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Sugiyama T, Sugimori H, Tang M, Ito Y, Gekka M, Uchino H, Ito M, Ogasawara K, Fujimura M. Deep learning-based video-analysis of instrument motion in microvascular anastomosis training. Acta Neurochir (Wien) 2024; 166:6. [PMID: 38214753 DOI: 10.1007/s00701-024-05896-4] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 10/15/2023] [Accepted: 12/11/2023] [Indexed: 01/13/2024]
Abstract
PURPOSE Attaining sufficient microsurgical skills is paramount for neurosurgical trainees. Kinematic analysis of surgical instruments using video offers the potential for an objective assessment of microsurgical proficiency, thereby enhancing surgical training and patient safety. The purposes of this study were to develop a deep-learning-based automated instrument tip-detection algorithm, and to validate its performance in microvascular anastomosis training. METHODS An automated instrument tip-tracking algorithm was developed and trained using YOLOv2, based on clinical microsurgical videos and microvascular anastomosis practice videos. With this model, we measured motion economy (procedural time and path distance) and motion smoothness (normalized jerk index) during the task of suturing artificial blood vessels for end-to-side anastomosis. These parameters were validated using traditional criteria-based rating scales and were compared across surgeons with varying microsurgical experience (novice, intermediate, and expert). The suturing task was deconstructed into four distinct phases, and parameters within each phase were compared between novice and expert surgeons. RESULTS The high accuracy of the developed model was indicated by a mean Dice similarity coefficient of 0.87. Deep learning-based parameters (procedural time, path distance, and normalized jerk index) exhibited correlations with traditional criteria-based rating scales and surgeons' years of experience. Experts completed the suturing task faster than novices. The total path distance for the right (dominant) side instrument movement was shorter for experts compared to novices. However, for the left (non-dominant) side, differences between the two groups were observed only in specific phases. The normalized jerk index for both the right and left sides was significantly lower in the expert than in the novice groups, and receiver operating characteristic analysis showed strong discriminative ability. CONCLUSION The deep learning-based kinematic analytic approach for surgical instruments proves beneficial in assessing performance in microvascular anastomosis. Moreover, this methodology can be adapted for use in clinical settings.
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Affiliation(s)
- Taku Sugiyama
- Department of Neurosurgery, Hokkaido University Graduate School of Medicine, North 15 West 7, Kita-Ku, Sapporo, 060-8638, Japan.
| | - Hiroyuki Sugimori
- Faculty of Health Sciences, Hokkaido University, Sapporo, 060-0812, Japan
| | - Minghui Tang
- Department of Diagnostic Imaging, Hokkaido University Graduate School of Medicine, North 15 West 7, Kita-Ku, Sapporo, 060-8638, Japan
| | - Yasuhiro Ito
- Department of Neurosurgery, Hokkaido University Graduate School of Medicine, North 15 West 7, Kita-Ku, Sapporo, 060-8638, Japan
| | - Masayuki Gekka
- Department of Neurosurgery, Hokkaido University Graduate School of Medicine, North 15 West 7, Kita-Ku, Sapporo, 060-8638, Japan
| | - Haruto Uchino
- Department of Neurosurgery, Hokkaido University Graduate School of Medicine, North 15 West 7, Kita-Ku, Sapporo, 060-8638, Japan
| | - Masaki Ito
- Department of Neurosurgery, Hokkaido University Graduate School of Medicine, North 15 West 7, Kita-Ku, Sapporo, 060-8638, Japan
| | | | - Miki Fujimura
- Department of Neurosurgery, Hokkaido University Graduate School of Medicine, North 15 West 7, Kita-Ku, Sapporo, 060-8638, Japan
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Sugiyama T, Ito M, Sugimori H, Tang M, Nakamura T, Ogasawara K, Matsuzawa H, Nakayama N, Lama S, Sutherland GR, Fujimura M. Tissue Acceleration as a Novel Metric for Surgical Performance During Carotid Endarterectomy. Oper Neurosurg (Hagerstown) 2023; 25:343-352. [PMID: 37427955 DOI: 10.1227/ons.0000000000000815] [Citation(s) in RCA: 1] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 01/20/2023] [Accepted: 05/08/2023] [Indexed: 07/11/2023] Open
Abstract
BACKGROUND AND OBJECTIVES Gentle tissue handling to avoid excessive motion of affected fragile vessels during surgical dissection is essential for both surgeon proficiency and patient safety during carotid endarterectomy (CEA). However, a void remains in the quantification of these aspects during surgery. The video-based measurement of tissue acceleration is presented as a novel metric for the objective assessment of surgical performance. This study aimed to evaluate whether such metrics correlate with both surgeons' skill proficiency and adverse events during CEA. METHODS In a retrospective study including 117 patients who underwent CEA, acceleration of the carotid artery was measured during exposure through a video-based analysis. Tissue acceleration values and threshold violation error frequencies were analyzed and compared among the surgeon groups with different surgical experience (3 groups: novice , intermediate , and expert ). Multiple patient-related variables, surgeon groups, and video-based surgical performance parameters were compared between the patients with and without adverse events during CEA. RESULTS Eleven patients (9.4%) experienced adverse events after CEA, and the rate of adverse events significantly correlated with the surgeon group. The mean maximum tissue acceleration and number of errors during surgical tasks significantly decreased from novice, to intermediate, to expert surgeons, and stepwise discriminant analysis showed that the combined use of surgical performance factors could accurately discriminate between surgeon groups. The multivariate logistic regression analysis revealed that the number of errors and vulnerable carotid plaques were associated with adverse events. CONCLUSION Tissue acceleration profiles can be a novel metric for the objective assessment of surgical performance and the prediction of adverse events during surgery. Thus, this concept can be introduced into futuristic computer-aided surgeries for both surgical education and patient safety.
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Affiliation(s)
- Taku Sugiyama
- Department of Neurosurgery, Hokkaido University Graduate School of Medicine, Sapporo, Japan
| | - Masaki Ito
- Department of Neurosurgery, Hokkaido University Graduate School of Medicine, Sapporo, Japan
| | | | - Minghui Tang
- Department of Diagnostic Imaging, Hokkaido University Graduate School of Medicine, Sapporo, Japan
| | - Toshitaka Nakamura
- Department of Neurosurgery, Sapporo Azabu Neurosurgical Hospital, Sapporo, Japan
| | | | - Hitoshi Matsuzawa
- Center for Integrated Human Brain Science, Brain Research Institute, University of Niigata, Niigata, Japan
- Department of Neurosurgery, Kashiwaba Neurosurgical Hospital, Sapporo, Japan
| | - Naoki Nakayama
- Department of Neurosurgery, Kashiwaba Neurosurgical Hospital, Sapporo, Japan
| | - Sanju Lama
- Department of Clinical Neurosciences and the Hotchkiss Brain Institute, University of Calgary, Calgary, AB, Canada
| | - Garnette R Sutherland
- Department of Clinical Neurosciences and the Hotchkiss Brain Institute, University of Calgary, Calgary, AB, Canada
| | - Miki Fujimura
- Department of Neurosurgery, Hokkaido University Graduate School of Medicine, Sapporo, Japan
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Baghdadi A, Guo E, Lama S, Singh R, Chow M, Sutherland GR. Force Profile as Surgeon-Specific Signature. ANNALS OF SURGERY OPEN 2023; 4:e326. [PMID: 37746608 PMCID: PMC10513276 DOI: 10.1097/as9.0000000000000326] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 01/27/2023] [Accepted: 07/22/2023] [Indexed: 09/26/2023] Open
Abstract
Objective To investigate the notion that a surgeon's force profile can be the signature of their identity and performance. Summary background data Surgeon performance in the operating room is an understudied topic. The advent of deep learning methods paired with a sensorized surgical device presents an opportunity to incorporate quantitative insight into surgical performance and processes. Using a device called the SmartForceps System and through automated analytics, we have previously reported surgeon force profile, surgical skill, and task classification. However, an investigation of whether an individual surgeon can be identified by surgical technique has yet to be studied. Methods In this study, we investigate multiple neural network architectures to identify the surgeon associated with their time-series tool-tissue forces using bipolar forceps data. The surgeon associated with each 10-second window of force data was labeled, and the data were randomly split into 80% for model training and validation (10% validation) and 20% for testing. Data imbalance was mitigated through subsampling from more populated classes with a random size adjustment based on 0.1% of sample counts in the respective class. An exploratory analysis of force segments was performed to investigate underlying patterns differentiating individual surgical techniques. Results In a dataset of 2819 ten-second time segments from 89 neurosurgical cases, the best-performing model achieved a micro-average area under the curve of 0.97, a testing F1-score of 0.82, a sensitivity of 82%, and a precision of 82%. This model was a time-series ResNet model to extract features from the time-series data followed by a linearized output into the XGBoost algorithm. Furthermore, we found that convolutional neural networks outperformed long short-term memory networks in performance and speed. Using a weighted average approach, an ensemble model was able to identify an expert surgeon with 83.8% accuracy using a validation dataset. Conclusions Our results demonstrate that each surgeon has a unique force profile amenable to identification using deep learning methods. We anticipate our models will enable a quantitative framework to provide bespoke feedback to surgeons and to track their skill progression longitudinally. Furthermore, the ability to recognize individual surgeons introduces the mechanism of correlating outcome to surgeon performance.
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Affiliation(s)
- Amir Baghdadi
- From the Project neuroArm, Department of Clinical Neurosciences, and Hotchkiss Brain Institute University of Calgary, Calgary, Alberta, Canada
| | - Eddie Guo
- From the Project neuroArm, Department of Clinical Neurosciences, and Hotchkiss Brain Institute University of Calgary, Calgary, Alberta, Canada
| | - Sanju Lama
- From the Project neuroArm, Department of Clinical Neurosciences, and Hotchkiss Brain Institute University of Calgary, Calgary, Alberta, Canada
| | - Rahul Singh
- From the Project neuroArm, Department of Clinical Neurosciences, and Hotchkiss Brain Institute University of Calgary, Calgary, Alberta, Canada
| | - Michael Chow
- Department of Surgery, Faculty of Medicine and Dentistry, University of Alberta, Edmonton, Canada
| | - Garnette R. Sutherland
- From the Project neuroArm, Department of Clinical Neurosciences, and Hotchkiss Brain Institute University of Calgary, Calgary, Alberta, Canada
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Baghdadi A, Lama S, Singh R, Sutherland GR. Tool-tissue force segmentation and pattern recognition for evaluating neurosurgical performance. Sci Rep 2023; 13:9591. [PMID: 37311965 DOI: 10.1038/s41598-023-36702-3] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 11/16/2022] [Accepted: 06/08/2023] [Indexed: 06/15/2023] Open
Abstract
Surgical data quantification and comprehension expose subtle patterns in tasks and performance. Enabling surgical devices with artificial intelligence provides surgeons with personalized and objective performance evaluation: a virtual surgical assist. Here we present machine learning models developed for analyzing surgical finesse using tool-tissue interaction force data in surgical dissection obtained from a sensorized bipolar forceps. Data modeling was performed using 50 neurosurgery procedures that involved elective surgical treatment for various intracranial pathologies. The data collection was conducted by 13 surgeons of varying experience levels using sensorized bipolar forceps, SmartForceps System. The machine learning algorithm constituted design and implementation for three primary purposes, i.e., force profile segmentation for obtaining active periods of tool utilization using T-U-Net, surgical skill classification into Expert and Novice, and surgical task recognition into two primary categories of Coagulation versus non-Coagulation using FTFIT deep learning architectures. The final report to surgeon was a dashboard containing recognized segments of force application categorized into skill and task classes along with performance metrics charts compared to expert level surgeons. Operating room data recording of > 161 h containing approximately 3.6 K periods of tool operation was utilized. The modeling resulted in Weighted F1-score = 0.95 and AUC = 0.99 for force profile segmentation using T-U-Net, Weighted F1-score = 0.71 and AUC = 0.81 for surgical skill classification, and Weighted F1-score = 0.82 and AUC = 0.89 for surgical task recognition using a subset of hand-crafted features augmented to FTFIT neural network. This study delivers a novel machine learning module in a cloud, enabling an end-to-end platform for intraoperative surgical performance monitoring and evaluation. Accessed through a secure application for professional connectivity, a paradigm for data-driven learning is established.
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Affiliation(s)
- Amir Baghdadi
- Project neuroArm, Department of Clinical Neurosciences, Hotchkiss Brain Institute University of Calgary, Calgary, AB, Canada
| | - Sanju Lama
- Project neuroArm, Department of Clinical Neurosciences, Hotchkiss Brain Institute University of Calgary, Calgary, AB, Canada
| | - Rahul Singh
- Project neuroArm, Department of Clinical Neurosciences, Hotchkiss Brain Institute University of Calgary, Calgary, AB, Canada
| | - Garnette R Sutherland
- Project neuroArm, Department of Clinical Neurosciences, Hotchkiss Brain Institute University of Calgary, Calgary, AB, Canada.
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Yilmaz R, Ledwos N, Sawaya R, Winkler-Schwartz A, Mirchi N, Bissonnette V, Fazlollahi AM, Bakhaidar M, Alsayegh A, Sabbagh AJ, Bajunaid K, Del Maestro R. Nondominant Hand Skills Spatial and Psychomotor Analysis During a Complex Virtual Reality Neurosurgical Task-A Case Series Study. Oper Neurosurg (Hagerstown) 2022; 23:22-30. [PMID: 35726926 DOI: 10.1227/ons.0000000000000232] [Citation(s) in RCA: 2] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 08/21/2021] [Accepted: 02/09/2022] [Indexed: 11/19/2022] Open
Abstract
BACKGROUND Virtual reality surgical simulators provide detailed psychomotor performance data, allowing qualitative and quantitative assessment of hand function. The nondominant hand plays an essential role in neurosurgery in exposing the operative area, assisting the dominant hand to optimize task execution, and hemostasis. Outlining expert-level nondominant hand skills may be critical to understand surgical expertise and aid learner training. OBJECTIVE To (1) provide validity for the simulated bimanual subpial tumor resection task and (2) to use this simulation in qualitative and quantitative evaluation of nondominant hand skills for bipolar forceps utilization. METHODS In this case series study, 45 right-handed participants performed a simulated subpial tumor resection using simulated bipolar forceps in the nondominant hand for assisting the surgery and hemostasis. A 10-item questionnaire was used to assess task validity. The nondominant hand skills across 4 expertise levels (neurosurgeons, senior trainees, junior trainees, and medical students) were analyzed by 2 visual models and performance metrics. RESULTS Neurosurgeon median (range) overall satisfaction with the simulated scenario was 4.0/5.0 (2.0-5.0). The visual models demonstrated a decrease in high force application areas on pial surface with increased expertise level. Bipolar-pia mater interactions were more focused around the tumoral region for neurosurgeons and senior trainees. These groups spent more time using the bipolar while interacting with pia. All groups spent significantly higher time in the left upper pial quadrant than other quadrants. CONCLUSION This work introduces new approaches for the evaluation of nondominant hand skills which may help surgical trainees by providing both qualitative and quantitative feedback.
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Affiliation(s)
- Recai Yilmaz
- Neurosurgical Simulation and Artificial Intelligence Learning Centre, Department of Neurology and Neurosurgery, Montreal Neurological Institute, McGill University, Montreal, Quebec, Canada
| | - Nicole Ledwos
- Neurosurgical Simulation and Artificial Intelligence Learning Centre, Department of Neurology and Neurosurgery, Montreal Neurological Institute, McGill University, Montreal, Quebec, Canada
| | - Robin Sawaya
- Neurosurgical Simulation and Artificial Intelligence Learning Centre, Department of Neurology and Neurosurgery, Montreal Neurological Institute, McGill University, Montreal, Quebec, Canada
| | - Alexander Winkler-Schwartz
- Neurosurgical Simulation and Artificial Intelligence Learning Centre, Department of Neurology and Neurosurgery, Montreal Neurological Institute, McGill University, Montreal, Quebec, Canada
- Department of Neurology and Neurosurgery, Montreal Neurological Institute and Hospital, McGill University, Montreal, Quebec, Canada
| | - Nykan Mirchi
- Neurosurgical Simulation and Artificial Intelligence Learning Centre, Department of Neurology and Neurosurgery, Montreal Neurological Institute, McGill University, Montreal, Quebec, Canada
| | - Vincent Bissonnette
- Neurosurgical Simulation and Artificial Intelligence Learning Centre, Department of Neurology and Neurosurgery, Montreal Neurological Institute, McGill University, Montreal, Quebec, Canada
- Division of Orthopaedic Surgery, Montreal General Hospital, McGill University, Montreal, Quebec, Canada
| | - Ali M Fazlollahi
- Neurosurgical Simulation and Artificial Intelligence Learning Centre, Department of Neurology and Neurosurgery, Montreal Neurological Institute, McGill University, Montreal, Quebec, Canada
| | - Mohamad Bakhaidar
- Neurosurgical Simulation and Artificial Intelligence Learning Centre, Department of Neurology and Neurosurgery, Montreal Neurological Institute, McGill University, Montreal, Quebec, Canada
- Department of Neurology and Neurosurgery, Montreal Neurological Institute and Hospital, McGill University, Montreal, Quebec, Canada
- Division of Neurosurgery, Department of Surgery, Faculty of Medicine, King Abdulaziz University, Jeddah, Saudi Arabia
| | - Ahmad Alsayegh
- Neurosurgical Simulation and Artificial Intelligence Learning Centre, Department of Neurology and Neurosurgery, Montreal Neurological Institute, McGill University, Montreal, Quebec, Canada
- Division of Neurosurgery, Department of Surgery, Faculty of Medicine, King Abdulaziz University, Jeddah, Saudi Arabia
| | - Abdulrahman J Sabbagh
- Division of Neurosurgery, Department of Surgery, Faculty of Medicine, King Abdulaziz University, Jeddah, Saudi Arabia
- Clinical Skills and Simulation Center, King Abdulaziz University, Jeddah, Saudi Arabia
| | - Khalid Bajunaid
- Department of Surgery, Faculty of Medicine, University of Jeddah, Jeddah, Saudi Arabia
| | - Rolando Del Maestro
- Neurosurgical Simulation and Artificial Intelligence Learning Centre, Department of Neurology and Neurosurgery, Montreal Neurological Institute, McGill University, Montreal, Quebec, Canada
- Department of Neurology and Neurosurgery, Montreal Neurological Institute and Hospital, McGill University, Montreal, Quebec, Canada
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Tool-Tissue Forces in Hemangioblastoma Surgery. World Neurosurg 2022; 160:e242-e249. [PMID: 34999009 DOI: 10.1016/j.wneu.2021.12.119] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 11/23/2021] [Revised: 12/30/2021] [Accepted: 12/31/2021] [Indexed: 10/19/2022]
Abstract
OBJECTIVE Surgical resection of intracranial hemangioblastoma poses technical challenges that may be difficult to impart to trainees. Here, we introduce knowledge of tool-tissue forces in Newton (N), observed during hemangioblastoma surgery. METHODS Seven surgeons (2 groups: trainees and mentor), with mentor (n = 1) and trainees (n = 6, PGY 1-6 including clinical fellowship), participated in 6 intracranial hemangioblastoma surgeries. Using sensorized bipolar forceps, we evaluated tool-tissue force profiles of 5 predetermined surgical tasks: 1) dissection, 2) coagulation, 3) retracting, 4) pulling, and 5) manipulating. Force profile for each trial included force duration, average, maximum, minimum, range, standard deviation (SD), and correlation coefficient. Force errors including unsuccessful trial bleeding or incomplete were compared between surgeons and with successful trials. RESULTS Force data from 718 trials were collected. The mean (standard deviation) of force used in all surgical tasks and across all surgical levels was 0.20 ± 0.17 N. The forces exerted by trainee surgeons were significantly lower than those of the mentor (0.15 vs. 0.24; P < 0.0001). A total of 18 (4.5%) trials were unsuccessful, 4 of them being unsuccessful trial-bleeding and the rest, unsuccessful trial-incomplete. The force in unsuccessful trial-bleeding was higher than successful trials (0.3 [0.09] vs. 0.17 [0.11]; P = 0.0401). Toward the end of surgery, higher force was observed (0.17 vs. 0.20; P < 0.0001). CONCLUSIONS The quantification of tool-tissue forces during hemangioblastoma surgery with feedback to the surgeon, could well enhance surgical training and allow avoidance of bleeding associated with high force error.
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Current concepts and perspectives on brain arteriovenous malformations: A review of pathogenesis and multidisciplinary treatment. World Neurosurg 2021; 159:314-326. [PMID: 34339893 DOI: 10.1016/j.wneu.2021.07.106] [Citation(s) in RCA: 6] [Impact Index Per Article: 2.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 06/20/2021] [Revised: 07/22/2021] [Accepted: 07/22/2021] [Indexed: 11/23/2022]
Abstract
Brain arteriovenous malformations (bAVMs) are unusual vascular pathologies characterized by the abnormal aggregation of dilated arteries and veins in the brain parenchyma and for which the absence of a normal vascular structure and capillary bed leads to direct connections between arteries and veins. Although bAVMs have long been believed to be congenital anomalies that develop during the prenatal period, current studies show that inflammation is associated with AVM genesis, growth, and rupture. Interventional treatment options include microsurgery, stereotactic radiosurgery, and endovascular embolization, and management often comprises a multidisciplinary combination of these modalities. The appropriate selection of patients with brain arteriovenous malformations for interventional treatment requires balancing the risk of treatment complications against the risk of hemorrhaging during the natural course of the pathology; however, no definitive guidelines have been established for the management of brain arteriovenous malformations. In this paper, we comprehensively review the current basic and clinical studies on bAVMs and discuss the contemporary status of multidisciplinary management of bAVMs.
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A data-driven performance dashboard for surgical dissection. Sci Rep 2021; 11:15013. [PMID: 34294827 PMCID: PMC8298519 DOI: 10.1038/s41598-021-94487-9] [Citation(s) in RCA: 5] [Impact Index Per Article: 1.7] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 03/11/2021] [Accepted: 07/06/2021] [Indexed: 11/30/2022] Open
Abstract
Surgical error and resulting complication have significant patient and economic consequences. Inappropriate exertion of tool-tissue force is a common variable for such error, that can be objectively monitored by sensorized tools. The rich digital output establishes a powerful skill assessment and sharing platform for surgical performance and training. Here we present SmartForceps data app incorporating an Expert Room environment for tracking and analysing the objective performance and surgical finesse through multiple interfaces specific for surgeons and data scientists. The app is enriched by incoming geospatial information, data distribution for engineered features, performance dashboard compared to expert surgeon, and interactive skill prediction and task recognition tools to develop artificial intelligence models. The study launches the concept of democratizing surgical data through a connectivity interface between surgeons with a broad and deep capability of geographic reach through mobile devices with highly interactive infographics and tools for performance monitoring, comparison, and improvement.
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Sugiyama T, Clapp T, Nelson J, Eitel C, Motegi H, Nakayama N, Sasaki T, Tokairin K, Ito M, Kazumata K, Houkin K. Immersive 3-Dimensional Virtual Reality Modeling for Case-Specific Presurgical Discussions in Cerebrovascular Neurosurgery. Oper Neurosurg (Hagerstown) 2021; 20:289-299. [PMID: 33294936 DOI: 10.1093/ons/opaa335] [Citation(s) in RCA: 23] [Impact Index Per Article: 7.7] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 04/24/2020] [Accepted: 08/12/2020] [Indexed: 11/13/2022] Open
Abstract
BACKGROUND Adequate surgical planning includes a precise understanding of patient-specific anatomy and is a necessity for neurosurgeons. Although the use of virtual reality (VR) technology is emerging in surgical planning and education, few studies have examined the effectiveness of immersive VR during surgical planning using a modern head-mounted display. OBJECTIVE To investigate if and how immersive VR aids presurgical discussions of cerebrovascular surgery. METHODS A multiuser immersive VR system, BananaVisionTM, was developed and used during presurgical discussions in a prospective patient cohort undergoing cerebrovascular surgery. A questionnaire/interview was administered to multiple surgeons after the surgeries to evaluate the effectiveness of the VR system compared to conventional imaging modalities. An objective assessment of the surgeon's knowledge of patient-specific anatomy was also conducted by rating surgeons' hand-drawn presurgical illustrations. RESULTS The VR session effectively enhanced surgeons' understanding of patient-specific anatomy in the majority of cases (83.3%). An objective assessment of surgeons' presurgical illustrations was consistent with this result. The VR session also effectively improved the decision-making process regarding minor surgical techniques in 61.1% of cases and even aided surgeons in making critical surgical decisions about cases involving complex and challenging anatomy. The utility of the VR system was rated significantly higher by trainees than by experts. CONCLUSION Although rated as more useful by trainees than by experts, immersive 3D VR modeling increased surgeons' understanding of patient-specific anatomy and improved surgical strategy in certain cases involving challenging anatomy.
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Affiliation(s)
- Taku Sugiyama
- Department of Neurosurgery, Hokkaido University Graduate School of Medicine, Sapporo, Japan
| | - Tod Clapp
- Department of Biomedical Sciences, Colorado State University, Fort Collins, Colorado
| | - Jordan Nelson
- Department of Biomedical Sciences, Colorado State University, Fort Collins, Colorado
| | - Chad Eitel
- Department of Biomedical Sciences, Colorado State University, Fort Collins, Colorado
| | - Hiroaki Motegi
- Department of Neurosurgery, Hokkaido University Graduate School of Medicine, Sapporo, Japan
| | - Naoki Nakayama
- Department of Neurosurgery, Hokkaido University Graduate School of Medicine, Sapporo, Japan
| | - Tsukasa Sasaki
- Department of Radiology, Hokkaido University Hospital, Sapporo, Japan
| | - Kikutaro Tokairin
- Department of Neurosurgery, Hokkaido University Graduate School of Medicine, Sapporo, Japan
| | - Masaki Ito
- Department of Neurosurgery, Hokkaido University Graduate School of Medicine, Sapporo, Japan
| | - Ken Kazumata
- Department of Neurosurgery, Hokkaido University Graduate School of Medicine, Sapporo, Japan
| | - Kiyohiro Houkin
- Department of Emergent Neurocognition, Faculty of Health Sciences, Hokkaido University, Sapporo, Japan
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Golahmadi AK, Khan DZ, Mylonas GP, Marcus HJ. Tool-tissue forces in surgery: A systematic review. Ann Med Surg (Lond) 2021; 65:102268. [PMID: 33898035 PMCID: PMC8058906 DOI: 10.1016/j.amsu.2021.102268] [Citation(s) in RCA: 22] [Impact Index Per Article: 7.3] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 03/23/2021] [Accepted: 03/26/2021] [Indexed: 11/30/2022] Open
Abstract
Background Excessive tool-tissue interaction forces often result in tissue damage and intraoperative complications, while insufficient forces prevent the completion of the task. This review sought to explore the tool-tissue interaction forces exerted by instruments during surgery across different specialities, tissues, manoeuvres and experience levels. Materials & methods A PRISMA-guided systematic review was carried out using Embase, Medline and Web of Science databases. Results Of 462 articles screened, 45 studies discussing surgical tool-tissue forces were included. The studies were categorized into 9 different specialities with the mean of average forces lowest for ophthalmology (0.04N) and highest for orthopaedic surgery (210N). Nervous tissue required the least amount of force to manipulate (mean of average: 0.4N), whilst connective tissue (including bone) required the most (mean of average: 45.8). For manoeuvres, drilling recorded the highest forces (mean of average: 14N), whilst sharp dissection recorded the lowest (mean of average: 0.03N). When comparing differences in the mean of average forces between groups, novices exerted 22.7% more force than experts, and presence of a feedback mechanism (e.g. audio) reduced exerted forces by 47.9%. Conclusions The measurement of tool-tissue forces is a novel but rapidly expanding field. The range of forces applied varies according to surgical speciality, tissue, manoeuvre, operator experience and feedback provided. Knowledge of the safe range of surgical forces will improve surgical safety whilst maintaining effectiveness. Measuring forces during surgery may provide an objective metric for training and assessment. Development of smart instruments, robotics and integrated feedback systems will facilitate this. This review explores tool-tissue forces during surgery, a new and expanding field. Forces were lowest in ophthalmology (0.04N) and highest in orthopaedics (210N). Forces were lowest during sharp dissection (0.03N) and highest when drilling (14N). Being an expert (vs. novice) and having feedback mechanisms (e.g. haptic) reduced exerted forces. Development of force metrics will facilitate training, assessment & novel technology.
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Affiliation(s)
- Aida Kafai Golahmadi
- Imperial College London School of Medicine, London, United Kingdom.,HARMS Laboratory, The Hamlyn Centre, Department of Surgery & Cancer, Imperial College London, London, United Kingdom
| | - Danyal Z Khan
- National Hospital for Neurology and Neurosurgery, London, United Kingdom.,Wellcome/EPSRC Centre for Interventional and Surgical Sciences, University College London, London, United Kingdom
| | - George P Mylonas
- HARMS Laboratory, The Hamlyn Centre, Department of Surgery & Cancer, Imperial College London, London, United Kingdom
| | - Hani J Marcus
- National Hospital for Neurology and Neurosurgery, London, United Kingdom.,Wellcome/EPSRC Centre for Interventional and Surgical Sciences, University College London, London, United Kingdom
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Schimmoeller T, Neumann EE, Nagle TF, Erdemir A. Reference tool kinematics-kinetics and tissue surface strain data during fundamental surgical acts. Sci Data 2020; 7:21. [PMID: 31941889 PMCID: PMC6962378 DOI: 10.1038/s41597-020-0359-0] [Citation(s) in RCA: 3] [Impact Index Per Article: 0.8] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 09/24/2019] [Accepted: 12/04/2019] [Indexed: 12/03/2022] Open
Abstract
Haptic based surgical simulations are popular training aids in medicine. Previously, surgical tool loads and motion were measured during cutting and needle insertion on non-human tissue and several haptic based simulations were developed to enhance surgical training. However, there was a lack of realistic foundational data regarding the mechanical responses of human tissue and tools during fundamental acts of surgery, i.e., cutting, suturing, retracting, pinching and indenting. This study used four recently developed surgical tools in a variety of procedures on a diverse set of cadaver leg specimens from human donors. The kinematics and kinetics of surgical tools were recorded along with topical three-dimensional strain during commonly performed surgical procedures. Full motion and load signatures of foundational surgical acts can also be used beyond the development of authentic visual and haptic simulations of surgery, i.e., they provide mechanical specifications for the development of autonomous surgical systems.
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Affiliation(s)
- Tyler Schimmoeller
- Department of Biomedical Engineering, Cleveland Clinic, Cleveland, Ohio, USA
- Computational Biomodeling (CoBi) Core, Lerner Research Institute, Cleveland Clinic, Cleveland, OH, USA
| | - Erica E Neumann
- Department of Biomedical Engineering, Cleveland Clinic, Cleveland, Ohio, USA
- Computational Biomodeling (CoBi) Core, Lerner Research Institute, Cleveland Clinic, Cleveland, OH, USA
| | - Tara F Nagle
- Department of Biomedical Engineering, Cleveland Clinic, Cleveland, Ohio, USA
- BioRobotics and Mechanical Testing Core, Medical Device Solutions, Lerner Research Institute, Cleveland Clinic, Cleveland, OH, USA
| | - Ahmet Erdemir
- Department of Biomedical Engineering, Cleveland Clinic, Cleveland, Ohio, USA.
- Computational Biomodeling (CoBi) Core, Lerner Research Institute, Cleveland Clinic, Cleveland, OH, USA.
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Sugiyama T, Nakamura T, Ito Y, Tokairin K, Kazumata K, Nakayama N, Houkin K. A Pilot Study on Measuring Tissue Motion During Carotid Surgery Using Video-Based Analyses for the Objective Assessment of Surgical Performance. World J Surg 2019; 43:2309-2319. [DOI: 10.1007/s00268-019-05018-7] [Citation(s) in RCA: 3] [Impact Index Per Article: 0.6] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 10/26/2022]
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Li T, Sunami Y, Zhang S. Perceptual Surgical Knife with Wavelet Denoising. MICROMACHINES 2018; 9:mi9020079. [PMID: 30393355 PMCID: PMC6187367 DOI: 10.3390/mi9020079] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.2] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Subscribe] [Scholar Register] [Received: 12/28/2017] [Revised: 01/22/2018] [Accepted: 02/11/2018] [Indexed: 12/03/2022]
Abstract
Robotic surgery is a new technology in medical applications and has been undergoing rapid development. The surgical knife, essential for robotic surgery, has the ability to determine the success of an operation. In this paper, on the basis of the principle of field-effect transistors (FETs), a perceptual surgical knife is proposed to detect the electrons or electric field of the human body with distinguishable signals. In addition, it is difficult to discriminate between the motions of surgical knives from the perceptual signals that are disturbed by high-frequency Gaussian white noise. Therefore, the wavelet denoising approach is chosen to reduce the high-frequency noise. The proposed perceptual surgical knife with the wavelet denoising method has the characteristics of high sensitivity, low cost, and good repeatability.
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Affiliation(s)
- Tao Li
- Institute of Innovative Science and Technology, Tokai University, Hiratsuka-shi 259-1292, Japan.
| | - Yuta Sunami
- Micro/Nano Technology Center, Tokai University, Hiratsuka-shi 259-1292, Japan.
- Department of Mechanical Engineering, Tokai University, Hiratsuka-shi 259-1292, Japan.
| | - Sheng Zhang
- Micro/Nano Technology Center, Tokai University, Hiratsuka-shi 259-1292, Japan.
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