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Cui Z, Ma R, Yang CH, Malpani A, Chu TN, Ghazi A, Davis JW, Miles BJ, Lau C, Liu Y, Hung AJ. Capturing relationships between suturing sub-skills to improve automatic suturing assessment. NPJ Digit Med 2024; 7:152. [PMID: 38862627 PMCID: PMC11167055 DOI: 10.1038/s41746-024-01143-3] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 12/19/2023] [Accepted: 05/22/2024] [Indexed: 06/13/2024] Open
Abstract
Suturing skill scores have demonstrated strong predictive capabilities for patient functional recovery. The suturing can be broken down into several substep components, including needle repositioning, needle entry angle, etc. Artificial intelligence (AI) systems have been explored to automate suturing skill scoring. Traditional approaches to skill assessment typically focus on evaluating individual sub-skills required for particular substeps in isolation. However, surgical procedures require the integration and coordination of multiple sub-skills to achieve successful outcomes. Significant associations among the technical sub-skill have been established by existing studies. In this paper, we propose a framework for joint skill assessment that takes into account the interconnected nature of sub-skills required in surgery. The prior known relationships among sub-skills are firstly identified. Our proposed AI system is then empowered by the prior known relationships to perform the suturing skill scoring for each sub-skill domain simultaneously. Our approach can effectively improve skill assessment performance through the prior known relationships among sub-skills. Through the proposed approach to joint skill assessment, we aspire to enhance the evaluation of surgical proficiency and ultimately improve patient outcomes in surgery.
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Affiliation(s)
- Zijun Cui
- University of Southern California, Los Angeles, CA, USA
| | - Runzhuo Ma
- Department of Urology, Cedars-Sinai Medical Center, Los Angeles, CA, USA
| | - Cherine H Yang
- Department of Urology, Cedars-Sinai Medical Center, Los Angeles, CA, USA
| | | | - Timothy N Chu
- University of Southern California, Los Angeles, CA, USA
| | - Ahmed Ghazi
- Johns Hopkins University, Baltimore, MD, USA
| | - John W Davis
- University of Texas MD Anderson Cancer Center, Houston, TX, USA
| | | | | | - Yan Liu
- University of Southern California, Los Angeles, CA, USA
| | - Andrew J Hung
- Department of Urology, Cedars-Sinai Medical Center, Los Angeles, CA, USA.
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Yanik E, Schwaitzberg S, De S. Deep Learning for Video-Based Assessment in Surgery. JAMA Surg 2024:2819793. [PMID: 38837128 DOI: 10.1001/jamasurg.2024.1510] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 06/06/2024]
Abstract
This surgical innovation explains how applying deep neural networks could ensure the continued use of video-based assessment.
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Affiliation(s)
- Erim Yanik
- College of Engineering, Florida Agriculture and Mechanical University, Florida State University, Tallahassee
| | - Steven Schwaitzberg
- School of Medicine and Biomedical Sciences, University at Buffalo, Buffalo, New York
| | - Suvranu De
- College of Engineering, Florida Agriculture and Mechanical University, Florida State University, Tallahassee
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Alghazawi L, Fadel MG, Chen JY, Das B, Robb H, Rodriguez-Luna MR, Fakih-Gomez N, Perretta S, Ashrafian H, Fehervari M. Development and Evaluation of a Quality Assessment Tool for Laparoscopic Sleeve Gastrectomy Videos: A Review and Comparison of Academic and Online Video Resources. Obes Surg 2024; 34:1909-1916. [PMID: 38581627 PMCID: PMC11031436 DOI: 10.1007/s11695-024-07199-0] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 11/19/2023] [Revised: 03/21/2024] [Accepted: 03/21/2024] [Indexed: 04/08/2024]
Abstract
BACKGROUND Video recording of surgical procedures is increasing in popularity. They are presented in various platforms, many of which are not peer-reviewed. Laparoscopic sleeve gastrectomy (LSG) videos are widely available; however, there is limited evidence supporting the use of reporting guidelines when uploading LSG videos to create a valuable educational video. We aimed to determine the variations and establish the quality of published LSG videos, in both peer-reviewed literature and on YouTube, using a newly designed checklist to improve the quality and enhance the transparency of video reporting. METHODS A quality assessment tool was designed by using existing research and society guidelines, such as the Bariatric Metabolic Surgery Standardization (BMSS). A systematic review using PRISMA guidelines was performed on MEDLINE and EMBASE databases to identify video case reports (academic videos) and a similar search was performed on the commercial YouTube platform (commercial videos) simultaneously. All videos displaying LSG were reviewed and scored using the quality assessment tool. Academic and commercial videos were subsequently compared and an evidence-based checklist was created. RESULTS A total of 93 LSG recordings including 26 academic and 67 commercial videos were reviewed. Mean score of the checklist was 5/11 and 4/11 for videos published in articles and YouTube, respectively. Academic videos had higher rates of describing instruments used, such as orogastric tube (P < 0.001) and stapler information (P = 0.04). Fifty-four percent of academic videos described short-term patient outcomes, while not reported in commercial videos (P < 0.001). Sleeve resection status was not universally reported. CONCLUSIONS Videos published in the academic literature are describing steps in greater detail with more emphasis on specific technical elements and patient outcomes and thus have a higher educational value. A new quality assessment tool has been proposed for video reporting guidelines to improve the reliability and value of published video research.
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Affiliation(s)
- Laith Alghazawi
- Department of Surgery and Cancer, Imperial College London, London, UK.
| | - Michael G Fadel
- Department of Surgery and Cancer, Imperial College London, London, UK
- Department of Bariatric and Metabolic Surgery, Chelsea and Westminster Hospital, London, UK
| | - Jun Yu Chen
- Department of Surgery and Cancer, Imperial College London, London, UK
| | - Bibek Das
- Department of Surgery and Cancer, Imperial College London, London, UK
| | - Henry Robb
- Department of Surgery and Cancer, Imperial College London, London, UK
| | - Maria Rita Rodriguez-Luna
- Research Institute Against Digestive Cancer (IRCAD), Strasbourg, France
- ICube Laboratory, Photonics Instrumentation for Health, Strasbourg, France
| | - Naim Fakih-Gomez
- Department of Bariatric and Metabolic Surgery, Chelsea and Westminster Hospital, London, UK
| | - Silvana Perretta
- Research Institute Against Digestive Cancer (IRCAD), Strasbourg, France
- Department of Digestive and Endocrine Surgery, University of Strasbourg, Strasbourg, France
- IHU-Strasbourg, Institute of Image-Guided Surgery, Strasbourg, France
| | - Hutan Ashrafian
- Department of Surgery and Cancer, Imperial College London, London, UK
| | - Matyas Fehervari
- Department of Surgery and Cancer, Imperial College London, London, UK
- Gastrointestinal Surgery, Maidstone and Tunbridge Wells NHS Trust, Tunbridge Wells, UK
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Ketel MHM, Klarenbeek BR, Abma I, Belgers EHJ, Coene PPLO, Dekker JWT, van Duijvendijk P, Emous M, Gisbertz SS, Haveman JW, Heisterkamp J, Nieuwenhuijzen GAP, Ruurda JP, van Sandick JW, van der Sluis PC, van Det MJ, van Esser S, Law S, de Steur WO, Sosef MN, Wijnhoven B, Hannink G, Rosman C, van Workum F. Nationwide Association of Surgical Performance of Minimally Invasive Esophagectomy With Patient Outcomes. JAMA Netw Open 2024; 7:e246556. [PMID: 38639938 PMCID: PMC11031683 DOI: 10.1001/jamanetworkopen.2024.6556] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [MESH Headings] [Track Full Text] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 11/14/2023] [Accepted: 01/31/2024] [Indexed: 04/20/2024] Open
Abstract
Importance Suboptimal surgical performance is hypothesized to be associated with less favorable patient outcomes in minimally invasive esophagectomy (MIE). Establishing this association may lead to programs that promote better surgical performance of MIE and improve patient outcomes. Objective To investigate associations between surgical performance and postoperative outcomes after MIE. Design, Setting, and Participants In this nationwide cohort study of 15 Dutch hospitals that perform more than 20 MIEs per year, 7 masked expert MIE surgeons assessed surgical performance using videos and a previously developed and validated competency assessment tool (CAT). Each hospital submitted 2 representative videos of MIEs performed between November 4, 2021, and September 13, 2022. Patients registered in the Dutch Upper Gastrointestinal Cancer Audit between January 1, 2020, and December 31, 2021, were included to examine patient outcomes. Exposure Hospitals were divided into quartiles based on their MIE-CAT performance score. Outcomes were compared between highest (top 25%) and lowest (bottom 25%) performing quartiles. Transthoracic MIE with gastric tube reconstruction. Main Outcome and Measure The primary outcome was severe postoperative complications (Clavien-Dindo ≥3) within 30 days after surgery. Multilevel logistic regression, with clustering of patients within hospitals, was used to analyze associations between performance and outcomes. Results In total, 30 videos and 970 patients (mean [SD] age, 66.6 [9.1] years; 719 men [74.1%]) were included. The mean (SD) MIE-CAT score was 113.6 (5.5) in the highest performance quartile vs 94.1 (5.9) in the lowest. Severe postoperative complications occurred in 18.7% (41 of 219) of patients in the highest performance quartile vs 39.2% (40 of 102) in the lowest (risk ratio [RR], 0.50; 95% CI, 0.24-0.99). The highest vs the lowest performance quartile showed lower rates of conversions (1.8% vs 8.9%; RR, 0.21; 95% CI, 0.21-0.21), intraoperative complications (2.7% vs 7.8%; RR, 0.21; 95% CI, 0.04-0.94), and overall postoperative complications (46.1% vs 65.7%; RR, 0.54; 95% CI, 0.24-0.96). The R0 resection rate (96.8% vs 94.2%; RR, 1.03; 95% CI, 0.97-1.05) and lymph node yield (mean [SD], 38.9 [14.7] vs 26.2 [9.0]; RR, 3.20; 95% CI, 0.27-3.21) increased with oncologic-specific performance (eg, hiatus dissection, lymph node dissection). In addition, a high anastomotic phase score was associated with a lower anastomotic leakage rate (4.6% vs 17.7%; RR, 0.14; 95% CI, 0.06-0.31). Conclusions and Relevance These findings suggest that better surgical performance is associated with fewer perioperative complications for patients with esophageal cancer on a national level. If surgical performance of MIE can be improved with MIE-CAT implementation, substantially better patient outcomes may be achievable.
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Affiliation(s)
- Mirte H. M. Ketel
- Department of Surgery, Radboud University Medical Center, Nijmegen, the Netherlands
| | | | - Inger Abma
- IQ Healthcare, Radboud University Medical Center, Nijmegen, the Netherlands
| | | | | | | | | | - Marloes Emous
- Department of Surgery, Medical Center Leeuwarden, Leeuwarden, the Netherlands
| | - Suzanne S. Gisbertz
- Department of Surgery, Amsterdam University Medical Center, Location University of Amsterdam, Amsterdam, the Netherlands
- Cancer Center Amsterdam, Cancer Treatment and Quality of Life, Amsterdam, the Netherlands
| | - Jan Willem Haveman
- Department of Surgery, University Medical Centre Groningen, University of Groningen, Groningen, the Netherlands
| | - Joos Heisterkamp
- Department of Surgery, Elisabeth Twee-Steden Hospital, Tilburg, the Netherlands
| | | | - Jelle P. Ruurda
- Department of Surgery, University Medical Center Utrecht, Utrecht University, Utrecht, the Netherlands
| | | | - Pieter C. van der Sluis
- Department of Surgery, Erasmus MC University Medical Center Rotterdam, Rotterdam, the Netherlands
| | - Marc J. van Det
- Department of Surgery, Hospital Group Twente (ZGT), Almelo, the Netherlands
| | - Stijn van Esser
- Department of Surgery, Reinier de Graaf Groep, Delft, the Netherlands
| | - Simon Law
- Department of Surgery, Queen Mary Hospital, School of Clinical Medicine, The University of Hong Kong, China
| | - Wobbe O. de Steur
- Department of Surgery, Leiden University Medical Center, Leiden, the Netherlands
| | | | - Bas Wijnhoven
- Department of Surgery, Antoni van Leeuwenhoek Ziekenhuis, Amsterdam, the Netherlands
| | - Gerjon Hannink
- Department of Medical Imaging, Radboud University Medical Center, Nijmegen, the Netherlands
| | - Camiel Rosman
- Department of Surgery, Radboud University Medical Center, Nijmegen, the Netherlands
| | - Frans van Workum
- Department of Surgery, Radboud University Medical Center, Nijmegen, the Netherlands
- Department of Surgery, Canisius-Wilhelmina Hospital, Nijmegen, the Netherlands
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Nikolian VC, Camacho D, Earle D, Lehmann R, Nau P, Ramshaw B, Stulberg J. Development and preliminary validation of a new task-based objective procedure-specific assessment of inguinal hernia repair procedural safety. Surg Endosc 2024; 38:1583-1591. [PMID: 38332173 DOI: 10.1007/s00464-024-10677-2] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 03/29/2023] [Accepted: 12/30/2023] [Indexed: 02/10/2024]
Abstract
BACKGROUND Surgical videos coupled with structured assessments enable surgical training programs to provide independent competency evaluations and align with the American Board of Surgery's entrustable professional activities initiative. Existing assessment instruments for minimally invasive inguinal hernia repair (IHR) have limitations with regards to reliability, validity, and usability. A cross-sectional study of six surgeons using a novel objective, procedure-specific, 8-item competency assessment for minimally invasive inguinal hernia repair (IHR-OPSA) was performed to assess inter-rater reliability using a "safe" vs. "unsafe" scoring rubric. METHODS The IHR-OPSA was developed by three expert IHR surgeons, field tested with five IHR surgeons, and revised based upon feedback. The final instrument included: (1) incision/port placement; (2) dissection of peritoneal flap (TAPP) or dissection of peritoneal flap (TEP); (3) exposure; (4) reducing the sac; (5) full dissection of the myopectineal orifice; (6) mesh insertion; (7) mesh fixation; and (8) operation flow. The IHR-OPSA was applied by six expert IHR surgeons to 20 IHR surgical videos selected to include a spectrum of hernia procedures (15 laparoscopic, 5 robotic), anatomy (14 indirect, 5 direct, 1 femoral), and Global Case Difficulty (easy, average, hard). Inter-rater reliability was assessed against Gwet's AC2. RESULTS The IHR-OPSA inter-rater reliability was good to excellent, ranging from 0.65 to 0.97 across the eight items. Assessments of robotic procedures had higher reliability with near perfect agreement for 7 of 8 items. In general, assessments of easier cases had higher levels of agreement than harder cases. CONCLUSIONS A novel 8-item minimally invasive IHR assessment tool was developed and tested for inter-rater reliability using a "safe" vs. "unsafe" rating system with promising results. To promote instrument validity the IHR-OPSA was designed and evaluated within the context of intended use with iterative engagement with experts and testing of constructs against real-world operative videos.
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Affiliation(s)
- Vahagn C Nikolian
- Department of Surgery, Oregon Health & Science University, 3181 S.W. Sam Jackson Park Rd., Portland, OR, 97239, USA.
| | - Diego Camacho
- Minimally Invasive and Endoscopic Surgery at Montefiore Medical Center, New York, NY, USA
| | - David Earle
- New England Hernia Center, Lowell, MA, USA
- Tufts University School of Medicine, Boston, MA, USA
| | - Ryan Lehmann
- Department of Surgery, Section of Bariatric Surgery, University of Iowa Hospitals & Clinics, Iowa City, IA, USA
| | - Peter Nau
- Department of Surgery, Section of Bariatric Surgery, University of Iowa Hospitals & Clinics, Iowa City, IA, USA
| | - Bruce Ramshaw
- CQInsights PBC, Knoxville, TN, USA
- Caresyntax Corporation, Boston, MA, USA
| | - Jonah Stulberg
- Department of Surgery, McGovern Medical School University of Texas Health Science Center at Houston, Houston, TX, USA
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Zuluaga L, Rich JM, Gupta R, Pedraza A, Ucpinar B, Okhawere KE, Saini I, Dwivedi P, Patel D, Zaytoun O, Menon M, Tewari A, Badani KK. AI-powered real-time annotations during urologic surgery: The future of training and quality metrics. Urol Oncol 2024; 42:57-66. [PMID: 38142209 DOI: 10.1016/j.urolonc.2023.11.002] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 07/11/2023] [Revised: 10/23/2023] [Accepted: 11/02/2023] [Indexed: 12/25/2023]
Abstract
INTRODUCTION AND OBJECTIVE Real-time artificial intelligence (AI) annotation of the surgical field has the potential to automatically extract information from surgical videos, helping to create a robust surgical atlas. This content can be used for surgical education and qualitative initiatives. We demonstrate the first use of AI in urologic robotic surgery to capture live surgical video and annotate key surgical steps and safety milestones in real-time. SUMMARY BACKGROUND DATA While AI models possess the capability to generate automated annotations based on a collection of video images, the real-time implementation of such technology in urological robotic surgery to aid surgeon and training staff it is still pending to be studied. METHODS We conducted an educational symposium, which broadcasted 2 live procedures, a robotic-assisted radical prostatectomy (RARP) and a robotic-assisted partial nephrectomy (RAPN). A surgical AI platform system (Theator, Palo Alto, CA) generated real-time annotations and identified operative safety milestones. This was achieved through trained algorithms, conventional video recognition, and novel Video Transfer Network technology which captures clips in full context, enabling automatic recognition and surgical mapping in real-time. RESULTS Real-time AI annotations for procedure #1, RARP, are found in Table 1. The safety milestone annotations included the apical safety maneuver and deliberate views of structures such as the external iliac vessels and the obturator nerve. Real-time AI annotations for procedure #2, RAPN, are found in Table 1. Safety milestones included deliberate views of structures such as the gonadal vessels and the ureter. AI annotated surgical events included intraoperative ultrasound, temporary clip application and removal, hemostatic powder application, and notable hemorrhage. CONCLUSIONS For the first time, surgical intelligence successfully showcased real-time AI annotations of 2 separate urologic robotic procedures during a live telecast. These annotations may provide the technological framework for send automatic notifications to clinical or operational stakeholders. This technology is a first step in real-time intraoperative decision support, leveraging big data to improve the quality of surgical care, potentially improve surgical outcomes, and support training and education.
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Affiliation(s)
- Laura Zuluaga
- Department of Urology, Icahn School of Medicine at Mount Sinai, New York City, NY.
| | - Jordan Miller Rich
- Department of Urology, Icahn School of Medicine at Mount Sinai, New York City, NY
| | - Raghav Gupta
- Department of Urology, Icahn School of Medicine at Mount Sinai, New York City, NY
| | - Adriana Pedraza
- Department of Urology, Icahn School of Medicine at Mount Sinai, New York City, NY
| | - Burak Ucpinar
- Department of Urology, Icahn School of Medicine at Mount Sinai, New York City, NY
| | - Kennedy E Okhawere
- Department of Urology, Icahn School of Medicine at Mount Sinai, New York City, NY
| | - Indu Saini
- Department of Urology, Icahn School of Medicine at Mount Sinai, New York City, NY
| | - Priyanka Dwivedi
- Department of Urology, Icahn School of Medicine at Mount Sinai, New York City, NY
| | - Dhruti Patel
- Department of Urology, Icahn School of Medicine at Mount Sinai, New York City, NY
| | - Osama Zaytoun
- Department of Urology, Icahn School of Medicine at Mount Sinai, New York City, NY
| | - Mani Menon
- Department of Urology, Icahn School of Medicine at Mount Sinai, New York City, NY
| | - Ashutosh Tewari
- Department of Urology, Icahn School of Medicine at Mount Sinai, New York City, NY
| | - Ketan K Badani
- Department of Urology, Icahn School of Medicine at Mount Sinai, New York City, NY
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Constable MD, Shum HPH, Clark S. Enhancing surgical performance in cardiothoracic surgery with innovations from computer vision and artificial intelligence: a narrative review. J Cardiothorac Surg 2024; 19:94. [PMID: 38355499 PMCID: PMC10865515 DOI: 10.1186/s13019-024-02558-5] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 07/01/2023] [Accepted: 01/30/2024] [Indexed: 02/16/2024] Open
Abstract
When technical requirements are high, and patient outcomes are critical, opportunities for monitoring and improving surgical skills via objective motion analysis feedback may be particularly beneficial. This narrative review synthesises work on technical and non-technical surgical skills, collaborative task performance, and pose estimation to illustrate new opportunities to advance cardiothoracic surgical performance with innovations from computer vision and artificial intelligence. These technological innovations are critically evaluated in terms of the benefits they could offer the cardiothoracic surgical community, and any barriers to the uptake of the technology are elaborated upon. Like some other specialities, cardiothoracic surgery has relatively few opportunities to benefit from tools with data capture technology embedded within them (as is possible with robotic-assisted laparoscopic surgery, for example). In such cases, pose estimation techniques that allow for movement tracking across a conventional operating field without using specialist equipment or markers offer considerable potential. With video data from either simulated or real surgical procedures, these tools can (1) provide insight into the development of expertise and surgical performance over a surgeon's career, (2) provide feedback to trainee surgeons regarding areas for improvement, (3) provide the opportunity to investigate what aspects of skill may be linked to patient outcomes which can (4) inform the aspects of surgical skill which should be focused on within training or mentoring programmes. Classifier or assessment algorithms that use artificial intelligence to 'learn' what expertise is from expert surgical evaluators could further assist educators in determining if trainees meet competency thresholds. With collaborative efforts between surgical teams, medical institutions, computer scientists and researchers to ensure this technology is developed with usability and ethics in mind, the developed feedback tools could improve cardiothoracic surgical practice in a data-driven way.
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Affiliation(s)
- Merryn D Constable
- Department of Psychology, Northumbria University, Newcastle-upon-Tyne, UK.
| | - Hubert P H Shum
- Department of Computer Science, Durham University, Durham, UK
| | - Stephen Clark
- Department of Applied Sciences, Northumbria University, Newcastle-upon-Tyne, UK
- Consultant Cardiothoracic and Transplant Surgeon, Freeman Hospital, Newcastle upon Tyne, UK
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Yule S, Dearani JA, Pugh C. Surgical Instant Replay-A National Video-Based Performance Assessment Toolbox. JAMA Surg 2023; 158:1344-1345. [PMID: 37755836 DOI: 10.1001/jamasurg.2023.1803] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 09/28/2023]
Abstract
This article discusses the widespread implementation of surgical video replay to improve technical and nontechnical performance of surgeons.
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Affiliation(s)
- Steven Yule
- School of Surgery, University of Edinburgh, Edinburgh, Scotland
| | - Joseph A Dearani
- Department of Cardiovascular Surgery, Mayo Clinic, Rochester, Minnesota
| | - Carla Pugh
- Department of Surgery, Stanford University School of Medicine, Stanford, California
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Oh DS, Ershad M, Wee JO, Sancheti MS, D'Souza DM, Herrera LJ, Schumacher LY, Shields M, Brown K, Yousaf S, Lazar JF. Comparison of Global Evaluative Assessment of Robotic Surgery with objective performance indicators for the assessment of skill during robotic-assisted thoracic surgery. Surgery 2023; 174:1349-1355. [PMID: 37718171 DOI: 10.1016/j.surg.2023.08.008] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 03/05/2023] [Revised: 06/30/2023] [Accepted: 08/08/2023] [Indexed: 09/19/2023]
Abstract
BACKGROUND The Global Evaluative Assessment of Robotic Skills is a popular but ultimately subjective assessment tool in robotic-assisted surgery. An alternative approach is to record system or console events or calculate instrument kinematics to derive objective performance indicators. The aim of this study was to compare these 2 approaches and correlate the Global Evaluative Assessment of Robotic Skills with different types of objective performance indicators during robotic-assisted lobectomy. METHODS Video, system event, and kinematic data were recorded from the robotic surgical system during left upper lobectomy on a standardized perfused and pulsatile ex vivo porcine heart-lung model. Videos were segmented into steps, and the superior vein dissection was graded independently by 2 blinded expert surgeons with Global Evaluative Assessment of Robotic Skills. Objective performance indicators representing categories for energy use, event data, movement, smoothness, time, and wrist articulation were calculated for the same task and compared to Global Evaluative Assessment of Robotic Skills scores. RESULTS Video and data from 51 cases were analyzed (44 fellows, 7 attendings). Global Evaluative Assessment of Robotic Skills scores were significantly higher for attendings (P < .05), but there was a significant difference in raters' scores of 31.4% (defined as >20% difference in total score). The interclass correlation was 0.44 for 1 rater and 0.61 for 2 raters. Objective performance indicators correlated with Global Evaluative Assessment of Robotic Skills to varying degrees. The most highly correlated Global Evaluative Assessment of Robotic Skills domain was efficiency. Instrument movement and smoothness were highly correlated among objective performance indicator categories. Of individual objective performance indicators, right-hand median jerk, an objective performance indicator of change of acceleration, had the highest correlation coefficient (0.55). CONCLUSION There was a relatively poor overall correlation between the Global Evaluative Assessment of Robotic Skills and objective performance indicators. However, both appear strongly correlated for certain metrics such as efficiency and smoothness. Objective performance indicators may be a potentially more quantitative and granular approach to assessing skill, given that they can be calculated mathematically and automatically without subjective interpretation.
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Affiliation(s)
- Daniel S Oh
- University of Southern California, Keck School of Medicine, Los Angeles, CA; Data and Analytics, Intuitive Surgical, Sunnyvale, CA.
| | | | - Jon O Wee
- Brigham and Women's Hospital, Harvard Medical School, Boston, MA
| | | | | | | | | | | | - Kristen Brown
- Data and Analytics, Intuitive Surgical, Sunnyvale, CA
| | - Sadia Yousaf
- Data and Analytics, Intuitive Surgical, Sunnyvale, CA
| | - John F Lazar
- Medstar Washington Hospital, Georgetown University, Washington, DC
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10
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Shafa G, Kiani P, Masino C, Okrainec A, Pasternak JD, Alseidi A, Madani A. Training for excellence: using a multimodal videoconferencing platform to coach surgeons and improve intraoperative performance. Surg Endosc 2023; 37:9406-9413. [PMID: 37670189 DOI: 10.1007/s00464-023-10374-6] [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: 04/03/2023] [Accepted: 07/30/2023] [Indexed: 09/07/2023]
Abstract
INTRODUCTION Continuing Professional Development opportunities for lifelong learning are fundamental to the acquisition of surgical expertise. However, few opportunities exist for longitudinal and structured learning to support the educational needs of surgeons in practice. While peer-to-peer coaching has been proposed as a potential solution, there remains significant logistical constraints and a lack of evidence to support its effectiveness. The purpose of this study is to determine whether the use of remote videoconferencing for video-based coaching improves operative performance. METHODS Early career surgeon mentees participated in a remote coaching intervention with a surgeon coach of their choice and using a virtual telestration platform (Zoom Video Communications, San Jose, CA). Feedback was articulated through annotating videos. The coach evaluated mentee performance using a modified Intraoperative Performance Assessment Tool (IPAT). Participants completed a 5-point Likert scale on the educational value of the coaching program. RESULTS Eight surgeons were enrolled in the study, six of whom completed a total of two coaching sessions (baseline, 6-month). Subspecialties included endocrine, hepatopancreatobiliary, and surgical oncology. Mean age of participants was 39 (SD 3.3), with mean 5 (SD 4.1) years in independent practice. Total IPAT scores increased significantly from the first session (mean 47.0, SD 1.9) to the second session (mean 51.8, SD 2.1), p = 0.03. Sub-category analysis showed a significant improvement in the Advanced Cognitive Skills domain with a mean of 33.2 (SD 2.5) versus a mean of 37.0 (SD 2.4), p < 0.01. There was no improvement in the psychomotor skills category. Participants agreed or strongly agreed that the coaching programs can improve surgical performance and decision-making (coaches 85%; mentees 100%). CONCLUSION Remote surgical coaching is feasible and has educational value using ubiquitous commercially available virtual platforms. Logistical issues with scheduling and finding cases aligned with learning objectives continue to challenge program adoption and widespread dissemination.
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Affiliation(s)
- Golsa Shafa
- Temerty Faculty of Medicine, University of Toronto, Toronto, ON, Canada
- Surgical Artificial Intelligence Research Academy, University Health Network, Toronto, ON, Canada
| | - Parmiss Kiani
- Surgical Artificial Intelligence Research Academy, University Health Network, Toronto, ON, Canada
| | - Caterina Masino
- Surgical Artificial Intelligence Research Academy, University Health Network, Toronto, ON, Canada
| | - Allan Okrainec
- Department of Surgery, University of Toronto, Toronto, ON, Canada
| | | | - Adnan Alseidi
- Department of Surgery, University of California, San Francisco, CA, USA
| | - Amin Madani
- Department of Surgery, University of Toronto, Toronto, ON, Canada.
- Surgical Artificial Intelligence Research Academy, University Health Network, Toronto, ON, Canada.
- University Health Network - Toronto Western Hospital, Main Pavilion, 13MP-312B, 399, Bathurst St, Toronto, ON, M5T 2S8, Canada.
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11
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De Backer P, Peraire Lores M, Demuynck M, Piramide F, Simoens J, Oosterlinck T, Bogaert W, Shan CV, Van Regemorter K, Wastyn A, Checcucci E, Debbaut C, Van Praet C, Farinha R, De Groote R, Gallagher A, Decaestecker K, Mottrie A. Surgical Phase Duration in Robot-Assisted Partial Nephrectomy: A Surgical Data Science Exploration for Clinical Relevance. Diagnostics (Basel) 2023; 13:3386. [PMID: 37958283 PMCID: PMC10650909 DOI: 10.3390/diagnostics13213386] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 08/25/2023] [Revised: 10/29/2023] [Accepted: 11/03/2023] [Indexed: 11/15/2023] Open
Abstract
(1) Background: Surgical phases form the basic building blocks for surgical skill assessment, feedback, and teaching. The phase duration itself and its correlation with clinical parameters at diagnosis have not yet been investigated. Novel commercial platforms provide phase indications but have not been assessed for accuracy yet. (2) Methods: We assessed 100 robot-assisted partial nephrectomy videos for phase durations based on previously defined proficiency metrics. We developed an annotation framework and subsequently compared our annotations to an existing commercial solution (Touch Surgery, Medtronic™). We subsequently explored clinical correlations between phase durations and parameters derived from diagnosis and treatment. (3) Results: An objective and uniform phase assessment requires precise definitions derived from an iterative revision process. A comparison to a commercial solution shows large differences in definitions across phases. BMI and the duration of renal tumor identification are positively correlated, as are tumor complexity and both tumor excision and renorrhaphy duration. (4) Conclusions: The surgical phase duration can be correlated with certain clinical outcomes. Further research should investigate whether the retrieved correlations are also clinically meaningful. This requires an increase in dataset sizes and facilitation through intelligent computer vision algorithms. Commercial platforms can facilitate this dataset expansion and help unlock the full potential, provided that the phase annotation details are disclosed.
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Affiliation(s)
- Pieter De Backer
- ORSI Academy, 9090 Melle, Belgium
- IbiTech-Biommeda, Department of Electronics and Information Systems, Faculty of Engineering and Architecture, Ghent University, 9000 Ghent, Belgium
- Department of Human Structure and Repair, Faculty of Medicine and Health Sciences, Ghent University, 9000 Ghent, Belgium (C.V.P.)
- Young Academic Urologist—Urotechnology Working Group, NL-6803 Arnhem, The Netherlands
- Department of Urology, ERN eUROGEN Accredited Centre, Ghent University Hospital, 9000 Ghent, Belgium
| | | | - Meret Demuynck
- Department of Human Structure and Repair, Faculty of Medicine and Health Sciences, Ghent University, 9000 Ghent, Belgium (C.V.P.)
| | - Federico Piramide
- ORSI Academy, 9090 Melle, Belgium
- Department of Surgery, Candiolo Cancer Institute, FPO-IRCCS, 10060 Turin, Italy
| | | | | | - Wouter Bogaert
- IbiTech-Biommeda, Department of Electronics and Information Systems, Faculty of Engineering and Architecture, Ghent University, 9000 Ghent, Belgium
| | - Chi Victor Shan
- Department of Human Structure and Repair, Faculty of Medicine and Health Sciences, Ghent University, 9000 Ghent, Belgium (C.V.P.)
| | - Karel Van Regemorter
- Department of Human Structure and Repair, Faculty of Medicine and Health Sciences, Ghent University, 9000 Ghent, Belgium (C.V.P.)
| | - Aube Wastyn
- Department of Human Structure and Repair, Faculty of Medicine and Health Sciences, Ghent University, 9000 Ghent, Belgium (C.V.P.)
| | - Enrico Checcucci
- Young Academic Urologist—Urotechnology Working Group, NL-6803 Arnhem, The Netherlands
- Department of Surgery, Candiolo Cancer Institute, FPO-IRCCS, 10060 Turin, Italy
| | - Charlotte Debbaut
- IbiTech-Biommeda, Department of Electronics and Information Systems, Faculty of Engineering and Architecture, Ghent University, 9000 Ghent, Belgium
| | - Charles Van Praet
- Department of Human Structure and Repair, Faculty of Medicine and Health Sciences, Ghent University, 9000 Ghent, Belgium (C.V.P.)
- Department of Urology, ERN eUROGEN Accredited Centre, Ghent University Hospital, 9000 Ghent, Belgium
| | | | - Ruben De Groote
- Department of Urology, Onze-Lieve Vrouwziekenhuis Hospital, 9300 Aalst, Belgium
| | | | - Karel Decaestecker
- Department of Human Structure and Repair, Faculty of Medicine and Health Sciences, Ghent University, 9000 Ghent, Belgium (C.V.P.)
- Department of Urology, ERN eUROGEN Accredited Centre, Ghent University Hospital, 9000 Ghent, Belgium
- Department of Urology, AZ Maria Middelares Hospital, 9000 Ghent, Belgium
| | - Alexandre Mottrie
- ORSI Academy, 9090 Melle, Belgium
- Department of Urology, Onze-Lieve Vrouwziekenhuis Hospital, 9300 Aalst, Belgium
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12
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Ketel MHM, Klarenbeek BR, Eddahchouri Y, Cuesta MA, van Daele E, Gutschow CA, Hölscher AH, Hubka M, Luyer MDP, Merritt RE, Nieuwenhuijzen GAP, Shen Y, Abma IL, Rosman C, van Workum F. Crowd-sourced and expert video assessment in minimally invasive esophagectomy. Surg Endosc 2023; 37:7819-7828. [PMID: 37605010 PMCID: PMC10520122 DOI: 10.1007/s00464-023-10297-2] [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: 04/24/2023] [Accepted: 07/02/2023] [Indexed: 08/23/2023]
Abstract
BACKGROUND Video-based assessment by experts may structurally measure surgical performance using procedure-specific competency assessment tools (CATs). A CAT for minimally invasive esophagectomy (MIE-CAT) was developed and validated previously. However, surgeon's time is scarce and video assessment is time-consuming and labor intensive. This study investigated non-procedure-specific assessment of MIE video clips by MIE experts and crowdsourcing, collective surgical performance evaluation by anonymous and untrained laypeople, to assist procedure-specific expert review. METHODS Two surgical performance scoring frameworks were used to assess eight MIE videos. First, global performance was assessed with the non-procedure-specific Global Operative Assessment of Laparoscopic Skills (GOALS) of 64 procedural phase-based video clips < 10 min. Each clip was assessed by two MIE experts and > 30 crowd workers. Second, the same experts assessed procedure-specific performance with the MIE-CAT of the corresponding full-length video. Reliability and convergent validity of GOALS for MIE were investigated using hypothesis testing with correlations (experience, blood loss, operative time, and MIE-CAT). RESULTS Less than 75% of hypothesized correlations between GOALS scores and experience of the surgical team (r < 0.3), blood loss (r = - 0.82 to 0.02), operative time (r = - 0.42 to 0.07), and the MIE-CAT scores (r = - 0.04 to 0.76) were met for both crowd workers and experts. Interestingly, experts' GOALS and MIE-CAT scores correlated strongly (r = 0.40 to 0.79), while crowd workers' GOALS and experts' MIE-CAT scores correlations were weak (r = - 0.04 to 0.49). Expert and crowd worker GOALS scores correlated poorly (ICC ≤ 0.42). CONCLUSION GOALS assessments by crowd workers lacked convergent validity and showed poor reliability. It is likely that MIE is technically too difficult to assess for laypeople. Convergent validity of GOALS assessments by experts could also not be established. GOALS might not be comprehensive enough to assess detailed MIE performance. However, expert's GOALS and MIE-CAT scores strongly correlated indicating video clip (instead of full-length video) assessments could be useful to shorten assessment time.
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Affiliation(s)
- Mirte H M Ketel
- Department of Surgery, Radboud University Medical Center, Nijmegen, The Netherlands.
| | | | - Yassin Eddahchouri
- Department of Surgery, Radboud University Medical Center, Nijmegen, The Netherlands
| | - Miguel A Cuesta
- Department of Surgery, Amsterdam University Medical Centers, Location VUmc, Amsterdam, The Netherlands
| | - Elke van Daele
- Department of Digestive Surgery, Ghent University Hospital, Ghent, Belgium
| | - Christian A Gutschow
- Department of Surgery and Transplantation, University Hospital Zurich, Zurich, Switzerland
| | - Arnulf H Hölscher
- Department for General, Visceral and Trauma Surgery, Elisabeth-Krankenhaus-Essen GmbH, Essen, Germany
| | - Michal Hubka
- Department of Thoracic Surgery, Virginia Mason Medical Center, Seattle, SE, USA
| | - Misha D P Luyer
- Department of Surgery, Catharina Hospital, Eindhoven, The Netherlands
| | - Robert E Merritt
- Department of Surgery, Ohio State University - Wexner Medical Center, Columbus, OH, USA
| | | | - Yaxing Shen
- Department of Thoracic Surgery, Zhongshan Hospital, Fudan University, Shanghai, China
| | - Inger L Abma
- IQ Healthcare, Radboud University Medical Center, Nijmegen, The Netherlands
| | - Camiel Rosman
- Department of Surgery, Radboud University Medical Center, Nijmegen, The Netherlands
| | - Frans van Workum
- Department of Surgery, Radboud University Medical Center, Nijmegen, The Netherlands
- Department of Surgery, Canisius-Wilhelmina Hospital, Nijmegen, The Netherlands
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13
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Nakamura W, Sumitomo M, Zennami K, Takenaka M, Ichino M, Takahara K, Teramoto A, Shiroki R. Combination of deep learning and ensemble machine learning using intraoperative video images strongly predicts recovery of urinary continence after robot-assisted radical prostatectomy. Cancer Rep (Hoboken) 2023; 6:e1861. [PMID: 37449339 PMCID: PMC10480482 DOI: 10.1002/cnr2.1861] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 05/12/2023] [Revised: 06/20/2023] [Accepted: 06/25/2023] [Indexed: 07/18/2023] Open
Abstract
BACKGROUND We recently reported the importance of deep learning (DL) of pelvic magnetic resonance imaging in predicting the degree of urinary incontinence (UI) following robot-assisted radical prostatectomy (RARP). However, our results were limited because the prediction accuracy was approximately 70%. AIM To develop a more precise prediction model that can inform patients about UI recovery post-RARP surgery using a DL model based on intraoperative video images. METHODS AND RESULTS The study cohort comprised of 101 patients with localized prostate cancer undergoing RARP. Three snapshots from intraoperative video recordings showing the pelvic cavity (prior to bladder neck incision, immediately following prostate removal, and after vesicourethral anastomosis) were evaluated, including pre- and intraoperative parameters. We evaluated the DL model plus simple or ensemble machine learning (ML), and the area under the receiver operating characteristic curve (AUC) was analyzed through sensitivity and specificity. Of 101, 64 and 37 patients demonstrated "early continence (using 0 or 1 safety pad at 3 months post-RARP)" and "late continence (others)," respectively, at 3 months postoperatively. The combination of DL and simple ML using intraoperative video snapshots with clinicopathological parameters had a notably high performance (AUC, 0.683-0.749) to predict early recovery from UI after surgery. Furthermore, combining DL with ensemble artificial neural network using intraoperative video snapshots had the highest performance (AUC, 0.882; sensitivity, 92.2%; specificity, 78.4%; overall accuracy, 85.3%) to predict early recovery from post-RARP incontinence, with similar results by internal validation. The addition of clinicopathological parameters showed no additive effects for each analysis using DL, EL and simple ML. CONCLUSION Our findings suggest that the DL algorithm with intraoperative video imaging is a reliable method for informing patients about the severity of their recovery from UI after RARP, although it is not clear if our methods are reproducible for predicting long-term UI and pad-free continence.
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Affiliation(s)
- Wataru Nakamura
- Department of Urology, School of MedicineFujita Health UniversityToyoakeJapan
| | - Makoto Sumitomo
- Department of Urology, School of MedicineFujita Health UniversityToyoakeJapan
- Fujita Cancer CenterFujita Health UniversityToyoakeJapan
| | - Kenji Zennami
- Department of Urology, School of MedicineFujita Health UniversityToyoakeJapan
| | - Masashi Takenaka
- Department of Urology, School of MedicineFujita Health UniversityToyoakeJapan
| | - Manabu Ichino
- Department of Urology, School of MedicineFujita Health UniversityToyoakeJapan
| | - Kiyoshi Takahara
- Department of Urology, School of MedicineFujita Health UniversityToyoakeJapan
| | - Atsushi Teramoto
- Faculty of Radiological Technology, School of Medical SciencesFujita Health UniversityToyoakeJapan
- Faculty of Information EngineeringMeijo UniversityNagoyaJapan
| | - Ryoichi Shiroki
- Department of Urology, School of MedicineFujita Health UniversityToyoakeJapan
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14
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Montgomery KB, Lindeman B. Using Graduating Surgical Resident Milestone Ratings to Predict Patient Outcomes: A Blunt Instrument for a Complex Problem. ACADEMIC MEDICINE : JOURNAL OF THE ASSOCIATION OF AMERICAN MEDICAL COLLEGES 2023; 98:765-768. [PMID: 36745875 PMCID: PMC10329982 DOI: 10.1097/acm.0000000000005165] [Citation(s) in RCA: 2] [Impact Index Per Article: 2.0] [Reference Citation Analysis] [Abstract] [MESH Headings] [Grants] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 06/18/2023]
Abstract
In 2013, U.S. general surgery residency programs implemented a milestones assessment framework in an effort to incorporate more competency-focused evaluation methods. Developed by a group of surgical education leaders and other stakeholders working with the Accreditation Council for Graduate Medical Education and recently updated in a version 2.0, the surgery milestones framework is centered around 6 "core competencies": patient care, medical knowledge, practice-based learning and improvement, interpersonal and communication skills, professionalism, and systems-based practice. While prior work has focused on the validity of milestones as a measure of resident performance, associations between general surgery resident milestone ratings and their post-training patient outcomes have only recently been explored in an analysis in this issue of Academic Medicine by Kendrick et al. Despite their well-designed efforts to tackle this complex problem, no relationships were identified. This accompanying commentary discusses the broader implications for the use of milestone ratings beyond their intended application, alternative assessment methods, and the challenges of developing predictive assessments in the complex setting of surgical care. Although milestone ratings have not been shown to provide the specificity needed to predict clinical outcomes in the complex settings studied by Kendrick et al, hope remains that utilization of other outcomes, assessment frameworks, and data analytic tools could augment these models and further our progress toward a predictive assessment in surgical education. Evaluation of residents in general surgery residency programs has grown both more sophisticated and complicated in the setting of increasing patient and case complexity, constraints on time, and regulation of resident supervision in the operating room. Over the last decade, surgical education research efforts related to resident assessment have focused on measuring performance through accurate and reproducible methods with evidence for their validity, as well as on attempting to refine decision making about resident preparedness for unsupervised practice.
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Affiliation(s)
- Kelsey B Montgomery
- K.B. Montgomery is a general surgery resident, Department of Surgery, University of Alabama at Birmingham, Birmingham, Alabama; ORCID: https://orcid.org/0000-0002-1284-1830
| | - Brenessa Lindeman
- B. Lindeman is associate professor, Department of Surgery, and assistant dean, Graduate Medical Education, University of Alabama at Birmingham, Birmingham, Alabama
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15
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Mizunuma K, Kurashima Y, Poudel S, Watanabe Y, Noji T, Nakamura T, Okamura K, Shichinohe T, Hirano S. Surgical skills assessment of pancreaticojejunostomy using a simulator may predict patient outcomes: A multicenter prospective observational study. Surgery 2023; 173:1374-1380. [PMID: 37003952 DOI: 10.1016/j.surg.2023.02.027] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 09/10/2022] [Revised: 12/30/2022] [Accepted: 02/23/2023] [Indexed: 04/03/2023]
Abstract
BACKGROUND Pancreatoduodenectomy, an advanced surgical procedure with a high complication rate, requires surgical skill in performing pancreaticojejunostomy, which correlates with operative outcomes. We aimed to analyze the correlation between pancreaticojejunostomy assessment conducted in a simulator environment and the operating room and patient clinical outcomes. METHODS We recruited 30 surgeons (with different experience levels in pancreatoduodenectomy) from 11 institutes. Three trained blinded raters assessed the videos of the pancreaticojejunostomy procedure performed in the operating room using a simulator according to an objective structured assessment of technical skill and a newly developed pancreaticojejunostomy assessment scale. The correlations between the assessment score of the pancreaticojejunostomy performed in the operating room and using the simulator and between each assessment score and patient outcomes were calculated. The participants were also surveyed regarding various aspects of the simulator as a training tool. RESULTS There was no correlation between the average score of the pancreaticojejunostomy performed in the operating room and that in the simulator environment (r = 0.047). Pancreaticojejunostomy scores using the simulator were significantly lower in patients with postoperative pancreatic fistula than in those without postoperative pancreatic fistula (P = .05). Multivariate analysis showed that pancreaticojejunostomy assessment scores were independent factors in postoperative pancreatic fistula (P = .09). The participants highly rated the simulator and considered that it had the potential to be used for training. CONCLUSION There was no correlation between pancreaticojejunostomy surgical performance in the operating room and the simulation environment. Surgical skills evaluated in the simulation setting could predict patient surgical outcomes.
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Affiliation(s)
- Kenichi Mizunuma
- Department of Gastroenterologial Surgery II, Hokkaido University Faculty of Medicine, Sapporo, Japan
| | - Yo Kurashima
- Department of Gastroenterologial Surgery II, Hokkaido University Faculty of Medicine, Sapporo, Japan; Clinical Simulation Center, Hokkaido University Graduate School of Medicine, Sapporo, Japan
| | - Saseem Poudel
- Department of Gastroenterologial Surgery II, Hokkaido University Faculty of Medicine, Sapporo, Japan.
| | - Yusuke Watanabe
- Clinical Research and Medical Innovation Center, Institute of Health Science Innovation for Medical Care, Hokkaido University Hospital, Sapporo, Japan
| | - Takehiro Noji
- Department of Gastroenterologial Surgery II, Hokkaido University Faculty of Medicine, Sapporo, Japan
| | - Toru Nakamura
- Department of Gastroenterologial Surgery II, Hokkaido University Faculty of Medicine, Sapporo, Japan
| | - Keisuke Okamura
- Department of Gastroenterologial Surgery II, Hokkaido University Faculty of Medicine, Sapporo, Japan
| | - Toshiaki Shichinohe
- Department of Gastroenterologial Surgery II, Hokkaido University Faculty of Medicine, Sapporo, Japan
| | - Satoshi Hirano
- Department of Gastroenterologial Surgery II, Hokkaido University Faculty of Medicine, Sapporo, Japan
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16
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Mascagni P, Alapatt D, Sestini L, Altieri MS, Madani A, Watanabe Y, Alseidi A, Redan JA, Alfieri S, Costamagna G, Boškoski I, Padoy N, Hashimoto DA. Computer vision in surgery: from potential to clinical value. NPJ Digit Med 2022; 5:163. [PMID: 36307544 PMCID: PMC9616906 DOI: 10.1038/s41746-022-00707-5] [Citation(s) in RCA: 35] [Impact Index Per Article: 17.5] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 07/15/2022] [Accepted: 10/10/2022] [Indexed: 11/09/2022] Open
Abstract
Hundreds of millions of operations are performed worldwide each year, and the rising uptake in minimally invasive surgery has enabled fiber optic cameras and robots to become both important tools to conduct surgery and sensors from which to capture information about surgery. Computer vision (CV), the application of algorithms to analyze and interpret visual data, has become a critical technology through which to study the intraoperative phase of care with the goals of augmenting surgeons' decision-making processes, supporting safer surgery, and expanding access to surgical care. While much work has been performed on potential use cases, there are currently no CV tools widely used for diagnostic or therapeutic applications in surgery. Using laparoscopic cholecystectomy as an example, we reviewed current CV techniques that have been applied to minimally invasive surgery and their clinical applications. Finally, we discuss the challenges and obstacles that remain to be overcome for broader implementation and adoption of CV in surgery.
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Affiliation(s)
- Pietro Mascagni
- Gemelli Hospital, Catholic University of the Sacred Heart, Rome, Italy. .,IHU-Strasbourg, Institute of Image-Guided Surgery, Strasbourg, France. .,Global Surgical Artificial Intelligence Collaborative, Toronto, ON, Canada.
| | - Deepak Alapatt
- ICube, University of Strasbourg, CNRS, IHU, Strasbourg, France
| | - Luca Sestini
- ICube, University of Strasbourg, CNRS, IHU, Strasbourg, France.,Department of Electronics, Information and Bioengineering, Politecnico di Milano, Milano, Italy
| | - Maria S Altieri
- Global Surgical Artificial Intelligence Collaborative, Toronto, ON, Canada.,Department of Surgery, University of Pennsylvania Perelman School of Medicine, Philadelphia, PA, USA
| | - Amin Madani
- Global Surgical Artificial Intelligence Collaborative, Toronto, ON, Canada.,Department of Surgery, University Health Network, Toronto, ON, Canada
| | - Yusuke Watanabe
- Global Surgical Artificial Intelligence Collaborative, Toronto, ON, Canada.,Department of Surgery, University of Hokkaido, Hokkaido, Japan
| | - Adnan Alseidi
- Global Surgical Artificial Intelligence Collaborative, Toronto, ON, Canada.,Department of Surgery, University of California San Francisco, San Francisco, CA, USA
| | - Jay A Redan
- Department of Surgery, AdventHealth-Celebration Health, Celebration, FL, USA
| | - Sergio Alfieri
- Fondazione Policlinico Universitario A. Gemelli IRCCS, Rome, Italy
| | - Guido Costamagna
- Fondazione Policlinico Universitario A. Gemelli IRCCS, Rome, Italy
| | - Ivo Boškoski
- Fondazione Policlinico Universitario A. Gemelli IRCCS, Rome, Italy
| | - Nicolas Padoy
- IHU-Strasbourg, Institute of Image-Guided Surgery, Strasbourg, France.,ICube, University of Strasbourg, CNRS, IHU, Strasbourg, France
| | - Daniel A Hashimoto
- Global Surgical Artificial Intelligence Collaborative, Toronto, ON, Canada.,Department of Surgery, University of Pennsylvania Perelman School of Medicine, Philadelphia, PA, USA
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