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Sakamoto T, Oda I, Okamura T, Cho H, Toyoshima N, Nonaka S, Suzuki H, Nakamura T, Watanabe D, Matsuo K, Hanano K, Takeyama T, Yoshinaga S, Saito Y. Exploratory investigation of virtual lesions in gastrointestinal endoscopy using a novel phase-shift method for three-dimensional shape measurement. DEN OPEN 2025; 5:e381. [PMID: 38725875 PMCID: PMC11079539 DOI: 10.1002/deo2.381] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Figures] [Subscribe] [Scholar Register] [Received: 02/15/2024] [Revised: 04/17/2024] [Accepted: 04/21/2024] [Indexed: 05/12/2024]
Abstract
Accurate measurement of the size of lesions or distances between any two points during endoscopic examination of the gastrointestinal tract is difficult owing to the fisheye lens used in endoscopy. To overcome this issue, we developed a phase-shift method to measure three-dimensional (3D) data on a curved surface, which we present herein. Our system allows the creation of 3D shapes on a curved surface by the phase-shift method using a stripe pattern projected from a small projecting device to an object. For evaluation, 88 measurement points were inserted in porcine stomach tissue, attached to a half-pipe jig, with an inner radius of 21 mm. The accuracy and precision of the measurement data for our shape measurement system were compared with the data obtained using an Olympus STM6 measurement microscope. The accuracy of the path length of a simulated protruded lesion was evaluated using a plaster model of the curved stomach and graph paper. The difference in height measures between the measurement microscope and measurement system data was 0.24 mm for the 88 measurement points on the curved surface of the porcine stomach. The error in the path length measurement for a lesion on an underlying curved surface was <1% for a 10-mm lesion. The software was developed for the automated calculation of the major and minor diameters of each lesion. The accuracy of our measurement system could improve the accuracy of determining the size of lesions, whether protruded or depressed, regardless of the curvature of the underlying surface.
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Affiliation(s)
- Taku Sakamoto
- Endoscopy DivisionNational Cancer Center HospitalTokyoJapan
| | - Ichiro Oda
- Endoscopy DivisionNational Cancer Center HospitalTokyoJapan
| | - Takuma Okamura
- Endoscopy DivisionNational Cancer Center HospitalTokyoJapan
| | - Hourin Cho
- Endoscopy DivisionNational Cancer Center HospitalTokyoJapan
| | - Naoya Toyoshima
- Endoscopy DivisionNational Cancer Center HospitalTokyoJapan
- Division of Science and Technology for EndoscopyExploratory Oncology Research and Clinical Trial CenterNational Cancer CenterTokyoJapan
| | - Satoru Nonaka
- Endoscopy DivisionNational Cancer Center HospitalTokyoJapan
- Division of Science and Technology for EndoscopyExploratory Oncology Research and Clinical Trial CenterNational Cancer CenterTokyoJapan
| | - Haruhisa Suzuki
- Endoscopy DivisionNational Cancer Center HospitalTokyoJapan
- Division of Science and Technology for EndoscopyExploratory Oncology Research and Clinical Trial CenterNational Cancer CenterTokyoJapan
| | - Tatsuya Nakamura
- Optical EngineeringOlympus Medical Systems CorporationTokyoJapan
| | - Daichi Watanabe
- Optical EngineeringOlympus Medical Systems CorporationTokyoJapan
| | - Keigo Matsuo
- Optical EngineeringOlympus Medical Systems CorporationTokyoJapan
| | - Kazunari Hanano
- Optical EngineeringOlympus Medical Systems CorporationTokyoJapan
| | | | - Shigetaka Yoshinaga
- Endoscopy DivisionNational Cancer Center HospitalTokyoJapan
- Division of Science and Technology for EndoscopyExploratory Oncology Research and Clinical Trial CenterNational Cancer CenterTokyoJapan
| | - Yutaka Saito
- Endoscopy DivisionNational Cancer Center HospitalTokyoJapan
- Division of Science and Technology for EndoscopyExploratory Oncology Research and Clinical Trial CenterNational Cancer CenterTokyoJapan
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Kafetzis I, Fuchs KH, Sodmann P, Troya J, Zoller W, Meining A, Hann A. Efficient artificial intelligence-based assessment of the gastroesophageal valve with Hill classification through active learning. Sci Rep 2024; 14:18825. [PMID: 39138220 PMCID: PMC11322637 DOI: 10.1038/s41598-024-68866-x] [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: 02/29/2024] [Accepted: 07/29/2024] [Indexed: 08/15/2024] Open
Abstract
Standardized assessment of the gastroesophageal valve during endoscopy, attainable via the Hill classification, is important for clinical assessment and therapeutic decision making. The Hill classification is associated with the presence of hiatal hernia (HH), a common endoscopic finding connected to gastro-esophageal reflux disease. A novel efficient medical artificial intelligence (AI) training pipeline using active learning (AL) is designed. We identified 21,970 gastroscopic images as training data and used our AL to train a model for predicting the Hill classification and detecting HH. Performance of the AL and traditionally trained models were evaluated on an external expert-annotated image collection. The AL model achieved accuracy of 76%. A traditionally trained model with 125% more training data achieved 77% accuracy. Furthermore, the AL model achieved higher precision than the traditional one for rare classes, with 0.54 versus 0.39 (p < 0.05) for grade 3 and 0.72 versus 0.61 (p < 0.05) for grade 4. In detecting HH, the AL model achieved 94% accuracy, 0.72 precision and 0.74 recall. Our AL pipeline is more efficient than traditional methods in training AI for endoscopy.
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Affiliation(s)
- Ioannis Kafetzis
- Interventional and Experimental Endoscopy (InExEn), Department of Internal Medicine 2, University Hospital Würzburg, Oberdürrbacherstr. 6, 97080, Würzburg, Germany.
| | - Karl-Hermann Fuchs
- Interventional and Experimental Endoscopy (InExEn), Department of Internal Medicine 2, University Hospital Würzburg, Oberdürrbacherstr. 6, 97080, Würzburg, Germany
| | - Philipp Sodmann
- Interventional and Experimental Endoscopy (InExEn), Department of Internal Medicine 2, University Hospital Würzburg, Oberdürrbacherstr. 6, 97080, Würzburg, Germany
| | - Joel Troya
- Interventional and Experimental Endoscopy (InExEn), Department of Internal Medicine 2, University Hospital Würzburg, Oberdürrbacherstr. 6, 97080, Würzburg, Germany
| | - Wolfram Zoller
- Clinic for General Internal Medicine, Gastroenterology, Hepatology and Infectiology, Pneumology, Klinikum Stuttgart-Katharinenhospital, Kriegsbergstr. 60, 70174, Stuttgart, Germany
| | - Alexander Meining
- Interventional and Experimental Endoscopy (InExEn), Department of Internal Medicine 2, University Hospital Würzburg, Oberdürrbacherstr. 6, 97080, Würzburg, Germany
| | - Alexander Hann
- Interventional and Experimental Endoscopy (InExEn), Department of Internal Medicine 2, University Hospital Würzburg, Oberdürrbacherstr. 6, 97080, Würzburg, Germany
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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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Mori Y. New horizons in polyp size estimation. Endoscopy 2024; 56:271-272. [PMID: 38216131 DOI: 10.1055/a-2224-0756] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [MESH Headings] [Track Full Text] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Indexed: 01/14/2024]
Affiliation(s)
- Yuichi Mori
- Clinical Effectiveness Research Group, Institute of Health and Society, University of Oslo, Oslo, Norway
- Department of Transplantation Medicine, Oslo University Hospital, Oslo, Norway
- Digestive Disease Center, Showa University Northern Yokohama Hospital, Yokohama, Japan
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Campion JR, O'Connor DB, Lahiff C. Human-artificial intelligence interaction in gastrointestinal endoscopy. World J Gastrointest Endosc 2024; 16:126-135. [PMID: 38577646 PMCID: PMC10989254 DOI: 10.4253/wjge.v16.i3.126] [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: 12/31/2023] [Revised: 01/18/2024] [Accepted: 02/23/2024] [Indexed: 03/14/2024] Open
Abstract
The number and variety of applications of artificial intelligence (AI) in gastrointestinal (GI) endoscopy is growing rapidly. New technologies based on machine learning (ML) and convolutional neural networks (CNNs) are at various stages of development and deployment to assist patients and endoscopists in preparing for endoscopic procedures, in detection, diagnosis and classification of pathology during endoscopy and in confirmation of key performance indicators. Platforms based on ML and CNNs require regulatory approval as medical devices. Interactions between humans and the technologies we use are complex and are influenced by design, behavioural and psychological elements. Due to the substantial differences between AI and prior technologies, important differences may be expected in how we interact with advice from AI technologies. Human–AI interaction (HAII) may be optimised by developing AI algorithms to minimise false positives and designing platform interfaces to maximise usability. Human factors influencing HAII may include automation bias, alarm fatigue, algorithm aversion, learning effect and deskilling. Each of these areas merits further study in the specific setting of AI applications in GI endoscopy and professional societies should engage to ensure that sufficient emphasis is placed on human-centred design in development of new AI technologies.
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Affiliation(s)
- John R Campion
- Department of Gastroenterology, Mater Misericordiae University Hospital, Dublin D07 AX57, Ireland
- School of Medicine, University College Dublin, Dublin D04 C7X2, Ireland
| | - Donal B O'Connor
- Department of Surgery, Trinity College Dublin, Dublin D02 R590, Ireland
| | - Conor Lahiff
- Department of Gastroenterology, Mater Misericordiae University Hospital, Dublin D07 AX57, Ireland
- School of Medicine, University College Dublin, Dublin D04 C7X2, Ireland
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Lux TJ, Herold K, Kafetzis I, Sodmann P, Sassmanshausen Z, Meining A, Hann A. Closing the Gap: A Critical Examination of Adherence, Inconsistency, and Improvements in Colonoscopy Reporting Practices. Digestion 2024; 105:224-231. [PMID: 38479373 PMCID: PMC11151964 DOI: 10.1159/000538113] [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: 10/02/2023] [Accepted: 02/16/2024] [Indexed: 06/05/2024]
Abstract
INTRODUCTION Comprehensive and standardized colonoscopy reports are crucial in colorectal cancer prevention, monitoring, and research. This study investigates adherence to national and international guidelines by analyzing reporting practices among 21 endoscopists in 7 German centers, with a focus on polyp reporting. METHODS We identified and assessed German, European, American, and World Health Organization-provided statements to identify key elements in colonoscopy reporting. Board-certified gastroenterologists rated the relevance of each element and estimated their reporting frequency. Adherence to the identified report elements was evaluated for 874 polyps from 351 colonoscopy reports ranging from March 2021 to March 2022. RESULTS We identified numerous recommendations for colonoscopy reporting. We categorized the reasoning behind those recommendations into clinical relevance, justification, and quality control and research. Although all elements were considered relevant by the surveyed gastroenterologists, discrepancies were observed in the evaluated reports. Particularly diminutive polyps or attributes which are rarely abnormal (e.g., surface integrity) respectively rarely performed (e.g., injection) were sparsely documented. Furthermore, the white light morphology of polyps was inconsistently documented using either the Paris classification or free text. In summary, the analysis of 874 reported polyps revealed heterogeneous adherence to the recommendations, with reporting frequencies ranging from 3% to 89%. CONCLUSION The inhomogeneous report practices may result from implicit reporting practices and recommendations with varying clinical relevance. Future recommendations should clearly differentiate between clinical relevance and research and quality control or explanatory purposes. Additionally, the role of computer-assisted documentation should be further evaluated to increase report frequencies of non-pathological findings and diminutive polyps.
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Affiliation(s)
- Thomas J Lux
- Interventional and Experimental Endoscopy (InExEn), Internal Medicine II, University Hospital Würzburg, Würzburg, Germany
| | - Katja Herold
- Interventional and Experimental Endoscopy (InExEn), Internal Medicine II, University Hospital Würzburg, Würzburg, Germany
| | - Ioannis Kafetzis
- Interventional and Experimental Endoscopy (InExEn), Internal Medicine II, University Hospital Würzburg, Würzburg, Germany
| | - Phillip Sodmann
- Interventional and Experimental Endoscopy (InExEn), Internal Medicine II, University Hospital Würzburg, Würzburg, Germany
| | - Zita Sassmanshausen
- Interventional and Experimental Endoscopy (InExEn), Internal Medicine II, University Hospital Würzburg, Würzburg, Germany
| | - Alexander Meining
- Interventional and Experimental Endoscopy (InExEn), Internal Medicine II, University Hospital Würzburg, Würzburg, Germany
| | - Alexander Hann
- Interventional and Experimental Endoscopy (InExEn), Internal Medicine II, University Hospital Würzburg, Würzburg, Germany
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Duan ZH, Zhou SY. Biopsy forceps are useful for measuring esophageal varices in vitro. World J Gastrointest Surg 2024; 16:539-545. [PMID: 38463364 PMCID: PMC10921203 DOI: 10.4240/wjgs.v16.i2.539] [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: 10/15/2023] [Revised: 12/20/2023] [Accepted: 01/09/2024] [Indexed: 02/25/2024] Open
Abstract
BACKGROUND To avoid acute variceal bleeding in cirrhosis, current guidelines recommend screening for high-risk esophageal varices (EVs) by determining variceal size and identifying red wale markings. However, visual measurements of EV during routine endoscopy are often inaccurate. AIM To determine whether biopsy forceps (BF) could be used as a reference to improve the accuracy of binary classification of variceal size. METHODS An in vitro self-made EV model with sizes ranging from 2 to 12 mm in diameter was constructed. An online image-based survey comprising 11 endoscopic images of simulated EV without BF and 11 endoscopic images of EV with BF was assembled and sent to 84 endoscopists. The endoscopists were blinded to the actual EV size and evaluated the 22 images in random order. RESULTS The respondents included 48 academic and four private endoscopists. The accuracy of EV size estimation was low in both the visual (13.81%) and BF-based (20.28%) groups. The use of open forceps improved the ability of the endoscopists to correctly classify the varices by size (small ≤ 5 mm, large > 5 mm) from 71.85% to 82.17% (P < 0.001). CONCLUSION BF may improve the accuracy of EV size assessment, and its use in clinical practice should be investigated.
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Affiliation(s)
- Zhi-Hui Duan
- Endoscopy Center, Xingtai People’s Hospital, Xingtai 054000, Hebei Province, China
| | - Sheng-Yun Zhou
- Endoscopy Center, Xingtai People’s Hospital, Xingtai 054000, Hebei Province, China
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