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Strong JS, Furube T, Takeuchi M, Kawakubo H, Maeda Y, Matsuda S, Fukuda K, Nakamura R, Kitagawa Y. Evaluating surgical expertise with AI-based automated instrument recognition for robotic distal gastrectomy. Ann Gastroenterol Surg 2024; 8:611-619. [PMID: 38957567 PMCID: PMC11216797 DOI: 10.1002/ags3.12784] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Figures] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 10/13/2023] [Revised: 12/11/2023] [Accepted: 02/09/2024] [Indexed: 07/04/2024] Open
Abstract
Introduction Complexities of robotic distal gastrectomy (RDG) give reason to assess physician's surgical skill. Varying levels in surgical skill affect patient outcomes. We aim to investigate how a novel artificial intelligence (AI) model can be used to evaluate surgical skill in RDG by recognizing surgical instruments. Methods Fifty-five consecutive robotic surgical videos of RDG for gastric cancer were analyzed. We used Deeplab, a multi-stage temporal convolutional network, and it trained on 1234 manually annotated images. The model was then tested on 149 annotated images for accuracy. Deep learning metrics such as Intersection over Union (IoU) and accuracy were assessed, and the comparison between experienced and non-experienced surgeons based on usage of instruments during infrapyloric lymph node dissection was performed. Results We annotated 540 Cadiere forceps, 898 Fenestrated bipolars, 359 Suction tubes, 307 Maryland bipolars, 688 Harmonic scalpels, 400 Staplers, and 59 Large clips. The average IoU and accuracy were 0.82 ± 0.12 and 87.2 ± 11.9% respectively. Moreover, the percentage of each instrument's usage to overall infrapyloric lymphadenectomy duration predicted by AI were compared. The use of Stapler and Large clip were significantly shorter in the experienced group compared to the non-experienced group. Conclusions This study is the first to report that surgical skill can be successfully and accurately determined by an AI model for RDG. Our AI gives us a way to recognize and automatically generate instance segmentation of the surgical instruments present in this procedure. Use of this technology allows unbiased, more accessible RDG surgical skill.
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Affiliation(s)
- James S. Strong
- Department of SurgeryKeio University School of MedicineTokyoJapan
- Harvard CollegeHarvard UniversityCambridgeMassachusettsUSA
| | - Tasuku Furube
- Department of SurgeryKeio University School of MedicineTokyoJapan
| | - Masashi Takeuchi
- Department of SurgeryKeio University School of MedicineTokyoJapan
| | | | - Yusuke Maeda
- Department of SurgeryKeio University School of MedicineTokyoJapan
| | - Satoru Matsuda
- Department of SurgeryKeio University School of MedicineTokyoJapan
| | - Kazumasa Fukuda
- Department of SurgeryKeio University School of MedicineTokyoJapan
| | - Rieko Nakamura
- Department of SurgeryKeio University School of MedicineTokyoJapan
| | - Yuko Kitagawa
- Department of SurgeryKeio University School of MedicineTokyoJapan
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Takács K, Lukács E, Levendovics R, Pekli D, Szijártó A, Haidegger T. Assessment of Surgeons' Stress Levels with Digital Sensors during Robot-Assisted Surgery: An Experimental Study. SENSORS (BASEL, SWITZERLAND) 2024; 24:2915. [PMID: 38733021 PMCID: PMC11086209 DOI: 10.3390/s24092915] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Subscribe] [Scholar Register] [Received: 03/04/2024] [Revised: 04/25/2024] [Accepted: 04/30/2024] [Indexed: 05/13/2024]
Abstract
Robot-Assisted Minimally Invasive Surgery (RAMIS) marks a paradigm shift in surgical procedures, enhancing precision and ergonomics. Concurrently it introduces complex stress dynamics and ergonomic challenges regarding the human-robot interface and interaction. This study explores the stress-related aspects of RAMIS, using the da Vinci XI Surgical System and the Sea Spikes model as a standard skill training phantom to establish a link between technological advancement and human factors in RAMIS environments. By employing different physiological and kinematic sensors for heart rate variability, hand movement tracking, and posture analysis, this research aims to develop a framework for quantifying the stress and ergonomic loads applied to surgeons. Preliminary findings reveal significant correlations between stress levels and several of the skill-related metrics measured by external sensors or the SURG-TLX questionnaire. Furthermore, early analysis of this preliminary dataset suggests the potential benefits of applying machine learning for surgeon skill classification and stress analysis. This paper presents the initial findings, identified correlations, and the lessons learned from the clinical setup, aiming to lay down the cornerstones for wider studies in the fields of clinical situation awareness and attention computing.
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Affiliation(s)
- Kristóf Takács
- Antal Bejczy Center for Intelligent Robotics (IROB), University Research and Innovation Center (EKIK), Óbuda University, 1034 Budapest, Hungary; (E.L.); (R.L.)
| | - Eszter Lukács
- Antal Bejczy Center for Intelligent Robotics (IROB), University Research and Innovation Center (EKIK), Óbuda University, 1034 Budapest, Hungary; (E.L.); (R.L.)
| | - Renáta Levendovics
- Antal Bejczy Center for Intelligent Robotics (IROB), University Research and Innovation Center (EKIK), Óbuda University, 1034 Budapest, Hungary; (E.L.); (R.L.)
- John von Neumann Faculty of Informatics (NIK), Óbuda University, 1034 Budapest, Hungary
- Austrian Center for Medical Innovation and Technology (ACMIT), 2700 Wiener Neustadt, Austria
| | - Damján Pekli
- Department of Surgery, Transplantation and Gastroenterology, Semmelweis University, 1082 Budapest, Hungary; (D.P.); (A.S.)
| | - Attila Szijártó
- Department of Surgery, Transplantation and Gastroenterology, Semmelweis University, 1082 Budapest, Hungary; (D.P.); (A.S.)
| | - Tamás Haidegger
- Antal Bejczy Center for Intelligent Robotics (IROB), University Research and Innovation Center (EKIK), Óbuda University, 1034 Budapest, Hungary; (E.L.); (R.L.)
- Austrian Center for Medical Innovation and Technology (ACMIT), 2700 Wiener Neustadt, Austria
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Gordon M, Daniel M, Ajiboye A, Uraiby H, Xu NY, Bartlett R, Hanson J, Haas M, Spadafore M, Grafton-Clarke C, Gasiea RY, Michie C, Corral J, Kwan B, Dolmans D, Thammasitboon S. A scoping review of artificial intelligence in medical education: BEME Guide No. 84. MEDICAL TEACHER 2024; 46:446-470. [PMID: 38423127 DOI: 10.1080/0142159x.2024.2314198] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [MESH Headings] [Track Full Text] [Subscribe] [Scholar Register] [Received: 12/04/2023] [Accepted: 01/31/2024] [Indexed: 03/02/2024]
Abstract
BACKGROUND Artificial Intelligence (AI) is rapidly transforming healthcare, and there is a critical need for a nuanced understanding of how AI is reshaping teaching, learning, and educational practice in medical education. This review aimed to map the literature regarding AI applications in medical education, core areas of findings, potential candidates for formal systematic review and gaps for future research. METHODS This rapid scoping review, conducted over 16 weeks, employed Arksey and O'Malley's framework and adhered to STORIES and BEME guidelines. A systematic and comprehensive search across PubMed/MEDLINE, EMBASE, and MedEdPublish was conducted without date or language restrictions. Publications included in the review spanned undergraduate, graduate, and continuing medical education, encompassing both original studies and perspective pieces. Data were charted by multiple author pairs and synthesized into various thematic maps and charts, ensuring a broad and detailed representation of the current landscape. RESULTS The review synthesized 278 publications, with a majority (68%) from North American and European regions. The studies covered diverse AI applications in medical education, such as AI for admissions, teaching, assessment, and clinical reasoning. The review highlighted AI's varied roles, from augmenting traditional educational methods to introducing innovative practices, and underscores the urgent need for ethical guidelines in AI's application in medical education. CONCLUSION The current literature has been charted. The findings underscore the need for ongoing research to explore uncharted areas and address potential risks associated with AI use in medical education. This work serves as a foundational resource for educators, policymakers, and researchers in navigating AI's evolving role in medical education. A framework to support future high utility reporting is proposed, the FACETS framework.
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Affiliation(s)
- Morris Gordon
- School of Medicine and Dentistry, University of Central Lancashire, Preston, UK
- Blackpool Hospitals NHS Foundation Trust, Blackpool, UK
| | - Michelle Daniel
- School of Medicine, University of California, San Diego, SanDiego, CA, USA
| | - Aderonke Ajiboye
- School of Medicine and Dentistry, University of Central Lancashire, Preston, UK
| | - Hussein Uraiby
- Department of Cellular Pathology, University Hospitals of Leicester NHS Trust, Leicester, UK
| | - Nicole Y Xu
- School of Medicine, University of California, San Diego, SanDiego, CA, USA
| | - Rangana Bartlett
- Department of Cognitive Science, University of California, San Diego, CA, USA
| | - Janice Hanson
- Department of Medicine and Office of Education, School of Medicine, Washington University in Saint Louis, Saint Louis, MO, USA
| | - Mary Haas
- Department of Emergency Medicine, University of Michigan Medical School, Ann Arbor, MI, USA
| | - Maxwell Spadafore
- Department of Emergency Medicine, University of Michigan Medical School, Ann Arbor, MI, USA
| | | | | | - Colin Michie
- School of Medicine and Dentistry, University of Central Lancashire, Preston, UK
| | - Janet Corral
- Department of Medicine, University of Nevada Reno, School of Medicine, Reno, NV, USA
| | - Brian Kwan
- School of Medicine, University of California, San Diego, SanDiego, CA, USA
| | - Diana Dolmans
- School of Health Professions Education, Faculty of Health, Maastricht University, Maastricht, NL, USA
| | - Satid Thammasitboon
- Center for Research, Innovation and Scholarship in Health Professions Education, Baylor College of Medicine, Houston, TX, USA
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Rueckert T, Rueckert D, Palm C. Methods and datasets for segmentation of minimally invasive surgical instruments in endoscopic images and videos: A review of the state of the art. Comput Biol Med 2024; 169:107929. [PMID: 38184862 DOI: 10.1016/j.compbiomed.2024.107929] [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: 08/23/2023] [Revised: 12/02/2023] [Accepted: 01/01/2024] [Indexed: 01/09/2024]
Abstract
In the field of computer- and robot-assisted minimally invasive surgery, enormous progress has been made in recent years based on the recognition of surgical instruments in endoscopic images and videos. In particular, the determination of the position and type of instruments is of great interest. Current work involves both spatial and temporal information, with the idea that predicting the movement of surgical tools over time may improve the quality of the final segmentations. The provision of publicly available datasets has recently encouraged the development of new methods, mainly based on deep learning. In this review, we identify and characterize datasets used for method development and evaluation and quantify their frequency of use in the literature. We further present an overview of the current state of research regarding the segmentation and tracking of minimally invasive surgical instruments in endoscopic images and videos. The paper focuses on methods that work purely visually, without markers of any kind attached to the instruments, considering both single-frame semantic and instance segmentation approaches, as well as those that incorporate temporal information. The publications analyzed were identified through the platforms Google Scholar, Web of Science, and PubMed. The search terms used were "instrument segmentation", "instrument tracking", "surgical tool segmentation", and "surgical tool tracking", resulting in a total of 741 articles published between 01/2015 and 07/2023, of which 123 were included using systematic selection criteria. A discussion of the reviewed literature is provided, highlighting existing shortcomings and emphasizing the available potential for future developments.
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Affiliation(s)
- Tobias Rueckert
- Regensburg Medical Image Computing (ReMIC), Ostbayerische Technische Hochschule Regensburg (OTH Regensburg), Germany.
| | - Daniel Rueckert
- Artificial Intelligence in Healthcare and Medicine, Klinikum rechts der Isar, Technical University of Munich, Germany; Department of Computing, Imperial College London, UK
| | - Christoph Palm
- Regensburg Medical Image Computing (ReMIC), Ostbayerische Technische Hochschule Regensburg (OTH Regensburg), Germany; Regensburg Center of Health Sciences and Technology (RCHST), OTH Regensburg, Germany
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5
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Boal MWE, Anastasiou D, Tesfai F, Ghamrawi W, Mazomenos E, Curtis N, Collins JW, Sridhar A, Kelly J, Stoyanov D, Francis NK. Evaluation of objective tools and artificial intelligence in robotic surgery technical skills assessment: a systematic review. Br J Surg 2024; 111:znad331. [PMID: 37951600 PMCID: PMC10771126 DOI: 10.1093/bjs/znad331] [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: 07/11/2023] [Revised: 09/18/2023] [Accepted: 09/19/2023] [Indexed: 11/14/2023]
Abstract
BACKGROUND There is a need to standardize training in robotic surgery, including objective assessment for accreditation. This systematic review aimed to identify objective tools for technical skills assessment, providing evaluation statuses to guide research and inform implementation into training curricula. METHODS A systematic literature search was conducted in accordance with the PRISMA guidelines. Ovid Embase/Medline, PubMed and Web of Science were searched. Inclusion criterion: robotic surgery technical skills tools. Exclusion criteria: non-technical, laparoscopy or open skills only. Manual tools and automated performance metrics (APMs) were analysed using Messick's concept of validity and the Oxford Centre of Evidence-Based Medicine (OCEBM) Levels of Evidence and Recommendation (LoR). A bespoke tool analysed artificial intelligence (AI) studies. The Modified Downs-Black checklist was used to assess risk of bias. RESULTS Two hundred and forty-seven studies were analysed, identifying: 8 global rating scales, 26 procedure-/task-specific tools, 3 main error-based methods, 10 simulators, 28 studies analysing APMs and 53 AI studies. Global Evaluative Assessment of Robotic Skills and the da Vinci Skills Simulator were the most evaluated tools at LoR 1 (OCEBM). Three procedure-specific tools, 3 error-based methods and 1 non-simulator APMs reached LoR 2. AI models estimated outcomes (skill or clinical), demonstrating superior accuracy rates in the laboratory with 60 per cent of methods reporting accuracies over 90 per cent, compared to real surgery ranging from 67 to 100 per cent. CONCLUSIONS Manual and automated assessment tools for robotic surgery are not well validated and require further evaluation before use in accreditation processes.PROSPERO: registration ID CRD42022304901.
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Affiliation(s)
- Matthew W E Boal
- The Griffin Institute, Northwick Park & St Marks’ Hospital, London, UK
- Wellcome/ESPRC Centre for Interventional Surgical Sciences (WEISS), University College London (UCL), London, UK
- Division of Surgery and Interventional Science, Research Department of Targeted Intervention, UCL, London, UK
| | - Dimitrios Anastasiou
- Wellcome/ESPRC Centre for Interventional Surgical Sciences (WEISS), University College London (UCL), London, UK
- Medical Physics and Biomedical Engineering, UCL, London, UK
| | - Freweini Tesfai
- The Griffin Institute, Northwick Park & St Marks’ Hospital, London, UK
- Wellcome/ESPRC Centre for Interventional Surgical Sciences (WEISS), University College London (UCL), London, UK
| | - Walaa Ghamrawi
- The Griffin Institute, Northwick Park & St Marks’ Hospital, London, UK
| | - Evangelos Mazomenos
- Wellcome/ESPRC Centre for Interventional Surgical Sciences (WEISS), University College London (UCL), London, UK
- Medical Physics and Biomedical Engineering, UCL, London, UK
| | - Nathan Curtis
- Department of General Surgey, Dorset County Hospital NHS Foundation Trust, Dorchester, UK
| | - Justin W Collins
- Division of Surgery and Interventional Science, Research Department of Targeted Intervention, UCL, London, UK
- University College London Hospitals NHS Foundation Trust, London, UK
| | - Ashwin Sridhar
- Division of Surgery and Interventional Science, Research Department of Targeted Intervention, UCL, London, UK
- University College London Hospitals NHS Foundation Trust, London, UK
| | - John Kelly
- Division of Surgery and Interventional Science, Research Department of Targeted Intervention, UCL, London, UK
- University College London Hospitals NHS Foundation Trust, London, UK
| | - Danail Stoyanov
- Wellcome/ESPRC Centre for Interventional Surgical Sciences (WEISS), University College London (UCL), London, UK
- Computer Science, UCL, London, UK
| | - Nader K Francis
- The Griffin Institute, Northwick Park & St Marks’ Hospital, London, UK
- Division of Surgery and Interventional Science, Research Department of Targeted Intervention, UCL, London, UK
- Yeovil District Hospital, Somerset Foundation NHS Trust, Yeovil, Somerset, UK
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6
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Shafiei SB, Shadpour S, Mohler JL, Sasangohar F, Gutierrez C, Seilanian Toussi M, Shafqat A. Surgical skill level classification model development using EEG and eye-gaze data and machine learning algorithms. J Robot Surg 2023; 17:2963-2971. [PMID: 37864129 PMCID: PMC10678814 DOI: 10.1007/s11701-023-01722-8] [Citation(s) in RCA: 1] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 05/19/2023] [Accepted: 08/19/2023] [Indexed: 10/22/2023]
Abstract
The aim of this study was to develop machine learning classification models using electroencephalogram (EEG) and eye-gaze features to predict the level of surgical expertise in robot-assisted surgery (RAS). EEG and eye-gaze data were recorded from 11 participants who performed cystectomy, hysterectomy, and nephrectomy using the da Vinci robot. Skill level was evaluated by an expert RAS surgeon using the modified Global Evaluative Assessment of Robotic Skills (GEARS) tool, and data from three subtasks were extracted to classify skill levels using three classification models-multinomial logistic regression (MLR), random forest (RF), and gradient boosting (GB). The GB algorithm was used with a combination of EEG and eye-gaze data to classify skill levels, and differences between the models were tested using two-sample t tests. The GB model using EEG features showed the best performance for blunt dissection (83% accuracy), retraction (85% accuracy), and burn dissection (81% accuracy). The combination of EEG and eye-gaze features using the GB algorithm improved the accuracy of skill level classification to 88% for blunt dissection, 93% for retraction, and 86% for burn dissection. The implementation of objective skill classification models in clinical settings may enhance the RAS surgical training process by providing objective feedback about performance to surgeons and their teachers.
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Affiliation(s)
- Somayeh B Shafiei
- Intelligent Cancer Care Laboratory, Department of Urology, Roswell Park Comprehensive Cancer Center, Buffalo, NY, 14263, USA.
| | - Saeed Shadpour
- Department of Animal Biosciences, University of Guelph, Guelph, ON, N1G 2W1, Canada
| | - James L Mohler
- Department of Urology, Roswell Park Comprehensive Cancer Center, Buffalo, NY, 14263, USA
| | - Farzan Sasangohar
- Mike and Sugar Barnes Faculty Fellow II, Wm Michael Barnes and Department of Industrial and Systems Engineering at Texas A&M University, College Station, TX, 77843, USA
| | - Camille Gutierrez
- Obstetrics and Gynecology Residency Program, Sisters of Charity Health System, Buffalo, NY, 14214, USA
| | - Mehdi Seilanian Toussi
- Intelligent Cancer Care Laboratory, Department of Urology, Roswell Park Comprehensive Cancer Center, Buffalo, NY, 14263, USA
| | - Ambreen Shafqat
- Intelligent Cancer Care Laboratory, Department of Urology, Roswell Park Comprehensive Cancer Center, Buffalo, NY, 14263, USA
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Dragosloveanu S, Petre MA, Capitanu BS, Dragosloveanu CDM, Cergan R, Scheau C. Initial Learning Curve for Robot-Assisted Total Knee Arthroplasty in a Dedicated Orthopedics Center. J Clin Med 2023; 12:6950. [PMID: 37959414 PMCID: PMC10649181 DOI: 10.3390/jcm12216950] [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: 10/16/2023] [Revised: 10/30/2023] [Accepted: 11/04/2023] [Indexed: 11/15/2023] Open
Abstract
Background and objectives: Our study aimed to assess the learning curve for robot-assisted (RA) total knee arthroplasty (TKA) in our hospital, compare operative times between RA-TKAs and manual TKAs, and assess the early complications rate between the two approaches. Methods: We included 39 patients who underwent RA-TKA and 45 control patients subjected to manual TKA in the same period and operated on by the same surgical staff. We collected demographic and patient-related data to assess potential differences between the two groups. Results: No statistical differences were recorded in regard to age, BMI, sex, Kellgren-Lawrence classification, or limb alignment between patients undergoing RA-TKA and manual TKA, respectively. Three surgeons transitioned from the learning to the proficiency phase in our study after a number of 6, 4, and 3 cases, respectively. The overall operative time for the learning phase was 111.54 ± 20.45 min, significantly longer compared to the average of 86.43 ± 19.09 min in the proficiency phase (p = 0.0154) and 80.56 ± 17.03 min for manual TKAs (p < 0.0001). No statistically significant difference was recorded between the global operative time for the proficiency phase TKAs versus the controls. No major complications were recorded in either RA-TKA or manual TKA groups. Conclusions: Our results suggest that experienced surgeons may adopt RA-TKA using this platform and quickly adapt without significant complications.
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Affiliation(s)
- Serban Dragosloveanu
- The “Carol Davila” University of Medicine and Pharmacy, 050474 Bucharest, Romania
- Department of Orthopaedics, “Foisor” Clinical Hospital of Orthopaedics, Traumatology and Osteoarticular TB, 021382 Bucharest, Romania
| | - Mihnea-Alexandru Petre
- Department of Orthopaedics, “Foisor” Clinical Hospital of Orthopaedics, Traumatology and Osteoarticular TB, 021382 Bucharest, Romania
| | - Bogdan Sorin Capitanu
- Department of Orthopaedics, “Foisor” Clinical Hospital of Orthopaedics, Traumatology and Osteoarticular TB, 021382 Bucharest, Romania
| | - Christiana Diana Maria Dragosloveanu
- The “Carol Davila” University of Medicine and Pharmacy, 050474 Bucharest, Romania
- Department of Ophthalmology, Clinical Hospital for Ophthalmological Emergencies, 010464 Bucharest, Romania
| | - Romica Cergan
- The “Carol Davila” University of Medicine and Pharmacy, 050474 Bucharest, Romania
- Department of Radiology and Medical Imaging, “Foisor” Clinical Hospital of Orthopaedics, Traumatology and Osteoarticular TB, 021382 Bucharest, Romania
| | - Cristian Scheau
- The “Carol Davila” University of Medicine and Pharmacy, 050474 Bucharest, Romania
- Department of Radiology and Medical Imaging, “Foisor” Clinical Hospital of Orthopaedics, Traumatology and Osteoarticular TB, 021382 Bucharest, Romania
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Park B, Chi H, Park B, Lee J, Jin HS, Park S, Hyung WJ, Choi MK. Visual modalities-based multimodal fusion for surgical phase recognition. Comput Biol Med 2023; 166:107453. [PMID: 37774560 DOI: 10.1016/j.compbiomed.2023.107453] [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: 04/14/2023] [Revised: 08/17/2023] [Accepted: 09/04/2023] [Indexed: 10/01/2023]
Abstract
Surgical workflow analysis is essential to help optimize surgery by encouraging efficient communication and the use of resources. However, the performance of phase recognition is limited by the use of information related to the presence of surgical instruments. To address the problem, we propose visual modality-based multimodal fusion for surgical phase recognition to overcome the limited diversity of information such as the presence of instruments. Using the proposed methods, we extracted a visual kinematics-based index related to using instruments, such as movement and their interrelations during surgery. In addition, we improved recognition performance using an effective convolutional neural network (CNN)-based fusion method for visual features and a visual kinematics-based index (VKI). The visual kinematics-based index improves the understanding of a surgical procedure since information is related to instrument interaction. Furthermore, these indices can be extracted in any environment, such as laparoscopic surgery, and help obtain complementary information for system kinematics log errors. The proposed methodology was applied to two multimodal datasets, a virtual reality (VR) simulator-based dataset (PETRAW) and a private distal gastrectomy surgery dataset, to verify that it can help improve recognition performance in clinical environments. We also explored the influence of a visual kinematics-based index to recognize each surgical workflow by the instrument's existence and the instrument's trajectory. Through the experimental results of a distal gastrectomy video dataset, we validated the effectiveness of our proposed fusion approach in surgical phase recognition. The relatively simple yet index-incorporated fusion we propose can yield significant performance improvements over only CNN-based training and exhibits effective training results compared to fusion based on Transformers, which require a large amount of pre-trained data.
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Affiliation(s)
- Bogyu Park
- AI Dev. Group, Hutom, Dokmak-ro 279, Mapo-gu, 04151, Seoul, Republic of Korea.
| | - Hyeongyu Chi
- AI Dev. Group, Hutom, Dokmak-ro 279, Mapo-gu, 04151, Seoul, Republic of Korea.
| | - Bokyung Park
- AI Dev. Group, Hutom, Dokmak-ro 279, Mapo-gu, 04151, Seoul, Republic of Korea.
| | - Jiwon Lee
- AI Dev. Group, Hutom, Dokmak-ro 279, Mapo-gu, 04151, Seoul, Republic of Korea.
| | - Hye Su Jin
- AI Dev. Group, Hutom, Dokmak-ro 279, Mapo-gu, 04151, Seoul, Republic of Korea.
| | - Sunghyun Park
- Yonsei University College of Medicine, Yonsei-ro 50, Seodaemun-gu, 03722, Seoul, Republic of Korea.
| | - Woo Jin Hyung
- AI Dev. Group, Hutom, Dokmak-ro 279, Mapo-gu, 04151, Seoul, Republic of Korea; Yonsei University College of Medicine, Yonsei-ro 50, Seodaemun-gu, 03722, Seoul, Republic of Korea.
| | - Min-Kook Choi
- AI Dev. Group, Hutom, Dokmak-ro 279, Mapo-gu, 04151, Seoul, Republic of Korea.
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9
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Wang Y, Wu Z, Dai J, Morgan TN, Garbens A, Kominsky H, Gahan J, Larson EC. Evaluating robotic-assisted partial nephrectomy surgeons with fully convolutional segmentation and multi-task attention networks. J Robot Surg 2023; 17:2323-2330. [PMID: 37368225 PMCID: PMC10492672 DOI: 10.1007/s11701-023-01657-0] [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: 05/17/2023] [Accepted: 06/17/2023] [Indexed: 06/28/2023]
Abstract
We use machine learning to evaluate surgical skill from videos during the tumor resection and renography steps of a robotic assisted partial nephrectomy (RAPN). This expands previous work using synthetic tissue to include actual surgeries. We investigate cascaded neural networks for predicting surgical proficiency scores (OSATS and GEARS) from RAPN videos recorded from the DaVinci system. The semantic segmentation task generates a mask and tracks the various surgical instruments. The movements from the instruments found via semantic segmentation are processed by a scoring network that regresses (predicts) GEARS and OSATS scoring for each subcategory. Overall, the model performs well for many subcategories such as force sensitivity and knowledge of instruments of GEARS and OSATS scoring, but can suffer from false positives and negatives that would not be expected of human raters. This is mainly attributed to limited training data variability and sparsity.
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Affiliation(s)
- Yihao Wang
- Department of Computer Science, Southern Methodist University, Dallas, USA
| | - Zhongjie Wu
- Department of Computer Science, Southern Methodist University, Dallas, USA
| | - Jessica Dai
- Department of Urology, University of Texas Southwestern Medical Center, Dallas, USA
| | - Tara N. Morgan
- Department of Urology, University of Texas Southwestern Medical Center, Dallas, USA
| | - Alaina Garbens
- Department of Urology, University of Texas Southwestern Medical Center, Dallas, USA
| | - Hal Kominsky
- Department of Urology, University of Texas Southwestern Medical Center, Dallas, USA
| | - Jeffrey Gahan
- Department of Urology, University of Texas Southwestern Medical Center, Dallas, USA
| | - Eric C. Larson
- Department of Computer Science, Southern Methodist University, Dallas, USA
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10
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Pedrett R, Mascagni P, Beldi G, Padoy N, Lavanchy JL. Technical skill assessment in minimally invasive surgery using artificial intelligence: a systematic review. Surg Endosc 2023; 37:7412-7424. [PMID: 37584774 PMCID: PMC10520175 DOI: 10.1007/s00464-023-10335-z] [Citation(s) in RCA: 4] [Impact Index Per Article: 4.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 05/15/2023] [Accepted: 07/20/2023] [Indexed: 08/17/2023]
Abstract
BACKGROUND Technical skill assessment in surgery relies on expert opinion. Therefore, it is time-consuming, costly, and often lacks objectivity. Analysis of intraoperative data by artificial intelligence (AI) has the potential for automated technical skill assessment. The aim of this systematic review was to analyze the performance, external validity, and generalizability of AI models for technical skill assessment in minimally invasive surgery. METHODS A systematic search of Medline, Embase, Web of Science, and IEEE Xplore was performed to identify original articles reporting the use of AI in the assessment of technical skill in minimally invasive surgery. Risk of bias (RoB) and quality of the included studies were analyzed according to Quality Assessment of Diagnostic Accuracy Studies criteria and the modified Joanna Briggs Institute checklists, respectively. Findings were reported according to the Preferred Reporting Items for Systematic Reviews and Meta-Analyses statement. RESULTS In total, 1958 articles were identified, 50 articles met eligibility criteria and were analyzed. Motion data extracted from surgical videos (n = 25) or kinematic data from robotic systems or sensors (n = 22) were the most frequent input data for AI. Most studies used deep learning (n = 34) and predicted technical skills using an ordinal assessment scale (n = 36) with good accuracies in simulated settings. However, all proposed models were in development stage, only 4 studies were externally validated and 8 showed a low RoB. CONCLUSION AI showed good performance in technical skill assessment in minimally invasive surgery. However, models often lacked external validity and generalizability. Therefore, models should be benchmarked using predefined performance metrics and tested in clinical implementation studies.
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Affiliation(s)
- Romina Pedrett
- Department of Visceral Surgery and Medicine, Inselspital, Bern University Hospital, University of Bern, Bern, Switzerland
| | - Pietro Mascagni
- IHU Strasbourg, Strasbourg, France
- Fondazione Policlinico Universitario A. Gemelli IRCCS, Rome, Italy
| | - Guido Beldi
- Department of Visceral Surgery and Medicine, Inselspital, Bern University Hospital, University of Bern, Bern, Switzerland
| | - Nicolas Padoy
- IHU Strasbourg, Strasbourg, France
- ICube, CNRS, University of Strasbourg, Strasbourg, France
| | - Joël L Lavanchy
- Department of Visceral Surgery and Medicine, Inselspital, Bern University Hospital, University of Bern, Bern, Switzerland.
- IHU Strasbourg, Strasbourg, France.
- University Digestive Health Care Center Basel - Clarunis, PO Box, 4002, Basel, Switzerland.
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11
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Igaki T, Kitaguchi D, Matsuzaki H, Nakajima K, Kojima S, Hasegawa H, Takeshita N, Kinugasa Y, Ito M. Automatic Surgical Skill Assessment System Based on Concordance of Standardized Surgical Field Development Using Artificial Intelligence. JAMA Surg 2023; 158:e231131. [PMID: 37285142 PMCID: PMC10248810 DOI: 10.1001/jamasurg.2023.1131] [Citation(s) in RCA: 9] [Impact Index Per Article: 9.0] [Reference Citation Analysis] [Abstract] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 09/15/2022] [Accepted: 01/28/2023] [Indexed: 06/08/2023]
Abstract
Importance Automatic surgical skill assessment with artificial intelligence (AI) is more objective than manual video review-based skill assessment and can reduce human burden. Standardization of surgical field development is an important aspect of this skill assessment. Objective To develop a deep learning model that can recognize the standardized surgical fields in laparoscopic sigmoid colon resection and to evaluate the feasibility of automatic surgical skill assessment based on the concordance of the standardized surgical field development using the proposed deep learning model. Design, Setting, and Participants This retrospective diagnostic study used intraoperative videos of laparoscopic colorectal surgery submitted to the Japan Society for Endoscopic Surgery between August 2016 and November 2017. Data were analyzed from April 2020 to September 2022. Interventions Videos of surgery performed by expert surgeons with Endoscopic Surgical Skill Qualification System (ESSQS) scores higher than 75 were used to construct a deep learning model able to recognize a standardized surgical field and output its similarity to standardized surgical field development as an AI confidence score (AICS). Other videos were extracted as the validation set. Main Outcomes and Measures Videos with scores less than or greater than 2 SDs from the mean were defined as the low- and high-score groups, respectively. The correlation between AICS and ESSQS score and the screening performance using AICS for low- and high-score groups were analyzed. Results The sample included 650 intraoperative videos, 60 of which were used for model construction and 60 for validation. The Spearman rank correlation coefficient between the AICS and ESSQS score was 0.81. The receiver operating characteristic (ROC) curves for the screening of the low- and high-score groups were plotted, and the areas under the ROC curve for the low- and high-score group screening were 0.93 and 0.94, respectively. Conclusions and Relevance The AICS from the developed model strongly correlated with the ESSQS score, demonstrating the model's feasibility for use as a method of automatic surgical skill assessment. The findings also suggest the feasibility of the proposed model for creating an automated screening system for surgical skills and its potential application to other types of endoscopic procedures.
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Affiliation(s)
- Takahiro Igaki
- Surgical Device Innovation Office, National Cancer Center Hospital East, Kashiwanoha, Kashiwa, Chiba, Japan
- Department of Colorectal Surgery, National Cancer Center Hospital East, Kashiwanoha, Kashiwa, Chiba, Japan
- Department of Gastrointestinal Surgery, Tokyo Medical and Dental University Graduate School of Medicine, Yushima, Bunkyo-Ku, Tokyo, Japan
| | - Daichi Kitaguchi
- Surgical Device Innovation Office, National Cancer Center Hospital East, Kashiwanoha, Kashiwa, Chiba, Japan
- Department of Colorectal Surgery, National Cancer Center Hospital East, Kashiwanoha, Kashiwa, Chiba, Japan
| | - Hiroki Matsuzaki
- Surgical Device Innovation Office, National Cancer Center Hospital East, Kashiwanoha, Kashiwa, Chiba, Japan
| | - Kei Nakajima
- Surgical Device Innovation Office, National Cancer Center Hospital East, Kashiwanoha, Kashiwa, Chiba, Japan
- Department of Colorectal Surgery, National Cancer Center Hospital East, Kashiwanoha, Kashiwa, Chiba, Japan
| | - Shigehiro Kojima
- Surgical Device Innovation Office, National Cancer Center Hospital East, Kashiwanoha, Kashiwa, Chiba, Japan
- Department of Colorectal Surgery, National Cancer Center Hospital East, Kashiwanoha, Kashiwa, Chiba, Japan
| | - Hiro Hasegawa
- Surgical Device Innovation Office, National Cancer Center Hospital East, Kashiwanoha, Kashiwa, Chiba, Japan
- Department of Colorectal Surgery, National Cancer Center Hospital East, Kashiwanoha, Kashiwa, Chiba, Japan
| | - Nobuyoshi Takeshita
- Surgical Device Innovation Office, National Cancer Center Hospital East, Kashiwanoha, Kashiwa, Chiba, Japan
- Department of Colorectal Surgery, National Cancer Center Hospital East, Kashiwanoha, Kashiwa, Chiba, Japan
| | - Yusuke Kinugasa
- Department of Gastrointestinal Surgery, Tokyo Medical and Dental University Graduate School of Medicine, Yushima, Bunkyo-Ku, Tokyo, Japan
| | - Masaaki Ito
- Surgical Device Innovation Office, National Cancer Center Hospital East, Kashiwanoha, Kashiwa, Chiba, Japan
- Department of Colorectal Surgery, National Cancer Center Hospital East, Kashiwanoha, Kashiwa, Chiba, Japan
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12
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Sheth NN, Srinivasan N, Patel S, Luciano CJ. Preliminary Evaluation of a Novel Neural Network-Based Hybrid Simulator for Surgical Training and Performance Assessment of Neonatal Thoracentesis. Simul Healthc 2023; 18:272-278. [PMID: 36111997 DOI: 10.1097/sih.0000000000000685] [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: 11/26/2022]
Abstract
INTRODUCTION Tension pneumothorax is a rare and life-threatening situation in neonates requiring immediate intervention through thoracentesis. Significant complications can arise while performing thoracentesis in the case of inadequate skill level or exposure to the condition. Although simulation-based training (SBT) has proven to be effective in learning surgical skills, training sessions are long, subjective, and expensive, because of which they cannot be held regularly. This article attempts to improve traditional SBT for neonatal thoracentesis through an autonomous simulator that can provide real-time objective feedback during surgical training and assessment. METHODS The simulator incorporates a custom manikin and virtual reality software interfaced through electromagnetic sensors that track the motion of surgical instruments. The software application reads and stores instrument motion information to replicate physical actions in the virtual environment, play back previously stored surgical performances and analyze data through a pretrained neural network. The simulator encapsulates the experience of SBT by allowing trainees to watch and replicate an ideal method of conducting the procedure, providing simplified, real-time autonomous guidance during practice and an objective taskwise assessment of the performance during testing. RESULTS The preliminary trial held at the University of Illinois Hospital in the presence of 1 neonatologist and 4 fellows revealed that all the participants used the autonomous guidance more than once, and all found simulation experience to be accurate and overall effective in learning thoracentesis. CONCLUSION Although the sample size is small, the simulator shows potential in being a viable alternative approach for training and assessment for thoracentesis.
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Affiliation(s)
- Nihar N Sheth
- From the Department of Biomedical Engineering (N.N.S., C.J.L.), University of Illinois at Chicago, Chicago, IL; and Department of Pediatrics (N.S., S.P.) at University of Illinois Hospital, Chicago, IL
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13
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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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14
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Pan M, Wang S, Li J, Li J, Yang X, Liang K. An Automated Skill Assessment Framework Based on Visual Motion Signals and a Deep Neural Network in Robot-Assisted Minimally Invasive Surgery. SENSORS (BASEL, SWITZERLAND) 2023; 23:s23094496. [PMID: 37177699 PMCID: PMC10181496 DOI: 10.3390/s23094496] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Subscribe] [Scholar Register] [Received: 03/06/2023] [Revised: 04/27/2023] [Accepted: 05/03/2023] [Indexed: 05/15/2023]
Abstract
Surgical skill assessment can quantify the quality of the surgical operation via the motion state of the surgical instrument tip (SIT), which is considered one of the effective primary means by which to improve the accuracy of surgical operation. Traditional methods have displayed promising results in skill assessment. However, this success is predicated on the SIT sensors, making these approaches impractical when employing the minimally invasive surgical robot with such a tiny end size. To address the assessment issue regarding the operation quality of robot-assisted minimally invasive surgery (RAMIS), this paper proposes a new automatic framework for assessing surgical skills based on visual motion tracking and deep learning. The new method innovatively combines vision and kinematics. The kernel correlation filter (KCF) is introduced in order to obtain the key motion signals of the SIT and classify them by using the residual neural network (ResNet), realizing automated skill assessment in RAMIS. To verify its effectiveness and accuracy, the proposed method is applied to the public minimally invasive surgical robot dataset, the JIGSAWS. The results show that the method based on visual motion tracking technology and a deep neural network model can effectively and accurately assess the skill of robot-assisted surgery in near real-time. In a fairly short computational processing time of 3 to 5 s, the average accuracy of the assessment method is 92.04% and 84.80% in distinguishing two and three skill levels. This study makes an important contribution to the safe and high-quality development of RAMIS.
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Affiliation(s)
- Mingzhang Pan
- College of Mechanical Engineering, Guangxi University, Nanning 530004, China
- State Key Laboratory for Conservation and Utilization of Subtropical Agro-Bioresources, Nanning 530004, China
| | - Shuo Wang
- College of Mechanical Engineering, Guangxi University, Nanning 530004, China
| | - Jingao Li
- College of Mechanical Engineering, Guangxi University, Nanning 530004, China
| | - Jing Li
- College of Mechanical Engineering, Guangxi University, Nanning 530004, China
| | - Xiuze Yang
- College of Mechanical Engineering, Guangxi University, Nanning 530004, China
| | - Ke Liang
- College of Mechanical Engineering, Guangxi University, Nanning 530004, China
- Guangxi Key Laboratory of Manufacturing System & Advanced Manufacturing Technology, School of Mechanical Engineering, Guangxi University, Nanning 530004, China
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15
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Yeh HH, Jain AM, Fox O, Sebov K, Wang SY. PhacoTrainer: Deep Learning for Cataract Surgical Videos to Track Surgical Tools. Transl Vis Sci Technol 2023; 12:23. [PMID: 36947046 PMCID: PMC10050900 DOI: 10.1167/tvst.12.3.23] [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: 08/21/2022] [Accepted: 02/13/2023] [Indexed: 03/23/2023] Open
Abstract
Purpose The purpose of this study was to build a deep-learning model that automatically analyzes cataract surgical videos for the locations of surgical landmarks, and to derive skill-related motion metrics. Methods The locations of the pupil, limbus, and 8 classes of surgical instruments were identified by a 2-step algorithm: (1) mask segmentation and (2) landmark identification from the masks. To perform mask segmentation, we trained the YOLACT model on 1156 frames sampled from 268 videos and the public Cataract Dataset for Image Segmentation (CaDIS) dataset. Landmark identification was performed by fitting ellipses or lines to the contours of the masks and deriving locations of interest, including surgical tooltips and the pupil center. Landmark identification was evaluated by the distance between the predicted and true positions in 5853 frames of 10 phacoemulsification video clips. We derived the total path length, maximal speed, and covered area using the tip positions and examined the correlation with human-rated surgical performance. Results The mean average precision score and intersection-over-union for mask detection were 0.78 and 0.82. The average distance between the predicted and true positions of the pupil center, phaco tip, and second instrument tip was 5.8, 9.1, and 17.1 pixels. The total path length and covered areas of these landmarks were negatively correlated with surgical performance. Conclusions We developed a deep-learning method to localize key anatomical portions of the eye and cataract surgical tools, which can be used to automatically derive metrics correlated with surgical skill. Translational Relevance Our system could form the basis of an automated feedback system that helps cataract surgeons evaluate their performance.
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Affiliation(s)
- Hsu-Hang Yeh
- Department of Biomedical Data Science, Stanford University, Palo Alto, CA, USA
| | - Anjal M. Jain
- Department of Ophthalmology, Byers Eye Institute, Stanford University, Palo Alto, CA, USA
| | - Olivia Fox
- Krieger School of Arts and Sciences, Johns Hopkins University, Baltimore, MD, USA
| | - Kostya Sebov
- Department of Biomedical Data Science, Stanford University, Palo Alto, CA, USA
| | - Sophia Y. Wang
- Department of Biomedical Data Science, Stanford University, Palo Alto, CA, USA
- Department of Ophthalmology, Byers Eye Institute, Stanford University, Palo Alto, CA, USA
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16
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Automated Capture of Intraoperative Adverse Events Using Artificial Intelligence: A Systematic Review and Meta-Analysis. J Clin Med 2023; 12:jcm12041687. [PMID: 36836223 PMCID: PMC9963108 DOI: 10.3390/jcm12041687] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 01/13/2023] [Revised: 02/08/2023] [Accepted: 02/14/2023] [Indexed: 02/22/2023] Open
Abstract
Intraoperative adverse events (iAEs) impact the outcomes of surgery, and yet are not routinely collected, graded, and reported. Advancements in artificial intelligence (AI) have the potential to power real-time, automatic detection of these events and disrupt the landscape of surgical safety through the prediction and mitigation of iAEs. We sought to understand the current implementation of AI in this space. A literature review was performed to PRISMA-DTA standards. Included articles were from all surgical specialties and reported the automatic identification of iAEs in real-time. Details on surgical specialty, adverse events, technology used for detecting iAEs, AI algorithm/validation, and reference standards/conventional parameters were extracted. A meta-analysis of algorithms with available data was conducted using a hierarchical summary receiver operating characteristic curve (ROC). The QUADAS-2 tool was used to assess the article risk of bias and clinical applicability. A total of 2982 studies were identified by searching PubMed, Scopus, Web of Science, and IEEE Xplore, with 13 articles included for data extraction. The AI algorithms detected bleeding (n = 7), vessel injury (n = 1), perfusion deficiencies (n = 1), thermal damage (n = 1), and EMG abnormalities (n = 1), among other iAEs. Nine of the thirteen articles described at least one validation method for the detection system; five explained using cross-validation and seven divided the dataset into training and validation cohorts. Meta-analysis showed the algorithms were both sensitive and specific across included iAEs (detection OR 14.74, CI 4.7-46.2). There was heterogeneity in reported outcome statistics and article bias risk. There is a need for standardization of iAE definitions, detection, and reporting to enhance surgical care for all patients. The heterogeneous applications of AI in the literature highlights the pluripotent nature of this technology. Applications of these algorithms across a breadth of urologic procedures should be investigated to assess the generalizability of these data.
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Katta MR, Kalluru PKR, Bavishi DA, Hameed M, Valisekka SS. Artificial intelligence in pancreatic cancer: diagnosis, limitations, and the future prospects-a narrative review. J Cancer Res Clin Oncol 2023:10.1007/s00432-023-04625-1. [PMID: 36739356 DOI: 10.1007/s00432-023-04625-1] [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: 11/17/2022] [Accepted: 01/27/2023] [Indexed: 02/06/2023]
Abstract
PURPOSE This review aims to explore the role of AI in the application of pancreatic cancer management and make recommendations to minimize the impact of the limitations to provide further benefits from AI use in the future. METHODS A comprehensive review of the literature was conducted using a combination of MeSH keywords, including "Artificial intelligence", "Pancreatic cancer", "Diagnosis", and "Limitations". RESULTS The beneficial implications of AI in the detection of biomarkers, diagnosis, and prognosis of pancreatic cancer have been explored. In addition, current drawbacks of AI use have been divided into subcategories encompassing statistical, training, and knowledge limitations; data handling, ethical and medicolegal aspects; and clinical integration and implementation. CONCLUSION Artificial intelligence (AI) refers to computational machine systems that accomplish a set of given tasks by imitating human intelligence in an exponential learning pattern. AI in gastrointestinal oncology has continued to provide significant advancements in the clinical, molecular, and radiological diagnosis and intervention techniques required to improve the prognosis of many gastrointestinal cancer types, particularly pancreatic cancer.
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Affiliation(s)
| | | | | | - Maha Hameed
- Clinical Research Department, King Faisal Specialist Hospital and Research Centre, Riyadh, Saudi Arabia.
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Kulkarni CS, Deng S, Wang T, Hartman-Kenzler J, Barnes LE, Parker SH, Safford SD, Lau N. Scene-dependent, feedforward eye gaze metrics can differentiate technical skill levels of trainees in laparoscopic surgery. Surg Endosc 2023; 37:1569-1580. [PMID: 36123548 PMCID: PMC11062149 DOI: 10.1007/s00464-022-09582-3] [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: 03/07/2022] [Accepted: 08/25/2022] [Indexed: 10/14/2022]
Abstract
INTRODUCTION In laparoscopic surgery, looking in the target areas is an indicator of proficiency. However, gaze behaviors revealing feedforward control (i.e., looking ahead) and their importance have been under-investigated in surgery. This study aims to establish the sensitivity and relative importance of different scene-dependent gaze and motion metrics for estimating trainee proficiency levels in surgical skills. METHODS Medical students performed the Fundamentals of Laparoscopic Surgery peg transfer task while recording their gaze on the monitor and tool activities inside the trainer box. Using computer vision and fixation algorithms, five scene-dependent gaze metrics and one tool speed metric were computed for 499 practice trials. Cluster analysis on the six metrics was used to group the trials into different clusters/proficiency levels, and ANOVAs were conducted to test differences between proficiency levels. A Random Forest model was trained to study metric importance at predicting proficiency levels. RESULTS Three clusters were identified, corresponding to three proficiency levels. The correspondence between the clusters and proficiency levels was confirmed by differences between completion times (F2,488 = 38.94, p < .001). Further, ANOVAs revealed significant differences between the three levels for all six metrics. The Random Forest model predicted proficiency level with 99% out-of-bag accuracy and revealed that scene-dependent gaze metrics reflecting feedforward behaviors were more important for prediction than the ones reflecting feedback behaviors. CONCLUSION Scene-dependent gaze metrics revealed skill levels of trainees more precisely than between experts and novices as suggested in the literature. Further, feedforward gaze metrics appeared to be more important than feedback ones at predicting proficiency.
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Affiliation(s)
- Chaitanya S Kulkarni
- Grado Department of Industrial and Systems Engineering, Virginia Tech, 250 Durham Hall (0118), 1145 Perry Street, Blacksburg, VA, 24061, USA
| | - Shiyu Deng
- Grado Department of Industrial and Systems Engineering, Virginia Tech, 250 Durham Hall (0118), 1145 Perry Street, Blacksburg, VA, 24061, USA
| | - Tianzi Wang
- Grado Department of Industrial and Systems Engineering, Virginia Tech, 250 Durham Hall (0118), 1145 Perry Street, Blacksburg, VA, 24061, USA
| | | | - Laura E Barnes
- Environmental and Systems Engineering, University of Virginia, Charlottesville, VA, USA
| | | | - Shawn D Safford
- Division of Pediatric General and Thoracic Surgery, UPMC Children's Hospital of Pittsburgh, Harrisburg, PA, USA
| | - Nathan Lau
- Grado Department of Industrial and Systems Engineering, Virginia Tech, 250 Durham Hall (0118), 1145 Perry Street, Blacksburg, VA, 24061, USA.
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Using AI and computer vision to analyze technical proficiency in robotic surgery. Surg Endosc 2022; 37:3010-3017. [PMID: 36536082 DOI: 10.1007/s00464-022-09781-y] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 07/25/2022] [Accepted: 11/27/2022] [Indexed: 12/23/2022]
Abstract
BACKGROUND Intraoperative skills assessment is time-consuming and subjective; an efficient and objective computer vision-based approach for feedback is desired. In this work, we aim to design and validate an interpretable automated method to evaluate technical proficiency using colorectal robotic surgery videos with artificial intelligence. METHODS 92 curated clips of peritoneal closure were characterized by both board-certified surgeons and a computer vision AI algorithm to compare the measures of surgical skill. For human ratings, six surgeons graded clips according to the GEARS assessment tool; for AI assessment, deep learning computer vision algorithms for surgical tool detection and tracking were developed and implemented. RESULTS For the GEARS category of efficiency, we observe a positive correlation between human expert ratings of technical efficiency and AI-determined total tool movement (r = - 0.72). Additionally, we show that more proficient surgeons perform closure with significantly less tool movement compared to less proficient surgeons (p < 0.001). For the GEARS category of bimanual dexterity, a positive correlation between expert ratings of bimanual dexterity and the AI model's calculated measure of bimanual movement based on simultaneous tool movement (r = 0.48) was also observed. On average, we also find that higher skill clips have significantly more simultaneous movement in both hands compared to lower skill clips (p < 0.001). CONCLUSIONS In this study, measurements of technical proficiency extracted from AI algorithms are shown to correlate with those given by expert surgeons. Although we target measurements of efficiency and bimanual dexterity, this work suggests that artificial intelligence through computer vision holds promise for efficiently standardizing grading of surgical technique, which may help in surgical skills training.
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Kitaguchi D, Fujino T, Takeshita N, Hasegawa H, Mori K, Ito M. Limited generalizability of single deep neural network for surgical instrument segmentation in different surgical environments. Sci Rep 2022; 12:12575. [PMID: 35869249 PMCID: PMC9307578 DOI: 10.1038/s41598-022-16923-8] [Citation(s) in RCA: 2] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 09/09/2021] [Accepted: 07/18/2022] [Indexed: 12/05/2022] Open
Abstract
Clarifying the generalizability of deep-learning-based surgical-instrument segmentation networks in diverse surgical environments is important in recognizing the challenges of overfitting in surgical-device development. This study comprehensively evaluated deep neural network generalizability for surgical instrument segmentation using 5238 images randomly extracted from 128 intraoperative videos. The video dataset contained 112 laparoscopic colorectal resection, 5 laparoscopic distal gastrectomy, 5 laparoscopic cholecystectomy, and 6 laparoscopic partial hepatectomy cases. Deep-learning-based surgical-instrument segmentation was performed for test sets with (1) the same conditions as the training set; (2) the same recognition target surgical instrument and surgery type but different laparoscopic recording systems; (3) the same laparoscopic recording system and surgery type but slightly different recognition target laparoscopic surgical forceps; (4) the same laparoscopic recording system and recognition target surgical instrument but different surgery types. The mean average precision and mean intersection over union for test sets 1, 2, 3, and 4 were 0.941 and 0.887, 0.866 and 0.671, 0.772 and 0.676, and 0.588 and 0.395, respectively. Therefore, the recognition accuracy decreased even under slightly different conditions. The results of this study reveal the limited generalizability of deep neural networks in the field of surgical artificial intelligence and caution against deep-learning-based biased datasets and models. Trial Registration Number: 2020-315, date of registration: October 5, 2020.
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Gendia A. Cloud Based AI-Driven Video Analytics (CAVs) in Laparoscopic Surgery: A Step Closer to a Virtual Portfolio. Cureus 2022; 14:e29087. [PMID: 36259009 PMCID: PMC9559410 DOI: 10.7759/cureus.29087] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Accepted: 09/12/2022] [Indexed: 11/17/2022] Open
Abstract
Aims: To outline the use of cloud-based artificial intelligence (AI)-driven video analytics (CAVs) in minimally invasive surgery and to propose their potential as a virtual portfolio for trainee and established surgeons. Methods: An independent online demonstration was requested from three platforms, namely Theator (Palo Alto, California, USA), Touch Surgery™ (Medtronic, London, England, UK), and C-SATS® (Seattle, Washington, USA). The assessed domains were online and app-based accessibility, the ability for timely trainee feedback, and AI integration for operation-specific steps and critical views. Results: The CAVs enable users to record surgeries with the advantage of limitless video storage through clouding and smart integration into theatre settings. This can be used to view surgeries and review trainee videos through a medium of communication and sharing with the ability to provide feedback. Theator and C-SATS® provide their users with surgical skills scoring systems with customizable options that can be used to provide structured feedback to trainees. Additionally, AI plays an important role in all three platforms by providing time-based analysis of steps and highlighting critical milestones. Conclusion: Cloud-based AI-driven video analytics is an emerging new technology that enables users to store, analyze, and review videos. This technology has the potential to improve training, governance, and standardization procedures. Moreover, with the future adaptation of the technology, CAVs can be integrated into the trainees’ portfolios as part of their virtual curriculum. This can enable a structured assessment of a surgeon’s progression and degree of experience throughout their surgical career.
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Kutana S, Bitner DP, Addison P, Chung PJ, Talamini MA, Filicori F. Objective assessment of robotic surgical skills: review of literature and future directions. Surg Endosc 2022; 36:3698-3707. [PMID: 35229215 DOI: 10.1007/s00464-022-09134-9] [Citation(s) in RCA: 2] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 08/04/2021] [Accepted: 02/13/2022] [Indexed: 01/29/2023]
Abstract
BACKGROUND Evaluation of robotic surgical skill has become increasingly important as robotic approaches to common surgeries become more widely utilized. However, evaluation of these currently lacks standardization. In this paper, we aimed to review the literature on robotic surgical skill evaluation. METHODS A review of literature on robotic surgical skill evaluation was performed and representative literature presented over the past ten years. RESULTS The study of reliability and validity in robotic surgical evaluation shows two main assessment categories: manual and automatic. Manual assessments have been shown to be valid but typically are time consuming and costly. Automatic evaluation and simulation are similarly valid and simpler to implement. Initial reports on evaluation of skill using artificial intelligence platforms show validity. Few data on evaluation methods of surgical skill connect directly to patient outcomes. CONCLUSION As evaluation in surgery begins to incorporate robotic skills, a simultaneous shift from manual to automatic evaluation may occur given the ease of implementation of these technologies. Robotic platforms offer the unique benefit of providing more objective data streams including kinematic data which allows for precise instrument tracking in the operative field. Such data streams will likely incrementally be implemented in performance evaluations. Similarly, with advances in artificial intelligence, machine evaluation of human technical skill will likely form the next wave of surgical evaluation.
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Affiliation(s)
- Saratu Kutana
- Intraoperative Performance Analytics Laboratory (IPAL), Department of General Surgery, Northwell Health, Lenox Hill Hospital, 186 E. 76th Street, 1st Floor, New York, NY, 10021, USA
| | - Daniel P Bitner
- Intraoperative Performance Analytics Laboratory (IPAL), Department of General Surgery, Northwell Health, Lenox Hill Hospital, 186 E. 76th Street, 1st Floor, New York, NY, 10021, USA.
| | - Poppy Addison
- Intraoperative Performance Analytics Laboratory (IPAL), Department of General Surgery, Northwell Health, Lenox Hill Hospital, 186 E. 76th Street, 1st Floor, New York, NY, 10021, USA
| | - Paul J Chung
- Intraoperative Performance Analytics Laboratory (IPAL), Department of General Surgery, Northwell Health, Lenox Hill Hospital, 186 E. 76th Street, 1st Floor, New York, NY, 10021, USA.,Donald and Barbara Zucker School of Medicine at Hofstra/Northwell, Hempstead, NY, USA
| | - Mark A Talamini
- Donald and Barbara Zucker School of Medicine at Hofstra/Northwell, Hempstead, NY, USA
| | - Filippo Filicori
- Intraoperative Performance Analytics Laboratory (IPAL), Department of General Surgery, Northwell Health, Lenox Hill Hospital, 186 E. 76th Street, 1st Floor, New York, NY, 10021, USA.,Donald and Barbara Zucker School of Medicine at Hofstra/Northwell, Hempstead, NY, USA
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23
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Ahn JC, Qureshi TA, Singal AG, Li D, Yang JD. Deep learning in hepatocellular carcinoma: Current status and future perspectives. World J Hepatol 2021; 13:2039-2051. [PMID: 35070007 PMCID: PMC8727204 DOI: 10.4254/wjh.v13.i12.2039] [Citation(s) in RCA: 15] [Impact Index Per Article: 5.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 05/28/2021] [Revised: 07/19/2021] [Accepted: 11/15/2021] [Indexed: 02/06/2023] Open
Abstract
Hepatocellular carcinoma (HCC) is among the leading causes of cancer incidence and death. Despite decades of research and development of new treatment options, the overall outcomes of patients with HCC continue to remain poor. There are areas of unmet need in risk prediction, early diagnosis, accurate prognostication, and individualized treatments for patients with HCC. Recent years have seen an explosive growth in the application of artificial intelligence (AI) technology in medical research, with the field of HCC being no exception. Among the various AI-based machine learning algorithms, deep learning algorithms are considered state-of-the-art techniques for handling and processing complex multimodal data ranging from routine clinical variables to high-resolution medical images. This article will provide a comprehensive review of the recently published studies that have applied deep learning for risk prediction, diagnosis, prognostication, and treatment planning for patients with HCC.
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Affiliation(s)
- Joseph C Ahn
- Gastroenterology and Hepatology, Mayo Clinic, Rochester, MN 55904, United States
| | - Touseef Ahmad Qureshi
- Biomedical Imaging Research Institute, Cedars-Sinai Medical Center, Los Angeles, CA 90048, United States
| | - Amit G Singal
- Internal Medicine, University of Texas Southwestern Medical Center, Dallas, TX 75390, United States
| | - Debiao Li
- Biomedical Imaging Research Institute, Cedars-Sinai Medical Center, Los Angeles, CA 90048, United States
| | - Ju-Dong Yang
- Karsh Division of Gastroenterology and Hepatology, Cedars-Sinai Medical Center, Los Angeles, CA 90048, United States
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24
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Azari DP, Frasier LL, Miller BL, Pavuluri Quamme SR, Le BV, Greenberg CC, Radwin RG. Modeling Performance of Open Surgical Cases. Simul Healthc 2021; 16:e188-e193. [PMID: 34860738 DOI: 10.1097/sih.0000000000000544] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.3] [Reference Citation Analysis] [Abstract] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/27/2022]
Abstract
INTRODUCTION Previous efforts used digital video to develop computer-generated assessments of surgical hand motion economy and fluidity of motion. This study tests how well previously trained assessment models match expert ratings of suturing and tying video clips recorded in a new operating room (OR) setting. METHODS Enabled through computer vision of the hands, this study tests the applicability of assessments born out of benchtop simulations to in vivo suturing and tying tasks recorded in the OR. RESULTS Compared with expert ratings, computer-generated assessments for fluidity of motion (slope = 0.83, intercept = 1.77, R2 = 0.55) performed better than motion economy (slope = 0.73, intercept = 2.04, R2 = 0.49), although 85% of ratings for both models were within ±2 of the expert response. Neither assessment performed as well in the OR as they did on the training data. Assessments were sensitive to changing hand postures, dropped ligatures, and poor tissue contact-features typically missing from training data. Computer-generated assessment of OR tasks was contingent on a clear, consistent view of both surgeon's hands. CONCLUSIONS Computer-generated assessment may help provide formative feedback during deliberate practice, albeit with greater variability in the OR compared with benchtop simulations. Future work will benefit from expanded available bimanual video records.
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Affiliation(s)
- David P Azari
- From the Department of Industrial and Systems Engineering (D.P.A., R.G.R.); Department of Surgery (S.R.P.Q., C.C.G.), Clinical Sciences Center; Department of Urology (B.V.L.); and Duane H. and Dorothy M. Bluemke Professor in the College of Engineering (R.G.R.), University of Wisconsin-Madison, Madison, WI; Department of Surgery (L.L.F.), Penn Medicine - University of Pennsylvania Health System, Philadelphia, PA; City of Hope National Comprehensive Cancer Center (B.L.M), Duarte, CA
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25
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Wang Y, Dai J, Morgan TN, Elsaied M, Garbens A, Qu X, Steinberg R, Gahan J, Larson EC. Evaluating robotic-assisted surgery training videos with multi-task convolutional neural networks. J Robot Surg 2021; 16:917-925. [PMID: 34709538 DOI: 10.1007/s11701-021-01316-2] [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: 07/02/2021] [Accepted: 10/03/2021] [Indexed: 10/20/2022]
Abstract
We seek to understand if an automated algorithm can replace human scoring of surgical trainees performing the urethrovesical anastomosis in radical prostatectomy with synthetic tissue. Specifically, we investigate neural networks for predicting the surgical proficiency score (GEARS score) from video clips. We evaluate videos of surgeons performing the urethral anastomosis using synthetic tissue. The algorithm tracks surgical instrument locations from video, saving the positions of key points on the instruments over time. These positional features are used to train a multi-task convolutional network to infer each sub-category of the GEARS score to determine the proficiency level of trainees. Experimental results demonstrate that the proposed method achieves good performance with scores matching manual inspection in 86.1% of all GEARS sub-categories. Furthermore, the model can detect the difference between proficiency (novice to expert) in 83.3% of videos. Evaluation of GEARS sub-categories with artificial neural networks is possible for novice and intermediate surgeons, but additional research is needed to understand if expert surgeons can be evaluated with a similar automated system.
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Affiliation(s)
- Yihao Wang
- Department of Computer Science, Southern Methodist University, Dallas, USA
| | - Jessica Dai
- Department of Urology, University of Texas Southwestern Medical Center, Dallas, USA
| | - Tara N Morgan
- Department of Urology, University of Texas Southwestern Medical Center, Dallas, USA
| | - Mohamed Elsaied
- Department of Computer Science, Southern Methodist University, Dallas, USA
| | - Alaina Garbens
- Department of Urology, University of Texas Southwestern Medical Center, Dallas, USA
| | - Xingming Qu
- Department of Computer Science, Southern Methodist University, Dallas, USA
| | - Ryan Steinberg
- Department of Urology, University of Texas Southwestern Medical Center, Dallas, USA
| | - Jeffrey Gahan
- Department of Urology, University of Texas Southwestern Medical Center, Dallas, USA
| | - Eric C Larson
- Department of Computer Science, Southern Methodist University, Dallas, USA.
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26
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Yu HW, Lee D, Lee K, Kim SJ, Chai YJ, Kim HC, Choi JY, Lee KE. Effect of an anti-adhesion agent on vision-based assessment of cervical adhesions after thyroid surgery: randomized, placebo-controlled trial. Sci Rep 2021; 11:19935. [PMID: 34620907 PMCID: PMC8497539 DOI: 10.1038/s41598-021-97919-8] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 01/08/2021] [Accepted: 09/01/2021] [Indexed: 11/23/2022] Open
Abstract
Many patients experience cervical adhesions after thyroid surgery. To date, however, no studies have objectively measured the effects of anti-adhesion agents on cervical adhesion symptoms. This study evaluated the effects of an anti-adhesion agent on cervical adhesions after thyroid surgery, as determined using a system that measures the extent of marker movement objectively. One hundred patients were randomized in a 1:1 ratio to undergo thyroid surgery with or without the anti-adhesion agent Collabarrier. Using specially manufactured recording equipment, the position of the marker on neck skin was measured before surgery, and 2 weeks, 3 months, and 9 months after surgery. Relative change in marker distance, calculated by subtracting the marker position before surgery from the marker positions 2 weeks, 3 months, and 9 months after surgery, differed significantly in the groups of patients who underwent thyroid surgery with and without the anti-adhesion agent (P < 0.05). A novel measuring system can objectively evaluate the effectiveness of a thyroid anti-adhesion agent. The anti-adhesion agent used significantly reduced adhesions compared with the control group. The trial is registered at www.cris.nih.go.kr (KCT0005745; date of registration, 08/01/2021).
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Affiliation(s)
- Hyeong Won Yu
- Department of Surgery, Seoul National University Bundang Hospital, Seongnam-si, Korea
| | - Dongheon Lee
- Department of Biomedical Engineering, Chungnam National University College of Medicine and Hospital, Daejeon, Korea
| | - Keunchul Lee
- Department of Surgery, Seoul National University Bundang Hospital, Seongnam-si, Korea
| | - Su-Jin Kim
- Department of Surgery, Seoul National University Hospital and College of Medicine, Seoul, Korea
| | - Young Jun Chai
- Department of Surgery, Seoul National University Boramae Medical Center, Seoul, Korea
| | - Hee Chan Kim
- Department of Biomedical Engineering and Institute of Medical & Biological Engineering, Medical Research Center, Seoul National University College of Medicine and Hospital, Seoul, Korea
| | - June Young Choi
- Department of Surgery, Seoul National University Bundang Hospital, Seongnam-si, Korea.
| | - Kyu Eun Lee
- Department of Surgery, Seoul National University Hospital and College of Medicine, Seoul, Korea
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27
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Zhang Z, Rosa B, Nageotte F. Surgical Tool Segmentation Using Generative Adversarial Networks With Unpaired Training Data. IEEE Robot Autom Lett 2021. [DOI: 10.1109/lra.2021.3092302] [Citation(s) in RCA: 2] [Impact Index Per Article: 0.7] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 12/29/2022]
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28
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Gumbs AA, Frigerio I, Spolverato G, Croner R, Illanes A, Chouillard E, Elyan E. Artificial Intelligence Surgery: How Do We Get to Autonomous Actions in Surgery? SENSORS (BASEL, SWITZERLAND) 2021; 21:5526. [PMID: 34450976 PMCID: PMC8400539 DOI: 10.3390/s21165526] [Citation(s) in RCA: 28] [Impact Index Per Article: 9.3] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Download PDF] [Figures] [Subscribe] [Scholar Register] [Received: 07/06/2021] [Revised: 08/03/2021] [Accepted: 08/11/2021] [Indexed: 12/30/2022]
Abstract
Most surgeons are skeptical as to the feasibility of autonomous actions in surgery. Interestingly, many examples of autonomous actions already exist and have been around for years. Since the beginning of this millennium, the field of artificial intelligence (AI) has grown exponentially with the development of machine learning (ML), deep learning (DL), computer vision (CV) and natural language processing (NLP). All of these facets of AI will be fundamental to the development of more autonomous actions in surgery, unfortunately, only a limited number of surgeons have or seek expertise in this rapidly evolving field. As opposed to AI in medicine, AI surgery (AIS) involves autonomous movements. Fortuitously, as the field of robotics in surgery has improved, more surgeons are becoming interested in technology and the potential of autonomous actions in procedures such as interventional radiology, endoscopy and surgery. The lack of haptics, or the sensation of touch, has hindered the wider adoption of robotics by many surgeons; however, now that the true potential of robotics can be comprehended, the embracing of AI by the surgical community is more important than ever before. Although current complete surgical systems are mainly only examples of tele-manipulation, for surgeons to get to more autonomously functioning robots, haptics is perhaps not the most important aspect. If the goal is for robots to ultimately become more and more independent, perhaps research should not focus on the concept of haptics as it is perceived by humans, and the focus should be on haptics as it is perceived by robots/computers. This article will discuss aspects of ML, DL, CV and NLP as they pertain to the modern practice of surgery, with a focus on current AI issues and advances that will enable us to get to more autonomous actions in surgery. Ultimately, there may be a paradigm shift that needs to occur in the surgical community as more surgeons with expertise in AI may be needed to fully unlock the potential of AIS in a safe, efficacious and timely manner.
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Affiliation(s)
- Andrew A. Gumbs
- Centre Hospitalier Intercommunal de POISSY/SAINT-GERMAIN-EN-LAYE 10, Rue Champ de Gaillard, 78300 Poissy, France;
| | - Isabella Frigerio
- Department of Hepato-Pancreato-Biliary Surgery, Pederzoli Hospital, 37019 Peschiera del Garda, Italy;
| | - Gaya Spolverato
- Department of Surgical, Oncological and Gastroenterological Sciences, University of Padova, 35122 Padova, Italy;
| | - Roland Croner
- Department of General-, Visceral-, Vascular- and Transplantation Surgery, University of Magdeburg, Haus 60a, Leipziger Str. 44, 39120 Magdeburg, Germany;
| | - Alfredo Illanes
- INKA–Innovation Laboratory for Image Guided Therapy, Medical Faculty, Otto-von-Guericke University Magdeburg, 39120 Magdeburg, Germany;
| | - Elie Chouillard
- Centre Hospitalier Intercommunal de POISSY/SAINT-GERMAIN-EN-LAYE 10, Rue Champ de Gaillard, 78300 Poissy, France;
| | - Eyad Elyan
- School of Computing, Robert Gordon University, Aberdeen AB10 7JG, UK;
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29
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Lajkó G, Nagyné Elek R, Haidegger T. Endoscopic Image-Based Skill Assessment in Robot-Assisted Minimally Invasive Surgery. SENSORS (BASEL, SWITZERLAND) 2021; 21:5412. [PMID: 34450854 PMCID: PMC8398563 DOI: 10.3390/s21165412] [Citation(s) in RCA: 9] [Impact Index Per Article: 3.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Figures] [Subscribe] [Scholar Register] [Received: 06/30/2021] [Revised: 08/02/2021] [Accepted: 08/05/2021] [Indexed: 02/06/2023]
Abstract
Objective skill assessment-based personal performance feedback is a vital part of surgical training. Either kinematic-acquired through surgical robotic systems, mounted sensors on tooltips or wearable sensors-or visual input data can be employed to perform objective algorithm-driven skill assessment. Kinematic data have been successfully linked with the expertise of surgeons performing Robot-Assisted Minimally Invasive Surgery (RAMIS) procedures, but for traditional, manual Minimally Invasive Surgery (MIS), they are not readily available as a method. 3D visual features-based evaluation methods tend to outperform 2D methods, but their utility is limited and not suited to MIS training, therefore our proposed solution relies on 2D features. The application of additional sensors potentially enhances the performance of either approach. This paper introduces a general 2D image-based solution that enables the creation and application of surgical skill assessment in any training environment. The 2D features were processed using the feature extraction techniques of a previously published benchmark to assess the attainable accuracy. We relied on the JHU-ISI Gesture and Skill Assessment Working Set dataset-co-developed by the Johns Hopkins University and Intuitive Surgical Inc. Using this well-established set gives us the opportunity to comparatively evaluate different feature extraction techniques. The algorithm reached up to 95.74% accuracy in individual trials. The highest mean accuracy-averaged over five cross-validation trials-for the surgical subtask of Knot-Tying was 83.54%, for Needle-Passing 84.23% and for Suturing 81.58%. The proposed method measured well against the state of the art in 2D visual-based skill assessment, with more than 80% accuracy for all three surgical subtasks available in JIGSAWS (Knot-Tying, Suturing and Needle-Passing). By introducing new visual features-such as image-based orientation and image-based collision detection-or, from the evaluation side, utilising other Support Vector Machine kernel methods, tuning the hyperparameters or using other classification methods (e.g., the boosted trees algorithm) instead, classification accuracy can be further improved. We showed the potential use of optical flow as an input for RAMIS skill assessment, highlighting the maximum accuracy achievable with these data by evaluating it with an established skill assessment benchmark, by evaluating its methods independently. The highest performing method, the Residual Neural Network, reached means of 81.89%, 84.23% and 83.54% accuracy for the skills of Suturing, Needle-Passing and Knot-Tying, respectively.
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Affiliation(s)
- Gábor Lajkó
- Autonomous Systems Track, Double Degree Programme, EIT Digital Master School, Technische Universität Berlin, Straße des 17. Juni 135, 10623 Berlin, Germany;
- ELTE Faculty of Informatics, Pázmány Péter Sétány 1/C, Eötvös Loránd University, Egyetem tér 1-3, 1117 Budapest, Hungary
| | - Renáta Nagyné Elek
- Antal Bejczy Center for Intelligent Robotics, University Research and Innovation Center, Óbuda University, 1034 Budapest, Hungary;
- Doctoral School of Applied Informatics and Applied Mathematics, Óbuda University, Bécsi út 96/b, 1034 Budapest, Hungary
- John von Neumann Faculty of Informatics, Óbuda University, Bécsi út 96/b, 1034 Budapest, Hungary
| | - Tamás Haidegger
- Antal Bejczy Center for Intelligent Robotics, University Research and Innovation Center, Óbuda University, 1034 Budapest, Hungary;
- Austrian Center for Medical Innovation and Technology, Viktor Kaplan-Straße 2/1, 2700 Wiener Neustadt, Austria
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30
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Battaglia E, Boehm J, Zheng Y, Jamieson AR, Gahan J, Majewicz Fey A. Rethinking Autonomous Surgery: Focusing on Enhancement over Autonomy. Eur Urol Focus 2021; 7:696-705. [PMID: 34246619 PMCID: PMC10394949 DOI: 10.1016/j.euf.2021.06.009] [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: 04/01/2021] [Revised: 05/28/2021] [Accepted: 06/17/2021] [Indexed: 12/12/2022]
Abstract
CONTEXT As robot-assisted surgery is increasingly used in surgical care, the engineering research effort towards surgical automation has also increased significantly. Automation promises to enhance surgical outcomes, offload mundane or repetitive tasks, and improve workflow. However, we must ask an important question: should autonomous surgery be our long-term goal? OBJECTIVE To provide an overview of the engineering requirements for automating control systems, summarize technical challenges in automated robotic surgery, and review sensing and modeling techniques to capture real-time human behaviors for integration into the robotic control loop for enhanced shared or collaborative control. EVIDENCE ACQUISITION We performed a nonsystematic search of the English language literature up to March 25, 2021. We included original studies related to automation in robot-assisted laparoscopic surgery and human-centered sensing and modeling. EVIDENCE SYNTHESIS We identified four comprehensive review papers that present techniques for automating portions of surgical tasks. Sixteen studies relate to human-centered sensing technologies and 23 to computer vision and/or advanced artificial intelligence or machine learning methods for skill assessment. Twenty-two studies evaluate or review the role of haptic or adaptive guidance during some learning task, with only a few applied to robotic surgery. Finally, only three studies discuss the role of some form of training in patient outcomes and none evaluated the effects of full or semi-autonomy on patient outcomes. CONCLUSIONS Rather than focusing on autonomy, which eliminates the surgeon from the loop, research centered on more fully understanding the surgeon's behaviors, goals, and limitations could facilitate a superior class of collaborative surgical robots that could be more effective and intelligent than automation alone. PATIENT SUMMARY We reviewed the literature for studies on automation in surgical robotics and on modeling of human behavior in human-machine interaction. The main application is to enhance the ability of surgical robotic systems to collaborate more effectively and intelligently with human surgeon operators.
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Affiliation(s)
- Edoardo Battaglia
- Department of Mechanical Engineering, University of Texas at Austin, Austin, TX, USA
| | - Jacob Boehm
- Department of Mechanical Engineering, University of Texas at Austin, Austin, TX, USA
| | - Yi Zheng
- Department of Mechanical Engineering, University of Texas at Austin, Austin, TX, USA
| | - Andrew R Jamieson
- Lyda Hill Department of Bioinformatics, UT Southwestern Medical Center, Dallas, TX, USA
| | - Jeffrey Gahan
- Department of Urology, UT Southwestern Medical Center, Dallas, TX, USA
| | - Ann Majewicz Fey
- Department of Mechanical Engineering, University of Texas at Austin, Austin, TX, USA.
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