Analysis of convolutional neural networks for fronto-temporal dementia biomarker discovery.

PURPOSE: Frontotemporal lobe dementia (FTD) results from the degeneration of the frontal and temporal lobes. It can manifest in several different ways, leading to the definition of variants characterised by their distinctive symptomatologies. As these variants are detected based on their symptoms, it can be unclear if they represent different types of FTD or different symptomatological axes. The goal of this paper is to investigate this question with a constrained cohort of FTD patients in order to see if the heterogeneity within this cohort can be inferred from medical images rather than symptom severity measurements. METHODS: An ensemble of convolutional neural networks (CNNs) is used to classify diffusion tensor images collected from two databases consisting of 72 patients with behavioural variant FTD and 120 healthy controls. FTD biomarkers were found using voxel-based analysis on the sensitivities of these CNNs. Sparse principal components analysis (sPCA) is then applied on the sensitivities arising from the patient cohort in order to identify the axes along which the patients express these biomarkers. Finally, this is correlated with their symptom severity measurements in order to interpret the clinical presentation of each axis. RESULTS: The CNNs result in sensitivities and specificities between 83 and 92%. As expected, our analysis determines that all the robust biomarkers arise from the frontal and temporal lobes. sPCA identified four axes in terms of biomarker expression which are correlated with symptom severity measurements. CONCLUSION: Our analysis confirms that behavioural variant FTD is not a singular type or spectrum of FTD, but rather that it has multiple symptomatological axes that relate to distinct regions of the frontal and temporal lobes. This analysis suggests that medical images can be used to understand the heterogeneity of FTD patients and the underlying anatomical changes that lead to their different clinical presentations.

The influence of virtual reality simulation on surgical residents' heart rate during an assessment of arthroscopic technical skills: a prospective, paired observational study.

HYPOTHESIS: To demonstrate that a virtual reality (VR) simulation training program reduces heart rate variability during an assessment of surgical trainees' technical skills in arthroscopy. STUDY DESIGN: Prospective observational matched study Materials & Methods: Thirty-six orthopaedic surgery residents, new to arthroscopy, received standard training in arthroscopic knee surgery, supplemented by additional monthly training for 6 months on a VR simulator for 16 of them. At inclusion, the 2 groups (VR and NON-VR) answered a questionnaire and performed a meniscectomy on a VR simulator. After 6 months of training, two independent trainers blinded to the inclusion arms evaluated the technical skills of the two groups during meniscectomies on a model and on an anatomical subject. Heart rate variability (HRV) was measured using a wireless heart rate monitor during baseline, VR training, and assessment. RESULTS: After removing incomplete data, the analysis focused on 10 VR residents matched at inclusion with 10 NON-VR residents. The VR group had a significantly lower heart rate at the final assessment (p=0.02) and lower overall HRV (p=0.05). The low/high frequency ratio (LF/HF) was not significantly different between the groups (1.84 vs 2.05, p=0.66) but the before-after training comparison showed a greater decrease in this ratio in the VR group compared to the NON-VR group -0.76 (-41%) vs -0.08 (-4%). CONCLUSION: This study demonstrates a significant difference in heart rate variability between trained residents versus untrained residents during the final assessment of their technical skills at 6 months. It appears that improving stress management should be an integral part of training programs in arthroscopic surgery. Clinical Interest: VR simulators in arthroscopy could improve non-technical skills such as heart rate variability, from the perspective of accountability. LEVEL OF EVIDENCE: III.

A preliminary exploration into top-down and bottom-up deep-learning approaches to localising neuro-interventional point targets in volumetric MRI.

PURPOSE: Point localisation is a critical aspect of many interventional planning procedures, specifically representing anatomical regions of interest or landmarks as individual points. This could be seen as analogous to the problem of visual search in cognitive psychology, in which this search is performed either: bottom-up, constructing increasingly abstract and coarse-resolution features over the entire image; or top-down, using contextual cues from the entire image to refine the scope of the region being investigated. Traditional convolutional neural networks use the former, but it is not clear if this is optimal. This article is a preliminary investigation as to how this motivation affects 3D point localisation in neuro-interventional planning. METHODS: Two neuro-imaging datasets were collected: one for cortical point localisation for repetitive transcranial magnetic stimulation and the other for sub-cortical anatomy localisation for deep brain stimulation. Four different frameworks were developed using top-down versus bottom-up paradigms as well as representing points as co-ordinates or heatmaps. These networks were applied to point localisation for transcranial magnetic stimulation and subcortical anatomy localisation. These networks were evaluated using cross-validation and a varying number of training datasets to analyse their sensitivity to quantity of training data. RESULTS: Each network shows increasing performance as the amount of available training data increases, with the co-ordinate-based top-down network consistently outperforming the others. Specifically, the top-down architectures tend to outperform the bottom-up ones. An analysis of their memory consumption also encourages the top-down co-ordinate based architecture as it requires significantly less memory than either bottom-up architectures or those representing their predictions via heatmaps. CONCLUSION: This paper is a preliminary foray into a fundamental aspect of machine learning architectural design: that of the top-down/bottom-up divide from cognitive psychology. Although there are additional considerations within the particular architectures investigated that could affect these results and the number of architectures investigated is limited, our results do indicate that the less commonly used top-down paradigm could lead to more efficient and effective architectures in the future.

sEMG-based Motion Recognition for Robotic Surgery Training - A Preliminary Study.

Robotic surgery represents a major breakthrough in the evolution of medical technology. Accordingly, efficient skill training and assessment methods should be developed to meet the surgeon's need of acquiring such robotic skills over a relatively short learning curve in a safe manner. Different from conventional training and assessment methods, we aim to explore the surface electromyography (sEMG) signal during the training process in order to obtain semantic and interpretable information to help the trainee better understand and improve his/her training performance. As a preliminary study, motion primitive recognition based on sEMG signal is studied in this work. Using machine learning (ML) technique, it is shown that the sEMG-based motion recognition method is feasible and promising for hand motions along 3 Cartesian axes in the virtual reality (VR) environment of a commercial robotic surgery training platform, which will hence serve as the basis for new robotic surgical skill assessment criterion and training guidance based on muscle activity information. Considering certain motion patterns were less accurately recognized than others, more data collection and deep learning-based analysis will be carried out to further improve the recognition accuracy in future research.

Virtual reality simulation training improve diagnostic knee arthroscopy and meniscectomy skills: a prospective transfer validity study.

PURPOSE: Limited data exist on the actual transfer of skills learned using a virtual reality (VR) simulator for arthroscopy training because studies mainly focused on VR performance improvement and not on transfer to real word (transfer validity). The purpose of this single-blinded, controlled trial was to objectively investigate transfer validity in the context of initial knee arthroscopy training. METHODS: For this study, 36 junior resident orthopaedic surgeons (postgraduate year one and year two) without prior experience in arthroscopic surgery were enrolled to receive standard knee arthroscopy surgery training (NON-VR group) or standard training plus training on a hybrid virtual reality knee arthroscopy simulator (1 h/month) (VR group). At inclusion, all participants completed a questionnaire on their current arthroscopic technical skills. After 6 months of training, both groups performed three exercises that were evaluated independently by two blinded trainers: i) arthroscopic partial meniscectomy on a bench-top knee simulator; ii) supervised diagnostic knee arthroscopy on a cadaveric knee; and iii) supervised knee partial meniscectomy on a cadaveric knee. Training level was determined with the Arthroscopic Surgical Skill Evaluation Tool (ASSET) score. RESULTS: Overall, performance (ASSET scores) was better in the VR group than NON-VR group (difference in the global scores: p < 0.001, in bench-top meniscectomy scores: p = 0.03, in diagnostic knee arthroscopy on a cadaveric knee scores: p = 0.04, and in partial meniscectomy on a cadaveric knee scores: p = 0.02). Subgroup analysis by postgraduate year showed that the year-one NON-VR subgroup performed worse than the other subgroups, regardless of the exercise. CONCLUSION: This study showed the transferability of the technical skills acquired by novice residents on a hybrid virtual reality simulator to the bench-top and cadaveric models. Surgical skill acquired with a VR arthroscopy surgical simulator might safely improve arthroscopy competences in the operating room, also helping to standardise resident training and follow their progress.

SAGES consensus recommendations on surgical video data use, structure, and exploration (for research in artificial intelligence, clinical quality improvement, and surgical education).

BACKGROUND: Surgery generates a vast amount of data from each procedure. Particularly video data provides significant value for surgical research, clinical outcome assessment, quality control, and education. The data lifecycle is influenced by various factors, including data structure, acquisition, storage, and sharing; data use and exploration, and finally data governance, which encompasses all ethical and legal regulations associated with the data. There is a universal need among stakeholders in surgical data science to establish standardized frameworks that address all aspects of this lifecycle to ensure data quality and purpose. METHODS: Working groups were formed, among 48 representatives from academia and industry, including clinicians, computer scientists and industry representatives. These working groups focused on: Data Use, Data Structure, Data Exploration, and Data Governance. After working group and panel discussions, a modified Delphi process was conducted. RESULTS: The resulting Delphi consensus provides conceptualized and structured recommendations for each domain related to surgical video data. We identified the key stakeholders within the data lifecycle and formulated comprehensive, easily understandable, and widely applicable guidelines for data utilization. Standardization of data structure should encompass format and quality, data sources, documentation, metadata, and account for biases within the data. To foster scientific data exploration, datasets should reflect diversity and remain adaptable to future applications. Data governance must be transparent to all stakeholders, addressing legal and ethical considerations surrounding the data. CONCLUSION: This consensus presents essential recommendations around the generation of standardized and diverse surgical video databanks, accounting for multiple stakeholders involved in data generation and use throughout its lifecycle. Following the SAGES annotation framework, we lay the foundation for standardization of data use, structure, and exploration. A detailed exploration of requirements for adequate data governance will follow.

Gaze behavior is related to objective technical skills assessment during virtual reality simulator-based surgical training: a proof of concept.

PURPOSE: Simulation-based training allows surgical skills to be learned safely. Most virtual reality-based surgical simulators address technical skills without considering non-technical skills, such as gaze use. In this study, we investigated surgeons' visual behavior during virtual reality-based surgical training where visual guidance is provided. Our hypothesis was that the gaze distribution in the environment is correlated with the simulator's technical skills assessment. METHODS: We recorded 25 surgical training sessions on an arthroscopic simulator. Trainees were equipped with a head-mounted eye-tracking device. A U-net was trained on two sessions to segment three simulator-specific areas of interest (AoI) and the background, to quantify gaze distribution. We tested whether the percentage of gazes in those areas was correlated with the simulator's scores. RESULTS: The neural network was able to segment all AoI with a mean Intersection over Union superior to 94% for each area. The gaze percentage in the AoI differed among trainees. Despite several sources of data loss, we found significant correlations between gaze position and the simulator scores. For instance, trainees obtained better procedural scores when their gaze focused on the virtual assistance (Spearman correlation test, N = 7, r = 0.800, p = 0.031). CONCLUSION: Our findings suggest that visual behavior should be quantified for assessing surgical expertise in simulation-based training environments, especially when visual guidance is provided. Ultimately visual behavior could be used to quantitatively assess surgeons' learning curve and expertise while training on VR simulators, in a way that complements existing metrics.

Reference-free Bayesian model for pointing errors of typein neurosurgical planning.

PURPOSE: Many neurosurgical planning tasks rely on identifying points of interest in volumetric images. Often, these points require significant expertise to identify correctly as, in some cases, they are not visible but instead inferred by the clinician. This leads to a high degree of variability between annotators selecting these points. In particular, errors of type are when the experts fundamentally select different points rather than the same point with some inaccuracy. This complicates research as their mean may not reflect any of the experts' intentions nor the ground truth. METHODS: We present a regularised Bayesian model for measuring errors of type in pointing tasks. This model is reference-free; in that it does not require a priori knowledge of the ground truth point but instead works on the basis of the level of consensus between multiple annotators. We apply this model to simulated data and clinical data from transcranial magnetic stimulation for chronic pain. RESULTS: Our model estimates the probabilities of selecting the correct point in the range of 82.6[Formula: see text]88.6% with uncertainties in the range of 2.8[Formula: see text]4.0%. This agrees with the literature where ground truth points are known. The uncertainty has not previously been explored in the literature and gives an indication of the dataset's strength. CONCLUSIONS: Our reference-free Bayesian framework easily models errors of type in pointing tasks. It allows for clinical studies to be performed with a limited number of annotators where the ground truth is not immediately known, which can be applied widely for better understanding human errors in neurosurgical planning.

A systematic review of annotation for surgical process model analysis in minimally invasive surgery based on video.

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.

PEg TRAnsfer Workflow recognition challenge report: Do multimodal data improve recognition?

BACKGROUND AND OBJECTIVE: In order to be context-aware, computer-assisted surgical systems require accurate, real-time automatic surgical workflow recognition. In the past several years, surgical video has been the most commonly-used modality for surgical workflow recognition. But with the democratization of robot-assisted surgery, new modalities, such as kinematics, are now accessible. Some previous methods use these new modalities as input for their models, but their added value has rarely been studied. This paper presents the design and results of the "PEg TRAnsfer Workflow recognition" (PETRAW) challenge with the objective of developing surgical workflow recognition methods based on one or more modalities and studying their added value. METHODS: The PETRAW challenge included a data set of 150 peg transfer sequences performed on a virtual simulator. This data set included videos, kinematic data, semantic segmentation data, and annotations, which described the workflow at three levels of granularity: phase, step, and activity. Five tasks were proposed to the participants: three were related to the recognition at all granularities simultaneously using a single modality, and two addressed the recognition using multiple modalities. The mean application-dependent balanced accuracy (AD-Accuracy) was used as an evaluation metric to take into account class balance and is more clinically relevant than a frame-by-frame score. RESULTS: Seven teams participated in at least one task with four participating in every task. The best results were obtained by combining video and kinematic data (AD-Accuracy of between 93% and 90% for the four teams that participated in all tasks). CONCLUSION: The improvement of surgical workflow recognition methods using multiple modalities compared with unimodal methods was significant for all teams. However, the longer execution time required for video/kinematic-based methods(compared to only kinematic-based methods) must be considered. Indeed, one must ask if it is wise to increase computing time by 2000 to 20,000% only to increase accuracy by 3%. The PETRAW data set is publicly available at www.synapse.org/PETRAW to encourage further research in surgical workflow recognition.

Deep neural network architecture for automated soft surgical skills evaluation using objective structured assessment of technical skills criteria.

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.

Virtual reality environments to train soft skills in medical and nursing education: a technical feasibility study between France and Japan.

PURPOSE: To meet the urgent and massive training needs of healthcare professionals, the use of digital technologies is proving increasingly relevant, and the rise of digital training platforms shows their usefulness and possibilities. However, despite the impact of these platforms on the medical skills learning, cultural differences are rarely factored in the implementation of these training environments. METHODS: By using the Scrub Nurse Non-Technical Skills Training System (SunSet), we developed a methodology enabling the adaptation of a virtual reality-based environment and scenarios from French to Japanese cultural and medical practices. We then conducted a technical feasibility study between France and Japan to assess virtual reality simulations acceptance among scrub nurses. RESULTS: Results in term of acceptance do not reveal major disparity between both populations, and the only emerging significant difference between both groups is on the Behavioral Intention, which is significantly higher for the French scrub nurses. In both cases, participants had a positive outlook. CONCLUSION: The findings suggest that the methodology we have implemented can be further used in the context of cultural adaptation of non-technical skills learning scenarios in virtual environments for the training and assessment of health care personnel.

"Stand-up straight!": human pose estimation to evaluate postural skills during orthopedic surgery simulations.

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.

A Delphi consensus statement for digital surgery.

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.

Ethical implications of AI in robotic surgical training: A Delphi consensus statement.

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.

Review of automated performance metrics to assess surgical technical skills in robot-assisted laparoscopy.

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.

Surgical data science - from concepts toward clinical translation.

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.

MIcro-surgical anastomose workflow recognition challenge report.

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.

Validation of virtual reality arthroscopy simulator relevance in characterising experienced surgeons.

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 (4.6.105mm.s-3 [3.1.105-7.2.105] vs. 1.5.106mm.s-3 [2.6.106-3.5.106], p=0.05), and economical in their motion (19.3mm2 [9.1-25.9] vs. 33.8mm2 [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.