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Cold KM, Vamadevan A, Vilmann AS, Svendsen MBS, Konge L, Bjerrum F. Computer-aided quality assessment of endoscopist competence during colonoscopy: a systematic review. Gastrointest Endosc 2024; 100:167-176.e1. [PMID: 38580134 DOI: 10.1016/j.gie.2024.04.004] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [MESH Headings] [Track Full Text] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 03/16/2024] [Revised: 03/28/2024] [Accepted: 03/28/2024] [Indexed: 04/07/2024]
Abstract
BACKGROUND AND AIMS Endoscopists' competence can vary widely, as shown in the variation in the adenoma detection rate (ADR). Computer-aided quality assessment (CAQ) can automatically assess performance during individual procedures. In this review we identified and described different CAQ systems for colonoscopy. METHODS A systematic review of the literature was done using MEDLINE, EMBASE, and Scopus based on 3 blocks of terms according to the inclusion criteria: colonoscopy, competence assessment, and automatic evaluation. Articles were systematically reviewed by 2 reviewers, first by abstract and then in full text. The methodological quality was assessed using the Medical Education Research Study Quality Instrument (MERSQI). RESULTS Of 12,575 identified studies, 6831 remained after removal of duplicates and 6806 did not pass the eligibility criteria and were excluded, leaving 25 studies, of which 13 studies were included in the final analysis. Five categories of CAQ systems were identified: withdrawal speedometer (7 studies), endoscope movement analysis (3 studies), effective withdrawal time (1 study), fold examination quality (1 study), and visual gaze pattern (1 study). The withdrawal speedometer was the only CAQ system that tested its feedback by examining changes in ADR. Three studies observed an improvement in ADR, and 2 studies did not. The methodological quality of the studies was high (mean MERSQI, 15.2 points; maximum, 18 points). CONCLUSIONS Thirteen studies developed or tested CAQ systems, most frequently by correlating it to the ADR. Only 5 studies tested feedback by implementing the CAQ system. A meta-analysis was impossible because of the heterogeneous study designs, and more studies are warranted.
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Affiliation(s)
- Kristoffer Mazanti Cold
- Copenhagen Academy for Medical Education and Simulation (CAMES), Center for HR & Education, the Capital Region of Denmark, Copenhagen, Denmark; Department of Clinical Medicine, Faculty of Health and Medical Sciences, University of Copenhagen, Copenhagen, Denmark
| | - Anishan Vamadevan
- Copenhagen Academy for Medical Education and Simulation (CAMES), Center for HR & Education, the Capital Region of Denmark, Copenhagen, Denmark
| | - Andreas Slot Vilmann
- Copenhagen Academy for Medical Education and Simulation (CAMES), Center for HR & Education, the Capital Region of Denmark, Copenhagen, Denmark; Department of Clinical Medicine, Faculty of Health and Medical Sciences, University of Copenhagen, Copenhagen, Denmark; Gastrounit, Surgical Section, Copenhagen University Hospital-Herlev and Gentofte, Herlev, Denmark
| | - Morten Bo Søndergaard Svendsen
- Copenhagen Academy for Medical Education and Simulation (CAMES), Center for HR & Education, the Capital Region of Denmark, Copenhagen, Denmark; Department of Computer Science, Faculty of Science, University of Copenhagen, Copenhagen, Denmark
| | - Lars Konge
- Copenhagen Academy for Medical Education and Simulation (CAMES), Center for HR & Education, the Capital Region of Denmark, Copenhagen, Denmark; Department of Clinical Medicine, Faculty of Health and Medical Sciences, University of Copenhagen, Copenhagen, Denmark
| | - Flemming Bjerrum
- Copenhagen Academy for Medical Education and Simulation (CAMES), Center for HR & Education, the Capital Region of Denmark, Copenhagen, Denmark; Department of Clinical Medicine, Faculty of Health and Medical Sciences, University of Copenhagen, Copenhagen, Denmark; Gastrounit, Surgical Section, Copenhagen University Hospital-Amager and Hvidovre, Hvidovre, Denmark
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Robertson DJ, Rex DK, Ciani O, Drummond MF. Colonoscopy vs the Fecal Immunochemical Test: Which is Best? Gastroenterology 2024; 166:758-771. [PMID: 38342196 DOI: 10.1053/j.gastro.2023.12.027] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 06/21/2023] [Revised: 12/18/2023] [Accepted: 12/29/2023] [Indexed: 02/13/2024]
Abstract
Although there is no debate around the effectiveness of colorectal cancer screening in reducing disease burden, there remains a question regarding the most effective and cost-effective screening modality. Current United States guidelines present a panel of options that include the 2 most commonly used modalities, colonoscopy and stool testing with the fecal immunochemical test (FIT). Large-scale comparative effectiveness trials comparing colonoscopy and FIT for colorectal cancer outcomes are underway, but results are not yet available. This review will separately state the "best case" for FIT and colonoscopy as the screening tool of first choice. In addition, the review will examine these modalities from a health economics perspective to provide the reader further context about the relative advantages of these commonly used tests.
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Affiliation(s)
- Douglas J Robertson
- VA Medical Center, White River Junction, Vermont; Geisel School of Medicine at Dartmouth, Hanover, New Hampshire.
| | - Douglas K Rex
- Division of Gastroenterology/Hepatology, Indiana University School of Medicine, Indianapolis, Indiana
| | - Oriana Ciani
- Centre for Research on Health and Social Care Management, SDA Bocconi School of Management, Milan, Italy
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Hashemi N, Svendsen MBS, Bjerrum F, Rasmussen S, Tolsgaard MG, Friis ML. Acquisition and usage of robotic surgical data for machine learning analysis. Surg Endosc 2023:10.1007/s00464-023-10214-7. [PMID: 37389741 PMCID: PMC10338401 DOI: 10.1007/s00464-023-10214-7] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Journal Information] [Subscribe] [Scholar Register] [Received: 02/19/2023] [Accepted: 06/12/2023] [Indexed: 07/01/2023]
Abstract
BACKGROUND The increasing use of robot-assisted surgery (RAS) has led to the need for new methods of assessing whether new surgeons are qualified to perform RAS, without the resource-demanding process of having expert surgeons do the assessment. Computer-based automation and artificial intelligence (AI) are seen as promising alternatives to expert-based surgical assessment. However, no standard protocols or methods for preparing data and implementing AI are available for clinicians. This may be among the reasons for the impediment to the use of AI in the clinical setting. METHOD We tested our method on porcine models with both the da Vinci Si and the da Vinci Xi. We sought to capture raw video data from the surgical robots and 3D movement data from the surgeons and prepared the data for the use in AI by a structured guide to acquire and prepare video data using the following steps: 'Capturing image data from the surgical robot', 'Extracting event data', 'Capturing movement data of the surgeon', 'Annotation of image data'. RESULTS 15 participant (11 novices and 4 experienced) performed 10 different intraabdominal RAS procedures. Using this method we captured 188 videos (94 from the surgical robot, and 94 corresponding movement videos of the surgeons' arms and hands). Event data, movement data, and labels were extracted from the raw material and prepared for use in AI. CONCLUSION With our described methods, we could collect, prepare, and annotate images, events, and motion data from surgical robotic systems in preparation for its use in AI.
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Affiliation(s)
- Nasseh Hashemi
- Department of Clinical Medicine, Aalborg University Hospital, Aalborg, Denmark.
- Nordsim-Centre for Skills Training and Simulation, Aalborg, Denmark.
- ROCnord-Robot Centre, Aalborg University Hospital, Aalborg, Denmark.
- Department of Urology, Aalborg University Hospital, Aalborg, Denmark.
| | - Morten Bo Søndergaard Svendsen
- Copenhagen Academy for Medical Education and Simulation, Center for Human Resources and Education, Copenhagen, Denmark
- Department of Computer Science, University of Copenhagen, Copenhagen, Denmark
| | - Flemming Bjerrum
- Copenhagen Academy for Medical Education and Simulation, Center for Human Resources and Education, Copenhagen, Denmark
- Department of Gastrointestinal and Hepatic Diseases, Copenhagen University Hospital - Herlev and Gentofte, Herlev, Denmark
| | - Sten Rasmussen
- Department of Clinical Medicine, Aalborg University Hospital, Aalborg, Denmark
| | - Martin G Tolsgaard
- Nordsim-Centre for Skills Training and Simulation, Aalborg, Denmark
- Copenhagen Academy for Medical Education and Simulation, Center for Human Resources and Education, Copenhagen, Denmark
| | - Mikkel Lønborg Friis
- Department of Clinical Medicine, Aalborg University Hospital, Aalborg, Denmark
- Nordsim-Centre for Skills Training and Simulation, Aalborg, Denmark
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Hassan C, Repici A. Targeting the low detector with artificial intelligence. Endoscopy 2022; 54:1015-1016. [PMID: 35595504 DOI: 10.1055/a-1819-6568] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Indexed: 12/10/2022]
Affiliation(s)
- Cesare Hassan
- IRCCS Humanitas Research Hospital, Rozzano, Milan, Italy.,Department of Biomedical Sciences, Humanitas University, Rozzano, Milan, Italy
| | - Alessandro Repici
- IRCCS Humanitas Research Hospital, Rozzano, Milan, Italy.,Department of Biomedical Sciences, Humanitas University, Rozzano, Milan, Italy
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Chiorean M. Quality assessment in screening colonoscopy: a computer will be watching you. Gastrointest Endosc 2022; 95:1011-1013. [PMID: 35282881 DOI: 10.1016/j.gie.2022.01.016] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 01/13/2022] [Accepted: 01/23/2022] [Indexed: 12/11/2022]
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