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Marchese Aizenman G, Salvagnini P, Cherubini A, Biffi C. Assessing clinical efficacy of polyp detection models using open-access datasets. Front Oncol 2024; 14:1422942. [PMID: 39148908 PMCID: PMC11324571 DOI: 10.3389/fonc.2024.1422942] [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: 04/24/2024] [Accepted: 07/08/2024] [Indexed: 08/17/2024] Open
Abstract
Background Ensuring accurate polyp detection during colonoscopy is essential for preventing colorectal cancer (CRC). Recent advances in deep learning-based computer-aided detection (CADe) systems have shown promise in enhancing endoscopists' performances. Effective CADe systems must achieve high polyp detection rates from the initial seconds of polyp appearance while maintaining low false positive (FP) detection rates throughout the procedure. Method We integrated four open-access datasets into a unified platform containing over 340,000 images from various centers, including 380 annotated polyps, with distinct data splits for comprehensive model development and benchmarking. The REAL-Colon dataset, comprising 60 full-procedure colonoscopy videos from six centers, is used as the fifth dataset of the platform to simulate clinical conditions for model evaluation on unseen center data. Performance assessment includes traditional object detection metrics and new metrics that better meet clinical needs. Specifically, by defining detection events as sequences of consecutive detections, we compute per-polyp recall at early detection stages and average per-patient FPs, enabling the generation of Free-Response Receiver Operating Characteristic (FROC) curves. Results Using YOLOv7, we trained and tested several models across the proposed data splits, showcasing the robustness of our open-access platform for CADe system development and benchmarking. The introduction of new metrics allows for the optimization of CADe operational parameters based on clinically relevant criteria, such as per-patient FPs and early polyp detection. Our findings also reveal that omitting full-procedure videos leads to non-realistic assessments and that detecting small polyp bounding boxes poses the greatest challenge. Conclusion This study demonstrates how newly available open-access data supports ongoing research progress in environments that closely mimic clinical settings. The introduced metrics and FROC curves illustrate CADe clinical efficacy and can aid in tuning CADe hyperparameters.
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Affiliation(s)
| | | | - Andrea Cherubini
- Cosmo Intelligent Medical Devices, Dublin, Ireland
- Milan Center for Neuroscience, University of Milano-Bicocca, Milano, Italy
| | - Carlo Biffi
- Cosmo Intelligent Medical Devices, Dublin, Ireland
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Spadaccini M, Troya J, Khalaf K, Facciorusso A, Maselli R, Hann A, Repici A. Artificial Intelligence-assisted colonoscopy and colorectal cancer screening: Where are we going? Dig Liver Dis 2024; 56:1148-1155. [PMID: 38458884 DOI: 10.1016/j.dld.2024.01.203] [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: 10/12/2023] [Revised: 01/22/2024] [Accepted: 01/23/2024] [Indexed: 03/10/2024]
Abstract
Colorectal cancer is a significant global health concern, necessitating effective screening strategies to reduce its incidence and mortality rates. Colonoscopy plays a crucial role in the detection and removal of colorectal neoplastic precursors. However, there are limitations and variations in the performance of endoscopists, leading to missed lesions and suboptimal outcomes. The emergence of artificial intelligence (AI) in endoscopy offers promising opportunities to improve the quality and efficacy of screening colonoscopies. In particular, AI applications, including computer-aided detection (CADe) and computer-aided characterization (CADx), have demonstrated the potential to enhance adenoma detection and optical diagnosis accuracy. Additionally, AI-assisted quality control systems aim to standardize the endoscopic examination process. This narrative review provides an overview of AI principles and discusses the current knowledge on AI-assisted endoscopy in the context of screening colonoscopies. It highlights the significant role of AI in improving lesion detection, characterization, and quality assurance during colonoscopy. However, further well-designed studies are needed to validate the clinical impact and cost-effectiveness of AI-assisted colonoscopy before its widespread implementation.
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Affiliation(s)
- Marco Spadaccini
- Department of Endoscopy, Humanitas Research Hospital, IRCCS, 20089 Rozzano, Italy; Department of Biomedical Sciences, Humanitas University, 20089 Rozzano, Italy.
| | - Joel Troya
- Interventional and Experimental Endoscopy (InExEn), Department of Internal Medicine II, University Hospital Würzburg, Würzburg, Germany
| | - Kareem Khalaf
- Division of Gastroenterology, St. Michael's Hospital, University of Toronto, Toronto, Canada
| | - Antonio Facciorusso
- Gastroenterology Unit, Department of Surgical and Medical Sciences, University of Foggia, Foggia, Italy
| | - Roberta Maselli
- Department of Endoscopy, Humanitas Research Hospital, IRCCS, 20089 Rozzano, Italy; Department of Biomedical Sciences, Humanitas University, 20089 Rozzano, Italy
| | - Alexander Hann
- Interventional and Experimental Endoscopy (InExEn), Department of Internal Medicine II, University Hospital Würzburg, Würzburg, Germany
| | - Alessandro Repici
- Department of Endoscopy, Humanitas Research Hospital, IRCCS, 20089 Rozzano, Italy; Department of Biomedical Sciences, Humanitas University, 20089 Rozzano, Italy
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van der Zander QEW, Roumans R, Kusters CHJ, Dehghani N, Masclee AAM, de With PHN, van der Sommen F, Snijders CCP, Schoon EJ. Appropriate trust in artificial intelligence for the optical diagnosis of colorectal polyps: The role of human/artificial intelligence interaction. Gastrointest Endosc 2024:S0016-5107(24)03324-8. [PMID: 38942330 DOI: 10.1016/j.gie.2024.06.029] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 12/12/2023] [Revised: 03/26/2024] [Accepted: 06/19/2024] [Indexed: 06/30/2024]
Abstract
BACKGROUND AND AIMS Computer-aided diagnosis (CADx) for the optical diagnosis of colorectal polyps is thoroughly investigated. However, studies on human-artificial intelligence interaction are lacking. Our aim was to investigate endoscopists' trust in CADx by evaluating whether communicating a calibrated algorithm confidence score improved trust. METHODS Endoscopists optically diagnosed 60 colorectal polyps. Initially, endoscopists diagnosed the polyps without CADx assistance (initial diagnosis). Immediately afterward, the same polyp was again shown with a CADx prediction: either only a prediction (benign or premalignant) or a prediction accompanied by a calibrated confidence score (0-100). A confidence score of 0 indicated a benign prediction, 100 a (pre)malignant prediction. In half of the polyps, CADx was mandatory, and for the other half, CADx was optional. After reviewing the CADx prediction, endoscopists made a final diagnosis. Histopathology was used as the gold standard. Endoscopists' trust in CADx was measured as CADx prediction utilization: the willingness to follow CADx predictions when the endoscopists initially disagreed with the CADx prediction. RESULTS Twenty-three endoscopists participated. Presenting CADx predictions increased the endoscopists' diagnostic accuracy (69.3% initial vs 76.6% final diagnosis, P < .001). The CADx prediction was used in 36.5% (n = 183 of 501) disagreements. Adding a confidence score led to lower CADx prediction utilization, except when the confidence score surpassed 60. Mandatory CADx decreased CADx prediction utilization compared to optional CADx. Appropriate trust-using correct or disregarding incorrect CADx predictions-was 48.7% (n = 244 of 501). CONCLUSIONS Appropriate trust was common, and CADx prediction utilization was highest for the optional CADx without confidence scores. These results express the importance of a better understanding of human-artificial intelligence interaction.
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Affiliation(s)
- Quirine E W van der Zander
- Department of Gastroenterology and Hepatology, Maastricht University Medical Center, Maastricht, The Netherlands; GROW, School for Oncology and Reproduction, Maastricht University, Maastricht, The Netherlands.
| | - Rachel Roumans
- Human-Technology Interaction, Eindhoven University of Technology, Eindhoven, The Netherlands
| | - Carolus H J Kusters
- Department of Electrical Engineering, Eindhoven University of Technology, Eindhoven The Netherlands
| | - Nikoo Dehghani
- Department of Electrical Engineering, Eindhoven University of Technology, Eindhoven The Netherlands
| | - Ad A M Masclee
- Department of Gastroenterology and Hepatology, Maastricht University Medical Center, Maastricht, The Netherlands
| | - Peter H N de With
- Department of Electrical Engineering, Eindhoven University of Technology, Eindhoven The Netherlands
| | - Fons van der Sommen
- Department of Electrical Engineering, Eindhoven University of Technology, Eindhoven The Netherlands
| | - Chris C P Snijders
- Human-Technology Interaction, Eindhoven University of Technology, Eindhoven, The Netherlands
| | - Erik J Schoon
- Department of Gastroenterology and Hepatology, Maastricht University Medical Center, Maastricht, The Netherlands; Division of Gastroenterology and Hepatology, Catharina Hospital Eindhoven, Eindhoven, The Netherlands
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4
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Biffi C, Antonelli G, Bernhofer S, Hassan C, Hirata D, Iwatate M, Maieron A, Salvagnini P, Cherubini A. REAL-Colon: A dataset for developing real-world AI applications in colonoscopy. Sci Data 2024; 11:539. [PMID: 38796533 PMCID: PMC11127922 DOI: 10.1038/s41597-024-03359-0] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 07/04/2023] [Accepted: 05/10/2024] [Indexed: 05/28/2024] Open
Abstract
Detection and diagnosis of colon polyps are key to preventing colorectal cancer. Recent evidence suggests that AI-based computer-aided detection (CADe) and computer-aided diagnosis (CADx) systems can enhance endoscopists' performance and boost colonoscopy effectiveness. However, most available public datasets primarily consist of still images or video clips, often at a down-sampled resolution, and do not accurately represent real-world colonoscopy procedures. We introduce the REAL-Colon (Real-world multi-center Endoscopy Annotated video Library) dataset: a compilation of 2.7 M native video frames from sixty full-resolution, real-world colonoscopy recordings across multiple centers. The dataset contains 350k bounding-box annotations, each created under the supervision of expert gastroenterologists. Comprehensive patient clinical data, colonoscopy acquisition information, and polyp histopathological information are also included in each video. With its unprecedented size, quality, and heterogeneity, the REAL-Colon dataset is a unique resource for researchers and developers aiming to advance AI research in colonoscopy. Its openness and transparency facilitate rigorous and reproducible research, fostering the development and benchmarking of more accurate and reliable colonoscopy-related algorithms and models.
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Affiliation(s)
- Carlo Biffi
- Cosmo Intelligent Medical Devices, Dublin, Ireland.
| | - Giulio Antonelli
- Gastroenterology and Digestive Endoscopy Unit, Ospedale dei Castelli (N.O.C.), Rome, Italy
| | - Sebastian Bernhofer
- Karl Landsteiner University of Health Sciences, Krems, Austria
- Department of Internal Medicine 2, University Hospital St. Pölten, St. Pölten, Austria
| | - Cesare Hassan
- Department of Biomedical Sciences, Humanitas University, Pieve Emanuele, Italy
- Endoscopy Unit, Humanitas Clinical and Research Center IRCCS, Rozzano, Italy
| | - Daizen Hirata
- Gastrointestinal Center, Sano Hospital, Hyogo, Japan
| | - Mineo Iwatate
- Gastrointestinal Center, Sano Hospital, Hyogo, Japan
| | - Andreas Maieron
- Karl Landsteiner University of Health Sciences, Krems, Austria
- Department of Internal Medicine 2, University Hospital St. Pölten, St. Pölten, Austria
| | | | - Andrea Cherubini
- Cosmo Intelligent Medical Devices, Dublin, Ireland.
- Milan Center for Neuroscience, University of Milano-Bicocca, Milano, Italy.
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Rondonotti E, Bergna IMB, Paggi S, Amato A, Andrealli A, Scardino G, Tamanini G, Lenoci N, Mandelli G, Terreni N, Rocchetto SI, Piagnani A, Di Paolo D, Bina N, Filippi E, Ambrosiani L, Hassan C, Correale L, Radaelli F. White light computer-aided optical diagnosis of diminutive colorectal polyps in routine clinical practice. Endosc Int Open 2024; 12:E676-E683. [PMID: 38774861 PMCID: PMC11108657 DOI: 10.1055/a-2303-0922] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 07/05/2023] [Accepted: 04/04/2024] [Indexed: 05/24/2024] Open
Abstract
Background and study aims Artificial Intelligence (AI) systems could make the optical diagnosis (OD) of diminutive colorectal polyps (DCPs) more reliable and objective. This study was aimed at prospectively evaluating feasibility and diagnostic performance of AI-standalone and AI-assisted OD of DCPs in a real-life setting by using a white light-based system (GI Genius, Medtronic Co, Minneapolis, Minnesota, United States). Patients and methods Consecutive colonoscopy outpatients with at least one DCP were evaluated by 11 endoscopists (5 experts and 6 non-experts in OD). DCPs were classified in real time by AI (AI-standalone OD) and by the endoscopist with the assistance of AI (AI-assisted OD), with histopathology as the reference standard. Results Of the 480 DCPs, AI provided the outcome "adenoma" or "non-adenoma" in 81.4% (95% confidence interval [CI]: 77.5-84.6). Sensitivity, specificity, positive and negative predictive value, and accuracy of AI-standalone OD were 97.0% (95% CI 94.0-98.6), 38.1% (95% CI 28.9-48.1), 80.1% (95% CI 75.2-84.2), 83.3% (95% CI 69.2-92.0), and 80.5% (95% CI 68.7-82.8%), respectively. Compared with AI-standalone, the specificity of AI-assisted OD was significantly higher (58.9%, 95% CI 49.7-67.5) and a trend toward an increase was observed for other diagnostic performance measures. Overall accuracy and negative predictive value of AI-assisted OD for experts and non-experts were 85.8% (95% CI 80.0-90.4) vs. 80.1% (95% CI 73.6-85.6) and 89.1% (95% CI 75.6-95.9) vs. 80.0% (95% CI 63.9-90.4), respectively. Conclusions Standalone AI is able to provide an OD of adenoma/non-adenoma in more than 80% of DCPs, with a high sensitivity but low specificity. The human-machine interaction improved diagnostic performance, especially when experts were involved.
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Affiliation(s)
| | - Irene Maria Bambina Bergna
- Gastroenterology Unit, Valduce Hospital, Como, Italy
- University of Milan, Milano, Italy
- Gastroenterology and Digestive Endoscopy Unit, Alessandro Manzoni Hospital, Lecco, Italy
| | - Silvia Paggi
- Gastroenterology Unit, Valduce Hospital, Como, Italy
| | - Arnaldo Amato
- Gastroenterology Unit, Valduce Hospital, Como, Italy
- Gastroenterology and Digestive Endoscopy Unit, Alessandro Manzoni Hospital, Lecco, Italy
| | | | | | | | | | | | | | - SImone Rocchetto
- Gastroenterology Unit, Valduce Hospital, Como, Italy
- University of Milan, Milano, Italy
| | - Alessandra Piagnani
- Gastroenterology Unit, Valduce Hospital, Como, Italy
- University of Milan, Milano, Italy
| | | | - Niccolò Bina
- Gastroenterology Unit, Valduce Hospital, Como, Italy
| | | | | | - Cesare Hassan
- Department of Gastroenterology, IRCCS Humanitas Research Hospital, Rozzano, Italy
- Department of Biomedical Sciences, Humanitas University, Pieve Emanuele, Italy
| | - Loredana Correale
- Department of Gastroenterology, IRCCS Humanitas Research Hospital, Rozzano, Italy
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Jukema JB, Kusters CHJ, Jong MR, Fockens KN, Boers T, van der Putten JA, Pouw RE, Duits LC, Weusten BLAM, Herrero LA, Houben MHMG, Nagengast WB, Westerhof J, Alkhalaf A, Mallant-Hent R, Scholten P, Ragunath K, Seewald S, Elbe P, Silva FB, Barret M, Ortiz Fernández-Sordo J, Moral Villarejo G, Pech O, Beyna T, Montazeri NSM, der Sommen FV, de With PH, de Groof AJ, Bergman JJ. Computer-aided diagnosis improves characterization of Barrett's neoplasia by general endoscopists (with video). Gastrointest Endosc 2024:S0016-5107(24)00233-5. [PMID: 38636819 DOI: 10.1016/j.gie.2024.04.013] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 09/05/2023] [Revised: 01/25/2024] [Accepted: 04/08/2024] [Indexed: 04/20/2024]
Abstract
BACKGROUND AND AIMS Characterization of visible abnormalities in patients with Barrett's esophagus (BE) can be challenging, especially for inexperienced endoscopists. This results in suboptimal diagnostic accuracy and poor interobserver agreement. Computer-aided diagnosis (CADx) systems may assist endoscopists. We aimed to develop, validate, and benchmark a CADx system for BE neoplasia. METHODS The CADx system received pretraining with ImageNet and then consecutive domain-specific pretraining with GastroNet, which includes 5 million endoscopic images. It was subsequently trained and internally validated using 1758 narrow-band imaging (NBI) images of early BE neoplasia (352 patients) and 1838 NBI images of nondysplastic BE (173 patients) from 8 international centers. CADx was tested prospectively on corresponding image and video test sets with 30 cases (20 patients) of BE neoplasia and 60 cases (31 patients) of nondysplastic BE. The test set was benchmarked by 44 general endoscopists in 2 phases (phase 1, no CADx assistance; phase 2, with CADx assistance). Ten international BE experts provided additional benchmark performance. RESULTS Stand-alone sensitivity and specificity of the CADx system were 100% and 98% for images and 93% and 96% for videos, respectively. CADx outperformed general endoscopists without CADx assistance in terms of sensitivity (P = .04). Sensitivity and specificity of general endoscopists increased from 84% to 96% and 90% to 98% with CAD assistance (P < .001). CADx assistance increased endoscopists' confidence in characterization (P < .001). CADx performance was similar to that of the BE experts. CONCLUSIONS CADx assistance significantly increased characterization performance of BE neoplasia by general endoscopists to the level of expert endoscopists. The use of this CADx system may thereby improve daily Barrett surveillance.
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Affiliation(s)
- Jelmer B Jukema
- Department of Gastroenterology and Hepatology, Amsterdam Gastroenterology Endocrinology Metabolism, Amsterdam UMC, University of Amsterdam, Amsterdam, the Netherlands
| | - Carolus H J Kusters
- Department of Electrical Engineering, Eindhoven University of Technology, Eindhoven, the Netherlands
| | - Martijn R Jong
- Department of Gastroenterology and Hepatology, Amsterdam Gastroenterology Endocrinology Metabolism, Amsterdam UMC, University of Amsterdam, Amsterdam, the Netherlands
| | - Kiki N Fockens
- Department of Gastroenterology and Hepatology, Amsterdam Gastroenterology Endocrinology Metabolism, Amsterdam UMC, University of Amsterdam, Amsterdam, the Netherlands
| | - Tim Boers
- Department of Electrical Engineering, Eindhoven University of Technology, Eindhoven, the Netherlands
| | - Joost A van der Putten
- Department of Electrical Engineering, Eindhoven University of Technology, Eindhoven, the Netherlands
| | - Roos E Pouw
- Department of Gastroenterology and Hepatology, Amsterdam Gastroenterology Endocrinology Metabolism, Amsterdam UMC, University of Amsterdam, Amsterdam, the Netherlands
| | - Lucas C Duits
- Department of Gastroenterology and Hepatology, Amsterdam Gastroenterology Endocrinology Metabolism, Amsterdam UMC, University of Amsterdam, Amsterdam, the Netherlands
| | - Bas L A M Weusten
- Department of Gastroenterology and Hepatology, UMC Utrecht, University of Utrecht, Utrecht, the Netherlands; Department of Gastroenterology and Hepatology, Sint Antonius Hospital, Nieuwegein, the Netherlands
| | - Lorenza Alvarez Herrero
- Department of Gastroenterology and Hepatology, Sint Antonius Hospital, Nieuwegein, the Netherlands
| | - Martin H M G Houben
- Department of Gastroenterology and Hepatology, HagaZiekenhuis Den Haag, Den Haag, the Netherlands
| | - Wouter B Nagengast
- Department of Gastroenterology and Hepatology, UMC Groningen, University of Groningen, Groningen, the Netherlands
| | - Jessie Westerhof
- Department of Gastroenterology and Hepatology, UMC Groningen, University of Groningen, Groningen, the Netherlands
| | - Alaa Alkhalaf
- Department of Gastroenterology and Hepatology, Isala Hospital Zwolle, Zwolle, the Netherlands
| | - Rosalie Mallant-Hent
- Department of Gastroenterology and Hepatology, Flevoziekenhuis Almere, Almere, the Netherlands
| | - Pieter Scholten
- Department of Gastroenterology and Hepatology, Onze Lieve Vrouwe Gasthuis, Amsterdam, the Netherlands
| | - Krish Ragunath
- Department of Gastroenterology and Hepatology, Royal Perth Hospital, Curtin University, Perth, WA, Australia
| | - Stefan Seewald
- Department of Gastroenterology and Hepatology, Hirslanden Klinik, Zurich, Switzerland
| | - Peter Elbe
- Department of Digestive Diseases, Karolinska University Hospital, Stockholm, Sweden; Division of Surgery, Department of Clinical Science, Intervention and Technology, CLINTEC, Karolinska Institutet, Stockholm, Sweden
| | - Francisco Baldaque Silva
- Department of Digestive Diseases, Karolinska University Hospital, Stockholm, Sweden; Center for Advanced Endoscopy Carlos Moreira da Silva, Gastroenterology Department, Pedro Hispano Hospital, ULSM Matosinhos, Portugal
| | - Maximilien Barret
- Department of Gastroenterology and Hepatology, Cochin Hospital Paris, Paris, France
| | - Jacobo Ortiz Fernández-Sordo
- Department of Gastroenterology and Hepatology, Nottingham University Hospitals NHS Trust, Nottingham, United Kingdom
| | - Guiomar Moral Villarejo
- Department of Gastroenterology and Hepatology, Nottingham University Hospitals NHS Trust, Nottingham, United Kingdom
| | - Oliver Pech
- Department of Gastroenterology and Hepatology, St. John of God Hospital, Regensburg, Germany
| | - Torsten Beyna
- Department of Gastroenterology and Hepatology, Evangalisches Krankenhaus Düsseldorf, Düsseldorf, Germany
| | - Nahid S M Montazeri
- Biostatistics Unit, Department of Gastroenterology and Hepatology, Amsterdam University Medical Center, location Academic Medical Center, University of Amsterdam, Amsterdam, the Netherlands
| | - Fons van der Sommen
- Department of Electrical Engineering, Eindhoven University of Technology, Eindhoven, the Netherlands
| | - Peter H de With
- Department of Electrical Engineering, Eindhoven University of Technology, Eindhoven, the Netherlands
| | - A Jeroen de Groof
- Department of Gastroenterology and Hepatology, Amsterdam Gastroenterology Endocrinology Metabolism, Amsterdam UMC, University of Amsterdam, Amsterdam, the Netherlands
| | - Jacques J Bergman
- Department of Gastroenterology and Hepatology, Amsterdam Gastroenterology Endocrinology Metabolism, Amsterdam UMC, University of Amsterdam, Amsterdam, the Netherlands.
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van der Zander QEW, Schreuder RM, Thijssen A, Kusters CHJ, Dehghani N, Scheeve T, Winkens B, van der Ende - van Loon MCM, de With PHN, van der Sommen F, Masclee AAM, Schoon EJ. Artificial intelligence for characterization of diminutive colorectal polyps: A feasibility study comparing two computer-aided diagnosis systems. Artif Intell Gastrointest Endosc 2024; 5:90574. [DOI: 10.37126/aige.v5.i1.90574] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 12/07/2023] [Revised: 01/11/2024] [Accepted: 02/02/2024] [Indexed: 02/20/2024] Open
Abstract
BACKGROUND Artificial intelligence (AI) has potential in the optical diagnosis of colorectal polyps.
AIM To evaluate the feasibility of the real-time use of the computer-aided diagnosis system (CADx) AI for ColoRectal Polyps (AI4CRP) for the optical diagnosis of diminutive colorectal polyps and to compare the performance with CAD EYETM (Fujifilm, Tokyo, Japan). CADx influence on the optical diagnosis of an expert endoscopist was also investigated.
METHODS AI4CRP was developed in-house and CAD EYE was proprietary software provided by Fujifilm. Both CADx-systems exploit convolutional neural networks. Colorectal polyps were characterized as benign or premalignant and histopathology was used as gold standard. AI4CRP provided an objective assessment of its characterization by presenting a calibrated confidence characterization value (range 0.0-1.0). A predefined cut-off value of 0.6 was set with values < 0.6 indicating benign and values ≥ 0.6 indicating premalignant colorectal polyps. Low confidence characterizations were defined as values 40% around the cut-off value of 0.6 (< 0.36 and > 0.76). Self-critical AI4CRP’s diagnostic performances excluded low confidence characterizations.
RESULTS AI4CRP use was feasible and performed on 30 patients with 51 colorectal polyps. Self-critical AI4CRP, excluding 14 low confidence characterizations [27.5% (14/51)], had a diagnostic accuracy of 89.2%, sensitivity of 89.7%, and specificity of 87.5%, which was higher compared to AI4CRP. CAD EYE had a 83.7% diagnostic accuracy, 74.2% sensitivity, and 100.0% specificity. Diagnostic performances of the endoscopist alone (before AI) increased non-significantly after reviewing the CADx characterizations of both AI4CRP and CAD EYE (AI-assisted endoscopist). Diagnostic performances of the AI-assisted endoscopist were higher compared to both CADx-systems, except for specificity for which CAD EYE performed best.
CONCLUSION Real-time use of AI4CRP was feasible. Objective confidence values provided by a CADx is novel and self-critical AI4CRP showed higher diagnostic performances compared to AI4CRP.
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Affiliation(s)
- Quirine Eunice Wennie van der Zander
- Department of Gastroenterology and Hepatology, Maastricht University Medical Center, Maastricht 6202 AZ, Netherlands
- GROW, School for Oncology and Reproduction, Maastricht University, Maastricht 6200 MD, Netherlands
| | - Ramon M Schreuder
- Division of Gastroenterology and Hepatology, Catharina Hospital Eindhoven, Eindhoven 5602 ZA, Netherlands
| | - Ayla Thijssen
- Department of Gastroenterology and Hepatology, Maastricht University Medical Center, Maastricht 6202 AZ, Netherlands
- GROW, School for Oncology and Reproduction, Maastricht University, Maastricht 6200 MD, Netherlands
| | - Carolus H J Kusters
- Department of Electrical Engineering, Eindhoven University of Technology, Eindhoven 5600 MB, Netherlands
| | - Nikoo Dehghani
- Department of Electrical Engineering, Eindhoven University of Technology, Eindhoven 5600 MB, Netherlands
| | - Thom Scheeve
- Department of Electrical Engineering, Eindhoven University of Technology, Eindhoven 5600 MB, Netherlands
| | - Bjorn Winkens
- Department of Methodology and Statistics, Maastricht University, Postbus 616, 6200 MD Maastricht, Netherlands
- School for Public Health and Primary Care, Maastricht University, Maastricht 6200 MD, Netherlands
| | | | - Peter H N de With
- Department of Electrical Engineering, Eindhoven University of Technology, Eindhoven 5600 MB, Netherlands
| | - Fons van der Sommen
- Department of Electrical Engineering, Eindhoven University of Technology, Eindhoven 5600 MB, Netherlands
| | - Ad A M Masclee
- Department of Gastroenterology and Hepatology, Maastricht University Medical Center, Maastricht 6202 AZ, Netherlands
| | - Erik J Schoon
- GROW, School for Oncology and Reproduction, Maastricht University, Maastricht 6200 MD, Netherlands
- Division of Gastroenterology and Hepatology, Catharina Hospital Eindhoven, Eindhoven 5602 ZA, Netherlands
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8
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Kato S, Kudo SE, Minegishi Y, Miyata Y, Maeda Y, Kuroki T, Takashina Y, Mochizuki K, Tamura E, Abe M, Sato Y, Sakurai T, Kouyama Y, Tanaka K, Ogawa Y, Nakamura H, Ichimasa K, Ogata N, Hisayuki T, Hayashi T, Wakamura K, Miyachi H, Baba T, Ishida F, Nemoto T, Misawa M. Impact of computer-aided characterization for diagnosis of colorectal lesions, including sessile serrated lesions: Multireader, multicase study. Dig Endosc 2024; 36:341-350. [PMID: 37937532 DOI: 10.1111/den.14612] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 02/23/2023] [Accepted: 06/06/2023] [Indexed: 11/09/2023]
Abstract
OBJECTIVES Computer-aided characterization (CADx) may be used to implement optical biopsy strategies into colonoscopy practice; however, its impact on endoscopic diagnosis remains unknown. We aimed to evaluate the additional diagnostic value of CADx when used by endoscopists for assessing colorectal polyps. METHODS This was a single-center, multicase, multireader, image-reading study using randomly extracted images of pathologically confirmed polyps resected between July 2021 and January 2022. Approved CADx that could predict two-tier classification (neoplastic or nonneoplastic) by analyzing narrow-band images of the polyps was used to obtain a CADx diagnosis. Participating endoscopists determined if the polyps were neoplastic or not and noted their confidence level using a computer-based, image-reading test. The test was conducted twice with a 4-week interval: the first test was conducted without CADx prediction and the second test with CADx prediction. Diagnostic performances for neoplasms were calculated using the pathological diagnosis as reference and performances with and without CADx prediction were compared. RESULTS Five hundred polyps were randomly extracted from 385 patients and diagnosed by 14 endoscopists (including seven experts). The sensitivity for neoplasia was significantly improved by referring to CADx (89.4% vs. 95.6%). CADx also had incremental effects on the negative predictive value (69.3% vs. 84.3%), overall accuracy (87.2% vs. 91.8%), and high-confidence diagnosis rate (77.4% vs. 85.8%). However, there was no significant difference in specificity (80.1% vs. 78.9%). CONCLUSIONS Computer-aided characterization has added diagnostic value for differentiating colorectal neoplasms and may improve the high-confidence diagnosis rate.
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Affiliation(s)
- Shun Kato
- Digestive Disease Center, Showa University Northern Yokohama Hospital, Kanagawa, Japan
| | - Shin-Ei Kudo
- Digestive Disease Center, Showa University Northern Yokohama Hospital, Kanagawa, Japan
| | - Yosuke Minegishi
- Digestive Disease Center, Showa University Northern Yokohama Hospital, Kanagawa, Japan
| | - Yuki Miyata
- Digestive Disease Center, Showa University Northern Yokohama Hospital, Kanagawa, Japan
| | - Yasuharu Maeda
- Digestive Disease Center, Showa University Northern Yokohama Hospital, Kanagawa, Japan
| | - Takanori Kuroki
- Digestive Disease Center, Showa University Northern Yokohama Hospital, Kanagawa, Japan
| | - Yuki Takashina
- Digestive Disease Center, Showa University Northern Yokohama Hospital, Kanagawa, Japan
| | - Kenichi Mochizuki
- Digestive Disease Center, Showa University Northern Yokohama Hospital, Kanagawa, Japan
| | - Eri Tamura
- Digestive Disease Center, Showa University Northern Yokohama Hospital, Kanagawa, Japan
| | - Masahiro Abe
- Digestive Disease Center, Showa University Northern Yokohama Hospital, Kanagawa, Japan
| | - Yuta Sato
- Digestive Disease Center, Showa University Northern Yokohama Hospital, Kanagawa, Japan
| | - Tatsuya Sakurai
- Digestive Disease Center, Showa University Northern Yokohama Hospital, Kanagawa, Japan
| | - Yuta Kouyama
- Digestive Disease Center, Showa University Northern Yokohama Hospital, Kanagawa, Japan
| | - Kenta Tanaka
- Digestive Disease Center, Showa University Northern Yokohama Hospital, Kanagawa, Japan
| | - Yushi Ogawa
- Digestive Disease Center, Showa University Northern Yokohama Hospital, Kanagawa, Japan
| | - Hiroki Nakamura
- Digestive Disease Center, Showa University Northern Yokohama Hospital, Kanagawa, Japan
| | - Katsuro Ichimasa
- Digestive Disease Center, Showa University Northern Yokohama Hospital, Kanagawa, Japan
- Department of Gastroenterology and Hepatology, National University Hospital, Singapore City, Singapore
| | - Noriyuki Ogata
- Digestive Disease Center, Showa University Northern Yokohama Hospital, Kanagawa, Japan
| | - Tomokazu Hisayuki
- Digestive Disease Center, Showa University Northern Yokohama Hospital, Kanagawa, Japan
| | - Takemasa Hayashi
- Digestive Disease Center, Showa University Northern Yokohama Hospital, Kanagawa, Japan
| | - Kunihiko Wakamura
- Digestive Disease Center, Showa University Northern Yokohama Hospital, Kanagawa, Japan
| | - Hideyuki Miyachi
- Digestive Disease Center, Showa University Northern Yokohama Hospital, Kanagawa, Japan
| | - Toshiyuki Baba
- Digestive Disease Center, Showa University Northern Yokohama Hospital, Kanagawa, Japan
| | - Fumio Ishida
- Digestive Disease Center, Showa University Northern Yokohama Hospital, Kanagawa, Japan
| | - Tetsuo Nemoto
- Department of Diagnostic Pathology and Laboratory Medicine, Showa University Northern Yokohama Hospital, Kanagawa, Japan
| | - Masashi Misawa
- Digestive Disease Center, Showa University Northern Yokohama Hospital, Kanagawa, Japan
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9
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Tham S, Koh FH, Ladlad J, Chue KM, Lin CL, Teo EK, Foo FJ. The imitation game: a review of the use of artificial intelligence in colonoscopy, and endoscopists' perceptions thereof. Ann Coloproctol 2023; 39:385-394. [PMID: 36907170 PMCID: PMC10626328 DOI: 10.3393/ac.2022.00878.0125] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 10/30/2022] [Revised: 12/22/2022] [Accepted: 01/09/2023] [Indexed: 03/14/2023] Open
Abstract
The development of deep learning systems in artificial intelligence (AI) has enabled advances in endoscopy, and AI-aided colonoscopy has recently been ushered into clinical practice as a clinical decision-support tool. This has enabled real-time AI-aided detection of polyps with a higher sensitivity than the average endoscopist, and evidence to support its use has been promising thus far. This review article provides a summary of currently published data relating to AI-aided colonoscopy, discusses current clinical applications, and introduces ongoing research directions. We also explore endoscopists' perceptions and attitudes toward the use of this technology, and discuss factors influencing its uptake in clinical practice.
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Affiliation(s)
- Sarah Tham
- Department of General Surgery, Sengkang General Hospital, SingHealth Services, Singapore
| | - Frederick H. Koh
- Colorectal Service, Department of General Surgery, Sengkang General Hospital, SingHealth Services, Singapore
- SKH Endoscopy Centre, Division of Hyperacute Care, Sengkang General Hospital, SingHealth Services, Singapore
| | - Jasmine Ladlad
- Colorectal Service, Department of General Surgery, Sengkang General Hospital, SingHealth Services, Singapore
- SKH Endoscopy Centre, Division of Hyperacute Care, Sengkang General Hospital, SingHealth Services, Singapore
| | - Koy-Min Chue
- Department of General Surgery, Sengkang General Hospital, SingHealth Services, Singapore
- SKH Endoscopy Centre, Division of Hyperacute Care, Sengkang General Hospital, SingHealth Services, Singapore
| | - SKH Endoscopy Centre
- SKH Endoscopy Centre, Division of Hyperacute Care, Sengkang General Hospital, SingHealth Services, Singapore
| | - Cui-Li Lin
- SKH Endoscopy Centre, Division of Hyperacute Care, Sengkang General Hospital, SingHealth Services, Singapore
- Department of Gastroenterology and Hepatology, Sengkang General Hospital, SingHealth Services, Singapore
| | - Eng-Kiong Teo
- SKH Endoscopy Centre, Division of Hyperacute Care, Sengkang General Hospital, SingHealth Services, Singapore
- Department of Gastroenterology and Hepatology, Sengkang General Hospital, SingHealth Services, Singapore
| | - Fung-Joon Foo
- Colorectal Service, Department of General Surgery, Sengkang General Hospital, SingHealth Services, Singapore
- SKH Endoscopy Centre, Division of Hyperacute Care, Sengkang General Hospital, SingHealth Services, Singapore
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10
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Kader R, Cid‐Mejias A, Brandao P, Islam S, Hebbar S, Puyal JG, Ahmad OF, Hussein M, Toth D, Mountney P, Seward E, Vega R, Stoyanov D, Lovat LB. Polyp characterization using deep learning and a publicly accessible polyp video database. Dig Endosc 2023; 35:645-655. [PMID: 36527309 PMCID: PMC10570984 DOI: 10.1111/den.14500] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 07/14/2022] [Accepted: 12/13/2022] [Indexed: 01/20/2023]
Abstract
OBJECTIVES Convolutional neural networks (CNN) for computer-aided diagnosis of polyps are often trained using high-quality still images in a single chromoendoscopy imaging modality with sessile serrated lesions (SSLs) often excluded. This study developed a CNN from videos to classify polyps as adenomatous or nonadenomatous using standard narrow-band imaging (NBI) and NBI-near focus (NBI-NF) and created a publicly accessible polyp video database. METHODS We trained a CNN with 16,832 high and moderate quality frames from 229 polyp videos (56 SSLs). It was evaluated with 222 polyp videos (36 SSLs) across two test-sets. Test-set I consists of 14,320 frames (157 polyps, 111 diminutive). Test-set II, which is publicly accessible, 3317 video frames (65 polyps, 41 diminutive), which was benchmarked with three expert and three nonexpert endoscopists. RESULTS Sensitivity for adenoma characterization was 91.6% in test-set I and 89.7% in test-set II. Specificity was 91.9% and 88.5%. Sensitivity for diminutive polyps was 89.9% and 87.5%; specificity 90.5% and 88.2%. In NBI-NF, sensitivity was 89.4% and 89.5%, with a specificity of 94.7% and 83.3%. In NBI, sensitivity was 85.3% and 91.7%, with a specificity of 87.5% and 90.0%, respectively. The CNN achieved preservation and incorporation of valuable endoscopic innovations (PIVI)-1 and PIVI-2 thresholds for each test-set. In the benchmarking of test-set II, the CNN was significantly more accurate than nonexperts (13.8% difference [95% confidence interval 3.2-23.6], P = 0.01) with no significant difference with experts. CONCLUSIONS A single CNN can differentiate adenomas from SSLs and hyperplastic polyps in both NBI and NBI-NF. A publicly accessible NBI polyp video database was created and benchmarked.
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Affiliation(s)
- Rawen Kader
- Wellcome/EPSRC Centre for Interventional and Surgical SciencesUniversity College LondonLondonUK
- Division of Surgery and Interventional SciencesUniversity College LondonLondonUK
- Gastrointestinal ServicesUniversity College London HospitalLondonUK
| | | | | | - Shahraz Islam
- Wellcome/EPSRC Centre for Interventional and Surgical SciencesUniversity College LondonLondonUK
- Division of Surgery and Interventional SciencesUniversity College LondonLondonUK
| | | | - Juana González‐Bueno Puyal
- Wellcome/EPSRC Centre for Interventional and Surgical SciencesUniversity College LondonLondonUK
- Odin Vision LtdLondonUK
| | - Omer F. Ahmad
- Wellcome/EPSRC Centre for Interventional and Surgical SciencesUniversity College LondonLondonUK
- Division of Surgery and Interventional SciencesUniversity College LondonLondonUK
- Gastrointestinal ServicesUniversity College London HospitalLondonUK
| | - Mohamed Hussein
- Wellcome/EPSRC Centre for Interventional and Surgical SciencesUniversity College LondonLondonUK
- Division of Surgery and Interventional SciencesUniversity College LondonLondonUK
- Gastrointestinal ServicesUniversity College London HospitalLondonUK
| | | | | | - Ed Seward
- Division of Surgery and Interventional SciencesUniversity College LondonLondonUK
- Gastrointestinal ServicesUniversity College London HospitalLondonUK
| | - Roser Vega
- Division of Surgery and Interventional SciencesUniversity College LondonLondonUK
- Gastrointestinal ServicesUniversity College London HospitalLondonUK
| | - Danail Stoyanov
- Wellcome/EPSRC Centre for Interventional and Surgical SciencesUniversity College LondonLondonUK
| | - Laurence B. Lovat
- Wellcome/EPSRC Centre for Interventional and Surgical SciencesUniversity College LondonLondonUK
- Division of Surgery and Interventional SciencesUniversity College LondonLondonUK
- Gastrointestinal ServicesUniversity College London HospitalLondonUK
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11
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Marwaha JS, Raza MM, Kvedar JC. The digital transformation of surgery. NPJ Digit Med 2023; 6:103. [PMID: 37258642 PMCID: PMC10232406 DOI: 10.1038/s41746-023-00846-3] [Citation(s) in RCA: 7] [Impact Index Per Article: 7.0] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 02/25/2023] [Accepted: 05/15/2023] [Indexed: 06/02/2023] Open
Abstract
Rapid advances in digital technology and artificial intelligence in recent years have already begun to transform many industries, and are beginning to make headway into healthcare. There is tremendous potential for new digital technologies to improve the care of surgical patients. In this piece, we highlight work being done to advance surgical care using machine learning, computer vision, wearable devices, remote patient monitoring, and virtual and augmented reality. We describe ways these technologies can be used to improve the practice of surgery, and discuss opportunities and challenges to their widespread adoption and use in operating rooms and at the bedside.
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Affiliation(s)
- Jayson S Marwaha
- Beth Israel Deaconess Medical Center, Boston, MA, USA.
- Harvard Medical School, Boston, MA, USA.
| | | | - Joseph C Kvedar
- Harvard Medical School, Boston, MA, USA
- Mass General Brigham, Boston, MA, USA
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12
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Keating E, Leyden J, O'Connor DB, Lahiff C. Unlocking quality in endoscopic mucosal resection. World J Gastrointest Endosc 2023; 15:338-353. [PMID: 37274555 PMCID: PMC10236981 DOI: 10.4253/wjge.v15.i5.338] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 12/28/2022] [Revised: 02/24/2023] [Accepted: 04/12/2023] [Indexed: 05/16/2023] Open
Abstract
A review of the development of the key performance metrics of endoscopic mucosal resection (EMR), learning from the experience of the establishment of widespread colonoscopy quality measurements. Potential future performance markers for both colonoscopy and EMR are also evaluated to ensure continued high quality performance is maintained with a focus service framework and predictors of patient outcome.
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Affiliation(s)
- Eoin Keating
- Department of Gastroenterology, Mater Misericordiae University Hospital, Dublin 7, Ireland
- School of Medicine, University College Dublin, Dublin 4, Ireland
| | - Jan Leyden
- Department of Gastroenterology, Mater Misericordiae University Hospital, Dublin 7, Ireland
- School of Medicine, University College Dublin, Dublin 4, Ireland
| | - Donal B O'Connor
- Department of Surgery, Tallaght University Hospital, Dublin 24, Ireland
- School of Medicine, Trinity College Dublin, Dublin 2, Ireland
| | - Conor Lahiff
- Department of Gastroenterology, Mater Misericordiae University Hospital, Dublin 7, Ireland
- School of Medicine, University College Dublin, Dublin 4, Ireland
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13
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Cherubini A, Dinh NN. A Review of the Technology, Training, and Assessment Methods for the First Real-Time AI-Enhanced Medical Device for Endoscopy. Bioengineering (Basel) 2023; 10:bioengineering10040404. [PMID: 37106592 PMCID: PMC10136070 DOI: 10.3390/bioengineering10040404] [Citation(s) in RCA: 2] [Impact Index Per Article: 2.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 01/25/2023] [Revised: 02/25/2023] [Accepted: 03/22/2023] [Indexed: 04/29/2023] Open
Abstract
Artificial intelligence (AI) has the potential to assist in endoscopy and improve decision making, particularly in situations where humans may make inconsistent judgments. The performance assessment of the medical devices operating in this context is a complex combination of bench tests, randomized controlled trials, and studies on the interaction between physicians and AI. We review the scientific evidence published about GI Genius, the first AI-powered medical device for colonoscopy to enter the market, and the device that is most widely tested by the scientific community. We provide an overview of its technical architecture, AI training and testing strategies, and regulatory path. In addition, we discuss the strengths and limitations of the current platform and its potential impact on clinical practice. The details of the algorithm architecture and the data that were used to train the AI device have been disclosed to the scientific community in the pursuit of a transparent AI. Overall, the first AI-enabled medical device for real-time video analysis represents a significant advancement in the use of AI for endoscopies and has the potential to improve the accuracy and efficiency of colonoscopy procedures.
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Affiliation(s)
- Andrea Cherubini
- Cosmo Intelligent Medical Devices, D02KV60 Dublin, Ireland
- Milan Center for Neuroscience, University of Milano-Bicocca, 20126 Milano, Italy
| | - Nhan Ngo Dinh
- Cosmo Intelligent Medical Devices, D02KV60 Dublin, Ireland
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14
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Hassan C, Sharma P, Mori Y, Bretthauer M, Rex DK, Repici A, Selvaggio C, Antonelli G, Khalaf K, Rizkala T, Ferrara E, Savevski V, Maselli R, Fugazza A, Capogreco A, Poletti V, Ferretti S, Alkandari A, Correale L. Comparative Performance of Artificial Intelligence Optical Diagnosis Systems for Leaving in Situ Colorectal Polyps. Gastroenterology 2023; 164:467-469.e4. [PMID: 36328079 DOI: 10.1053/j.gastro.2022.10.021] [Citation(s) in RCA: 14] [Impact Index Per Article: 14.0] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 08/10/2022] [Revised: 10/14/2022] [Accepted: 10/16/2022] [Indexed: 01/17/2023]
Affiliation(s)
- Cesare Hassan
- Department of Biomedical Sciences, Humanitas University, Pieve Emanuele, Italy; Endoscopy Unit, Humanitas Clinical and Research Hospital, IRCCS, Rozzano, Italy.
| | - Prateek Sharma
- Division of Gastroenterology, VA Medical Center, University of Kansas School of Medicine, Kansas City, Kansas
| | - Yuichi Mori
- Clinical Effectiveness Research Group, University of Oslo, Oslo, Norway; Digestive Disease Center, Showa University Northern Yokohama Hospital, Yokohama, Japan
| | | | - Douglas K Rex
- Division of Gastroenterology/Hepatology, Indiana University School of Medicine, Indianapolis, Indiana
| | | | - Alessandro Repici
- Department of Biomedical Sciences, Humanitas University, Pieve Emanuele, Italy; Endoscopy Unit, Humanitas Clinical and Research Hospital, IRCCS, Rozzano, Italy
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15
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Cherubini A, East JE. Gorilla in the room: Even experts can miss polyps at colonoscopy and how AI helps complex visual perception tasks. Dig Liver Dis 2023; 55:151-153. [PMID: 36804032 DOI: 10.1016/j.dld.2022.10.004] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 09/28/2022] [Accepted: 10/06/2022] [Indexed: 02/23/2023]
Affiliation(s)
- Andrea Cherubini
- Artificial Intelligence Group, Cosmo AI/Linkverse, Lainate 20045, Italy; Milan Center for Neuroscience, University of Milano-Bicocca, Milano 20126, Italy.
| | - James E East
- Translational Gastroenterology Unit, Nuffield Department of Medicine, Experimental Medicine Division, John Radcliffe Hospital, University of Oxford, Oxford, UK; Division of Gastroenterology and Hepatology, Mayo Clinic Healthcare, London
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16
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ELKarazle K, Raman V, Then P, Chua C. Detection of Colorectal Polyps from Colonoscopy Using Machine Learning: A Survey on Modern Techniques. SENSORS (BASEL, SWITZERLAND) 2023; 23:1225. [PMID: 36772263 PMCID: PMC9953705 DOI: 10.3390/s23031225] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Figures] [Subscribe] [Scholar Register] [Received: 12/27/2022] [Revised: 01/08/2023] [Accepted: 01/17/2023] [Indexed: 06/18/2023]
Abstract
Given the increased interest in utilizing artificial intelligence as an assistive tool in the medical sector, colorectal polyp detection and classification using deep learning techniques has been an active area of research in recent years. The motivation for researching this topic is that physicians miss polyps from time to time due to fatigue and lack of experience carrying out the procedure. Unidentified polyps can cause further complications and ultimately lead to colorectal cancer (CRC), one of the leading causes of cancer mortality. Although various techniques have been presented recently, several key issues, such as the lack of enough training data, white light reflection, and blur affect the performance of such methods. This paper presents a survey on recently proposed methods for detecting polyps from colonoscopy. The survey covers benchmark dataset analysis, evaluation metrics, common challenges, standard methods of building polyp detectors and a review of the latest work in the literature. We conclude this paper by providing a precise analysis of the gaps and trends discovered in the reviewed literature for future work.
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Affiliation(s)
- Khaled ELKarazle
- School of Information and Communication Technologies, Swinburne University of Technology, Sarawak Campus, Kuching 93350, Malaysia
| | - Valliappan Raman
- Department of Artificial Intelligence and Data Science, Coimbatore Institute of Technology, Coimbatore 641014, India
| | - Patrick Then
- School of Information and Communication Technologies, Swinburne University of Technology, Sarawak Campus, Kuching 93350, Malaysia
| | - Caslon Chua
- Department of Computer Science and Software Engineering, Swinburne University of Technology, Melbourne 3122, Australia
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17
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van der Zander QEW, der Sommen FV, Schoon EJ. Assessing the Level of Expertise of Endoscopists in Optical Diagnosis of Colorectal Polyps-Not Every Expert Is an Expert. Gastroenterology 2023:S0016-5085(23)00037-9. [PMID: 36646392 DOI: 10.1053/j.gastro.2022.12.045] [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: 12/13/2022] [Accepted: 12/17/2022] [Indexed: 01/18/2023]
Affiliation(s)
- Quirine E W van der Zander
- Division of Gastroenterology and Hepatology, Maastricht University Medical Center, Maastricht, the Netherlands; GROW, School for Oncology and Reproduction, Maastricht University, Maastricht, the Netherlands
| | - Fons van der Sommen
- Department of Electrical Engineering, Eindhoven University of Technology, Eindhoven, the Netherlands
| | - Erik J Schoon
- GROW, School for Oncology and Reproduction, Maastricht University, Maastricht, the Netherlands; Division of Gastroenterology and Hepatology, Catharina Hospital Eindhoven, Eindhoven, the Netherlands
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18
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Reverberi C, Rigon T, Solari A, Hassan C, Cherubini P, Cherubini A. Experimental evidence of effective human-AI collaboration in medical decision-making. Sci Rep 2022; 12:14952. [PMID: 36056152 PMCID: PMC9440124 DOI: 10.1038/s41598-022-18751-2] [Citation(s) in RCA: 19] [Impact Index Per Article: 9.5] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 03/10/2022] [Accepted: 08/18/2022] [Indexed: 11/25/2022] Open
Abstract
Artificial Intelligence (AI) systems are precious support for decision-making, with many applications also in the medical domain. The interaction between MDs and AI enjoys a renewed interest following the increased possibilities of deep learning devices. However, we still have limited evidence-based knowledge of the context, design, and psychological mechanisms that craft an optimal human-AI collaboration. In this multicentric study, 21 endoscopists reviewed 504 videos of lesions prospectively acquired from real colonoscopies. They were asked to provide an optical diagnosis with and without the assistance of an AI support system. Endoscopists were influenced by AI ([Formula: see text]), but not erratically: they followed the AI advice more when it was correct ([Formula: see text]) than incorrect ([Formula: see text]). Endoscopists achieved this outcome through a weighted integration of their and the AI opinions, considering the case-by-case estimations of the two reliabilities. This Bayesian-like rational behavior allowed the human-AI hybrid team to outperform both agents taken alone. We discuss the features of the human-AI interaction that determined this favorable outcome.
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Affiliation(s)
- Carlo Reverberi
- Department of Psychology, University of Milano-Bicocca, 20126, Milan, Italy.
- Milan Center for Neuroscience, University of Milano-Bicocca, 20126, Milan, Italy.
| | - Tommaso Rigon
- Department of Economics, Management and Statistics, University of Milano-Bicocca, 20126, Milan, Italy
| | - Aldo Solari
- Milan Center for Neuroscience, University of Milano-Bicocca, 20126, Milan, Italy
- Department of Economics, Management and Statistics, University of Milano-Bicocca, 20126, Milan, Italy
| | - Cesare Hassan
- Department of Biomedical Sciences, Humanitas University, 20072, Pieve Emanuele, Italy
- Endoscopy Unit, Humanitas Clinical and Research Center IRCCS, Rozzano, Italy
| | - Paolo Cherubini
- Department of Psychology, University of Milano-Bicocca, 20126, Milan, Italy
- Milan Center for Neuroscience, University of Milano-Bicocca, 20126, Milan, Italy
- Department of Neural and Behavioral Sciences, University of Pavia, Pavia, Italy
| | - Andrea Cherubini
- Milan Center for Neuroscience, University of Milano-Bicocca, 20126, Milan, Italy.
- Artificial Intelligence Group, Cosmo AI/Linkverse, Lainate, 20045, Milan, Italy.
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19
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Computer copilots for endoscopic diagnosis. NPJ Digit Med 2022; 5:129. [PMID: 36050460 PMCID: PMC9436955 DOI: 10.1038/s41746-022-00678-7] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Track Full Text] [Download PDF] [Journal Information] [Subscribe] [Scholar Register] [Received: 07/23/2022] [Accepted: 08/15/2022] [Indexed: 11/08/2022] Open
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