or recognition of anatomical areas only. The risk of bias was evaluated with the Newcastle Ottawa Quality assessment tool. Data from the studies were visually presented in table using the SPIDER tool., Results: Of the 2806 articles identified, 34 were selected for review. Twenty-two were in the field of digestive surgery, six in ophthalmologic surgery only, one in neurosurgery, three in gynecologic surgery, and two in mixed fields. Thirty-one studies (88.2%) were dedicated to phase, step, or action recognition and mainly relied on a very simple formalization (29, 85.2%). Clinical information in the datasets was lacking for studies using available public datasets. The process of annotation for surgical process model was lacking and poorly described, and description of the surgical procedures was highly variable between studies., Conclusion: Surgical video annotation lacks a rigorous and reproducible framework. This leads to difficulties in sharing videos between institutions and hospitals because of the different languages used. There is a need to develop and use common ontology to improve libraries of annotated surgical videos., (© 2023. The Author(s), under exclusive licence to Springer Science+Business Media, LLC, part of Springer Nature.)

Purpose: Classic methods of surgery skills evaluation tend to classify the surgeon performance in multi-categorical discrete classes. If this classification scheme has proven to be effective, it does not provide in-between evaluation levels. If these intermediate scoring levels were available, they would provide more accurate evaluation of the surgeon trainee., Methods: We propose a novel approach to assess surgery skills on a continuous scale ranging from 1 to 5. We show that the proposed approach is flexible enough to be used either for scores of global performance or several sub-scores based on a surgical criteria set called Objective Structured Assessment of Technical Skills (OSATS). We established a combined CNN+BiLSTM architecture to take advantage of both temporal and spatial features of kinematic data. Our experimental validation relies on real-world data obtained from JIGSAWS database. The surgeons are evaluated on three tasks: Knot-Tying, Needle-Passing and Suturing. The proposed framework of neural networks takes as inputs a sequence of 76 kinematic variables and produces an output float score ranging from 1 to 5, reflecting the quality of the performed surgical task., Results: Our proposed model achieves high-quality OSATS scores predictions with means of Spearman correlation coefficients between the predicted outputs and the ground-truth outputs of 0.82, 0.60 and 0.65 for Knot-Tying, Needle-Passing and Suturing, respectively. To our knowledge, we are the first to achieve this regression performance using the OSATS criteria and the JIGSAWS kinematic data., Conclusion: An effective deep learning tool was created for the purpose of surgical skills assessment. It was shown that our method could be a promising surgical skills evaluation tool for surgical training programs., (© 2023. CARS.)

Disinhibition is a core symptom in behavioural variant frontotemporal dementia (bvFTD) particularly affecting the daily lives of both patients and caregivers. Yet, characterisation of inhibition disorders is still unclear and management options of these disorders are limited. Questionnaires currently used to investigate behavioural disinhibition do not differentiate between several subtypes of disinhibition, encompass observation biases and lack of ecological validity. In the present work, we explored disinhibition in an original semi-ecological situation, by distinguishing three categories of disinhibition: compulsivity, impulsivity and social disinhibition. First, we measured prevalence and frequency of these disorders in 23 bvFTD patients and 24 healthy controls (HC) in order to identify the phenotypical heterogeneity of disinhibition. Then, we examined the relationships between these metrics, the neuropsychological scores and the behavioural states to propose a more comprehensive view of these neuropsychiatric manifestations. Finally, we studied the context of occurrence of these disorders by investigating environmental factors potentially promoting or reducing them. As expected, we found that patients were more compulsive, impulsive and socially disinhibited than HC. We found that 48% of patients presented compulsivity (e.g., repetitive actions), 48% impulsivity (e.g., oral production) and 100% of the patients group showed social disinhibition (e.g., disregards for rules or investigator). Compulsivity was negatively related with emotions recognition. BvFTD patients were less active if not encouraged in an activity, and their social disinhibition decreased as activity increased. Finally, impulsivity and social disinhibition decreased when patients were asked to focus on a task. Summarising, this study underlines the importance to differentiate subtypes of disinhibition as well as the setting in which they are exhibited, and points to stimulating area for non-pharmacological management., Competing Interests: Declaration of competing interest The authors report no competing interests., (Copyright © 2022 The Author(s). Published by Elsevier Ltd.. All rights reserved.)

Purpose: Surgery simulators can be used to learn technical and non-technical skills and, to analyse posture. Ergonomic skill can be automatically detected with a Human Pose Estimation algorithm to help improve the surgeon's work quality. The objective of this study was to analyse the postural behaviour of surgeons and identify expertise-dependent movements. Our hypothesis was that hesitation and the occurrence of surgical instruments interfering with movement (defined as interfering movements) decrease with expertise., Material and Methods: Sixty surgeons with three expertise levels (novice, intermediate, and expert) were recruited. During a training session using an arthroscopic simulator, each participant's movements were video-recorded with an RGB camera. A modified OpenPose algorithm was used to detect the surgeon's joints. The detection frequency of each joint in a specific area was visualized with a heatmap-like approach and used to calculate a mobility score., Results: This analysis allowed quantifying surgical movements. Overall, the mean mobility score was 0.823, 0.816, and 0.820 for novice, intermediate and expert surgeons, respectively. The mobility score alone was not enough to identify postural behaviour differences. A visual analysis of each participants' movements highlighted expertise-dependent interfering movements., Conclusion: Video-recording and analysis of surgeon's movements are a non-invasive approach to obtain quantitative and qualitative ergonomic information in order to provide feedback during training. Our findings suggest that the interfering movements do not decrease with expertise but differ in function of the surgeon's level., (© 2022. CARS.)

Improving surgical training by means of technology assistance is an important challenge that aims to directly impact surgical quality. Surgical training includes the acquisition of two categories of knowledge: declarative knowledge (i.e. 'knowing what') and procedural knowledge (i.e. 'knowing how'). It is essential to acquire both before performing any particular surgery. There are currently many tools for acquiring procedural knowledge, such as simulators. However, few approaches or tools allow a trainer to formalize and record surgical declarative knowledge, and a trainee to have easy access to it. In this paper, we propose an approach for structuring surgical declarative knowledge according to procedural knowledge and based on surgical process modeling. A dedicated software application has been implemented. We evaluated the concept and the software usability on two procedures with different medical populations: endoscopic third ventriculostomy involving 6 neurosurgeons and preparation of a surgical table for craniotomy involving 4 scrub nurses. The results of both studies show that surgical process models could be a well-adapted approach for structuring and visualizing surgical declarative knowledge. The software application was perceived by neurosurgeons and scrub nurses as an innovative tool for managing and presenting surgical knowledge. The preliminary results show that the feasibility of the proposed approach and the acceptability and usability of the corresponding software. Future experiments will study impact of such an approach on knowledge acquisition.

Extracting population-wise information from medical images, specifically in the neurological domain, is crucial to better understanding disease processes and progression. This is frequently done in a whole-brain voxel-wise manner, in which a population of patients and healthy controls are registered to a common co-ordinate space and a statistical test is performed on the distribution of image intensities for each location. Although this method has yielded a number of scientific insights, it is further from clinical applicability as the differences are often small and altogether do not permit for a high-performing classifier. In this article, we take the opposite approach of using a high-performing classifier, specifically a traditional convolutional neural network, and then extracting insights from it which can be applied in a population-wise manner, a method we call voxel-based diktiometry. We have applied this method to diffusion tensor imaging (DTI) analysis for Parkinson's disease (PD), using the Parkinson's Progression Markers Initiative database. By using the network sensitivity information, we can decompose what elements of the DTI contribute the most to the network's performance, drawing conclusions about diffusion biomarkers for PD that are based on metrics which are not readily expressed in the voxel-wise approach., (© 2022 The Authors. Human Brain Mapping published by Wiley Periodicals LLC.)

The use of digital technology is increasing rapidly across surgical specialities, yet there is no consensus for the term 'digital surgery'. This is critical as digital health technologies present technical, governance, and legal challenges which are unique to the surgeon and surgical patient. We aim to define the term digital surgery and the ethical issues surrounding its clinical application, and to identify barriers and research goals for future practice. 38 international experts, across the fields of surgery, AI, industry, law, ethics and policy, participated in a four-round Delphi exercise. Issues were generated by an expert panel and public panel through a scoping questionnaire around key themes identified from the literature and voted upon in two subsequent questionnaire rounds. Consensus was defined if >70% of the panel deemed the statement important and <30% unimportant. A final online meeting was held to discuss consensus statements. The definition of digital surgery as the use of technology for the enhancement of preoperative planning, surgical performance, therapeutic support, or training, to improve outcomes and reduce harm achieved 100% consensus agreement. We highlight key ethical issues concerning data, privacy, confidentiality and public trust, consent, law, litigation and liability, and commercial partnerships within digital surgery and identify barriers and research goals for future practice. Developers and users of digital surgery must not only have an awareness of the ethical issues surrounding digital applications in healthcare, but also the ethical considerations unique to digital surgery. Future research into these issues must involve all digital surgery stakeholders including patients., (© 2022. The Author(s).)

Context: As the role of AI in healthcare continues to expand there is increasing awareness of the potential pitfalls of AI and the need for guidance to avoid them., Objectives: To provide ethical guidance on developing narrow AI applications for surgical training curricula. We define standardised approaches to developing AI driven applications in surgical training that address current recognised ethical implications of utilising AI on surgical data. We aim to describe an ethical approach based on the current evidence, understanding of AI and available technologies, by seeking consensus from an expert committee., Evidence Acquisition: The project was carried out in 3 phases: (1) A steering group was formed to review the literature and summarize current evidence. (2) A larger expert panel convened and discussed the ethical implications of AI application based on the current evidence. A survey was created, with input from panel members. (3) Thirdly, panel-based consensus findings were determined using an online Delphi process to formulate guidance. 30 experts in AI implementation and/or training including clinicians, academics and industry contributed. The Delphi process underwent 3 rounds. Additions to the second and third-round surveys were formulated based on the answers and comments from previous rounds. Consensus opinion was defined as ≥ 80% agreement., Evidence Synthesis: There was 100% response from all 3 rounds. The resulting formulated guidance showed good internal consistency, with a Cronbach alpha of >0.8. There was 100% consensus that there is currently a lack of guidance on the utilisation of AI in the setting of robotic surgical training. Consensus was reached in multiple areas, including: 1. Data protection and privacy; 2. Reproducibility and transparency; 3. Predictive analytics; 4. Inherent biases; 5. Areas of training most likely to benefit from AI., Conclusions: Using the Delphi methodology, we achieved international consensus among experts to develop and reach content validation for guidance on ethical implications of AI in surgical training. Providing an ethical foundation for launching narrow AI applications in surgical training. This guidance will require further validation., Patient Summary: As the role of AI in healthcare continues to expand there is increasing awareness of the potential pitfalls of AI and the need for guidance to avoid them.In this paper we provide guidance on ethical implications of AI in surgical training., (Copyright © 2021 European Association of Urology. Published by Elsevier B.V. All rights reserved.)

Introduction: Robot-assisted laparoscopy is a safe surgical approach with several studies suggesting correlations between complication rates and the surgeon's technical skills. Surgical skills are usually assessed by questionnaires completed by an expert observer. With the advent of surgical robots, automated surgical performance metrics (APMs)-objective measures related to instrument movements-can be computed. The aim of this systematic review was thus to assess APMs use in robot-assisted laparoscopic procedures. The primary outcome was the assessment of surgical skills by APMs and the secondary outcomes were the association between APM and surgeon parameters and the prediction of clinical outcomes., Methods: A systematic review following the PRISMA guidelines was conducted. PubMed and Scopus electronic databases were screened with the query "robot-assisted surgery OR robotic surgery AND performance metrics" between January 2010 and January 2021. The quality of the studies was assessed by the medical education research study quality instrument. The study settings, metrics, and applications were analysed., Results: The initial search yielded 341 citations of which 16 studies were finally included. The study settings were either simulated virtual reality (VR) (4 studies) or real clinical environment (12 studies). Data to compute APMs were kinematics (motion tracking), and system and specific events data (actions from the robot console). APMs were used to differentiate expertise levels, and thus validate VR modules, predict outcomes, and integrate datasets for automatic recognition models. APMs were correlated with clinical outcomes for some studies., Conclusions: APMs constitute an objective approach for assessing technical skills. Evidence of associations between APMs and clinical outcomes remain to be confirmed by further studies, particularly, for non-urological procedures. Concurrent validation is also required., (© 2021. The Author(s), under exclusive licence to Springer Science+Business Media, LLC, part of Springer Nature.)

Recent developments in data science in general and machine learning in particular have transformed the way experts envision the future of surgery. Surgical Data Science (SDS) is a new research field that aims to improve the quality of interventional healthcare through the capture, organization, analysis and modeling of data. While an increasing number of data-driven approaches and clinical applications have been studied in the fields of radiological and clinical data science, translational success stories are still lacking in surgery. In this publication, we shed light on the underlying reasons and provide a roadmap for future advances in the field. Based on an international workshop involving leading researchers in the field of SDS, we review current practice, key achievements and initiatives as well as available standards and tools for a number of topics relevant to the field, namely (1) infrastructure for data acquisition, storage and access in the presence of regulatory constraints, (2) data annotation and sharing and (3) data analytics. We further complement this technical perspective with (4) a review of currently available SDS products and the translational progress from academia and (5) a roadmap for faster clinical translation and exploitation of the full potential of SDS, based on an international multi-round Delphi process., Competing Interests: Declaration of Competing Interest Anand Malpani is a future employee at Mimic Technologies Inc. (Seattle, WA, US). Johannes Fallert and Lars Mündermann are employed at KARL STORZ SE & Co. KG (Tuttlingen, Germany). Hirenkumar Nakawala is employed at CMR Surgical Ltd (Cambridge, UK). Nicolas Padoy is a scientific advisor of Caresyntax (Berlin, Germany). Daniel A. Hashimoto is a consultant for Johnson & Johnson (New Brunswick, NJ, USA), Verily Life Sciences (San Francisco, CA, USA), and Activ Surgical (Boston, MA, USA). He has received research support from Olympus Corporation and the Intuitive Foundation. Carla Pugh is the founder of 10 Newtons Inc. (Madison, WI, US). Danail Stoyanov is employed at Digital Surgery Ltd (London, UK) and Odin Vision Ltd (London, UK). Teodor Grantcharov is the founder of Surgical Safety Technologies Inc. (Toronto, Ontario, Canada). Tobias Roß is employed at Quality Match GmbH (Heidelberg, Germany). All other authors do not declare any conflicts of interest., (Copyright © 2021. Published by Elsevier B.V.)

Disinhibition is a core symptom of many neurodegenerative diseases, particularly frontotemporal dementia, and is a major cause of stress for caregivers. While a distinction between behavioural and cognitive disinhibition is common, an operational definition of behavioural disinhibition is still missing. Furthermore, conventional assessment of behavioural disinhibition, based on questionnaires completed by the caregivers, often lacks ecological validity. Therefore, their neuroanatomical correlates are non-univocal. In the present work, we used an original behavioural approach in a semi-ecological situation to assess two specific dimensions of behavioural disinhibition: compulsivity and social disinhibition. First, we investigated disinhibition profile in patients compared to controls. Then, to validate our approach, compulsivity and social disinhibition scores were correlated with classic cognitive tests measuring disinhibition (Hayling Test) and social cognition (mini-Social cognition & Emotional Assessment). Finally, we disentangled the anatomical networks underlying these two subtypes of behavioural disinhibition, taking in account the grey (voxel-based morphometry) and white matter (diffusion tensor imaging tractography). We included 17 behavioural variant frontotemporal dementia patients and 18 healthy controls. We identified patients as more compulsive and socially disinhibited than controls. We found that behavioural metrics in the semi-ecological task were related to cognitive performance: compulsivity correlated with the Hayling test and both compulsivity and social disinhibition were associated with the emotion recognition test. Based on voxel-based morphometry and tractography, compulsivity correlated with atrophy in the bilateral orbitofrontal cortex, the right temporal region and subcortical structures, as well as with alterations of the bilateral cingulum and uncinate fasciculus, the right inferior longitudinal fasciculus and the right arcuate fasciculus. Thus, the network of regions related to compulsivity matched the "semantic appraisal" network. Social disinhibition was associated with bilateral frontal atrophy and impairments in the forceps minor, the bilateral cingulum and the left uncinate fasciculus, regions corresponding to the frontal component of the "salience" network. Summarizing, this study validates our semi-ecological approach, through the identification of two subtypes of behavioural disinhibition, and highlights different neural networks underlying compulsivity and social disinhibition. Taken together, these findings are promising for clinical practice by providing a better characterisation of inhibition disorders, promoting their detection and consequently a more adapted management of patients., (Copyright © 2022 The Authors. Published by Elsevier Inc. All rights reserved.)

Background: Virtual reality (VR) simulation is particularly suitable for learning arthroscopy skills. Despite significant research, one drawback often outlined is the difficulty in distinguishing performance levels (Construct Validity) in experienced surgeons. Therefore, it seems adequate to search new methods of performance measurements using probe trajectories instead of commonly used metrics., Hypothesis: It was hypothesized that a larger experience in surgical shoulder arthroscopy would be correlated with better performance on a VR shoulder arthroscopy simulator and that experienced operators would share similar probe trajectories., Materials & Methods: After answering to standardized questionnaires, 104 trajectories from 52 surgeons divided into 2 cohorts (26 intermediates and 26 experts) were recorded on a shoulder arthroscopy simulator. The procedure analysed was the "loose body removal" in a right shoulder joint. 10 metrics were computed on the trajectories including procedure duration, overall path length, economy of motion and smoothness. Additionally, Dynamic Time Warping (DTW) was computed on the trajectories for unsupervised hierarchical clustering of the surgeons., Results: Experts were significantly faster (Median 70.9s Interquartile range [56.4-86.3] vs. 116.1s [82.8-154.2], p<0.01), more fluid (4.6.10 5 mm.s -3 [3.1.10 5 -7.2.10 5 ] vs. 1.5.10 6 mm.s -3 [2.6.10 6 -3.5.10 6 ], p=0.05), and economical in their motion (19.3mm 2 [9.1-25.9] vs. 33.8mm 2 [14.8-50.5], p<0.01), but there was no significant difference in performance for path length (671.4mm [503.8-846.1] vs 694.6mm [467.0-1090.1], p=0.62). The DTW clustering differentiates two expertise related groups of trajectories with performance similarities, respectively including 48 expert trajectories for the first group and 52 intermediates and 4 expert trajectories for the second group (Sensitivity of 92%, Specificity of 100%). Hierarchical clustering with DTW significantly identified expert operators from intermediate operators and found trajectory similarities among 24/26 experts., Conclusion: This study demonstrated the Construct Validity of the VR shoulder arthroscopy simulator within groups of experienced surgeons. With new types of metrics simply based on the simulator's raw trajectories, it was possible to significantly distinguish levels of expertise. We demonstrated that clustering analysis with Dynamic Time Warping was able to reliably discriminate between expert operators and intermediate operators., Clinical Relevance: The results have implications for the future of arthroscopic surgical training or post-graduate accreditation programs using virtual reality simulation., Level of Evidence: III; prospective comparative study., (Copyright © 2021 Elsevier Masson SAS. All rights reserved.)

Deep Brain Stimulation (DBS) is an increasingly common therapy for a large range of neurological disorders, such as abnormal movement disorders. The effectiveness of DBS in terms of controlling patient symptomatology has made this procedure increasingly used over the past few decades. Concurrently, the popularity of Machine Learning (ML), a subfield of artificial intelligence, has skyrocketed and its influence has more recently extended to medical domains such as neurosurgery. Despite its growing research interest, there has yet to be a literature review specifically on the use of ML in DBS. We have followed a fully systematic methodology to obtain a corpus of 73 papers. In each paper, we identified the clinical application, the type/amount of data used, the method employed, and the validation strategy, further decomposed into 12 different sub-categories. The papers overall illustrated some existing trends in how ML is used in the context of DBS, including the breath of the problem domain and evolving techniques, as well as common frameworks and limitations. This systematic review analyzes at a broad level how ML have been recently used to address clinical problems on DBS, giving insight into how these new computational methods are helping to push the state-of-the-art of functional neurosurgery. DBS clinical workflow is complex, involves many specialists, and raises several clinical issues which have partly been addressed with artificial intelligence. However, several areas remain and those that have been recently addressed with ML are by no means considered "solved" by the community nor are they closed to new and evolving methods., (Copyright © 2021. Published by Elsevier B.V.)

Background and Objective: Automatic surgical workflow recognition is an essential step in developing context-aware computer-assisted surgical systems. Video recordings of surgeries are becoming widely accessible, as the operational field view is captured during laparoscopic surgeries. Head and ceiling mounted cameras are also increasingly being used to record videos in open surgeries. This makes videos a common choice in surgical workflow recognition. Additional modalities, such as kinematic data captured during robot-assisted surgeries, could also improve workflow recognition. This paper presents the design and results of the MIcro-Surgical Anastomose Workflow recognition on training sessions (MISAW) challenge whose objective was to develop workflow recognition models based on kinematic data and/or videos., Methods: The MISAW challenge provided a data set of 27 sequences of micro-surgical anastomosis on artificial blood vessels. This data set was composed of videos, kinematics, and workflow annotations. The latter described the sequences at three different granularity levels: phase, step, and activity. Four tasks were proposed to the participants: three of them were related to the recognition of surgical workflow at three different granularity levels, while the last one addressed the recognition of all granularity levels in the same model. We used the average application-dependent balanced accuracy (AD-Accuracy) as the evaluation metric. This takes unbalanced classes into account and it is more clinically relevant than a frame-by-frame score., Results: Six teams participated in at least one task. All models employed deep learning models, such as convolutional neural networks (CNN), recurrent neural networks (RNN), or a combination of both. The best models achieved accuracy above 95%, 80%, 60%, and 75% respectively for recognition of phases, steps, activities, and multi-granularity. The RNN-based models outperformed the CNN-based ones as well as the dedicated modality models compared to the multi-granularity except for activity recognition., Conclusion: For high levels of granularity, the best models had a recognition rate that may be sufficient for applications such as prediction of remaining surgical time. However, for activities, the recognition rate was still low for applications that can be employed clinically. The MISAW data set is publicly available at http://www.synapse.org/MISAW to encourage further research in surgical workflow recognition., Competing Interests: Declaration of Competing Interest The authors declare that they have no conflict of interest., (Copyright © 2021 Elsevier B.V. All rights reserved.)

Purpose: Deep Brain Stimulation (DBS) is a proven therapy for Parkinson's Disease (PD), frequently resulting in an enhancement of motor function. Nonetheless, several undesirable side effects can occur after DBS, which can worsen the quality of life of the patient. Thus, the clinical team has to carefully select patients on whom to perform DBS. Over the past decade, there have been some attempts to relate pre-operative data and DBS clinical outcomes, with most focused on the motor symptomatology. In this paper, we propose a machine learning-based method able to predict a large number of DBS clinical outcomes for PD., Methods: We propose a multimodal pipeline, referred to as PassFlow, which predicts 84 clinical post-operative clinical scores. PassFlow is composed of an artificial neural network to compress clinical information, an image processing method from the state-of-the-art to extract morphological biomarkers our of T1 imaging, and an SVM to perform the regressions. We validated PassFlow on 196 PD patients who undergone a DBS., Results: PassFlow showed correlation coefficients as high as 0.71 and were able to significantly predict 63 out of the 84 scores, outperforming a comparative linear method. The number of metrics that are predicted with this pre-operative information was also found to be correlated with the number of patients with this information available, indicating that the PassFlow method is still actively learning., Conclusion: We presented a novel, machine learning-based pipeline to predict a variety of post-operative clinical outcomes of DBS for PD patients. PassFlow took into account various bio-markers, arising from different data modalities, showing high correlation coefficients for some scores from pre-operative data only. It indicates that many clinical outcomes of DBS can be predicted agnostic to the specific simulation parameters, as PassFlow has been validated without such stimulation-related information., (© 2021. CARS.)

Purpose: Deep brain stimulation (DBS) is a common treatment for a variety of neurological disorders which involves the precise placement of electrodes at particular subcortical locations such as the subthalamic nucleus. This placement is often guided by auditory analysis of micro-electrode recordings (MERs) which informs the clinical team as to the anatomic region in which the electrode is currently positioned. Recent automation attempts have lacked flexibility in terms of the amount of signal recorded, not allowing them to collect more signal when higher certainty is needed or less when the anatomy is unambiguous., Methods: We have addressed this problem by evaluating a simple algorithm that allows for MER signal collection to terminate once the underlying model has sufficient confidence. We have parameterized this approach and explored its performance using three underlying models composed of one neural network and two Bayesian extensions of said network., Results: We have shown that one particular configuration, a Bayesian model of the underlying network's certainty, outperforms the others and is relatively insensitive to parameterization. Further investigation shows that this model also allows for signals to be classified earlier without increasing the error rate., Conclusion: We have presented a simple algorithm that records the confidence of an underlying neural network, thus allowing for MER data collection to be terminated early when sufficient confidence is reached. This has the potential to improve the efficiency of DBS electrode implantation by reducing the time required to identify anatomical structures using MERs., (© 2021. CARS.)

Purpose: Transcranial magnetic stimulation (TMS) is a growing therapy for a variety of psychiatric and neurological disorders that arise from or are modulated by cortical regions of the brain represented by singular 3D target points. These target points are often determined manually with assistance from a pre-operative T1-weighted MRI, although there is growing interest in automatic target point localisation using an atlas. However, both approaches can be time-consuming which has an effect on the clinical workflow, and the latter does not take into account patient variability such as the varying number of cortical gyri where these targets are located., Methods: This paper proposes a multi-resolution convolutional neural network for point localisation in MR images for a priori defined points in increasingly finely resolved versions of the input image. This approach is both fast and highly memory efficient, allowing it to run in high-throughput centres, and has the capability of distinguishing between patients with high levels of anatomical variability., Results: Preliminary experiments have found the accuracy of this network to be [Formula: see text] mm, compared to [Formula: see text] mm for deformable registration and [Formula: see text] mm for a human expert. For most treatment points, the human expert and proposed CNN statistically significantly outperform registration, but neither statistically significantly outperforms the other, suggesting that the proposed network has human-level performance., Conclusions: The human-level performance of this network indicates that it can improve TMS planning by automatically localising target points in seconds, avoiding more time-consuming registration or manual point localisation processes. This is particularly beneficial for out-of-hospital centres with limited computational resources where TMS is increasingly being administered.

Introduction: We aimed to investigate phenotypic heterogeneity in the behavioral variant of frontotemporal dementia (bvFTD) through assessment of inhibition deficits., Methods: We assessed occurrences of 16 behavioral inhibition deficits from video recordings of 15 bvFTD patients (early stage) and 15 healthy controls (HC) in an ecological setting. We extracted dimensions of inhibition deficit and analyzed their correlations with cognitive and clinical measures. Using these dimensions, we isolated patient clusters whose atrophy patterns were explored., Results: After identifying two patterns of inhibition deficit (compulsive automatic behaviors and socially unconventional behaviors), we isolated three behavioral clusters with distinct atrophy patterns. BvFTD-G0 (N = 3), an outlier group, showed severe behavioral disturbances and more severe ventromedial prefrontal cortex/orbitofrontal cortex atrophy. Compared to bvFTD-G1 (N = 6), bvFTD-G2 (N = 6) presented higher anxiety and depression along with less diffuse atrophy especially in midline regions., Discussion: Identifying clinico-anatomical profiles through behavior observation could help to stratify bvFTD patients for adapted treatments., Competing Interests: The authors declare no competing interests., (© 2021 The Authors. Alzheimer's & Dementia: Diagnosis, Assessment & Disease Monitoring published by Wiley Periodicals, LLC on behalf of Alzheimer's Association.)

Medical questionnaires are a valuable source of information but are often difficult to analyse due to both their size and the high possibility of them having missing values. This is a problematic issue in biomedical data science as it may complicate how individual questionnaire data is represented for statistical or machine learning analysis. In this paper, we propose a deeply-learnt residual autoencoder to simultaneously perform non-linear data imputation and dimensionality reduction. We present an extensive analysis of the dynamics of the performance of this autoencoder regarding the compression rate and the proportion of missing values. This method is evaluated on motor and non-motor clinical questionnaires of the Parkinson's Progression Markers Initiative (PPMI) database and consistently outperforms linear coupled imputation and reduction approaches., (Copyright © 2021 Elsevier B.V. All rights reserved.)

Obstetric ultrasound simulators are now used for training and evaluating OB/GYN students but there is a lack of literature about evaluation metrics in this setting. In this literature review, we searched MEDLINE and the COCHRANE database using the keywords: (Obstetric OR Fetal) AND (Sonography OR Ultrasound) AND Simulation. Of a total of 263 studies screened, we selected nine articles from the title and the abstract in PubMed, in the past 5 years. Two more article were added from bibliographies. A total of 11 articles were therefore included. from which nine articles were selected from the title and the abstract in PubMed. Two more articles were added from the bibliographies For each study, data about the type of simulation, and the metrics (qualitative or quantitative) used for assessment were collected. The selection of studies shows that evaluation criteria for ultrasound training were qualitative metrics (binary success/fail exercise ; dexterity quoted by an external observer ; Objective Structured Assessment of Ultrasound Skills (OSAUS) Score ; quality of images according to Salomon's score) or quantitative criteria (Accuracy of Biometry - Simulator generated metrics). Most studies used a combination of both. To date, simulator metrics used to discriminate ultrasound skills are performance score quoted by external observers and image quality scoring. Whether probe trajectory metrics can be used to discriminate skills is unknown., Competing Interests: Declaration of Competing Interest The authors report no conflict of interest., (Copyright © 2020. Published by Elsevier B.V.)

The number of biomedical image analysis challenges organized per year is steadily increasing. These international competitions have the purpose of benchmarking algorithms on common data sets, typically to identify the best method for a given problem. Recent research, however, revealed that common practice related to challenge reporting does not allow for adequate interpretation and reproducibility of results. To address the discrepancy between the impact of challenges and the quality (control), the Biomedical Image Analysis ChallengeS (BIAS) initiative developed a set of recommendations for the reporting of challenges. The BIAS statement aims to improve the transparency of the reporting of a biomedical image analysis challenge regardless of field of application, image modality or task category assessed. This article describes how the BIAS statement was developed and presents a checklist which authors of biomedical image analysis challenges are encouraged to include in their submission when giving a paper on a challenge into review. The purpose of the checklist is to standardize and facilitate the review process and raise interpretability and reproducibility of challenge results by making relevant information explicit., Competing Interests: Declaration of Competing Interest Anne Martel is a co-founder, CSO of Pathcore, Toronto, Canada. The remaining authors declare no competing interests., (Copyright © 2020. Published by Elsevier B.V.)

Background: Neurosurgical teams are exposed to various stressors: complexity of surgical procedures, environment, time pressure and interruptions contribute to increasing the perceived workload., Objective: This study aimed to evaluate the impact of interruptions and surgical complexity on neurosurgical team workload., Methods: A prospective observational study was conducted on thirty surgical procedures of graduated complexity recorded in our Department of Neurosurgery. A scale was created and used by neurosurgeons to evaluate the perceived complexity of the surgical procedure. Interruptions and severity of interruptions were noted. The workloads of the neurosurgeon, surgical assistant, scrub nurse and circulating nurse were measured on the Surgery Task Load Index (SURG-TLX) at the end of the procedure., Results: A mean 24.6 interruptions per hour were recorded. The mean interference level of the interruptions was 3.5/7. Mean surgical complexity was 4.3/10. Mean sterile team workload was 43.4/100. The multiple linear regression model showed that sterile team workload increased with surgical complexity (β=6.692, P=.0002) but decreased in spite of increases in the number of interruptions per hour (β=-0.855, P=.027). Neurosurgeon and surgical assistant workload increased with surgical complexity (β=11.53, P<0.0001 and β=7.42, P=0.0007, respectively). Scrub nurse workload decreased in spite of increases in the number of interruptions per hour (β=-1.11, P=.026)., Conclusion: Our study suggests positive effects of some interruptions during elective neurosurgical procedures with strong team familiarity., (Copyright © 2020 Elsevier Masson SAS. All rights reserved.)

Micro-electrode recording (MER) is a powerful way of localizing target structures during neurosurgical procedures such as the implantation of deep brain stimulation electrodes, which is a common treatment for Parkinson's disease and other neurological disorders. While Micro-electrode Recording (MER) provides adjunctive information to guidance assisted by pre-operative imaging, it is not unanimously used in the operating room. The lack of standard use of MER may be in part due to its long duration, which can lead to complications during the operation, or due to high degree of expertise required for their interpretation. Over the past decade, various approaches addressing automating MER analysis for target localization have been proposed, which have mainly focused on feature engineering. While the accuracies obtained are acceptable in certain configurations, one issue with handcrafted MER features is that they do not necessarily capture more subtle differences in MER that could be detected auditorily by an expert neurophysiologist. In this paper, we propose and validate a deep learning-based pipeline for subthalamic nucleus (STN) localization with micro-electrode recordings motivated by the human auditory system. Our proposed Convolutional Neural Network (CNN), referred as SepaConvNet, shows improved accuracy over two comparative networks for locating the STN from one second MER samples.

Purpose: Annotation of surgical videos is a time-consuming task which requires specific knowledge. In this paper, we present and evaluate a deep learning-based method that includes pre-annotation of the phases and steps in surgical videos and user assistance in the annotation process., Methods: We propose a classification function that automatically detects errors and infers temporal coherence in predictions made by a convolutional neural network. First, we trained three different architectures of neural networks to assess the method on two surgical procedures: cholecystectomy and cataract surgery. The proposed method was then implemented in an annotation software to test its ability to assist surgical video annotation. A user study was conducted to validate our approach, in which participants had to annotate the phases and the steps of a cataract surgery video. The annotation and the completion time were recorded., Results: The participants who used the assistance system were 7% more accurate on the step annotation and 10 min faster than the participants who used the manual system. The results of the questionnaire showed that the assistance system did not disturb the participants and did not complicate the task., Conclusion: The annotation process is a difficult and time-consuming task essential to train deep learning algorithms. In this publication, we propose a method to assist the annotation of surgical workflows which was validated through a user study. The proposed assistance system significantly improved annotation duration and accuracy.

Objective: According to a meta-analysis of 7 studies, the median number of patients with at least one adverse event during the surgery is 14.4%, and a third of those adverse events were preventable. The occurrence of adverse events forces surgeons to implement corrective strategies and, thus, deviate from the standard surgical process. Therefore, it is clear that the automatic identification of adverse events is a major challenge for patient safety. In this paper, we have proposed a method enabling us to identify such deviations. We have focused on identifying surgeons' deviations from standard surgical processes due to surgical events rather than anatomic specificities. This is particularly challenging, given the high variability in typical surgical procedure workflows., Methods: We have introduced a new approach designed to automatically detect and distinguish surgical process deviations based on multi-dimensional non-linear temporal scaling with a hidden semi-Markov model using manual annotation of surgical processes. The approach was then evaluated using cross-validation., Results: The best results have over 90% accuracy. Recall and precision for event deviations, i.e. related to adverse events, are respectively below 80% and 40%. To understand these results, we have provided a detailed analysis of the incorrectly-detected observations., Conclusion: Multi-dimensional non-linear temporal scaling with a hidden semi-Markov model provides promising results for detecting deviations. Our error analysis of the incorrectly-detected observations offers different leads in order to further improve our method., Significance: Our method demonstrated the feasibility of automatically detecting surgical deviations that could be implemented for both skill analysis and developing situation awareness-based computer-assisted surgical systems., (Copyright © 2020 Elsevier B.V. All rights reserved.)

Hybrid simulation is defined as the use of a patient actor combined with a task trainer within the same session. We sought to investigate the level of evidence about the clinical benefits of hybrid simulation training in obstetrics. We searched MEDLINE using the keywords: Obstetrics AND Medical Education AND (Standardized patient OR Hybrid simulation). A total of 155 studies were screened, from which we selected 11 articles were selected from the title and the abstract in PubMed. For each study, data about the type of simulation, the level of evidence according KirkPatrick's hierarchy was collected. There is evidence that clinical benefit for patients exists for Shoulder Dystocia, and Cord prolapse. For Non-technical skills, such as communication or team training, hybrid simulation was also effective. Whether hybrid simulation offers better training for communication and better immersion than high-fidelity simulation for learners remains to be investigated., Competing Interests: Declaration of Competing Interest The authors declare that they have no known competing financial interests or personal relationships that could have appeared to influence the work reported in this paper., (Copyright © 2019. Published by Elsevier B.V.)

PURPOSE : Evaluating the quality of surgical procedures is a major concern in minimally invasive surgeries. We propose a bottom-up approach based on the study of Sleeve Gastrectomy procedures, for which we analyze what we assume to be an important indicator of the surgical expertise: the exposure of the surgical scene. We first aim at predicting this indicator with features extracted from the laparoscopic video feed, and second to analyze how the extracted features describing the surgical practice influence this indicator. METHOD : Twenty-nine patients underwent Sleeve Gastrectomy performed by two confirmed surgeons in a monocentric study. Features were extracted from spatial and procedural annotations of the videos, and an expert surgeon evaluated the quality of the surgical exposure at specific instants. The features were used as input of a classifier (linear discriminant analysis followed by a support vector machine) to predict the expertise indicator. Features selected in different configurations of the algorithm were compared to understand their relationships with the surgical exposure and the surgeon's practice. RESULTS : The optimized algorithm giving the best performance used spatial features as input ([Formula: see text]). It also predicted equally the two classes of the indicator, despite their strong imbalance. Analyzing the selection of input features in the algorithm allowed a comparison of different configurations of the algorithm and showed a link between the surgical exposure and the surgeon's practice. CONCLUSION : This preliminary study validates that a prediction of the surgical exposure from spatial features is possible. The analysis of the clusters of feature selected by the algorithm also shows encouraging results and potential clinical interpretations.

Objective: Conventional surgical assistance and skill analysis for suturing mostly focus on the motions of the tools. As the quality of the suturing is determined by needle motions relative to the tissues, having knowledge of the needle motion would be useful for surgical assistance and skill analysis. As the first step toward demonstrating the usefulness of the knowledge of the needle motion, we developed a needle detection algorithm., Methods: Owing to the small needle size, attaching sensors to it is difficult. Therefore, we developed a real-time video-based needle detection algorithm using a region-based convolutional neural network., Results: Our method successfully detected the needle with an average precision of 89.2%. The needle was robustly detected even when the needle was heavily occluded by the tools and/or the blood vessels during microvascular anastomosis. However, there were some incorrect detections, including partial detection., Conclusion: To the best of our knowledge, this is the first time deep neural networks have been applied to real-time needle detection. In the future, we will develop a needle pose estimation algorithm using the predicted needle location toward computer-aided surgical assistance and surgical skill analysis.

Parkinson's Disease provokes alterations of subcortical deep gray matter, leading to subtle changes in the shape of several subcortical structures even before the manifestation of motor and non-motor clinical symptoms. We used an automated registration and segmentation pipeline to measure this structural alteration in one early and one advanced Parkinson's Disease (PD) cohorts, one prodromal stage cohort and one healthy control cohort. These structural alterations are then passed to a machine learning pipeline to classify these populations. Our workflow is able to distinguish different stages of PD based solely on shape analysis of the bilateral caudate nucleus and putamen, with balanced accuracies in the range of 59% to 85%. Furthermore, we compared the significance of each of these subcortical structure, compared the performances of different classifiers on this task, thus quantifying the informativeness of striatal shape alteration as a staging bio-marker for PD., (Copyright © 2020 The Author(s). Published by Elsevier Inc. All rights reserved.)

Background: Undesirable events in the neurosurgery operating theater have less often been the result of a technical error than of a dysfunction linked to nontechnical skills (NTSs). The essential aim of our study was to perform a systematic review of the reported data on NTS in neurosurgery. Our secondary objective was to identify the NTSs more specific to neurosurgery to define the training needs of neurosurgery trainees., Methods: The MEDLINE and Cochrane Database of Systematic Reviews were searched according to the PRISMA (preferred reporting items for systematic reviews and meta-analysis) guidelines. The search initially identified 2132 reports, of which 21 were eligible for systematic study. Data were extracted from the report regarding study design, sample size, NTSs assessed, and assessment tools, and the key results were collected., Results: Interpersonal skills (communication, teamwork), cognitive skills (decision making, situational awareness), and personal resource factors (coping with stress or fatigue) were specifically identified. No study had used assessment tools such as NOTECHS (nontechnical skills), NOTSS (nontechnical skills for surgeons), or OTAS (observational teamwork assessment for surgery). They were performed in a real environment in 11 cases, in a simulated environment in 9, and during theoretical teaching in 1 case., Conclusions: Very few studies have been performed concerning neurosurgical NTSs, despite the increasing the number of studies during the past few years on NTSs in other domains of surgery. Society has been concentrating more and more on the quality and safety of medical care. The development and application of NTS assessment tools is, therefore, essential to provide assistance in the training of future neurosurgeons., (Copyright © 2019 Elsevier Inc. All rights reserved.)

Purpose: Annotation of surgical activities becomes increasingly important for many recent applications such as surgical workflow analysis, surgical situation awareness, and the design of the operating room of the future, especially to train machine learning methods in order to develop intelligent assistance. Currently, annotation is mostly performed by observers with medical background and is incredibly costly and time-consuming, creating a major bottleneck for the above-mentioned technologies. In this paper, we propose a way to eliminate, or at least limit, the human intervention in the annotation process., Methods: Meaningful information about interaction between objects is inherently available in virtual reality environments. We propose a strategy to convert automatically this information into annotations in order to provide as output individual surgical process models., Validation: We implemented our approach through a peg-transfer task simulator and compared it to manual annotations. To assess the impact of our contribution, we studied both intra- and inter-observer variability., Results and Conclusion: In average, manual annotations took more than 12 min for 1 min of video to achieve low-level physical activity annotation, whereas automatic annotation is achieved in less than a second for the same video period. We also demonstrated that manual annotation introduced mistakes as well as intra- and inter-observer variability that our method is able to suppress due to the high precision and reproducibility.

Cognitive action control depends on cortical-subcortical circuits, involving notably the subthalamic nucleus (STN), as evidenced by local field potentials recordings (LFPs) studies. The STN consistently shows an increase in theta oscillations power during conflict resolution. Some studies have shown that cognitive action control in Parkinson's disease (PD) could be influenced by the occurrence of monetary reward. In this study, we investigated whether incentive motivation could modulate STN activity, and notably STN theta activity, during response conflict resolution. To achieve this objective, we recorded STN LFPs during a motivated Simon task in PD patients who had undergone deep brain stimulation surgery. Behavioral results revealed that promised rewards increased the difficulty in resolving conflict situations, thus replicating previous findings. Signal analyses locked on the imperative stimulus onset revealed the typical pattern of increased theta power in a conflict situation. However, this conflict-related modulation of theta power was not influenced by the size of the reward cued. We nonetheless identified a significant effect of the reward size on local functional organization (indexed by inter-trial phase clustering) of theta oscillations, with higher organization associated with high rewards while resolving conflict. When focusing on the period following the onset of the reward cue, we unveiled a stronger beta power decrease in higher reward conditions. However, these LFPs results were not correlated to behavioral results. Our study suggests that the STN is involved in how reward information can influence computations during conflict resolution. However, considering recent studies as well as the present results, we suspect that these effects are subtle., (Copyright © 2019 The Authors. Published by Elsevier Inc. All rights reserved.)

Purpose: To assess surgical skills in robot-assisted partial nephrectomy (RAPN) with and without surgical navigation (SN)., Methods: We employed an SN system that synchronizes the real-time endoscopic image with a virtual reality three-dimensional (3D) model for RAPN and evaluated the skills of two expert surgeons with regard to the identification and dissection of the renal artery (non-SN group, n = 21 [first surgeon n = 9, second surgeon n = 12]; SN group, n = 32 [first surgeon n = 11, second surgeon n = 21]). We converted all movements of the robotic forceps during RAPN into a dedicated vocabulary. Using RAPN videos, we classified all movements of the robotic forceps into direct action (defined as movements of the robotic forceps that directly affect tissues) and connected motion (defined as movements that link actions). In addition, we analyzed the frequency, duration, and occupancy rate of the connected motion., Results: In the SN group, the R.E.N.A.L nephrometry score was lower (7 vs. 6, P = 0.019) and the time to identify and dissect the renal artery (16 vs. 9 min, P = 0.008) was significantly shorter. The connected motions of inefficient "insert," "pull," and "rotate" motions were significantly improved by SN. SN significantly improved the frequency, duration, and occupancy rate of connected motions of the right hand of the first surgeon and of both hands of the second surgeon. The improvements in connected motions were positively associated with SN for both surgeons., Conclusion: This is the first study to investigate SN for nephron-sparing surgery. SN with 3D models might help improve the connected motions of expert surgeons to ensure efficient RAPN.

Statement: This systematic review, conducted in accordance with the Preferred Reporting Items for Systematic Reviews and Meta-Analyses guidelines, is aimed to review current research in virtual reality (VR) for healthcare training, specifically pertaining to nontechnical skills. PsycInfo and Medline databases were queried for relevant articles published through December 2017. Of the 1377 publications identified, 80 were assessed for eligibility and 26 were finally included in the qualitative synthesis. Overall, the use of virtual training for nontechnical skills is recent in healthcare education and has increased since 2010. Screen-based VR simulators or virtual worlds are the most frequently used systems. The nontechnical skills addressed in VR simulation include mainly teamwork, communication, and situation awareness. Most studies evaluate the usability and acceptability of VR simulation, and few studies have measured the effects of VR simulation on nontechnical skills development.

Background: Teamwork is an essential factor in reducing workflow disruption (WD) in the operating room. Team familiarity (TF) has been recognized as an antecedent to surgical quality and safety. To date, no study has examined the link between team members' role and expertise, TF and WD in surgical setting. This study aimed to examine the relationships between expertise, surgeon-scrub nurse familiarity and WD., Methods: We observed a convenience sample of 12 elective neurosurgical procedures carried out by 4 surgeons and 11 SN with different levels of expertise and different degrees of familiarity between surgeons and SN. We calculated the number of WD per unit of coding time to control for the duration of operation. We explored the type and frequency of WD, and the differences between the surgeons and SN. We examined the relationships between duration of WD, staff expertise and surgeon-scrub nurse familiarity., Results: 9.91% of the coded surgical time concerned WD. The most frequent causes of WD were distractions (29.7%) and colleagues' interruptions (25.2%). This proportion was seen for SN, whereas teaching moments and colleagues' interruptions were the most frequent WD for surgeons. The WD was less high among expert surgeons and less frequent when surgeon was familiar with SN., Conclusions: The frequency of WD during surgical time can compromise surgical quality and patient safety. WD seems to decrease in teams with high levels of surgeon-scrub nurse familiarity and with development of surgical expertise. Favoring TF and giving feedback to the team about WD issues could be interesting ways to improve teamwork.

In the original version of this Article the values in the rightmost column of Table 1 were inadvertently shifted relative to the other columns. This has now been corrected in the PDF and HTML versions of the Article.

International challenges have become the standard for validation of biomedical image analysis methods. Given their scientific impact, it is surprising that a critical analysis of common practices related to the organization of challenges has not yet been performed. In this paper, we present a comprehensive analysis of biomedical image analysis challenges conducted up to now. We demonstrate the importance of challenges and show that the lack of quality control has critical consequences. First, reproducibility and interpretation of the results is often hampered as only a fraction of relevant information is typically provided. Second, the rank of an algorithm is generally not robust to a number of variables such as the test data used for validation, the ranking scheme applied and the observers that make the reference annotations. To overcome these problems, we recommend best practice guidelines and define open research questions to be addressed in the future.

Purpose: Lack of annotated training data hinders automatic recognition and prediction of surgical activities necessary for situation-aware operating rooms. We propose using knowledge transfer to compensate for data deficit and improve prediction., Methods: We used two approaches to extract and transfer surgical process knowledge. First, we encoded semantic information about surgical terms using word embedding. Secondly, we passed knowledge between different clinical datasets of neurosurgical procedures using transfer learning., Results: The combination of two methods provided 22% improvement of activity prediction. We also made several pertinent observations about surgical practices based on the results of the performed transfer., Conclusion: Word embedding boosts learning process. Transfer learning was shown to be more effective than a simple combination of data, especially for less similar procedures.

Purpose: The development of common ontologies has recently been identified as one of the key challenges in the emerging field of surgical data science (SDS). However, past and existing initiatives in the domain of surgery have mainly been focussing on individual groups and failed to achieve widespread international acceptance by the research community. To address this challenge, the authors of this paper launched a European initiative-OntoSPM Collaborative Action-with the goal of establishing a framework for joint development of ontologies in the field of SDS. This manuscript summarizes the goals and the current status of the international initiative., Methods: A workshop was organized in 2016, gathering the main European research groups having experience in developing and using ontologies in this domain. It led to the conclusion that a common ontology for surgical process models (SPM) was absolutely needed, and that the existing OntoSPM ontology could provide a good starting point toward the collaborative design and promotion of common, standard ontologies on SPM., Results: The workshop led to the OntoSPM Collaborative Action-launched in mid-2016-with the objective to develop, maintain and promote the use of common ontologies of SPM relevant to the whole domain of SDS. The fundamental concept, the architecture, the management and curation of the common ontology have been established, making it ready for wider public use., Conclusion: The OntoSPM Collaborative Action has been in operation for 24 months, with a growing dedicated membership. Its main result is a modular ontology, undergoing constant updates and extensions, based on the experts' suggestions. It remains an open collaborative action, which always welcomes new contributors and applications.

Objective: The analysis of surgical motion has received a growing interest with the development of devices allowing their automatic capture. In this context, the use of advanced surgical training systems makes an automated assessment of surgical trainee possible. Automatic and quantitative evaluation of surgical skills is a very important step in improving surgical patient care., Material and Method: In this paper, we present an approach for the discovery and ranking of discriminative and interpretable patterns of surgical practice from recordings of surgical motions. A pattern is defined as a series of actions or events in the kinematic data that together are distinctive of a specific gesture or skill level. Our approach is based on the decomposition of continuous kinematic data into a set of overlapping gestures represented by strings (bag of words) for which we compute comparative numerical statistic (tf-idf) enabling the discriminative gesture discovery via its relative occurrence frequency., Results: We carried out experiments on three surgical motion datasets. The results show that the patterns identified by the proposed method can be used to accurately classify individual gestures, skill levels and surgical interfaces. We also present how the patterns provide a detailed feedback on the trainee skill assessment., Conclusions: The proposed approach is an interesting addition to existing learning tools for surgery as it provides a way to obtain a feedback on which parts of an exercise have been used to classify the attempt as correct or incorrect., (Copyright © 2018 Elsevier B.V. All rights reserved.)

Purpose: Surgical processes are generally only studied by identifying differences in populations such as participants or level of expertise. But the similarity between this population is also important in understanding the process. We therefore proposed to study these two aspects., Methods: In this article, we show how similarities in process workflow within a population can be identified as sequential surgical signatures. To this purpose, we have proposed a pattern mining approach to identify these signatures., Validation: We validated our method with a data set composed of seventeen micro-surgical suturing tasks performed by four participants with two levels of expertise., Results: We identified sequential surgical signatures specific to each participant, shared between participants with and without the same level of expertise. These signatures are also able to perfectly define the level of expertise of the participant who performed a new micro-surgical suturing task. However, it is more complicated to determine who the participant is, and the method correctly determines this information in only 64% of cases., Conclusion: We show for the first time the concept of sequential surgical signature. This new concept has the potential to further help to understand surgical procedures and provide useful knowledge to define future CAS systems.

Anatomical atlases have been developed to improve the targeting of basal ganglia in deep brain stimulation. However, the sole anatomy cannot predict the functional outcome of this surgery. Deep brain stimulation is often a compromise between several functional outcomes: motor, fluency and neuropsychological outcomes in particular. In this study, we have developed anatomo-clinical atlases for the targeting of subthalamic and medial globus pallidus deep brain stimulation. The activated electrode coordinates of 42 patients implanted in the subthalamic nucleus and 29 patients in the medial globus pallidus were studied. The atlas was built using the representation of the volume of tissue theoretically activated by the stimulation. The UPDRS score was used to represent the motor outcome. The Stroop test was represented as well as semantic and phonemic fluencies. For the subthalamic nucleus, best motor outcomes were obtained when the supero-lateral part of the nucleus was stimulated whereas the semantic fluency was impaired in this same region. For the medial globus pallidus, best outcomes were obtained when the postero ventral part of the nucleus was stimulated whereas the phonemic fluency was impaired in this same region. There was no significant neuropsychological impairment. We have proposed new anatomo-clinical atlases to visualize the motor and neuropsychological consequences at 6 months of subthalamic nucleus and pallidal stimulation in patients with Parkinson's disease., Competing Interests: The authors have declared that no competing interests exist.

Purpose: Deep brain stimulation (DBS) is a procedure requiring accurate targeting and electrode placement. The two key elements for successful planning are preserving patient safety by ensuring a safe trajectory and creating treatment efficacy through optimal selection of the stimulation point. In this work, we present the first approach of computer-assisted preoperative DBS planning to automatically optimize both the safety of the electrode's trajectory and location of the stimulation point so as to provide the best clinical outcome., Methods: Building upon the findings of previous works focused on electrode trajectory, we added a set of constraints guiding the choice of stimulation point. These took into account retrospective data represented by anatomo-clinical atlases and intersections between the stimulation region and sensitive anatomical structures causing side effects. We implemented our method into automatic preoperative planning software to assess if the algorithm was able to simultaneously optimize electrode trajectory and the stimulation point., Results: Leave-one-out cross-validation on a dataset of 18 cases demonstrated an improvement in the expected outcome when using the new constraints. The distance to critical structures was not reduced. The intersection between the stimulation region and structures sensitive to stimulation was minimized., Conclusions: Introducing these new constraints guided the planning to select locations showing a trend toward symptom improvement, while minimizing the risks of side effects, and there was no cost in terms of trajectory safety.

Objective: Deep brain stimulation (DBS) is an increasingly common treatment for neurodegenerative diseases. Neurosurgeons must have thorough procedural, anatomical, and functional knowledge to plan electrode trajectories and thus ensure treatment efficacy and patient safety. Developing this knowledge requires extensive training. We propose a training approach with objective assessment of neurosurgeon proficiency in DBS planning., Methods: To assess proficiency, we propose analyzing both the viability of the planned trajectory and the manner in which the operator arrived at the trajectory. To improve understanding, we suggest a self-guided training course for DBS planning using real-time feedback. To validate the proposed measures of proficiency and training course, two experts and six novices followed the training course, and we monitored their proficiency measures throughout., Results: At baseline, experts planned higher quality trajectories and did so more efficiently. As novices progressed through the training course, their proficiency measures increased significantly, trending toward expert measures., Conclusion: We developed and validated measures which reliably discriminate proficiency levels. These measures are integrated into a training course, which quantitatively improves trainee performance. The proposed training course can be used to improve trainees' proficiency, and the quantitative measures allow trainees' progress to be monitored.

Purpose: Surgery is one of the riskiest and most important medical acts that are performed today. The need to improve patient outcomes and surgeon training, and to reduce the costs of surgery, has motivated the equipment of operating rooms with sensors that record surgical interventions. The richness and complexity of the data that are collected call for new methods to support computer-assisted surgery. The aim of this paper is to support the monitoring of junior surgeons learning their surgical skill sets., Methods: Our method is fully automatic and takes as input a series of surgical interventions each represented by a low-level recording of all activities performed by the surgeon during the intervention (e.g., cut the skin with a scalpel). Our method produces a curve describing the process of standardization of the behavior of junior surgeons. Given the fact that junior surgeons receive constant feedback from senior surgeons during surgery, these curves can be directly interpreted as learning curves., Results: Our method is assessed using the behavior of a junior surgeon in anterior cervical discectomy and fusion surgery over his first three years after residency. They revealed the ability of the method to accurately represent the surgical skill evolution. We also showed that the learning curves can be computed by phases allowing a finer evaluation of the skill progression., Conclusion: Preliminary results suggest that our approach constitutes a useful addition to surgical training monitoring.

Background: Moving from awake surgery under local anesthesia to asleep surgery under general anesthesia will require to precisely predict the outcome of deep brain stimulation., Objective: To propose a data-driven prediction of both the therapeutic effect and side effects of the surgery., Methods: The retrospective intraoperative data from 30 patients operated on in the subthalamic nucleus were used to train an artificial neural network to predict the deep brain stimulation outcome. A leave-one-out validation was undertaken to give a predictive performance that would reflect the performance of the predictive model in clinical practice. Three-dimensional coordinates and the amount of current of the electrodes were used to train the model., Results: 130 electrode positions were reviewed. The areas under the curve were 0.902 and 0.89 for therapeutic and side effects, respectively. The mean sensitivity and specificity were 93.07% (SD 0.95) and 69.24% (SD 5.27) for the therapeutic effect, 73.47% (SD 10.55) and 91.82% (SD 0.12) for the side effect., Conclusion: Data-driven prediction could be an additional modality to predict deep brain stimulation outcome. Further validation is needed to precisely use this method for performing surgery under general anesthesia., (© 2018 S. Karger AG, Basel.)

Objective: Surgery is one of the riskiest and most important medical acts that is performed today. Understanding the ways in which surgeries are similar or different from each other is of major interest to understand and analyze surgical behaviors. This article addresses the issue of identifying discriminative patterns of surgical practice from recordings of surgeries. These recordings are sequences of low-level surgical activities representing the actions performed by surgeons during surgeries., Materials and Method: To discover patterns that are specific to a group of surgeries, we use the vector space model (VSM) which is originally an algebraic model for representing text documents. We split long sequences of surgical activities into subsequences of consecutive activities. We then compute the relative frequencies of these subsequences using the tf*idf framework and we use the Cosine similarity to classify the sequences. This process makes it possible to discover which patterns discriminate one set of surgeries recordings from another set., Results: Experiments were performed on 40 neurosurgeries of anterior cervical discectomy (ACD). The results demonstrate that our method accurately identifies patterns that can discriminate between (1) locations where the surgery took place, (2) levels of expertise of surgeons (i.e., expert vs. intermediate) and even (3) individual surgeons who performed the intervention. We also show how the tf*idf weight vector can be used to both visualize the most interesting patterns and to highlight the parts of a given surgery that are the most interesting., Conclusions: Identifying patterns that discriminate groups of surgeon is a very important step in improving the understanding of surgical processes. The proposed method finds discriminative and interpretable patterns in sequences of surgical activities. Our approach provides intuitive results, as it identifies automatically the set of patterns explaining the differences between the groups., (Copyright © 2017 Elsevier B.V. All rights reserved.)