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Sugino S, Yoshida N, Guo Z, Zhang R, Inoue K, Hirose R, Dohi O, Itoh Y, Nemoto D, Togashi K, Yamamoto H, Zhu X. Non-polypoid Colorectal Lesions Detection and False Positive Detection by Artificial Intelligence under Blue Laser Imaging and Linked Color Imaging. J Anus Rectum Colon 2024; 8:212-220. [PMID: 39086882 PMCID: PMC11286363 DOI: 10.23922/jarc.2023-070] [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: 11/28/2023] [Accepted: 03/28/2024] [Indexed: 08/02/2024] Open
Abstract
Objectives Artificial intelligence (AI) with white light imaging (WLI) is not enough for detecting non-polypoid colorectal polyps and it still has high false positive rate (FPR). We developed AIs using blue laser imaging (BLI) and linked color imaging (LCI) to detect them with specific learning sets (LS). Methods The contents of LS were as follows, LS (WLI): 1991 WLI images of lesion of 2-10 mm, LS (IEE): 5920 WLI, BLI, and LCI images of non-polypoid and small lesions of 2-20 mm. LS (IEE) was extracted from videos and included both in-focus and out-of-focus images. We designed three AIs as follows: AI (WLI) finetuned by LS (WLI), AI (IEE) finetuned by LS (WLI)+LS (IEE), and AI (HQ) finetuned by LS (WLI)+LS (IEE) only with images in focus. Polyp detection using a test set of WLI, BLI, and LCI videos of 100 non-polypoid or non-reddish lesions of 2-20 mm and FPR using movies of 15 total colonoscopy were analyzed, compared to 2 experts and 2 trainees. Results The sensitivity for LCI in AI (IEE) (83%) was compared to that for WLI in AI (IEE) (76%: p=0.02), WLI in AI (WLI) (57%: p<0.01), BLI in AI (IEE) (78%: p=0.14), and LCI in trainees (74%: p<0.01). The sensitivity for LCI in AI (IEE) (83%) was significantly higher than that in AI (HQ) (78%: p<0.01). The FPR for LCI (6.5%) in AI (IEE) were significantly lower than that in AI (HQ) (17.3%: p<0.01). Conclusions AI finetuned by appropriate LS detected non-reddish and non-polypoid polyps under LCI.
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Affiliation(s)
- Satoshi Sugino
- Department of Gastroenterology, Asahi University Hospital, Gifu, Japan
| | - Naohisa Yoshida
- Department of Gastroenterology, Kyoto Prefectural University of Medicine, Kyoto, Japan
| | - Zhe Guo
- Biomedical Information Engineering Lab, The University of Aizu, Fukushima, Japan
| | - Ruiyao Zhang
- Biomedical Information Engineering Lab, The University of Aizu, Fukushima, Japan
| | - Ken Inoue
- Department of Gastroenterology, Kyoto Prefectural University of Medicine, Kyoto, Japan
| | - Ryohei Hirose
- Department of Gastroenterology, Kyoto Prefectural University of Medicine, Kyoto, Japan
| | - Osamu Dohi
- Department of Gastroenterology, Kyoto Prefectural University of Medicine, Kyoto, Japan
| | - Yoshito Itoh
- Department of Gastroenterology, Kyoto Prefectural University of Medicine, Kyoto, Japan
| | - Daiki Nemoto
- Department of Coloproctology, Aizu Medical Center, Fukushima Medical University, Fukushima, Japan
| | - Kazutomo Togashi
- Department of Coloproctology, Aizu Medical Center, Fukushima Medical University, Fukushima, Japan
| | - Hironori Yamamoto
- Department of Gastroenterology, Jichi Medical University, Tochigi, Japan
| | - Xin Zhu
- Biomedical Information Engineering Lab, The University of Aizu, Fukushima, Japan
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Mandarino FV, Danese S, Uraoka T, Parra-Blanco A, Maeda Y, Saito Y, Kudo SE, Bourke MJ, Iacucci M. Precision endoscopy in colorectal polyps' characterization and planning of endoscopic therapy. Dig Endosc 2024; 36:761-777. [PMID: 37988279 DOI: 10.1111/den.14727] [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: 08/25/2023] [Accepted: 11/19/2023] [Indexed: 11/23/2023]
Abstract
Precision endoscopy in the management of colorectal polyps and early colorectal cancer has emerged as the standard of care. It includes optical characterization of polyps and estimation of submucosal invasion depth of large nonpedunculated colorectal polyps to select the appropriate endoscopic resection modality. Over time, several imaging modalities have been implemented in endoscopic practice to improve optical performance. Among these, image-enhanced endoscopy systems and magnification endoscopy represent now well-established tools. New advanced technologies, such as endocytoscopy and confocal laser endomicroscopy, have recently shown promising results in predicting the histology of colorectal polyps. In recent years, artificial intelligence has continued to enhance endoscopic performance in the characterization of colorectal polyps, overcoming the limitations of other imaging modes. In this review we retrace the path of precision endoscopy, analyzing the yield of various endoscopic imaging techniques in personalizing management of colorectal polyps and early colorectal cancer.
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Affiliation(s)
- Francesco Vito Mandarino
- Department of Gastroenterology and Gastrointestinal Endoscopy, San Raffaele Hospital IRCSS, Milan, Italy
- Department of Gastrointestinal Endoscopy, Westmead Hospital, Sydney, NSW, Australia
| | - Silvio Danese
- Department of Gastroenterology and Gastrointestinal Endoscopy, San Raffaele Hospital IRCSS, Milan, Italy
| | - Toshio Uraoka
- Department of Gastroenterology and Hepatology, Gunma University Graduate School of Medicine, Gumma, Japan
| | - Adolfo Parra-Blanco
- NIHR Nottingham Biomedical Research Centre, Nottingham University Hospitals NHS Trust and the University of Nottingham, Nottingham, UK
| | - Yasuharu Maeda
- Digestive Disease Center, Showa University Northern Yokohama Hospital, Kanagawa, Japan
| | - Yutaka Saito
- Endoscopy Division, National Cancer Center Hospital, Tokyo, Japan
| | - Shin-Ei Kudo
- Digestive Disease Center, Showa University Northern Yokohama Hospital, Kanagawa, Japan
| | - Michael J Bourke
- Department of Gastrointestinal Endoscopy, Westmead Hospital, Sydney, NSW, Australia
| | - Marietta Iacucci
- Department of Gastroenterology, University College Cork, Cork, Ireland
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Weng W, Yoshida N, Morinaga Y, Sugino S, Tomita Y, Kobayashi R, Inoue K, Hirose R, Dohi O, Itoh Y, Zhu X. Development of high-quality artificial intelligence for computer-aided diagnosis in determining subtypes of colorectal cancer. J Gastroenterol Hepatol 2024. [PMID: 38923607 DOI: 10.1111/jgh.16661] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 12/22/2023] [Revised: 03/14/2024] [Accepted: 06/09/2024] [Indexed: 06/28/2024]
Abstract
BACKGROUND AND AIM There are no previous studies in which computer-aided diagnosis (CAD) diagnosed colorectal cancer (CRC) subtypes correctly. In this study, we developed an original CAD for the diagnosis of CRC subtypes. METHODS Pretraining for the CAD based on ResNet was performed using ImageNet and five open histopathological pretraining image datasets (HiPreD) containing 3 million images. In addition, sparse attention was introduced to improve the CAD compared to other attention networks. One thousand and seventy-two histopathological images from 29 early CRC cases at Kyoto Prefectural University of Medicine from 2019 to 2022 were collected (857 images for training and validation, 215 images for test). All images were annotated by a qualified histopathologist for segmentation of normal mucosa, adenoma, pure well-differentiated adenocarcinoma (PWDA), and moderately/poorly differentiated adenocarcinoma (MPDA). Diagnostic ability including dice sufficient coefficient (DSC) and diagnostic accuracy were evaluated. RESULTS Our original CAD, named Colon-seg, with the pretraining of both HiPreD and ImageNET showed a better DSC (88.4%) compared to CAD without both pretraining (76.8%). Regarding the attentional mechanism, Colon-seg with sparse attention showed a better DSC (88.4%) compared to other attentional mechanisms (dual: 79.7%, ECA: 80.7%, shuffle: 84.7%, SK: 86.9%). In addition, the DSC of Colon-seg (88.4%) was better than other types of CADs (TransUNet: 84.7%, MultiResUnet: 86.1%, Unet++: 86.7%). The diagnostic accuracy of Colon-seg for each histopathological type was 94.3% for adenoma, 91.8% for PWDA, and 92.8% for MPDA. CONCLUSION A deep learning-based CAD for CRC subtype differentiation was developed with pretraining and fine-tuning of abundant histopathological images.
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Affiliation(s)
- Weihao Weng
- Graduate School of Computer Science and Engineering, The University of Aizu, Aizuwakamatsu, Japan
| | - Naohisa Yoshida
- Department of Molecular Gastroenterology and Hepatology, Graduate School of Medical Science, Kyoto Prefectural University of Medicine, Kyoto, Japan
| | - Yukiko Morinaga
- Department of Surgical Pathology, Graduate School of Medical Science, Kyoto Prefectural University of Medicine, Kyoto, Japan
| | - Satoshi Sugino
- Department of Gastroenterology, Asahi University Hospital, Gifu, Japan
| | - Yuri Tomita
- Department of Gastroenterology, Koseikai Takeda Hospital, Kyoto, Japan
| | - Reo Kobayashi
- Department of Molecular Gastroenterology and Hepatology, Graduate School of Medical Science, Kyoto Prefectural University of Medicine, Kyoto, Japan
| | - Ken Inoue
- Department of Molecular Gastroenterology and Hepatology, Graduate School of Medical Science, Kyoto Prefectural University of Medicine, Kyoto, Japan
| | - Ryohei Hirose
- Department of Molecular Gastroenterology and Hepatology, Graduate School of Medical Science, Kyoto Prefectural University of Medicine, Kyoto, Japan
| | - Osamu Dohi
- Department of Molecular Gastroenterology and Hepatology, Graduate School of Medical Science, Kyoto Prefectural University of Medicine, Kyoto, Japan
| | - Yoshito Itoh
- Department of Molecular Gastroenterology and Hepatology, Graduate School of Medical Science, Kyoto Prefectural University of Medicine, Kyoto, Japan
| | - Xin Zhu
- Graduate School of Computer Science and Engineering, The University of Aizu, Aizuwakamatsu, Japan
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Shimada Y, Ojima T, Takaoka Y, Sugano A, Someya Y, Hirabayashi K, Homma T, Kitamura N, Akemoto Y, Tanabe K, Sato F, Yoshimura N, Tsuchiya T. Prediction of visceral pleural invasion of clinical stage I lung adenocarcinoma using thoracoscopic images and deep learning. Surg Today 2024; 54:540-550. [PMID: 37864054 DOI: 10.1007/s00595-023-02756-z] [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: 03/20/2023] [Accepted: 09/13/2023] [Indexed: 10/22/2023]
Abstract
PURPOSE To develop deep learning models using thoracoscopic images to identify visceral pleural invasion (VPI) in patients with clinical stage I lung adenocarcinoma, and to verify if these models can be applied clinically. METHODS Two deep learning models, one based on a convolutional neural network (CNN) and the other based on a vision transformer (ViT), were applied and trained via 463 images (VPI negative: 269 images, VPI positive: 194 images) captured from surgical videos of 81 patients. Model performances were validated via an independent test dataset containing 46 images (VPI negative: 28 images, VPI positive: 18 images) from 46 test patients. RESULTS The areas under the receiver operating characteristic curves of the CNN-based and ViT-based models were 0.77 and 0.84 (p = 0.304), respectively. The accuracy, sensitivity, specificity, and positive and negative predictive values were 73.91, 83.33, 67.86, 62.50, and 86.36% for the CNN-based model and 78.26, 77.78, 78.57, 70.00, and 84.62% for the ViT-based model, respectively. These models' diagnostic abilities were comparable to those of board-certified thoracic surgeons and tended to be superior to those of non-board-certified thoracic surgeons. CONCLUSION The deep learning model systems can be utilized in clinical applications via data expansion.
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Affiliation(s)
- Yoshifumi Shimada
- Department of Thoracic Surgery, University of Toyama, 2630 Sugitani, Toyama, Japan
| | - Toshihiro Ojima
- Department of Thoracic Surgery, University of Toyama, 2630 Sugitani, Toyama, Japan
| | - Yutaka Takaoka
- Data Science Center for Medicine and Hospital Management, Toyama University Hospital, 2630 Sugitani, Toyama, Japan
- Center for Data Science and Artificial Intelligence Research Promotion, Toyama University Hospital, 2630 Sugitani, Toyama, Japan
| | - Aki Sugano
- Data Science Center for Medicine and Hospital Management, Toyama University Hospital, 2630 Sugitani, Toyama, Japan
- Center for Clinical Research, Toyama University Hospital, 2630 Sugitani, Toyama, Japan
| | - Yoshiaki Someya
- Center for Data Science and Artificial Intelligence Research Promotion, Toyama University Hospital, 2630 Sugitani, Toyama, Japan
| | - Kenichi Hirabayashi
- Department of Diagnostic Pathology, University of Toyama, 2630 Sugitani, Toyama, Japan
| | - Takahiro Homma
- Department of Thoracic Surgery, University of Toyama, 2630 Sugitani, Toyama, Japan
| | - Naoya Kitamura
- Department of Thoracic Surgery, University of Toyama, 2630 Sugitani, Toyama, Japan
| | - Yushi Akemoto
- Department of Thoracic Surgery, University of Toyama, 2630 Sugitani, Toyama, Japan
| | - Keitaro Tanabe
- Department of Thoracic Surgery, University of Toyama, 2630 Sugitani, Toyama, Japan
| | - Fumitaka Sato
- Department of Thoracic Surgery, University of Toyama, 2630 Sugitani, Toyama, Japan
| | - Naoki Yoshimura
- Department of Cardiovascular Surgery, University of Toyama, 2630 Sugitani, Toyama, Japan
| | - Tomoshi Tsuchiya
- Department of Thoracic Surgery, University of Toyama, 2630 Sugitani, Toyama, Japan.
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Cheng Y, Li L, Bi Y, Su S, Zhang B, Feng X, Wang N, Zhang W, Yao Y, Ru N, Xiang J, Sun L, Hu K, Wen F, Wang Z, Bai L, Wang X, Wang R, Lv X, Wang P, Meng F, Xiao W, Linghu E, Chai N. Computer-aided diagnosis system for optical diagnosis of colorectal polyps under white light imaging. Dig Liver Dis 2024:S1590-8658(24)00723-0. [PMID: 38744557 DOI: 10.1016/j.dld.2024.04.023] [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: 12/31/2023] [Revised: 03/21/2024] [Accepted: 04/23/2024] [Indexed: 05/16/2024]
Abstract
OBJECTIVES This study presents a novel computer-aided diagnosis (CADx) designed for optically diagnosing colorectal polyps using white light imaging (WLI).We aimed to evaluate the effectiveness of the CADx and its auxiliary role among endoscopists with different levels of expertise. METHODS We collected 2,324 neoplastic and 3,735 nonneoplastic polyp WLI images for model training, and 838 colorectal polyp images from 740 patients for model validation. We compared the diagnostic accuracy of the CADx with that of 15 endoscopists under WLI and narrow band imaging (NBI). The auxiliary benefits of CADx for endoscopists of different experience levels and for identifying different types of colorectal polyps was also evaluated. RESULTS The CADx demonstrated an optical diagnostic accuracy of 84.49%, showing considerable superiority over all endoscopists, irrespective of whether WLI or NBI was used (P < 0.001). Assistance from the CADx significantly improved the diagnostic accuracy of the endoscopists from 68.84% to 77.49% (P = 0.001), with the most significant impact observed among novice endoscopists. Notably, novices using CADx-assisted WLI outperform junior and expert endoscopists without such assistance. CONCLUSIONS The CADx demonstrated a crucial role in substantially enhancing the precision of optical diagnosis for colorectal polyps under WLI and showed the greatest auxiliary benefits for novice endoscopists.
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Affiliation(s)
- Yaxuan Cheng
- Chinese PLA Medical School, Beijing, 100853, PR China; Department of Gastroenterology, The First Medical Center of Chinese PLA General Hospital, Beijing, 100853, PR China
| | - Longsong Li
- Department of Gastroenterology, The First Medical Center of Chinese PLA General Hospital, Beijing, 100853, PR China
| | - Yawei Bi
- Department of Gastroenterology, The First Medical Center of Chinese PLA General Hospital, Beijing, 100853, PR China
| | - Song Su
- Department of Gastroenterology, The First Medical Center of Chinese PLA General Hospital, Beijing, 100853, PR China
| | - Bo Zhang
- Department of Gastroenterology, The First Medical Center of Chinese PLA General Hospital, Beijing, 100853, PR China
| | - Xiuxue Feng
- Department of Gastroenterology, The First Medical Center of Chinese PLA General Hospital, Beijing, 100853, PR China
| | - Nanjun Wang
- Department of Gastroenterology, The First Medical Center of Chinese PLA General Hospital, Beijing, 100853, PR China
| | - Wengang Zhang
- Department of Gastroenterology, The First Medical Center of Chinese PLA General Hospital, Beijing, 100853, PR China
| | - Yi Yao
- Department of Gastroenterology, The First Medical Center of Chinese PLA General Hospital, Beijing, 100853, PR China
| | - Nan Ru
- Department of Gastroenterology, The First Medical Center of Chinese PLA General Hospital, Beijing, 100853, PR China
| | - Jingyuan Xiang
- Department of Gastroenterology, The First Medical Center of Chinese PLA General Hospital, Beijing, 100853, PR China
| | - Lihua Sun
- Department of Gastroenterology, The First Medical Center of Chinese PLA General Hospital, Beijing, 100853, PR China
| | - Kang Hu
- Department of Gastroenterology, The 987 Hospital of PLA Joint Logistic Support Force, Baoji, 721004, PR China
| | - Feng Wen
- Department of Gastroenterology, General Hospital of Central Theater Command of PLA,Wuhan 430070, PR China
| | - Zixin Wang
- Department of Gastroenterology, The First Medical Center of Chinese PLA General Hospital, Beijing, 100853, PR China
| | - Lu Bai
- Department of Gastroenterology, The First Medical Center of Chinese PLA General Hospital, Beijing, 100853, PR China
| | - Xueting Wang
- Department of Gastroenterology, The First Medical Center of Chinese PLA General Hospital, Beijing, 100853, PR China
| | - Runzi Wang
- Department of Gastroenterology, The First Medical Center of Chinese PLA General Hospital, Beijing, 100853, PR China
| | - Xingping Lv
- Department of Gastroenterology, The First Medical Center of Chinese PLA General Hospital, Beijing, 100853, PR China
| | - Pengju Wang
- Chinese PLA Medical School, Beijing, 100853, PR China; Department of Gastroenterology, The First Medical Center of Chinese PLA General Hospital, Beijing, 100853, PR China
| | - Fanqi Meng
- Medical Department, HighWise Medical Technology Co, Ltd, Changsha, 410000, PR China
| | - Wen Xiao
- Medical Department, HighWise Medical Technology Co, Ltd, Changsha, 410000, PR China
| | - Enqiang Linghu
- Department of Gastroenterology, The First Medical Center of Chinese PLA General Hospital, Beijing, 100853, PR China.
| | - Ningli Chai
- Department of Gastroenterology, The First Medical Center of Chinese PLA General Hospital, Beijing, 100853, PR China.
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Zhao X, Zhang G, Chen J, Li Z, Shi Y, Li G, Zhai C, Nie L. A rationally designed nuclei-targeting FAPI 04-based molecular probe with enhanced tumor uptake for PET/CT and fluorescence imaging. Eur J Nucl Med Mol Imaging 2024; 51:1593-1604. [PMID: 38512485 DOI: 10.1007/s00259-024-06691-0] [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: 12/18/2023] [Accepted: 03/12/2024] [Indexed: 03/23/2024]
Abstract
PURPOSE Fibroblast activation protein inhibitor (FAPI) -based probes have been widely studied in the diagnosis of various malignant tumors with positron emission tomography/computed tomography (PET/CT). However, current imaging studies of FAPI-based probes face challenges in rapid clearance rate and potential false-negative results. Furthermore, FAPI has been rarely explored in optical imaging. Considering this, further modifications are imperative to improve the properties of FAPI-based probes to address existing limitations and broaden their application scenarios. In this study, we rationally introduced methylene blue (MB) to FAPIs, thereby imparting nuclei-targeting and fluorescence imaging capabilities to the probes. Furthermore, we evaluated the added value of FAPI-based fluorescence imaging to traditional PET/CT, exploring the potential application of FAPI-based probes in intraoperative fluorescence imaging. METHODS A new FAPI-based probe, namely NOTA-FAPI-MB, was designed for both PET/CT and fluorescence imaging by conjugation of MB. The targeting efficacy of the probe was evaluated on fibroblast activation protein (FAP)-transfected cell line and human primary cancer-associated fibroblasts (CAFs). Subsequently, PET/CT and fluorescence imaging were conducted on tumor-bearing mice. The tumor detection and boundary delineation were assessed by fluorescence imaging of tissues from hepatocellular carcinoma (HCC) patients. RESULTS NOTA-FAPI-MB demonstrated exceptional targeting ability towards FAP-transfected cells and CAFs in comparison to NOTA-FAPI. This benefit arises from the cationic methylene blue (MB) affinity for anionic nucleic acids. PET/CT imaging of tumor-bearing mice revealed significantly higher tumor uptake of [18F]F-NOTA-FAPI-MB (standard uptake value of 2.20 ± 0.31) compared to [18F]F-FDG (standard uptake value of 1.66 ± 0.14). In vivo fluorescence imaging indicated prolonged retention at the tumor site, with retention lasting up to 24 h. In addition, the fluorescent probes enabled more precise lesion detection and tumor margin delineation than clinically used indocyanine green (ICG), achieving a 100.0% (6/6) tumor-positive rate for NOTA-FAPI-MB while 33.3% (2/6) for ICG. These findings highlighted the potential of NOTA-FAPI-MB in guiding intraoperative surgical procedures. CONCLUSIONS The NOTA-FAPI-MB was successfully synthesized, in which FAPI and MB simultaneously contributed to the targeting effect. Notably, the nuclear delivery mechanism of the probes improved intracellular retention time and targeting efficacy, broadening the imaging time window for fluorescence imaging. In vivo PET/CT demonstrated favorable performance of NOTA-FAPI-MB compared to [18F]F-FDG. This study highlights the significance of fluorescence imaging as an adjunct technique to PET/CT. Furthermore, the encouraging results obtained from the imaging of human HCC tissues hold promise for the potential application of NOTA-FAPI-MB in intraoperative fluorescent surgery guidance within clinical settings.
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Affiliation(s)
- Xingyang Zhao
- Medical Research Institute, Guangdong Provincial People's Hospital (Guangdong Academy of Medical Sciences), Southern Medical University, Guangzhou, 510080, China
| | - Guojin Zhang
- PET Center, Department of Nuclear Medicine, Guangdong Provincial People's Hospital (Guangdong Academy of Medical Sciences), Southern Medical University, Guangzhou, 510080, China
| | - Jiali Chen
- Medical Research Institute, Guangdong Provincial People's Hospital (Guangdong Academy of Medical Sciences), Southern Medical University, Guangzhou, 510080, China
| | - Zirong Li
- Medical Research Institute, Guangdong Provincial People's Hospital (Guangdong Academy of Medical Sciences), Southern Medical University, Guangzhou, 510080, China
| | - Yusheng Shi
- Department of Radiation Oncology, Zhuhai People's Hospital, Zhuhai Hospital Affiliated with Jinan University, Zhuhai, 519000, China
| | - Guiting Li
- Research and Development Center, Guangdong Huixuan Pharmaceutical Technology Co., Ltd, Guangzhou, 510765, China
| | - Chuangyan Zhai
- PET Center, Department of Nuclear Medicine, Guangdong Provincial People's Hospital (Guangdong Academy of Medical Sciences), Southern Medical University, Guangzhou, 510080, China.
- School of Forensic Medicine, Southern Medical University, Guangzhou, 510515, China.
| | - Liming Nie
- Medical Research Institute, Guangdong Provincial People's Hospital (Guangdong Academy of Medical Sciences), Southern Medical University, Guangzhou, 510080, China.
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Rymarczyk D, Schultz W, Borowa A, Friedman JR, Danel T, Branigan P, Chałupczak M, Bracha A, Krawiec T, Warchoł M, Li K, De Hertogh G, Zieliński B, Ghanem LR, Stojmirovic A. Deep Learning Models Capture Histological Disease Activity in Crohn's Disease and Ulcerative Colitis with High Fidelity. J Crohns Colitis 2024; 18:604-614. [PMID: 37814351 PMCID: PMC11037111 DOI: 10.1093/ecco-jcc/jjad171] [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: 05/11/2023] [Indexed: 10/11/2023]
Abstract
BACKGROUND AND AIMS Histological disease activity in inflammatory bowel disease [IBD] is associated with clinical outcomes and is an important endpoint in drug development. We developed deep learning models for automating histological assessments in IBD. METHODS Histology images of intestinal mucosa from phase 2 and phase 3 clinical trials in Crohn's disease [CD] and ulcerative colitis [UC] were used to train artificial intelligence [AI] models to predict the Global Histology Activity Score [GHAS] for CD and Geboes histopathology score for UC. Three AI methods were compared. AI models were evaluated on held-back testing sets, and model predictions were compared against an expert central reader and five independent pathologists. RESULTS The model based on multiple instance learning and the attention mechanism [SA-AbMILP] demonstrated the best performance among competing models. AI-modelled GHAS and Geboes subgrades matched central readings with moderate to substantial agreement, with accuracies ranging from 65% to 89%. Furthermore, the model was able to distinguish the presence and absence of pathology across four selected histological features, with accuracies for colon in both CD and UC ranging from 87% to 94% and for CD ileum ranging from 76% to 83%. For both CD and UC and across anatomical compartments [ileum and colon] in CD, comparable accuracies against central readings were found between the model-assigned scores and scores by an independent set of pathologists. CONCLUSIONS Deep learning models based upon GHAS and Geboes scoring systems were effective at distinguishing between the presence and absence of IBD microscopic disease activity.
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Affiliation(s)
- Dawid Rymarczyk
- AI Lab, Ardigen SA, Kraków, Poland
- Faculty of Mathematics and Computer Science, Jagiellonian University, Kraków, Poland
| | - Weiwei Schultz
- Data Science & Digital Health, Janssen Research & Development, LLC, Spring House, Pennsylvania
| | - Adriana Borowa
- AI Lab, Ardigen SA, Kraków, Poland
- Faculty of Mathematics and Computer Science, Jagiellonian University, Kraków, Poland
| | - Joshua R Friedman
- Data Science & Digital Health, Janssen Research & Development, LLC, Spring House, Pennsylvania
| | - Tomasz Danel
- AI Lab, Ardigen SA, Kraków, Poland
- Faculty of Mathematics and Computer Science, Jagiellonian University, Kraków, Poland
| | - Patrick Branigan
- Immunology TA, Janssen Research & Development, LLC, Spring House, Pennsylvania
| | | | | | | | | | - Katherine Li
- Immunology TA, Janssen Research & Development, LLC, Spring House, Pennsylvania
| | - Gert De Hertogh
- Department of Pathology, University Hospitals KU Leuven, Belgium
| | - Bartosz Zieliński
- AI Lab, Ardigen SA, Kraków, Poland
- Faculty of Mathematics and Computer Science, Jagiellonian University, Kraków, Poland
| | - Louis R Ghanem
- Immunology TA, Janssen Research & Development, LLC, Spring House, Pennsylvania
| | - Aleksandar Stojmirovic
- Data Science & Digital Health, Janssen Research & Development, LLC, Spring House, Pennsylvania
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8
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Tiankanon K, Karuehardsuwan J, Aniwan S, Mekaroonkamol P, Sunthornwechapong P, Navadurong H, Tantitanawat K, Mekritthikrai K, Samutrangsi S, Vateekul P, Rerknimitr R. Performance comparison between two computer-aided detection colonoscopy models by trainees using different false positive thresholds: a cross-sectional study in Thailand. Clin Endosc 2024; 57:217-225. [PMID: 38556473 PMCID: PMC10984740 DOI: 10.5946/ce.2023.145] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 05/31/2023] [Revised: 07/24/2023] [Accepted: 09/25/2023] [Indexed: 04/02/2024] Open
Abstract
BACKGROUND/AIMS This study aims to compare polyp detection performance of "Deep-GI," a newly developed artificial intelligence (AI) model, to a previously validated AI model computer-aided polyp detection (CADe) using various false positive (FP) thresholds and determining the best threshold for each model. METHODS Colonoscopy videos were collected prospectively and reviewed by three expert endoscopists (gold standard), trainees, CADe (CAD EYE; Fujifilm Corp.), and Deep-GI. Polyp detection sensitivity (PDS), polyp miss rates (PMR), and false-positive alarm rates (FPR) were compared among the three groups using different FP thresholds for the duration of bounding boxes appearing on the screen. RESULTS In total, 170 colonoscopy videos were used in this study. Deep-GI showed the highest PDS (99.4% vs. 85.4% vs. 66.7%, p<0.01) and the lowest PMR (0.6% vs. 14.6% vs. 33.3%, p<0.01) when compared to CADe and trainees, respectively. Compared to CADe, Deep-GI demonstrated lower FPR at FP thresholds of ≥0.5 (12.1 vs. 22.4) and ≥1 second (4.4 vs. 6.8) (both p<0.05). However, when the threshold was raised to ≥1.5 seconds, the FPR became comparable (2 vs. 2.4, p=0.3), while the PMR increased from 2% to 10%. CONCLUSION Compared to CADe, Deep-GI demonstrated a higher PDS with significantly lower FPR at ≥0.5- and ≥1-second thresholds. At the ≥1.5-second threshold, both systems showed comparable FPR with increased PMR.
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Affiliation(s)
- Kasenee Tiankanon
- Division of Gastroenterology, Department of Medicine, Faculty of Medicine, Chulalongkorn University and King Chulalongkorn Memorial Hospital, Thai red cross, Bangkok, Thailand
| | - Julalak Karuehardsuwan
- Division of Gastroenterology, Department of Medicine, Faculty of Medicine, Chulalongkorn University and King Chulalongkorn Memorial Hospital, Thai red cross, Bangkok, Thailand
| | - Satimai Aniwan
- Division of Gastroenterology, Department of Medicine, Faculty of Medicine, Chulalongkorn University and King Chulalongkorn Memorial Hospital, Thai red cross, Bangkok, Thailand
| | - Parit Mekaroonkamol
- Division of Gastroenterology, Department of Medicine, Faculty of Medicine, Chulalongkorn University and King Chulalongkorn Memorial Hospital, Thai red cross, Bangkok, Thailand
| | | | - Huttakan Navadurong
- Division of Gastroenterology, Department of Medicine, Faculty of Medicine, Chulalongkorn University and King Chulalongkorn Memorial Hospital, Thai red cross, Bangkok, Thailand
| | - Kittithat Tantitanawat
- Division of Gastroenterology, Department of Medicine, Faculty of Medicine, Chulalongkorn University and King Chulalongkorn Memorial Hospital, Thai red cross, Bangkok, Thailand
| | - Krittaya Mekritthikrai
- Division of Gastroenterology, Department of Medicine, Faculty of Medicine, Chulalongkorn University and King Chulalongkorn Memorial Hospital, Thai red cross, Bangkok, Thailand
| | - Salin Samutrangsi
- Division of Gastroenterology, Department of Medicine, Faculty of Medicine, Chulalongkorn University and King Chulalongkorn Memorial Hospital, Thai red cross, Bangkok, Thailand
| | - Peerapon Vateekul
- Department of Computer Engineering, Faculty of Engineering, Chulalongkorn University, Bangkok, Thailand
| | - Rungsun Rerknimitr
- Division of Gastroenterology, Department of Medicine, Faculty of Medicine, Chulalongkorn University and King Chulalongkorn Memorial Hospital, Thai red cross, Bangkok, Thailand
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9
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Zhang H, Wu Q, Sun J, Wang J, Zhou L, Cai W, Zou D. A computer-aided system improves the performance of endoscopists in detecting colorectal polyps: a multi-center, randomized controlled trial. Front Med (Lausanne) 2024; 10:1341259. [PMID: 38327275 PMCID: PMC10847558 DOI: 10.3389/fmed.2023.1341259] [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: 11/20/2023] [Accepted: 12/28/2023] [Indexed: 02/09/2024] Open
Abstract
Background Up to 45.9% of polyps are missed during colonoscopy, which is the major cause of post-colonoscopy colorectal cancer (CRC). Computer-aided detection (CADe) techniques based on deep learning might improve endoscopists' performance in detecting polyps. We aimed to evaluate the effectiveness of the CADe system in assisting endoscopists in a real-world clinical setting. Methods The CADe system was trained to detect colorectal polyps, recognize the ileocecal region, and monitor the speed of withdrawal during colonoscopy in real-time. Between 17 January 2021 and 16 July 2021. We recruited consecutive patients aged 18-75 years from three centers in China. We randomized patients in 1:1 groups to either colonoscopy with the CADe system or unassisted (control). The primary outcomes were the sensitivity and specificity of the endoscopists. We used subgroup analysis to examine the polyp detection rate (PDR) and the miss detection rate of endoscopists. Results A total of 1293 patients were included. The sensitivity of the endoscopists in the experimental group was significantly higher than that of the control group (84.97 vs. 72.07%, p < 0.001), and the specificity of the endoscopists in these two groups was comparable (100.00 vs. 100.00%). In a subgroup analysis, the CADe system improved the PDR of the 6-9 mm polyps (18.04 vs. 13.85%, p < 0.05) and reduced the miss detection rate, especially at 10:00-12:00 am (12.5 vs. 39.81%, p < 0.001). Conclusion The CADe system can potentially improve the sensitivity of endoscopists in detecting polyps, reduce the missed detection of polyps in colonoscopy, and reduce the risk of CRC. Registration This clinical trial was registered with the Chinese Clinical Trial Registry (Trial Registration Number: ChiCTR2100041988). Clinical trial registration website www.chictr.org.cn, identifier ChiCTR2100041988.
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Affiliation(s)
- Heng Zhang
- Department of Gastroenterology, The Central Hospital of Wuhan, Tongji Medical College, Huazhong University of Science and Technology, Wuhan, China
| | - Qi Wu
- Endoscopy Center, Peking University Cancer Hospital and Institute, Beijing, China
| | - Jing Sun
- Department of Gastroenterology, Ruijin Hospital, Shanghai Jiao Tong University School of Medicine, Shanghai, China
| | - Jing Wang
- Endoscopy Center, Peking University Cancer Hospital and Institute, Beijing, China
| | - Lei Zhou
- Department of Gastroenterology, The Central Hospital of Wuhan, Tongji Medical College, Huazhong University of Science and Technology, Wuhan, China
| | - Wei Cai
- Department of Gastrointestinal Surgery, The Central Hospital of Wuhan, Tongji Medical College, Huazhong University of Science and Technology, Wuhan, China
| | - Duowu Zou
- Department of Gastroenterology, Ruijin Hospital, Shanghai Jiao Tong University School of Medicine, Shanghai, China
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10
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Nduma BN, Nkeonye S, Uwawah TD, Kaur D, Ekhator C, Ambe S. Use of Artificial Intelligence in the Diagnosis of Colorectal Cancer. Cureus 2024; 16:e53024. [PMID: 38410294 PMCID: PMC10895204 DOI: 10.7759/cureus.53024] [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] [Accepted: 01/26/2024] [Indexed: 02/28/2024] Open
Abstract
Colorectal cancer (CRC) is one of the most common forms of cancer. Therefore, diagnosing the condition early and accurately is critical for improved patient outcomes and effective treatment. Recently, artificial intelligence (AI) algorithms such as support vector machine (SVM) and convolutional neural network (CNN) have demonstrated promise in medical image analysis. This paper, conducted from a systematic review perspective, aimed to determine the effectiveness of AI integration in CRC diagnosis, emphasizing accuracy, sensitivity, and specificity. From a methodological perspective, articles that were included were those that had been conducted in the past decade. Also, the articles needed to have been documented in English, with databases such as Embase, PubMed, and Google Scholar used to obtain relevant research studies. Similarly, keywords were used to arrive at relevant articles. These keywords included AI, CRC, specificity, sensitivity, accuracy, efficacy, effectiveness, disease diagnosis, screening, machine learning, area under the curve (AUC), and deep learning. From the results, most scholarly studies contend that AI is superior in medical image analysis, the development of subtle patterns, and decision support. However, while deploying these algorithms, a key theme is that the collaboration between medical experts and AI systems needs to be seamless. In addition, the AI algorithms ought to be refined continuously in the current world of big data and ensure that they undergo rigorous validation to provide more informed decision-making for or against adopting those AI tools in clinical settings. In conclusion, therefore, balancing between human expertise and technological innovation is likely to pave the way for the realization of AI's full potential concerning its promising role in improving CRC diagnosis, upon which there might be significant patient outcome improvements, disease detection, and the achievement of a more effective healthcare system.
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Affiliation(s)
| | - Stephen Nkeonye
- Oncology, University of Texas MD Anderson Cancer Center, Houston, USA
| | | | - Davinder Kaur
- Internal Medicine, Medical City, North Richland Hills, USA
| | - Chukwuyem Ekhator
- Neuro-Oncology, New York Institute of Technology College of Osteopathic Medicine, Old Westbury, USA
| | - Solomon Ambe
- Neurology, Baylor Scott & White Health, McKinney, USA
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11
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Zhang H, Yang X, Tao Y, Zhang X, Huang X. Diagnostic accuracy of endocytoscopy via artificial intelligence in colorectal lesions: A systematic review and meta‑analysis. PLoS One 2023; 18:e0294930. [PMID: 38113199 PMCID: PMC10729963 DOI: 10.1371/journal.pone.0294930] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 05/06/2023] [Accepted: 11/10/2023] [Indexed: 12/21/2023] Open
Abstract
BACKGROUND Endocytoscopy (EC) is a nuclei and micro-vessels visualization in real-time and can facilitate "optical biopsy" and "virtual histology" of colorectal lesions. This study aimed to investigate the significance of employing artificial intelligence (AI) in the field of endoscopy, specifically in diagnosing colorectal lesions. The research was conducted under the supervision of experienced professionals and trainees. METHODS EMBASE, PubMed, Cochrane Library, Web of Science, Chinese National Knowledge Infrastructure (CNKI) database, and other potential databases were surveyed for articles related to the EC with AI published before September 2023. RevMan (5.40), Stata (14.0), and R software (4.1.0) were used for statistical assessment. Studies that measured the accuracy of EC using AI for colorectal lesions were included. Two authors independently assessed the selected studies and their extracted data. This included information such as the country, literature, total study population, study design, characteristics of the fundamental study and control groups, sensitivity, number of samples, assay methodology, specificity, true positives or negatives, and false positives or negatives. The diagnostic accuracy of EC by AI was determined by a bivariate random-effects model, avoiding a high heterogeneity effect. The ANOVA model was employed to determine the more effective approach. RESULTS A total of 223 studies were reviewed; 8 articles were selected that included 2984 patients (4241 lesions) for systematic review and meta-analysis. AI assessed 4069 lesions; experts diagnosed 3165 and 5014 by trainees. AI demonstrated high accuracy, sensitivity, and specificity levels in detecting colorectal lesions, with values of 0.93 (95% CI: 0.90, 0.95) and 0.94 (95% CI: 0.73, 0.99). Expert diagnosis was 0.90 (95% CI: 0.85, 0.94), 0.87 (95% CI: 0.78, 0.93), and trainee diagnosis was 0.74 (95% CI: 0.67, 0.79), 0.72 (95% CI: 0.62, 0.80). With the EC by AI, the AUC from SROC was 0.95 (95% CI: 0.93, 0.97), therefore classified as excellent category, expert showed 0.95 (95% CI: 0.93, 0.97), and the trainee had 0.79 (95% CI: 0.75, 0.82). The superior index from the ANOVA model was 4.00 (1.15,5.00), 2.00 (1.15,5.00), and 0.20 (0.20,0.20), respectively. The examiners conducted meta-regression and subgroup analyses to evaluate the presence of heterogeneity. The findings of these investigations suggest that the utilization of NBI technology was correlated with variability in sensitivity and specificity. There was a lack of solid evidence indicating the presence of publishing bias. CONCLUSIONS The present findings indicate that using AI in EC can potentially enhance the efficiency of diagnosing colorectal abnormalities. As a valuable instrument, it can enhance prognostic outcomes in ordinary EC procedures, exhibiting superior diagnostic accuracy compared to trainee-level endoscopists and demonstrating comparability to expert endoscopists. The research is subject to certain constraints, namely a limited number of clinical investigations and variations in the methodologies used for identification. Consequently, it is imperative to conduct comprehensive and extensive research to enhance the precision of diagnostic procedures.
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Affiliation(s)
- Hangbin Zhang
- Department of Gastroenterology, The First Affiliated Hospital of Zhejiang Chinese Medical University, Hangzhou, China
| | - Xinyu Yang
- Department of Gastroenterology, The First Affiliated Hospital of Zhejiang Chinese Medical University, Hangzhou, China
| | - Ye Tao
- Department of Gastroenterology, The First Affiliated Hospital of Zhejiang Chinese Medical University, Hangzhou, China
| | - Xinyi Zhang
- Department of Gastroenterology, The First Affiliated Hospital of Zhejiang Chinese Medical University, Hangzhou, China
| | - Xuan Huang
- Department of Gastroenterology, The First Affiliated Hospital of Zhejiang Chinese Medical University, Hangzhou, China
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12
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Xin Y, Zhang Q, Liu X, Li B, Mao T, Li X. Application of artificial intelligence in endoscopic gastrointestinal tumors. Front Oncol 2023; 13:1239788. [PMID: 38144533 PMCID: PMC10747923 DOI: 10.3389/fonc.2023.1239788] [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: 06/14/2023] [Accepted: 11/17/2023] [Indexed: 12/26/2023] Open
Abstract
With an increasing number of patients with gastrointestinal cancer, effective and accurate early diagnostic clinical tools are required provide better health care for patients with gastrointestinal cancer. Recent studies have shown that artificial intelligence (AI) plays an important role in the diagnosis and treatment of patients with gastrointestinal tumors, which not only improves the efficiency of early tumor screening, but also significantly improves the survival rate of patients after treatment. With the aid of efficient learning and judgment abilities of AI, endoscopists can improve the accuracy of diagnosis and treatment through endoscopy and avoid incorrect descriptions or judgments of gastrointestinal lesions. The present article provides an overview of the application status of various artificial intelligence in gastric and colorectal cancers in recent years, and the direction of future research and clinical practice is clarified from a clinical perspective to provide a comprehensive theoretical basis for AI as a promising diagnostic and therapeutic tool for gastrointestinal cancer.
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Affiliation(s)
| | | | | | | | | | - Xiaoyu Li
- Department of Gastroenterology, The Affiliated Hospital of Qingdao University, Qingdao, China
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13
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Hsu WF, Chiu HM. Optimization of colonoscopy quality: Comprehensive review of the literature and future perspectives. Dig Endosc 2023; 35:822-834. [PMID: 37381701 DOI: 10.1111/den.14627] [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: 05/14/2023] [Accepted: 06/27/2023] [Indexed: 06/30/2023]
Abstract
Colonoscopy is crucial in preventing colorectal cancer (CRC) and reducing associated mortality. This comprehensive review examines the importance of high-quality colonoscopy and associated quality indicators, including bowel preparation, cecal intubation rate, withdrawal time, adenoma detection rate (ADR), complete resection, specimen retrieval, complication rates, and patient satisfaction, while also discussing other ADR-related metrics. Additionally, the review draws attention to often overlooked quality aspects, such as nonpolypoid lesion detection, as well as insertion and withdrawal skills. Moreover, it explores the potential of artificial intelligence in enhancing colonoscopy quality and highlights specific considerations for organized screening programs. The review also emphasizes the implications of organized screening programs and the need for continuous quality improvement. A high-quality colonoscopy is crucial for preventing postcolonoscopy CRC- and CRC-related deaths. Health-care professionals must develop a thorough understanding of colonoscopy quality components, including technical quality, patient safety, and patient experience. By prioritizing ongoing evaluation and refinement of these quality indicators, health-care providers can contribute to improved patient outcomes and develop more effective CRC screening programs.
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Affiliation(s)
- Wen-Feng Hsu
- Department of Internal Medicine, National Taiwan University Hospital, Taipei, Taiwan
| | - Han-Mo Chiu
- Department of Internal Medicine, National Taiwan University Hospital, Taipei, Taiwan
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14
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Young E, Edwards L, Singh R. The Role of Artificial Intelligence in Colorectal Cancer Screening: Lesion Detection and Lesion Characterization. Cancers (Basel) 2023; 15:5126. [PMID: 37958301 PMCID: PMC10647850 DOI: 10.3390/cancers15215126] [Citation(s) in RCA: 4] [Impact Index Per Article: 4.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 09/12/2023] [Revised: 10/14/2023] [Accepted: 10/14/2023] [Indexed: 11/15/2023] Open
Abstract
Colorectal cancer remains a leading cause of cancer-related morbidity and mortality worldwide, despite the widespread uptake of population surveillance strategies. This is in part due to the persistent development of 'interval colorectal cancers', where patients develop colorectal cancer despite appropriate surveillance intervals, implying pre-malignant polyps were not resected at a prior colonoscopy. Multiple techniques have been developed to improve the sensitivity and accuracy of lesion detection and characterisation in an effort to improve the efficacy of colorectal cancer screening, thereby reducing the incidence of interval colorectal cancers. This article presents a comprehensive review of the transformative role of artificial intelligence (AI), which has recently emerged as one such solution for improving the quality of screening and surveillance colonoscopy. Firstly, AI-driven algorithms demonstrate remarkable potential in addressing the challenge of overlooked polyps, particularly polyp subtypes infamous for escaping human detection because of their inconspicuous appearance. Secondly, AI empowers gastroenterologists without exhaustive training in advanced mucosal imaging to characterise polyps with accuracy similar to that of expert interventionalists, reducing the dependence on pathologic evaluation and guiding appropriate resection techniques or referrals for more complex resections. AI in colonoscopy holds the potential to advance the detection and characterisation of polyps, addressing current limitations and improving patient outcomes. The integration of AI technologies into routine colonoscopy represents a promising step towards more effective colorectal cancer screening and prevention.
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Affiliation(s)
- Edward Young
- Faculty of Health and Medical Sciences, University of Adelaide, Lyell McEwin Hospital, Haydown Rd, Elizabeth Vale, SA 5112, Australia
| | - Louisa Edwards
- Faculty of Health and Medical Sciences, University of Adelaide, Queen Elizabeth Hospital, Port Rd, Woodville South, SA 5011, Australia
| | - Rajvinder Singh
- Faculty of Health and Medical Sciences, University of Adelaide, Lyell McEwin Hospital, Haydown Rd, Elizabeth Vale, SA 5112, Australia
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15
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Brunori A, Daca-Alvarez M, Pellisé M. pT1 colorectal cancer: A treatment dilemma. Best Pract Res Clin Gastroenterol 2023; 66:101854. [PMID: 37852711 DOI: 10.1016/j.bpg.2023.101854] [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: 05/17/2023] [Revised: 07/04/2023] [Accepted: 07/30/2023] [Indexed: 10/20/2023]
Abstract
The implementation of population screening programs for colorectal cancer (CRC) has led to a considerable increase in the prevalence pT1-CRC originating on polyps amenable by local treatments. However, a high proportion of patients are referred for unnecessary oncological surgeries without a clear benefit in terms of survival. Selecting the appropriate endoscopic resection technique in the moment of diagnosis becomes crucial to provide the best treatment alternative to each individual polyp and patient. For this, it is imperative to increase the optical diagnostic skill for differentiating pT1-CRCs and decide the appropriate initial therapy. En bloc resection is crucial to obtain an adequate histological specimen that might allow organ preserving therapeutic management. In this review, we address key challenges in T1 CRC management, explore the efficacy and safety of the available diagnostic and therapeutic approaches, and shed light on upcoming advances in the field.
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Affiliation(s)
- Angelo Brunori
- Gastroenterology and Digestive Endoscopy, Università degli Studi di Perugia, Italy
| | - Maria Daca-Alvarez
- Department of Gastroenterology Hospital Clinic de Barcelona, Institut d'Investigacions Biomediques August Pi I Sunyer (IDIBAPS), Hospital Clinic of Barcelona, Centro de Investigación Biomédica en Red de EnfermedadesHepáticas y Digestivas (CIBERehd), Spain
| | - Maria Pellisé
- Department of Gastroenterology Hospital Clinic de Barcelona, Institut d'Investigacions Biomediques August Pi I Sunyer (IDIBAPS), Hospital Clinic of Barcelona, Centro de InvestigaciónBiomé, dica en Red de EnfermedadesHepáticas y Digestivas (CIBERehd), Universitat de Barcelona, Barcelona, Spain.
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16
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Arif AA, Jiang SX, Byrne MF. Artificial intelligence in endoscopy: Overview, applications, and future directions. Saudi J Gastroenterol 2023; 29:269-277. [PMID: 37787347 PMCID: PMC10644999 DOI: 10.4103/sjg.sjg_286_23] [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: 08/08/2023] [Accepted: 08/16/2023] [Indexed: 09/15/2023] Open
Abstract
Since the emergence of artificial intelligence (AI) in medicine, endoscopy applications in gastroenterology have been at the forefront of innovations. The ever-increasing number of studies necessitates the need to organize and classify applications in a useful way. Separating AI capabilities by computer aided detection (CADe), diagnosis (CADx), and quality assessment (CADq) allows for a systematic evaluation of each application. CADe studies have shown promise in accurate detection of esophageal, gastric and colonic neoplasia as well as identifying sources of bleeding and Crohn's disease in the small bowel. While more advanced CADx applications employ optical biopsies to give further information to characterize neoplasia and grade inflammatory disease, diverse CADq applications ensure quality and increase the efficiency of procedures. Future applications show promise in advanced therapeutic modalities and integrated systems that provide multimodal capabilities. AI is set to revolutionize clinical decision making and performance of endoscopy.
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Affiliation(s)
- Arif A. Arif
- Department of Medicine, University of British Columbia, Vancouver, BC, Canada
| | - Shirley X. Jiang
- Department of Medicine, University of British Columbia, Vancouver, BC, Canada
| | - Michael F. Byrne
- Division of Gastroenterology, Department of Medicine, University of British Columbia, Vancouver, BC, Canada
- Satisfai Health, Vancouver, BC, Canada
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17
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Kubo M, Ono S, Yokota I, Matsumoto S, Nishimura Y, Ono M, Yamamoto K, Sakamoto N. Quantitative diagnostic algorithm using endocytoscopy for superficial nonampullary duodenal epithelial tumors. J Gastroenterol Hepatol 2023; 38:1496-1502. [PMID: 37129220 DOI: 10.1111/jgh.16207] [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: 01/11/2023] [Revised: 03/03/2023] [Accepted: 04/20/2023] [Indexed: 05/03/2023]
Abstract
BACKGROUND AND AIM Optical biopsy using endocytoscopy for superficial nonampullary duodenal epithelial tumors (SNADETs) is practical; however, a diagnostic algorithm has not been established. The aim of this study was to determine correlations of endocytoscopic findings of SNADETs with histology using computer analysis and to establish an algorithm. METHODS Endocytoscopic images and histological images of duodenal lesions from 70 patients were retrospectively collected. The numbers of glands and densely stained areas with methylene blue (DSMs) per 1 mm2 and the percentage of DSMs per screen in endocytoscopy were determined. Moreover, correlations in DSMs and glands between endocytoscopy and histological images were analyzed. Histopathological diagnoses were assessed according to the revised Vienna classification. The primary outcome was correlation between the number of glands in endocytoscopy and that in histology. Finally, a diagnostic algorithm for endoscopic intervention of SNADETs with a statistical program command was established. RESULTS The number of glands in endocytoscopic images was correlated with that in histopathological images (ρ 0.64, P < 0.001). There were significant differences in the mean number of glands between category 4/5 and category 3 (P = 0.03) and the mean percentage of DSMs between category 4/5 and category 1 (P < 0.001). When the cutoffs for the number of glands and percentage of DSMs were set at 47 per 1 mm2 and 20.8% in one screen, respectively, the area under the ROC curve was 0.89. CONCLUSIONS Endocytoscopic images of SNADETs reflect histopathological atypia, and computer analysis provides a practical diagnostic algorithm for endoscopic intervention.
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Affiliation(s)
- Marina Kubo
- Department of Gastroenterology and Hepatology, Faculty of Medicine and Graduate School of Medicine, Hokkaido University, Sapporo, Hokkaido, 060-8638, Japan
| | - Shoko Ono
- Division of Endoscopy, Hokkaido University Hospital, Sapporo, Hokkaido, 060-8648, Japan
| | - Isao Yokota
- Department of Biostatistics, Graduate School of Medicine, Hokkaido University, Sapporo, Hokkaido, 060-8638, Japan
| | - Shogo Matsumoto
- Department of Gastroenterology and Hepatology, Faculty of Medicine and Graduate School of Medicine, Hokkaido University, Sapporo, Hokkaido, 060-8638, Japan
| | - Yusuke Nishimura
- Department of Gastroenterology and Hepatology, Faculty of Medicine and Graduate School of Medicine, Hokkaido University, Sapporo, Hokkaido, 060-8638, Japan
| | - Masayoshi Ono
- Department of Gastroenterology and Hepatology, Faculty of Medicine and Graduate School of Medicine, Hokkaido University, Sapporo, Hokkaido, 060-8638, Japan
| | - Keiko Yamamoto
- Division of Endoscopy, Hokkaido University Hospital, Sapporo, Hokkaido, 060-8648, Japan
| | - Naoya Sakamoto
- Department of Gastroenterology and Hepatology, Faculty of Medicine and Graduate School of Medicine, Hokkaido University, Sapporo, Hokkaido, 060-8638, Japan
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18
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Sánchez-Peralta LF, Glover B, Saratxaga CL, Ortega-Morán JF, Nazarian S, Picón A, Pagador JB, Sánchez-Margallo FM. Clinical Validation Benchmark Dataset and Expert Performance Baseline for Colorectal Polyp Localization Methods. J Imaging 2023; 9:167. [PMID: 37754931 PMCID: PMC10532435 DOI: 10.3390/jimaging9090167] [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: 07/28/2023] [Revised: 08/18/2023] [Accepted: 08/18/2023] [Indexed: 09/28/2023] Open
Abstract
Colorectal cancer is one of the leading death causes worldwide, but, fortunately, early detection highly increases survival rates, with the adenoma detection rate being one surrogate marker for colonoscopy quality. Artificial intelligence and deep learning methods have been applied with great success to improve polyp detection and localization and, therefore, the adenoma detection rate. In this regard, a comparison with clinical experts is required to prove the added value of the systems. Nevertheless, there is no standardized comparison in a laboratory setting before their clinical validation. The ClinExpPICCOLO comprises 65 unedited endoscopic images that represent the clinical setting. They include white light imaging and narrow band imaging, with one third of the images containing a lesion but, differently to another public datasets, the lesion does not appear well-centered in the image. Together with the dataset, an expert clinical performance baseline has been established with the performance of 146 gastroenterologists, who were required to locate the lesions in the selected images. Results shows statistically significant differences between experience groups. Expert gastroenterologists' accuracy was 77.74, while sensitivity and specificity were 86.47 and 74.33, respectively. These values can be established as minimum values for a DL method before performing a clinical trial in the hospital setting.
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Affiliation(s)
- Luisa F. Sánchez-Peralta
- Jesús Usón Minimally Invasive Surgery Centre, E-10071 Cáceres, Spain; (L.F.S.-P.); (J.F.O.-M.); (F.M.S.-M.)
- AI4polypNET Thematic Network, E-08193 Barcelona, Spain
| | - Ben Glover
- Imperial College London, London SW7 2BU, UK; (B.G.); (S.N.)
| | - Cristina L. Saratxaga
- TECNALIA, Basque Research and Technology Alliance (BRTA), E-48160 Derio, Spain; (C.L.S.); (A.P.)
| | - Juan Francisco Ortega-Morán
- Jesús Usón Minimally Invasive Surgery Centre, E-10071 Cáceres, Spain; (L.F.S.-P.); (J.F.O.-M.); (F.M.S.-M.)
- AI4polypNET Thematic Network, E-08193 Barcelona, Spain
| | | | - Artzai Picón
- TECNALIA, Basque Research and Technology Alliance (BRTA), E-48160 Derio, Spain; (C.L.S.); (A.P.)
- Department of Automatic Control and Systems Engineering, University of the Basque Country, E-48013 Bilbao, Spain
| | - J. Blas Pagador
- Jesús Usón Minimally Invasive Surgery Centre, E-10071 Cáceres, Spain; (L.F.S.-P.); (J.F.O.-M.); (F.M.S.-M.)
- AI4polypNET Thematic Network, E-08193 Barcelona, Spain
| | - Francisco M. Sánchez-Margallo
- Jesús Usón Minimally Invasive Surgery Centre, E-10071 Cáceres, Spain; (L.F.S.-P.); (J.F.O.-M.); (F.M.S.-M.)
- AI4polypNET Thematic Network, E-08193 Barcelona, Spain
- RICORS-TERAV Network, ISCIII, E-28029 Madrid, Spain
- Centro de Investigación Biomédica en Red de Enfermedades Cardiovasculares (CIBERCV), Instituto de Salud Carlos III, E-28029 Madrid, Spain
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19
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Zammarchi I, Santacroce G, Iacucci M. Next-Generation Endoscopy in Inflammatory Bowel Disease. Diagnostics (Basel) 2023; 13:2547. [PMID: 37568910 PMCID: PMC10417286 DOI: 10.3390/diagnostics13152547] [Citation(s) in RCA: 5] [Impact Index Per Article: 5.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 07/10/2023] [Revised: 07/28/2023] [Accepted: 07/29/2023] [Indexed: 08/13/2023] Open
Abstract
Endoscopic healing is recognized as a primary treatment goal in Inflammatory Bowel Disease (IBD). However, endoscopic remission may not reflect histological remission, which is crucial to achieving favorable long-term outcomes. The development of new advanced techniques has revolutionized the field of IBD assessment and management. These tools can accurately assess vascular and mucosal features, drawing endoscopy closer to histology. Moreover, they can enhance the detection and characterization of IBD-related dysplasia. Given the persistent challenge of interobserver variability, a more standardized approach to endoscopy is warranted, and the integration of artificial intelligence (AI) holds promise for addressing this limitation. Additionally, although molecular endoscopy is still in its infancy, it is a promising tool to forecast response to therapy. This review provides an overview of advanced endoscopic techniques, including dye-based and dye-less chromoendoscopy, and in vivo histological examinations with probe-based confocal laser endomicroscopy and endocytoscopy. The remarkable contribution of these tools to IBD management, especially when integrated with AI, is discussed. Specific attention is given to their role in improving disease assessment, detection, and characterization of IBD-associated lesions, and predicting disease-related outcomes.
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Affiliation(s)
| | | | - Marietta Iacucci
- APC Microbiome Ireland, College of Medicine and Health, University College Cork, T12 R229 Cork, Ireland; (I.Z.); (G.S.)
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20
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Biamonte P, D’Amico F, Fasulo E, Barà R, Bernardi F, Allocca M, Zilli A, Danese S, Furfaro F. New Technologies in Digestive Endoscopy for Ulcerative Colitis Patients. Biomedicines 2023; 11:2139. [PMID: 37626636 PMCID: PMC10452412 DOI: 10.3390/biomedicines11082139] [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: 07/13/2023] [Revised: 07/25/2023] [Accepted: 07/27/2023] [Indexed: 08/27/2023] Open
Abstract
Ulcerative colitis (UC) is a chronic inflammatory bowel disease primarily affecting the colon and rectum. Endoscopy plays a crucial role in the diagnosis and management of UC. Recent advancements in endoscopic technology, including chromoendoscopy, confocal laser endomicroscopy, endocytoscopy and the use of artificial intelligence, have revolutionized the assessment and treatment of UC patients. These innovative techniques enable early detection of dysplasia and cancer, more precise characterization of disease extent and severity and more targeted biopsies, leading to improved diagnosis and disease monitoring. Furthermore, these advancements have significant implications for therapeutic decision making, empowering clinicians to carefully consider a range of treatment options, including pharmacological therapies, endoscopic interventions and surgical approaches. In this review, we provide an overview of the latest endoscopic technologies and their applications for diagnosing and monitoring UC. We also discuss their impact on treatment decision making, highlighting the potential benefits and limitations of each technique.
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Affiliation(s)
- Paolo Biamonte
- Gastroenterology and Endoscopy, IRCCS San Raffaele Hospital, 20132 Milan, Italy; (P.B.); (E.F.); (R.B.); (F.B.); (M.A.); (A.Z.); (S.D.); (F.F.)
| | - Ferdinando D’Amico
- Gastroenterology and Endoscopy, IRCCS San Raffaele Hospital, 20132 Milan, Italy; (P.B.); (E.F.); (R.B.); (F.B.); (M.A.); (A.Z.); (S.D.); (F.F.)
- Department of Biomedical Sciences, Humanitas University, Pieve Emanuele, 20090 Milan, Italy
| | - Ernesto Fasulo
- Gastroenterology and Endoscopy, IRCCS San Raffaele Hospital, 20132 Milan, Italy; (P.B.); (E.F.); (R.B.); (F.B.); (M.A.); (A.Z.); (S.D.); (F.F.)
| | - Rukaia Barà
- Gastroenterology and Endoscopy, IRCCS San Raffaele Hospital, 20132 Milan, Italy; (P.B.); (E.F.); (R.B.); (F.B.); (M.A.); (A.Z.); (S.D.); (F.F.)
| | - Francesca Bernardi
- Gastroenterology and Endoscopy, IRCCS San Raffaele Hospital, 20132 Milan, Italy; (P.B.); (E.F.); (R.B.); (F.B.); (M.A.); (A.Z.); (S.D.); (F.F.)
| | - Mariangela Allocca
- Gastroenterology and Endoscopy, IRCCS San Raffaele Hospital, 20132 Milan, Italy; (P.B.); (E.F.); (R.B.); (F.B.); (M.A.); (A.Z.); (S.D.); (F.F.)
| | - Alessandra Zilli
- Gastroenterology and Endoscopy, IRCCS San Raffaele Hospital, 20132 Milan, Italy; (P.B.); (E.F.); (R.B.); (F.B.); (M.A.); (A.Z.); (S.D.); (F.F.)
| | - Silvio Danese
- Gastroenterology and Endoscopy, IRCCS San Raffaele Hospital, 20132 Milan, Italy; (P.B.); (E.F.); (R.B.); (F.B.); (M.A.); (A.Z.); (S.D.); (F.F.)
- Gastroenterology and Endoscopy, Vita-Salute San Raffaele University, 20132 Milan, Italy
| | - Federica Furfaro
- Gastroenterology and Endoscopy, IRCCS San Raffaele Hospital, 20132 Milan, Italy; (P.B.); (E.F.); (R.B.); (F.B.); (M.A.); (A.Z.); (S.D.); (F.F.)
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21
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Misumi Y, Nonaka K, Takeuchi M, Kamitani Y, Uechi Y, Watanabe M, Kishino M, Omori T, Yonezawa M, Isomoto H, Tokushige K. Comparison of the Ability of Artificial-Intelligence-Based Computer-Aided Detection (CAD) Systems and Endoscopists to Detect Colorectal Neoplastic Lesions on Endoscopy Video. J Clin Med 2023; 12:4840. [PMID: 37510955 PMCID: PMC10381252 DOI: 10.3390/jcm12144840] [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: 06/23/2023] [Revised: 07/13/2023] [Accepted: 07/19/2023] [Indexed: 07/30/2023] Open
Abstract
Artificial-intelligence-based computer-aided diagnosis (CAD) systems have developed remarkably in recent years. These systems can help increase the adenoma detection rate (ADR), an important quality indicator in colonoscopies. While there have been many still-image-based studies on the usefulness of CAD, few have reported on its usefulness using actual clinical videos. However, no studies have compared the CAD group and control groups using the exact same case videos. This study aimed to determine whether CAD or endoscopists were superior in identifying colorectal neoplastic lesions in videos. In this study, we examined 34 lesions from 21 cases. CAD performed better than four of the six endoscopists (three experts and three beginners), including all the beginners. The time to lesion detection with beginners and experts was 2.147 ± 1.118 s and 1.394 ± 0.805 s, respectively, with significant differences between beginners and experts (p < 0.001) and between beginners and CAD (both p < 0.001). The time to lesion detection was significantly shorter for experts and CAD than for beginners. No significant difference was found between experts and CAD (p = 1.000). CAD could be useful as a diagnostic support tool for beginners to bridge the experience gap with experts.
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Affiliation(s)
- Yoshitsugu Misumi
- Department of Digestive Endoscopy, Tokyo Women's Medical University Hospital, 8-1, Kawada-Chou, Shinjuku-Ku, Tokyo 162-8666, Japan
| | - Kouichi Nonaka
- Department of Digestive Endoscopy, Tokyo Women's Medical University Hospital, 8-1, Kawada-Chou, Shinjuku-Ku, Tokyo 162-8666, Japan
| | - Miharu Takeuchi
- Department of Digestive Endoscopy, Tokyo Women's Medical University Hospital, 8-1, Kawada-Chou, Shinjuku-Ku, Tokyo 162-8666, Japan
| | - Yu Kamitani
- Department of Digestive Endoscopy, Tokyo Women's Medical University Hospital, 8-1, Kawada-Chou, Shinjuku-Ku, Tokyo 162-8666, Japan
| | - Yasuhiro Uechi
- Department of Digestive Endoscopy, Tokyo Women's Medical University Hospital, 8-1, Kawada-Chou, Shinjuku-Ku, Tokyo 162-8666, Japan
| | - Mai Watanabe
- Department of Digestive Endoscopy, Tokyo Women's Medical University Hospital, 8-1, Kawada-Chou, Shinjuku-Ku, Tokyo 162-8666, Japan
| | - Maiko Kishino
- Department of Digestive Endoscopy, Tokyo Women's Medical University Hospital, 8-1, Kawada-Chou, Shinjuku-Ku, Tokyo 162-8666, Japan
| | - Teppei Omori
- Institute of Gastroenterology, Tokyo Women's Medical University Hospital, 8-1, Kawada-Chou, Shinjuku-Ku, Tokyo 162-8666, Japan
| | - Maria Yonezawa
- Institute of Gastroenterology, Tokyo Women's Medical University Hospital, 8-1, Kawada-Chou, Shinjuku-Ku, Tokyo 162-8666, Japan
| | - Hajime Isomoto
- Division of Gastroenterology and Nephrology, Department of Multidisciplinary Internal Medicine, Faculty of Medicine, Tottori University, 36-1, Nishi-Chou, Yonago 683-8504, Japan
| | - Katsutoshi Tokushige
- Institute of Gastroenterology, Tokyo Women's Medical University Hospital, 8-1, Kawada-Chou, Shinjuku-Ku, Tokyo 162-8666, Japan
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22
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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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23
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Xu M, Chen Z, Zheng J, Zhao Q, Yuan Z. Artificial Intelligence-Aided Optical Imaging for Cancer Theranostics. Semin Cancer Biol 2023:S1044-579X(23)00094-9. [PMID: 37302519 DOI: 10.1016/j.semcancer.2023.06.003] [Citation(s) in RCA: 3] [Impact Index Per Article: 3.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 10/31/2022] [Revised: 06/08/2023] [Accepted: 06/08/2023] [Indexed: 06/13/2023]
Abstract
The use of artificial intelligence (AI) to assist biomedical imaging have demonstrated its high accuracy and high efficiency in medical decision-making for individualized cancer medicine. In particular, optical imaging methods are able to visualize both the structural and functional information of tumors tissues with high contrast, low cost, and noninvasive property. However, no systematic work has been performed to inspect the recent advances on AI-aided optical imaging for cancer theranostics. In this review, we demonstrated how AI can guide optical imaging methods to improve the accuracy on tumor detection, automated analysis and prediction of its histopathological section, its monitoring during treatment, and its prognosis by using computer vision, deep learning and natural language processing. By contrast, the optical imaging techniques involved mainly consisted of various tomography and microscopy imaging methods such as optical endoscopy imaging, optical coherence tomography, photoacoustic imaging, diffuse optical tomography, optical microscopy imaging, Raman imaging, and fluorescent imaging. Meanwhile, existing problems, possible challenges and future prospects for AI-aided optical imaging protocol for cancer theranostics were also discussed. It is expected that the present work can open a new avenue for precision oncology by using AI and optical imaging tools.
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Affiliation(s)
- Mengze Xu
- Center for Cognition and Neuroergonomics, State Key Laboratory of Cognitive Neuroscience and Learning, Beijing Normal University, Zhuhai, China; Cancer Center, Faculty of Health Sciences, University of Macau, Macau SAR, China; Centre for Cognitive and Brain Sciences, University of Macau, Macau SAR, China
| | - Zhiyi Chen
- Institute of Medical Imaging, Hengyang Medical School, University of South China, Hengyang, China
| | - Junxiao Zheng
- Cancer Center, Faculty of Health Sciences, University of Macau, Macau SAR, China; Centre for Cognitive and Brain Sciences, University of Macau, Macau SAR, China
| | - Qi Zhao
- Cancer Center, Faculty of Health Sciences, University of Macau, Macau SAR, China
| | - Zhen Yuan
- Cancer Center, Faculty of Health Sciences, University of Macau, Macau SAR, China; Centre for Cognitive and Brain Sciences, University of Macau, Macau SAR, China.
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24
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Sharma A, Kumar R, Yadav G, Garg P. Artificial intelligence in intestinal polyp and colorectal cancer prediction. Cancer Lett 2023; 565:216238. [PMID: 37211068 DOI: 10.1016/j.canlet.2023.216238] [Citation(s) in RCA: 5] [Impact Index Per Article: 5.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 04/06/2023] [Revised: 05/17/2023] [Accepted: 05/17/2023] [Indexed: 05/23/2023]
Abstract
Artificial intelligence (AI) algorithms and their application to disease detection and decision support for healthcare professions have greatly evolved in the recent decade. AI has been widely applied and explored in gastroenterology for endoscopic analysis to diagnose intestinal cancers, premalignant polyps, gastrointestinal inflammatory lesions, and bleeding. Patients' responses to treatments and prognoses have both been predicted using AI by combining multiple algorithms. In this review, we explored the recent applications of AI algorithms in the identification and characterization of intestinal polyps and colorectal cancer predictions. AI-based prediction models have the potential to help medical practitioners diagnose, establish prognoses, and find accurate conclusions for the treatment of patients. With the understanding that rigorous validation of AI approaches using randomized controlled studies is solicited before widespread clinical use by health authorities, the article also discusses the limitations and challenges associated with deploying AI systems to diagnose intestinal malignancies and premalignant lesions.
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Affiliation(s)
- Anju Sharma
- Department of Pharmacoinformatics, National Institute of Pharmaceutical Education and Research, S.A.S Nagar, 160062, Punjab, India
| | - Rajnish Kumar
- Amity Institute of Biotechnology, Amity University Uttar Pradesh, Lucknow Campus, Uttar Pradesh, 226010, India; Department of Veterinary Medicine and Surgery, College of Veterinary Medicine, University of Missouri, Columbia, MO, USA
| | - Garima Yadav
- Amity Institute of Biotechnology, Amity University Uttar Pradesh, Lucknow Campus, Uttar Pradesh, 226010, India
| | - Prabha Garg
- Department of Pharmacoinformatics, National Institute of Pharmaceutical Education and Research, S.A.S Nagar, 160062, Punjab, India.
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25
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Ren Y, Zou D, Xu W, Zhao X, Lu W, He X. Bimodal segmentation and classification of endoscopic ultrasonography images for solid pancreatic tumor. Biomed Signal Process Control 2023. [DOI: 10.1016/j.bspc.2023.104591] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 02/05/2023]
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26
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Hong SM, Baek DH. A Review of Colonoscopy in Intestinal Diseases. Diagnostics (Basel) 2023; 13:diagnostics13071262. [PMID: 37046479 PMCID: PMC10093393 DOI: 10.3390/diagnostics13071262] [Citation(s) in RCA: 2] [Impact Index Per Article: 2.0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 03/01/2023] [Revised: 03/25/2023] [Accepted: 03/26/2023] [Indexed: 03/30/2023] Open
Abstract
Since the development of the fiberoptic colonoscope in the late 1960s, colonoscopy has been a useful tool to diagnose and treat various intestinal diseases. This article reviews the clinical use of colonoscopy for various intestinal diseases based on present and future perspectives. Intestinal diseases include infectious diseases, inflammatory bowel disease (IBD), neoplasms, functional bowel disorders, and others. In cases of infectious diseases, colonoscopy is helpful in making the differential diagnosis, revealing endoscopic gross findings, and obtaining the specimens for pathology. Additionally, colonoscopy provides clues for distinguishing between infectious disease and IBD, and aids in the post-treatment monitoring of IBD. Colonoscopy is essential for the diagnosis of neoplasms that are diagnosed through only pathological confirmation. At present, malignant tumors are commonly being treated using endoscopy because of the advancement of endoscopic resection procedures. Moreover, the characteristics of tumors can be described in more detail by image-enhanced endoscopy and magnifying endoscopy. Colonoscopy can be helpful for the endoscopic decompression of colonic volvulus in large bowel obstruction, balloon dilatation as a treatment for benign stricture, and colon stenting as a treatment for malignant obstruction. In the diagnosis of functional bowel disorder, colonoscopy is used to investigate other organic causes of the symptom.
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27
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van der Laan JJH, van der Putten JA, Zhao X, Karrenbeld A, Peters FTM, Westerhof J, de With PHN, van der Sommen F, Nagengast WB. Optical Biopsy of Dysplasia in Barrett's Oesophagus Assisted by Artificial Intelligence. Cancers (Basel) 2023; 15:cancers15071950. [PMID: 37046611 PMCID: PMC10093622 DOI: 10.3390/cancers15071950] [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: 02/03/2023] [Revised: 03/17/2023] [Accepted: 03/21/2023] [Indexed: 04/14/2023] Open
Abstract
Optical biopsy in Barrett's oesophagus (BE) using endocytoscopy (EC) could optimize endoscopic screening. However, the identification of dysplasia is challenging due to the complex interpretation of the highly detailed images. Therefore, we assessed whether using artificial intelligence (AI) as second assessor could help gastroenterologists in interpreting endocytoscopic BE images. First, we prospectively videotaped 52 BE patients with EC. Then we trained and tested the AI pm distinct datasets drawn from 83,277 frames, developed an endocytoscopic BE classification system, and designed online training and testing modules. We invited two successive cohorts for these online modules: 10 endoscopists to validate the classification system and 12 gastroenterologists to evaluate AI as second assessor by providing six of them with the option to request AI assistance. Training the endoscopists in the classification system established an improved sensitivity of 90.0% (+32.67%, p < 0.001) and an accuracy of 77.67% (+13.0%, p = 0.020) compared with the baseline. However, these values deteriorated at follow-up (-16.67%, p < 0.001 and -8.0%, p = 0.009). Contrastingly, AI-assisted gastroenterologists maintained high sensitivity and accuracy at follow-up, subsequently outperforming the unassisted gastroenterologists (+20.0%, p = 0.025 and +12.22%, p = 0.05). Thus, best diagnostic scores for the identification of dysplasia emerged through human-machine collaboration between trained gastroenterologists with AI as the second assessor. Therefore, AI could support clinical implementation of optical biopsies through EC.
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Affiliation(s)
- Jouke J H van der Laan
- Department of Gastroenterology and Hepatology, University Medical Center Groningen, University of Groningen, 9713 GZ Groningen, The Netherlands
| | - Joost A van der Putten
- Department of Electrical Engineering, Video Coding and Architectures, Eindhoven University of Technology, 5612 AZ Eindhoven, The Netherlands
| | - Xiaojuan Zhao
- Department of Gastroenterology and Hepatology, University Medical Center Groningen, University of Groningen, 9713 GZ Groningen, The Netherlands
| | - Arend Karrenbeld
- Department of Pathology, University Medical Center Groningen, University of Groningen, 9713 GZ Groningen, The Netherlands
| | - Frans T M Peters
- Department of Gastroenterology and Hepatology, University Medical Center Groningen, University of Groningen, 9713 GZ Groningen, The Netherlands
| | - Jessie Westerhof
- Department of Gastroenterology and Hepatology, University Medical Center Groningen, University of Groningen, 9713 GZ Groningen, The Netherlands
| | - Peter H N de With
- Department of Electrical Engineering, Video Coding and Architectures, Eindhoven University of Technology, 5612 AZ Eindhoven, The Netherlands
| | - Fons van der Sommen
- Department of Electrical Engineering, Video Coding and Architectures, Eindhoven University of Technology, 5612 AZ Eindhoven, The Netherlands
| | - Wouter B Nagengast
- Department of Gastroenterology and Hepatology, University Medical Center Groningen, University of Groningen, 9713 GZ Groningen, The Netherlands
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28
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Artificial intelligence model for analyzing colonic endoscopy images to detect changes associated with irritable bowel syndrome. PLOS DIGITAL HEALTH 2023; 2:e0000058. [PMID: 36812592 PMCID: PMC9937744 DOI: 10.1371/journal.pdig.0000058] [Citation(s) in RCA: 2] [Impact Index Per Article: 2.0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Figures] [Subscribe] [Scholar Register] [Received: 05/14/2022] [Accepted: 01/12/2023] [Indexed: 02/19/2023]
Abstract
IBS is not considered to be an organic disease and usually shows no abnormality on lower gastrointestinal endoscopy, although biofilm formation, dysbiosis, and histological microinflammation have recently been reported in patients with IBS. In this study, we investigated whether an artificial intelligence (AI) colorectal image model can identify minute endoscopic changes, which cannot typically be detected by human investigators, that are associated with IBS. Study subjects were identified based on electronic medical records and categorized as IBS (Group I; n = 11), IBS with predominant constipation (IBS-C; Group C; n = 12), and IBS with predominant diarrhea (IBS-D; Group D; n = 12). The study subjects had no other diseases. Colonoscopy images from IBS patients and from asymptomatic healthy subjects (Group N; n = 88) were obtained. Google Cloud Platform AutoML Vision (single-label classification) was used to construct AI image models to calculate sensitivity, specificity, predictive value, and AUC. A total of 2479, 382, 538, and 484 images were randomly selected for Groups N, I, C and D, respectively. The AUC of the model discriminating between Group N and I was 0.95. Sensitivity, specificity, positive predictive value, and negative predictive value of Group I detection were 30.8%, 97.6%, 66.7%, and 90.2%, respectively. The overall AUC of the model discriminating between Groups N, C, and D was 0.83; sensitivity, specificity, and positive predictive value of Group N were 87.5%, 46.2%, and 79.9%, respectively. Using the image AI model, colonoscopy images of IBS could be discriminated from healthy subjects at AUC 0.95. Prospective studies are needed to further validate whether this externally validated model has similar diagnostic capabilities at other facilities and whether it can be used to determine treatment efficacy.
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29
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Artificial Intelligence-The Rising Star in the Field of Gastroenterology and Hepatology. Diagnostics (Basel) 2023; 13:diagnostics13040662. [PMID: 36832150 PMCID: PMC9955763 DOI: 10.3390/diagnostics13040662] [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: 12/05/2022] [Revised: 01/31/2023] [Accepted: 02/07/2023] [Indexed: 02/12/2023] Open
Abstract
Artificial intelligence (AI) is a term that covers a multitude of techniques that are used in a manner that tries to reproduce human intelligence. AI is helpful in various medical specialties that use imaging for diagnostic purposes, and gastroenterology is no exception. In this field, AI has several applications, such as detecting and classifying polyps, detecting the malignancy in polyps, diagnosing Helicobacter pylori infection, gastritis, inflammatory bowel disease, gastric cancer, esophageal neoplasia, and pancreatic and hepatic lesions. The aim of this mini-review is to analyze the currently available studies regarding AI in the field of gastroenterology and hepatology and to discuss its main applications as well as its main limitations.
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30
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Huang P, Feng Z, Shu X, Wu A, Wang Z, Hu T, Cao Y, Tu Y, Li Z. A bibliometric and visual analysis of publications on artificial intelligence in colorectal cancer (2002-2022). Front Oncol 2023; 13:1077539. [PMID: 36824138 PMCID: PMC9941644 DOI: 10.3389/fonc.2023.1077539] [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: 10/23/2022] [Accepted: 01/27/2023] [Indexed: 02/10/2023] Open
Abstract
Background Colorectal cancer (CRC) has the third-highest incidence and second-highest mortality rate of all cancers worldwide. Early diagnosis and screening of CRC have been the focus of research in this field. With the continuous development of artificial intelligence (AI) technology, AI has advantages in many aspects of CRC, such as adenoma screening, genetic testing, and prediction of tumor metastasis. Objective This study uses bibliometrics to analyze research in AI in CRC, summarize the field's history and current status of research, and predict future research directions. Method We searched the SCIE database for all literature on CRC and AI. The documents span the period 2002-2022. we used bibliometrics to analyze the data of these papers, such as authors, countries, institutions, and references. Co-authorship, co-citation, and co-occurrence analysis were the main methods of analysis. Citespace, VOSviewer, and SCImago Graphica were used to visualize the results. Result This study selected 1,531 articles on AI in CRC. China has published a maximum number of 580 such articles in this field. The U.S. had the most quality publications, boasting an average citation per article of 46.13. Mori Y and Ding K were the two authors with the highest number of articles. Scientific Reports, Cancers, and Frontiers in Oncology are this field's most widely published journals. Institutions from China occupy the top 9 positions among the most published institutions. We found that research on AI in this field mainly focuses on colonoscopy-assisted diagnosis, imaging histology, and pathology examination. Conclusion AI in CRC is currently in the development stage with good prospects. AI is currently widely used in colonoscopy, imageomics, and pathology. However, the scope of AI applications is still limited, and there is a lack of inter-institutional collaboration. The pervasiveness of AI technology is the main direction of future housing development in this field.
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Affiliation(s)
- Pan Huang
- Department of General Surgery, First Affiliated Hospital of Nanchang University, Nanchang, China,Department of Digestive Surgery, Digestive Disease Hospital, The First Affiliated Hospital of Nanchang University, Nanchang, China,Medical Innovation Center, The First Affiliated Hospital of Nanchang University, Nanchang, China
| | - Zongfeng Feng
- Department of General Surgery, First Affiliated Hospital of Nanchang University, Nanchang, China,Department of Digestive Surgery, Digestive Disease Hospital, The First Affiliated Hospital of Nanchang University, Nanchang, China,Medical Innovation Center, The First Affiliated Hospital of Nanchang University, Nanchang, China
| | - Xufeng Shu
- Department of General Surgery, First Affiliated Hospital of Nanchang University, Nanchang, China,Department of Digestive Surgery, Digestive Disease Hospital, The First Affiliated Hospital of Nanchang University, Nanchang, China,Medical Innovation Center, The First Affiliated Hospital of Nanchang University, Nanchang, China
| | - Ahao Wu
- Department of General Surgery, First Affiliated Hospital of Nanchang University, Nanchang, China,Department of Digestive Surgery, Digestive Disease Hospital, The First Affiliated Hospital of Nanchang University, Nanchang, China,Medical Innovation Center, The First Affiliated Hospital of Nanchang University, Nanchang, China
| | - Zhonghao Wang
- Department of General Surgery, First Affiliated Hospital of Nanchang University, Nanchang, China,Department of Digestive Surgery, Digestive Disease Hospital, The First Affiliated Hospital of Nanchang University, Nanchang, China,Medical Innovation Center, The First Affiliated Hospital of Nanchang University, Nanchang, China
| | - Tengcheng Hu
- Department of General Surgery, First Affiliated Hospital of Nanchang University, Nanchang, China,Department of Digestive Surgery, Digestive Disease Hospital, The First Affiliated Hospital of Nanchang University, Nanchang, China,Medical Innovation Center, The First Affiliated Hospital of Nanchang University, Nanchang, China
| | - Yi Cao
- Department of General Surgery, First Affiliated Hospital of Nanchang University, Nanchang, China,Department of Digestive Surgery, Digestive Disease Hospital, The First Affiliated Hospital of Nanchang University, Nanchang, China,Medical Innovation Center, The First Affiliated Hospital of Nanchang University, Nanchang, China
| | - Yi Tu
- Department of Pathology, The First Affiliated Hospital of Nanchang University, Nanchang, China,*Correspondence: Yi Tu, ; Zhengrong Li,
| | - Zhengrong Li
- Department of General Surgery, First Affiliated Hospital of Nanchang University, Nanchang, China,Department of Digestive Surgery, Digestive Disease Hospital, The First Affiliated Hospital of Nanchang University, Nanchang, China,Medical Innovation Center, The First Affiliated Hospital of Nanchang University, Nanchang, China,*Correspondence: Yi Tu, ; Zhengrong Li,
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Murino A, Rimondi A. Automated artificial intelligence scoring systems for the endoscopic assessment of ulcerative colitis: How far are we from clinical application? Gastrointest Endosc 2023; 97:347-349. [PMID: 36509572 DOI: 10.1016/j.gie.2022.10.010] [Citation(s) in RCA: 2] [Impact Index Per Article: 2.0] [Reference Citation Analysis] [MESH Headings] [Track Full Text] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 09/25/2022] [Accepted: 10/04/2022] [Indexed: 12/15/2022]
Affiliation(s)
- Alberto Murino
- Royal Free Unit for Endoscopy, The Royal Free Hospital and University College London Institute for Liver and Digestive Health, Hampstead; Department of Gastroenterology, Cleveland Clinic London, London, United Kingdom
| | - Alessandro Rimondi
- Royal Free Unit for Endoscopy, The Royal Free Hospital and University College London Institute for Liver and Digestive Health, Hampstead; Department of Gastroenterology, Cleveland Clinic London, London, United Kingdom; Department of Pathophysiology and Transplantation, University of Milan, Italy, Milan, Italy; Center for Prevention and Diagnosis of Celiac Disease and Division of Gastroenterology and Endoscopy, Foundation IRCCS Ca' Granda Ospedale Maggiore Policlinico, Milan, Italy
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Greenberg A, Samueli B, Fahoum I, Farkash S, Greenberg O, Zemser-Werner V, Sabo E, Hagege RR, Hershkovitz D. Short Training Significantly Improves Ganglion Cell Detection Using an Algorithm-Assisted Approach. Arch Pathol Lab Med 2023; 147:215-221. [PMID: 35738006 DOI: 10.5858/arpa.2021-0481-oa] [Citation(s) in RCA: 2] [Impact Index Per Article: 2.0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Accepted: 01/17/2022] [Indexed: 02/05/2023]
Abstract
CONTEXT.— Medical education in pathology relies on the accumulation of experience gained through inspection of numerous samples from each entity. Acquiring sufficient teaching material for rare diseases, such as Hirschsprung disease (HSCR), may be difficult, especially in smaller institutes. The current study makes use of a previously developed decision support system using a decision support algorithm meant to aid pathologists in the diagnosis of HSCR. OBJECTIVE.— To assess the effect of a short training session on algorithm-assisted HSCR diagnosis. DESIGN.— Five pathologists reviewed a data set of 568 image sets (1704 images in total) selected from 50 cases by the decision support algorithm and were tasked with scoring the images for the presence or absence of ganglion cells. The task was repeated a total of 3 times. Each pathologist had to complete a short educational presentation between the second and third iterations. RESULTS.— The training resulted in a significantly increased rate of correct diagnoses (true positive/negative) and a decreased need for referrals for expert consultation. No statistically significant changes in the rate of false positives/negatives were detected. CONCLUSIONS.— A very short (<10 minutes) training session can greatly improve the pathologist's performance in the algorithm-assisted diagnosis of HSCR. The same approach may be feasible in training for the diagnosis of other rare diseases.
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Affiliation(s)
- Ariel Greenberg
- From Institute of Pathology, Tel Aviv Sourasky Medical Center, Tel Aviv, Israel (A Greenberg, Fahoum, O Greenberg, Zemser-Werner, Hagege, Hershkovitz)
| | - Benzion Samueli
- From the Department of Pathology, Soroka University Medical Center, Be'er Sheva, Israel (Samueli)
| | - Ibrahim Fahoum
- From Institute of Pathology, Tel Aviv Sourasky Medical Center, Tel Aviv, Israel (A Greenberg, Fahoum, O Greenberg, Zemser-Werner, Hagege, Hershkovitz)
| | - Shai Farkash
- From the Institute of Pathology, Emek Medical Center, Afula, Israel (Farkash)
| | - Orli Greenberg
- From Institute of Pathology, Tel Aviv Sourasky Medical Center, Tel Aviv, Israel (A Greenberg, Fahoum, O Greenberg, Zemser-Werner, Hagege, Hershkovitz)
| | - Valentina Zemser-Werner
- From Institute of Pathology, Tel Aviv Sourasky Medical Center, Tel Aviv, Israel (A Greenberg, Fahoum, O Greenberg, Zemser-Werner, Hagege, Hershkovitz)
| | - Edmond Sabo
- From the Institute of Pathology, Carmel Medical Center, Haifa, Israel (Sabo).,From the Rappaport Faculty of Medicine, Technion, Haifa, Israel (Sabo)
| | - Rami R Hagege
- From Institute of Pathology, Tel Aviv Sourasky Medical Center, Tel Aviv, Israel (A Greenberg, Fahoum, O Greenberg, Zemser-Werner, Hagege, Hershkovitz).,Hagege and Hershkovitz contributed equally to the research
| | - Dov Hershkovitz
- From the Sackler Faculty of Medicine, Tel Aviv University, Tel Aviv, Israel (Hershkovitz).,Hagege and Hershkovitz contributed equally to the research
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Singh Y, Gogtay M, Yekula A, Soni A, Mishra AK, Tripathi K, Abraham GM. Detection of colorectal adenomas using artificial intelligence models in patients with chronic hepatitis C. World J Hepatol 2023; 15:107-115. [PMID: 36744168 PMCID: PMC9896503 DOI: 10.4254/wjh.v15.i1.107] [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: 09/21/2022] [Revised: 10/21/2022] [Accepted: 11/14/2022] [Indexed: 01/16/2023] Open
Abstract
BACKGROUND Hepatitis C virus is known for its oncogenic potential, especially in hepatocellular carcinoma and non-Hodgkin lymphoma. Several studies have shown that chronic hepatitis C (CHC) has an increased risk of the development of colorectal cancer (CRC).
AIM To analyze this positive relationship and develop an artificial intelligence (AI)-based tool using machine learning (ML) algorithms to stratify these patient populations into risk groups for CRC/adenoma detection.
METHODS To develop the AI automated calculator, we applied ML to train models to predict the probability and the number of adenomas detected on colonoscopy. Data sets were split into 70:30 ratios for training and internal validation. The Scikit-learn standard scaler was used to scale values of continuous variables. Colonoscopy findings were used as the gold standard and deep learning architecture was used to train six ML models for prediction. A Flask (customizable Python framework) application programming interface (API) was used to deploy the trained ML model with the highest accuracy as a web application. Finally, Heroku was used for the deployment of the web-based API to https://adenomadetection.herokuapp.com.
RESULTS Of 415 patients, 206 had colonoscopy results. On internal validation, the Bernoulli naive Bayes model predicted the probability of adenoma detection with the highest accuracy of 56%, precision of 55%, recall of 55%, and F1 measure of 54%. Support vector regressor predicted the number of adenomas with the least mean absolute error of 0.905.
CONCLUSION Our AI-based tool can help providers stratify patients with CHC for early referral for screening colonoscopy. Along with providing a numerical percentage, the calculator can also comment on the number of adenomatous polyps a gastroenterologist can expect, prompting a higher adenoma detection rate.
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Affiliation(s)
- Yuvaraj Singh
- Department of Internal Medicine, Saint Vincent Hospital, Worcester, MA 01608, United States
| | - Maya Gogtay
- Hospice and Palliative Medicine, University of Texas Health-San Antonio, San Antonio, TX 78201, United States
| | - Anuroop Yekula
- Department of Internal Medicine, Saint Vincent Hospital, Worcester, MA 01608, United States
| | - Aakriti Soni
- Department of Internal Medicine, Saint Vincent Hospital, Worcester, MA 01608, United States
| | - Ajay Kumar Mishra
- Division of Cardiology, Saint Vincent Hospital, Worcester, MA 01608, United States
| | - Kartikeya Tripathi
- Division of Gastroenterology and Hepatology, UMass Chan School-Baystate Medical Center, Springfield, MA 01199, United States
| | - GM Abraham
- Division of Infectious Disease, Chief of Medicine, Saint Vincent Hospital, Worcester, MA 01608, United States
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Zuo W, Dai Y, Huang X, Peng RQ, Li X, Liu H. Evaluation of the competence of an artificial intelligence-assisted colonoscopy system in clinical practice: A post hoc analysis. Front Med (Lausanne) 2023; 10:1158574. [PMID: 37089592 PMCID: PMC10118043 DOI: 10.3389/fmed.2023.1158574] [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: 02/04/2023] [Accepted: 03/20/2023] [Indexed: 04/25/2023] Open
Abstract
Background Artificial intelligence-assisted colonoscopy (AIAC) has been proposed and validated in recent years, but the effectiveness of clinic application remains unclear since it was only validated in some clinical trials rather than normal conditions. In addition, previous clinical trials were mostly concerned with colorectal polyp identification, while fewer studies are focusing on adenoma identification and polyps size measurement. In this study, we validated the effectiveness of AIAC in the clinical environment and further investigated its capacity for adenoma identification and polyps size measurement. Methods The information of 174 continued patients who went for coloscopy in Chongqing Rongchang District People's hospital with detected colon polyps was retrospectively collected, and their coloscopy images were divided into three validation datasets, polyps dataset, polyps/adenomas dataset (all containing narrow band image, NBI images), and polyp size measurement dataset (images with biopsy forceps and polyps) to assess the competence of the artificial intelligence system, and compare its diagnostic ability with endoscopists with different experiences. Results A total of 174 patients were included, and the sensitivity of the colorectal polyp recognition model was 99.40%, the accuracy of the colorectal adenoma diagnostic model was 93.06%, which was higher than that of endoscopists, and the mean absolute error of the polyp size measurement model was 0.62 mm and the mean relative error was 10.89%, which was lower than that of endoscopists. Conclusion Artificial intelligence-assisted model demonstrated higher competence compared with endoscopists and stable diagnosis ability in clinical use.
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Katrevula A, Katukuri GR, Singh AP, Inavolu P, Rughwani H, Alla SR, Ramchandani M, Duvvur NR. Real-World Experience of AI-Assisted Endocytoscopy Using EndoBRAIN—An Observational Study from a Tertiary Care Center. JOURNAL OF DIGESTIVE ENDOSCOPY 2022. [DOI: 10.1055/s-0042-1758535] [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] [Indexed: 12/25/2022] Open
Abstract
Abstract
Background and Aims Precise optical diagnosis of colorectal polyps could improve the cost-effectiveness of colonoscopy and reduce polypectomy-related complications. We conducted this study to estimate the diagnostic performance of visual inspection alone (WLI + NBI) and of EndoBRAIN (endocytoscopy-computer-aided diagnosis [EC-CAD]) in identifying a lesion as neoplastic or nonneoplastic using EC in real-world scenario.
Methods In this observational, prospective, pilot study, a total of 55 polyps were studied in the patients aged more than or equal to 18 years. EndoBRAIN is an artificial intelligence (AI)-based system that analyzes cell nuclei, crypt structure, and vessel pattern in differentiating neoplastic and nonneoplastic lesion in real-time. Endoscopist assessed polyps using white light imaging (WLI), narrow band imaging (NBI) initially followed by assessment using EC with NBI and EC with methylene blue staining. The sensitivity, specificity, positive predictive value, negative predictive value, and accuracy of endoscopist and EndoBRAIN in identifying the neoplastic from nonneoplastic polyp was compared using histopathology as gold-standard.
Results A total of 55 polyps were studied, in which most of them were diminutive (36/55) and located in rectum (21/55). The image acquisition rate was 78% (43/55) and histopathology of the majority was identified to be hyperplastic (20/43) and low-grade adenoma (16/43). EndoBRAIN identified colonic polyps with 100% sensitivity, 81.82% specificity (95% confidence interval [CI], 59.7–94.8%), 90.7% accuracy (95% CI, 77.86–97.41%), 84% positive predictive value (95% CI, 68.4–92.72%), and 100% negative predictive value. The sensitivity and negative predictive value were significantly greater than visual inspection of endoscopist. The diagnostic accuracy seems to be superior; however, it did not reach statistical significance. Specificity and positive predictive value were similar in both groups.
Conclusion Optical diagnosis using EC and EC-CAD has a potential role in predicting the histopathological diagnosis. The diagnostic performance of CAD seems to be better than endoscopist using EC for predicting neoplastic lesions.
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Affiliation(s)
- Anudeep Katrevula
- Department of Gastroenterology, AIG Hospitals, Hyderabad, Telangana, India
| | | | | | - Pradev Inavolu
- Department of Gastroenterology, AIG Hospitals, Hyderabad, Telangana, India
| | - Hardik Rughwani
- Department of Gastroenterology, AIG Hospitals, Hyderabad, Telangana, India
| | | | - Mohan Ramchandani
- Department of Gastroenterology, AIG Hospitals, Hyderabad, Telangana, India
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Young EJ, Rajandran A, Philpott HL, Sathananthan D, Hoile SF, Singh R. Mucosal imaging in colon polyps: New advances and what the future may hold. World J Gastroenterol 2022; 28:6632-6661. [PMID: 36620337 PMCID: PMC9813932 DOI: 10.3748/wjg.v28.i47.6632] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Download PDF] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 09/03/2022] [Revised: 10/23/2022] [Accepted: 11/23/2022] [Indexed: 12/19/2022] Open
Abstract
An expanding range of advanced mucosal imaging technologies have been developed with the goal of improving the detection and characterization of lesions in the gastrointestinal tract. Many technologies have targeted colorectal neoplasia given the potential for intervention prior to the development of invasive cancer in the setting of widespread surveillance programs. Improvement in adenoma detection reduces miss rates and prevents interval cancer development. Advanced imaging technologies aim to enhance detection without significantly increasing procedural time. Accurate polyp characterisation guides resection techniques for larger polyps, as well as providing the platform for the “resect and discard” and “do not resect” strategies for small and diminutive polyps. This review aims to collate and summarise the evidence regarding these technologies to guide colonoscopic practice in both interventional and non-interventional endoscopists.
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Affiliation(s)
- Edward John Young
- Department of Gastroenterology, Lyell McEwin Hospital, Northern Adelaide Local Health Network, Elizabeth Vale 5031, South Australia, Australia
- Faculty of Health and Medical Sciences, University of Adelaide, Adelaide 5000, South Australia, Australia
| | - Arvinf Rajandran
- Department of Gastroenterology, Lyell McEwin Hospital, Northern Adelaide Local Health Network, Elizabeth Vale 5031, South Australia, Australia
| | - Hamish Lachlan Philpott
- Department of Gastroenterology, Lyell McEwin Hospital, Northern Adelaide Local Health Network, Elizabeth Vale 5031, South Australia, Australia
- Faculty of Health and Medical Sciences, University of Adelaide, Adelaide 5000, South Australia, Australia
| | - Dharshan Sathananthan
- Department of Gastroenterology, Lyell McEwin Hospital, Northern Adelaide Local Health Network, Elizabeth Vale 5031, South Australia, Australia
- Faculty of Health and Medical Sciences, University of Adelaide, Adelaide 5000, South Australia, Australia
| | - Sophie Fenella Hoile
- Department of Gastroenterology, Lyell McEwin Hospital, Northern Adelaide Local Health Network, Elizabeth Vale 5031, South Australia, Australia
- Faculty of Health and Medical Sciences, University of Adelaide, Adelaide 5000, South Australia, Australia
| | - Rajvinder Singh
- Department of Gastroenterology, Lyell McEwin Hospital, Northern Adelaide Local Health Network, Elizabeth Vale 5031, South Australia, Australia
- Faculty of Health and Medical Sciences, University of Adelaide, Adelaide 5000, South Australia, Australia
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Alfarone L, Parigi TL, Gabbiadini R, Dal Buono A, Spinelli A, Hassan C, Iacucci M, Repici A, Armuzzi A. Technological advances in inflammatory bowel disease endoscopy and histology. Front Med (Lausanne) 2022; 9:1058875. [PMID: 36438050 PMCID: PMC9691880 DOI: 10.3389/fmed.2022.1058875] [Citation(s) in RCA: 4] [Impact Index Per Article: 2.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 09/30/2022] [Accepted: 10/26/2022] [Indexed: 09/29/2023] Open
Abstract
Accurate disease characterization is the pillar of modern treatment of inflammatory bowel disease (IBD) and endoscopy is the mainstay of disease assessment and colorectal cancer surveillance. Recent technological progress has enhanced and expanded the use of endoscopy in IBD. In particular, numerous artificial intelligence (AI)-powered systems have shown to support human endoscopists' evaluations, improving accuracy and consistency while saving time. Moreover, advanced optical technologies such as endocytoscopy (EC), allowing high magnification in vivo, can bridge endoscopy with histology. Furthermore, molecular imaging, through probe based confocal laser endomicroscopy allows the real-time detection of specific biomarkers on gastrointestinal surface, and could be used to predict therapeutic response, paving the way to precision medicine. In parallel, as the applications of AI spread, computers are positioned to resolve some of the limitations of human histopathology evaluation, such as interobserver variability and inconsistencies in assessment. The aim of this review is to summarize the most promising advances in endoscopic and histologic assessment of IBD.
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Affiliation(s)
- Ludovico Alfarone
- Department of Biomedical Sciences, Humanitas University, Milan, Italy
- IBD Center, IRCCS Humanitas Research Hospital, Milan, Italy
| | - Tommaso Lorenzo Parigi
- Department of Biomedical Sciences, Humanitas University, Milan, Italy
- IBD Center, IRCCS Humanitas Research Hospital, Milan, Italy
| | | | | | - Antonino Spinelli
- Department of Biomedical Sciences, Humanitas University, Milan, Italy
- Colon and Rectal Surgery Division, IRCCS Humanitas Research Hospital, Milan, Italy
| | - Cesare Hassan
- Department of Biomedical Sciences, Humanitas University, Milan, Italy
- Endoscopy Unit, IRCCS Humanitas Research Hospital, Milan, Italy
| | - Marietta Iacucci
- Institute of Immunology and Immunotherapy, University of Birmingham, Birmingham, United Kingdom
- Department of Gastroenterology, University Hospitals Birmingham NHS Foundation Trust, Birmingham, United Kingdom
| | - Alessandro Repici
- Department of Biomedical Sciences, Humanitas University, Milan, Italy
- Endoscopy Unit, IRCCS Humanitas Research Hospital, Milan, Italy
| | - Alessandro Armuzzi
- Department of Biomedical Sciences, Humanitas University, Milan, Italy
- IBD Center, IRCCS Humanitas Research Hospital, Milan, Italy
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Parkash O, Siddiqui ATS, Jiwani U, Rind F, Padhani ZA, Rizvi A, Hoodbhoy Z, Das JK. Diagnostic accuracy of artificial intelligence for detecting gastrointestinal luminal pathologies: A systematic review and meta-analysis. Front Med (Lausanne) 2022; 9:1018937. [PMID: 36405592 PMCID: PMC9672666 DOI: 10.3389/fmed.2022.1018937] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.5] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 08/15/2022] [Accepted: 10/03/2022] [Indexed: 11/06/2022] Open
Abstract
Background Artificial Intelligence (AI) holds considerable promise for diagnostics in the field of gastroenterology. This systematic review and meta-analysis aims to assess the diagnostic accuracy of AI models compared with the gold standard of experts and histopathology for the diagnosis of various gastrointestinal (GI) luminal pathologies including polyps, neoplasms, and inflammatory bowel disease. Methods We searched PubMed, CINAHL, Wiley Cochrane Library, and Web of Science electronic databases to identify studies assessing the diagnostic performance of AI models for GI luminal pathologies. We extracted binary diagnostic accuracy data and constructed contingency tables to derive the outcomes of interest: sensitivity and specificity. We performed a meta-analysis and hierarchical summary receiver operating characteristic curves (HSROC). The risk of bias was assessed using Quality Assessment for Diagnostic Accuracy Studies-2 (QUADAS-2) tool. Subgroup analyses were conducted based on the type of GI luminal disease, AI model, reference standard, and type of data used for analysis. This study is registered with PROSPERO (CRD42021288360). Findings We included 73 studies, of which 31 were externally validated and provided sufficient information for inclusion in the meta-analysis. The overall sensitivity of AI for detecting GI luminal pathologies was 91.9% (95% CI: 89.0–94.1) and specificity was 91.7% (95% CI: 87.4–94.7). Deep learning models (sensitivity: 89.8%, specificity: 91.9%) and ensemble methods (sensitivity: 95.4%, specificity: 90.9%) were the most commonly used models in the included studies. Majority of studies (n = 56, 76.7%) had a high risk of selection bias while 74% (n = 54) studies were low risk on reference standard and 67% (n = 49) were low risk for flow and timing bias. Interpretation The review suggests high sensitivity and specificity of AI models for the detection of GI luminal pathologies. There is a need for large, multi-center trials in both high income countries and low- and middle- income countries to assess the performance of these AI models in real clinical settings and its impact on diagnosis and prognosis. Systematic review registration [https://www.crd.york.ac.uk/prospero/display_record.php?RecordID=288360], identifier [CRD42021288360].
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Affiliation(s)
- Om Parkash
- Department of Medicine, Aga Khan University, Karachi, Pakistan
| | | | - Uswa Jiwani
- Center of Excellence in Women and Child Health, Aga Khan University, Karachi, Pakistan
| | - Fahad Rind
- Head and Neck Oncology, The Ohio State University, Columbus, OH, United States
| | - Zahra Ali Padhani
- Institute for Global Health and Development, Aga Khan University, Karachi, Pakistan
| | - Arjumand Rizvi
- Center of Excellence in Women and Child Health, Aga Khan University, Karachi, Pakistan
| | - Zahra Hoodbhoy
- Department of Pediatrics and Child Health, Aga Khan University, Karachi, Pakistan
| | - Jai K. Das
- Institute for Global Health and Development, Aga Khan University, Karachi, Pakistan
- Department of Pediatrics and Child Health, Aga Khan University, Karachi, Pakistan
- *Correspondence: Jai K. Das,
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Minoda Y, Ihara E, Fujimori N, Nagatomo S, Esaki M, Hata Y, Bai X, Tanaka Y, Ogino H, Chinen T, Hu Q, Oki E, Yamamoto H, Ogawa Y. Efficacy of ultrasound endoscopy with artificial intelligence for the differential diagnosis of non-gastric gastrointestinal stromal tumors. Sci Rep 2022; 12:16640. [PMID: 36198726 PMCID: PMC9534932 DOI: 10.1038/s41598-022-20863-8] [Citation(s) in RCA: 4] [Impact Index Per Article: 2.0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 02/26/2022] [Accepted: 09/20/2022] [Indexed: 12/15/2022] Open
Abstract
Gastrointestinal stromal tumors (GISTs) are common subepithelial lesions (SELs) and require treatment considering their malignant potential. We recently developed an endoscopic ultrasound-based artificial intelligence (EUS-AI) system to differentiate GISTs from non-GISTs in gastric SELs, which were used to train the system. We assessed whether the EUS-AI system designed for diagnosing gastric GISTs could be applied to non-gastric GISTs. Between January 2015 and January 2021, 52 patients with non-gastric SELs (esophagus, n = 15; duodenum, n = 26; colon, n = 11) were enrolled. The ability of EUS-AI to differentiate GISTs from non-GISTs in non-gastric SELs was examined. The accuracy, sensitivity, and specificity of EUS-AI for discriminating GISTs from non-GISTs in non-gastric SELs were 94.4%, 100%, and 86.1%, respectively, with an area under the curve of 0.98 based on the cutoff value set using the Youden index. In the subanalysis, the accuracy, sensitivity, and specificity of EUS-AI were highest in the esophagus (100%, 100%, 100%; duodenum, 96.2%, 100%, 0%; colon, 90.9%, 100%, 0%); the cutoff values were determined using the Youden index or the value determined using stomach cases. The diagnostic accuracy of EUS-AI increased as lesion size increased, regardless of lesion location. EUS-AI based on gastric SELs had good diagnostic ability for non-gastric GISTs.
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Affiliation(s)
- Yosuke Minoda
- Department of Medicine and Bioregulatory Science, Graduate School of Medical Sciences, Kyushu University, 3-1-1 Maidashi, Higashi-Ku, Fukuoka, 812-8582, Japan.,Department of Endoscopic Diagnostics and Therapeutics, Kyushu University Hospital, 3-1-1 Maidashi, Higashi-Ku, Fukuoka, 812-8582, Japan
| | - Eikichi Ihara
- Department of Medicine and Bioregulatory Science, Graduate School of Medical Sciences, Kyushu University, 3-1-1 Maidashi, Higashi-Ku, Fukuoka, 812-8582, Japan. .,Department of Gastroenterology and Metabolism, Graduate School of Medical Sciences, Kyushu University, 3-1-1 Maidashi, Higashi-Ku, Fukuoka, 812-8582, Japan.
| | - Nao Fujimori
- Department of Medicine and Bioregulatory Science, Graduate School of Medical Sciences, Kyushu University, 3-1-1 Maidashi, Higashi-Ku, Fukuoka, 812-8582, Japan
| | - Shuzaburo Nagatomo
- Department of Medicine and Bioregulatory Science, Graduate School of Medical Sciences, Kyushu University, 3-1-1 Maidashi, Higashi-Ku, Fukuoka, 812-8582, Japan
| | - Mitsuru Esaki
- Department of Medicine and Bioregulatory Science, Graduate School of Medical Sciences, Kyushu University, 3-1-1 Maidashi, Higashi-Ku, Fukuoka, 812-8582, Japan
| | - Yoshitaka Hata
- Department of Medicine and Bioregulatory Science, Graduate School of Medical Sciences, Kyushu University, 3-1-1 Maidashi, Higashi-Ku, Fukuoka, 812-8582, Japan
| | - Xiaopeng Bai
- Department of Medicine and Bioregulatory Science, Graduate School of Medical Sciences, Kyushu University, 3-1-1 Maidashi, Higashi-Ku, Fukuoka, 812-8582, Japan
| | - Yoshimasa Tanaka
- Department of Medicine and Bioregulatory Science, Graduate School of Medical Sciences, Kyushu University, 3-1-1 Maidashi, Higashi-Ku, Fukuoka, 812-8582, Japan
| | - Haruei Ogino
- Department of Medicine and Bioregulatory Science, Graduate School of Medical Sciences, Kyushu University, 3-1-1 Maidashi, Higashi-Ku, Fukuoka, 812-8582, Japan
| | - Takatoshi Chinen
- Department of Medicine and Bioregulatory Science, Graduate School of Medical Sciences, Kyushu University, 3-1-1 Maidashi, Higashi-Ku, Fukuoka, 812-8582, Japan
| | - Qingjiang Hu
- Department of Surgery and Science, Graduate School of Medical Sciences, Kyushu University, 3-1-1 Maidashi, Higashi-Ku, Fukuoka, 812-8582, Japan
| | - Eiji Oki
- Department of Surgery and Science, Graduate School of Medical Sciences, Kyushu University, 3-1-1 Maidashi, Higashi-Ku, Fukuoka, 812-8582, Japan
| | - Hidetaka Yamamoto
- Department of Pathological Sciences, Graduate School of Medical Sciences, Kyushu University, 3-1-1 Maidashi, Higashi-Ku, Fukuoka, 812-8582, Japan
| | - Yoshihiro Ogawa
- Department of Medicine and Bioregulatory Science, Graduate School of Medical Sciences, Kyushu University, 3-1-1 Maidashi, Higashi-Ku, Fukuoka, 812-8582, Japan
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Agrawal LS, Acharya S, Shukla S, Parekh YC. Future of Endoscopy in Inflammatory Bowel Diseases (IBDs). Cureus 2022; 14:e29567. [PMID: 36312686 PMCID: PMC9596090 DOI: 10.7759/cureus.29567] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.5] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 08/15/2022] [Accepted: 09/25/2022] [Indexed: 12/15/2022] Open
Abstract
Gastrointestinal (GI) endoscopy has transformed over the years in scope, safety, accuracy, acceptability, and cost effectiveness of the clinical practice. There has been a reduction in the superiority of the endoscopic devices as innovations have taken place and increased the diagnostic values with certain limitations. There are particular difficulties in striking a balance between the development of new technology and the device's acceptance. The wide use of endoscopy for investigating GI lesions and diagnosis has led to an increase in more advanced methods and their broad application. It can simultaneously diagnose pre-malignant and malignant lesions, and newer interventions have made the biopsy specimen uptake possible. In this review article, we focus on the more recent roles, indications, applications, and usage of the innovative methods of endoscopy.
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Polyp detection on video colonoscopy using a hybrid 2D/3D CNN. Med Image Anal 2022; 82:102625. [DOI: 10.1016/j.media.2022.102625] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 06/17/2021] [Revised: 08/22/2022] [Accepted: 09/10/2022] [Indexed: 12/15/2022]
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Sasaki K, Ito M, Kobayashi S, Kitaguchi D, Matsuzaki H, Kudo M, Hasegawa H, Takeshita N, Sugimoto M, Mitsunaga S, Gotohda N. Automated surgical workflow identification by artificial intelligence in laparoscopic hepatectomy: Experimental research. Int J Surg 2022; 105:106856. [PMID: 36031068 DOI: 10.1016/j.ijsu.2022.106856] [Citation(s) in RCA: 4] [Impact Index Per Article: 2.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 07/21/2022] [Accepted: 08/19/2022] [Indexed: 12/15/2022]
Abstract
BACKGROUND To perform accurate laparoscopic hepatectomy (LH) without injury, novel intraoperative systems of computer-assisted surgery (CAS) for LH are expected. Automated surgical workflow identification is a key component for developing CAS systems. This study aimed to develop a deep-learning model for automated surgical step identification in LH. MATERIALS AND METHODS We constructed a dataset comprising 40 cases of pure LH videos; 30 and 10 cases were used for the training and testing datasets, respectively. Each video was divided into 30 frames per second as static images. LH was divided into nine surgical steps (Steps 0-8), and each frame was annotated as being within one of these steps in the training set. After extracorporeal actions (Step 0) were excluded from the video, two deep-learning models of automated surgical step identification for 8-step and 6-step models were developed using a convolutional neural network (Models 1 & 2). Each frame in the testing dataset was classified using the constructed model performed in real-time. RESULTS Above 8 million frames were annotated for surgical step identification from the pure LH videos. The overall accuracy of Model 1 was 0.891, which was increased to 0.947 in Model 2. Median and average accuracy for each case in Model 2 was 0.927 (range, 0.884-0.997) and 0.937 ± 0.04 (standardized difference), respectively. Real-time automated surgical step identification was performed at 21 frames per second. CONCLUSIONS We developed a highly accurate deep-learning model for surgical step identification in pure LH. Our model could be applied to intraoperative systems of CAS.
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Affiliation(s)
- Kimimasa Sasaki
- Surgical Device Innovation Office, National Cancer Center Hospital East, 6-5-1, Kashiwanoha, Kashiwa-City, Chiba, 277-8577, Japan; Department of Hepatobiliary and Pancreatic Surgery, National Cancer Center Hospital East, 6-5-1, Kashiwanoha, Kashiwa-City, Chiba, 277-8577, Japan; Course of Advanced Clinical Research of Cancer, Juntendo University Graduate School of Medicine, 2-1-1, Hongo, Bunkyo-Ward, Tokyo, 113-8421, Japan
| | - Masaaki Ito
- Surgical Device Innovation Office, National Cancer Center Hospital East, 6-5-1, Kashiwanoha, Kashiwa-City, Chiba, 277-8577, Japan.
| | - Shin Kobayashi
- Department of Hepatobiliary and Pancreatic Surgery, National Cancer Center Hospital East, 6-5-1, Kashiwanoha, Kashiwa-City, Chiba, 277-8577, Japan
| | - Daichi Kitaguchi
- Surgical Device Innovation Office, National Cancer Center Hospital East, 6-5-1, Kashiwanoha, Kashiwa-City, Chiba, 277-8577, Japan
| | - Hiroki Matsuzaki
- Surgical Device Innovation Office, National Cancer Center Hospital East, 6-5-1, Kashiwanoha, Kashiwa-City, Chiba, 277-8577, Japan
| | - Masashi Kudo
- Department of Hepatobiliary and Pancreatic Surgery, National Cancer Center Hospital East, 6-5-1, Kashiwanoha, Kashiwa-City, Chiba, 277-8577, Japan
| | - Hiro Hasegawa
- Surgical Device Innovation Office, National Cancer Center Hospital East, 6-5-1, Kashiwanoha, Kashiwa-City, Chiba, 277-8577, Japan
| | - Nobuyoshi Takeshita
- Surgical Device Innovation Office, National Cancer Center Hospital East, 6-5-1, Kashiwanoha, Kashiwa-City, Chiba, 277-8577, Japan
| | - Motokazu Sugimoto
- Department of Hepatobiliary and Pancreatic Surgery, National Cancer Center Hospital East, 6-5-1, Kashiwanoha, Kashiwa-City, Chiba, 277-8577, Japan
| | - Shuichi Mitsunaga
- Course of Advanced Clinical Research of Cancer, Juntendo University Graduate School of Medicine, 2-1-1, Hongo, Bunkyo-Ward, Tokyo, 113-8421, Japan; Department of Hepatobiliary and Pancreatic Oncology, National Cancer Center Hospital East, 6-5-1, Kashiwanoha, Kashiwa-City, Chiba, 277-8577, Japan
| | - Naoto Gotohda
- Department of Hepatobiliary and Pancreatic Surgery, National Cancer Center Hospital East, 6-5-1, Kashiwanoha, Kashiwa-City, Chiba, 277-8577, Japan; Course of Advanced Clinical Research of Cancer, Juntendo University Graduate School of Medicine, 2-1-1, Hongo, Bunkyo-Ward, Tokyo, 113-8421, Japan
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Pecere S, Antonelli G, Dinis-Ribeiro M, Mori Y, Hassan C, Fuccio L, Bisschops R, Costamagna G, Ji EH, Lee D, Misawa M, Messmann H, Iacopini F, Petruzziello L, Repici A, Saito Y, Sharma P, Yamada M, Spada C, Frazzoni L. Endoscopists performance in optical diagnosis of colorectal polyps in artificial intelligence studies. United European Gastroenterol J 2022; 10:817-826. [PMID: 35984903 PMCID: PMC9557953 DOI: 10.1002/ueg2.12285] [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: 03/15/2022] [Accepted: 07/18/2022] [Indexed: 12/15/2022] Open
Abstract
Widespread adoption of optical diagnosis of colorectal neoplasia is prevented by suboptimal endoscopist performance and lack of standardized training and competence evaluation. We aimed to assess diagnostic accuracy of endoscopists in optical diagnosis of colorectal neoplasia in the framework of artificial intelligence (AI) validation studies. Literature searches of databases (PubMed/MEDLINE, EMBASE, Scopus) up to April 2022 were performed to identify articles evaluating accuracy of individual endoscopists in performing optical diagnosis of colorectal neoplasia within studies validating AI against a histologically verified ground-truth. The main outcomes were endoscopists' pooled sensitivity, specificity, positive and negative predictive value (PPV/NPV), positive and negative likelihood ratio (LR) and area under the curve (AUC for sROC) for predicting adenomas versus non-adenomas. Six studies with 67 endoscopists and 2085 (IQR: 115-243,5) patients were evaluated. Pooled sensitivity and specificity for adenomatous histology was respectively 84.5% (95% CI 80.3%-88%) and 83% (95% CI 79.6%-85.9%), corresponding to a PPV, NPV, LR+, LR- of 89.5% (95% CI 87.1%-91.5%), 75.7% (95% CI 70.1%-80.7%), 5 (95% CI 3.9%-6.2%) and 0.19 (95% CI 0.14%-0.25%). The AUC was 0.82 (CI 0.76-0.90). Expert endoscopists showed a higher sensitivity than non-experts (90.5%, [95% CI 87.6%-92.7%] vs. 75.5%, [95% CI 66.5%-82.7%], p < 0.001), and Eastern endoscopists showed a higher sensitivity than Western (85%, [95% CI 80.5%-88.6%] vs. 75.8%, [95% CI 70.2%-80.6%]). Quality was graded high for 3 studies and low for 3 studies. We show that human accuracy for diagnosis of colorectal neoplasia in the setting of AI studies is suboptimal. Educational interventions could benefit by AI validation settings which seem a feasible framework for competence assessment.
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Affiliation(s)
- Silvia Pecere
- Digestive Endoscopy Unit, Fondazione Policlinico Universitario A. Gemelli IRCCS, Rome, Italy.,Centre for Endoscopic Research Therapeutics and Training (CERTT), Università Cattolica del Sacro Cuore, Rome, Italy
| | - Giulio Antonelli
- Department of Anatomical, Histological, Forensic Medicine and Orthopedics Sciences, "Sapienza" University of Rome, Rome, Italy.,Gastroenterology and Digestive Endoscopy Unit, Ospedale dei Castelli Hospital, Rome, Italy
| | | | - Yuichi Mori
- Clinical Effectiveness Research Group, University of Oslo, Oslo, Norway.,Digestive Disease Center, Showa University Northern Yokohama Hospital, Yokohama, Japan
| | - Cesare Hassan
- Department of Biomedical Sciences, Humanitas University, Milan, Italy.,Department of Gastroenterology, IRCCS Humanitas Research Hospital, Milan, Italy
| | - Lorenzo Fuccio
- Department of Medical and Surgical Sciences (DIMEC), University of Bologna, S. Orsola-Malpighi Hospital, Bologna, Italy
| | - Raf Bisschops
- Department of Gastroenterology and Hepatology, University Hospitals Leuven, TARGID, KU Leuven, Belgium
| | - Guido Costamagna
- Digestive Endoscopy Unit, Fondazione Policlinico Universitario A. Gemelli IRCCS, Rome, Italy.,Centre for Endoscopic Research Therapeutics and Training (CERTT), Università Cattolica del Sacro Cuore, Rome, Italy
| | - Eun Hyo Ji
- Department of Internal Medicine, Healthcare Research Institute, Seoul National University Hospital Healthcare System Gangnam Center, Seoul, Korea
| | - Dongheon Lee
- Department of Internal Medicine, Healthcare Research Institute, Seoul National University Hospital Healthcare System Gangnam Center, Seoul, Korea
| | - Masashi Misawa
- Digestive Disease Center, Showa University Northern Yokohama Hospital, Yokohama, Japan
| | - Helmut Messmann
- III Medizinische Klinik, Universitatsklinikum Augsburg, Augsburg, Germany
| | - Federico Iacopini
- Gastroenterology and Digestive Endoscopy Unit, Ospedale dei Castelli Hospital, Rome, Italy
| | - Lucio Petruzziello
- Digestive Endoscopy Unit, Fondazione Policlinico Universitario A. Gemelli IRCCS, Rome, Italy.,Centre for Endoscopic Research Therapeutics and Training (CERTT), Università Cattolica del Sacro Cuore, Rome, Italy
| | - Alessandro Repici
- Department of Biomedical Sciences, Humanitas University, Milan, Italy
| | - Yutaka Saito
- Endoscopy Division, National Cancer Center Hospital, Tokyo, Japan
| | - Prateek Sharma
- Department of Gastroenterology and Hepatology, University of Kansas Medical Center, Kansas City, Kansas, USA
| | - Masayoshi Yamada
- Endoscopy Division, National Cancer Center Hospital, Tokyo, Japan
| | - Cristiano Spada
- Centre for Endoscopic Research Therapeutics and Training (CERTT), Università Cattolica del Sacro Cuore, Rome, Italy.,Fondazione Poliambulanza, Brescia, Italy
| | - Leonardo Frazzoni
- Department of Medical and Surgical Sciences (DIMEC), University of Bologna, S. Orsola-Malpighi Hospital, Bologna, Italy
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Nemoto D, Guo Z, Peng B, Zhang R, Nakajima Y, Hayashi Y, Yamashina T, Aizawa M, Utano K, Lefor AK, Zhu X, Togashi K. Computer-aided diagnosis of serrated colorectal lesions using non-magnified white-light endoscopic images. Int J Colorectal Dis 2022; 37:1875-1884. [PMID: 35861862 DOI: 10.1007/s00384-022-04210-x] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.5] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Accepted: 06/23/2022] [Indexed: 02/04/2023]
Abstract
PURPOSE Computer-aided diagnosis systems for polyp characterization are commercially available but cannot recognize subtypes of sessile lesions. This study aimed to develop a computer-aided diagnosis system to characterize polyps using non-magnified white-light endoscopic images. METHODS A total of 2249 non-magnified white-light images from 1030 lesions including 534 tubular adenomas, 225 sessile serrated adenoma/polyps, and 271 hyperplastic polyps in the proximal colon were consecutively extracted from an image library and divided into training and testing datasets (4:1), based on the date of colonoscopy. Using ResNet-50 networks, we developed a classifier (1) to differentiate adenomas from serrated lesions, and another classifier (2) to differentiate sessile serrated adenoma/polyps from hyperplastic polyps. Diagnostic performance was assessed using the testing dataset. The computer-aided diagnosis system generated a probability score for each image, and a probability score for each lesion was calculated as the weighted mean with a log10-transformation. Two experts (E1, E2) read the identical testing dataset with a probability score. RESULTS The area under the curve of classifier (1) for adenomas was equivalent to E1 and superior to E2 (classifier 86%, E1 86%, E2 69%; classifier vs. E2, p < 0.001). In contrast, the area under the curve of classifier (2) for sessile serrated adenoma/polyps was inferior to both experts (classifier 55%, E1 68%, E2 79%; classifier vs. E2, p < 0.001). CONCLUSION The classifier (1) developed using white-light images alone compares favorably with experts in differentiating adenomas from serrated lesions. However, the classifier (2) to identify sessile serrated adenoma/polyps is inferior to experts.
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Affiliation(s)
- Daiki Nemoto
- Department of Coloproctology & Gastroenterology, Aizu Medical Center, Fukushima Medical University, 21-2 Maeda, Tanisawa, Aizuwakamatsu, Fukushima, 969-3492, Japan
| | - Zhe Guo
- Biomedical Information Engineering Lab, The University of Aizu, Aizuwakamatsu, Fukushima, Japan
| | - Boyuan Peng
- Biomedical Information Engineering Lab, The University of Aizu, Aizuwakamatsu, Fukushima, Japan
| | - Ruiyao Zhang
- Biomedical Information Engineering Lab, The University of Aizu, Aizuwakamatsu, Fukushima, Japan
| | - Yuki Nakajima
- Department of Coloproctology & Gastroenterology, Aizu Medical Center, Fukushima Medical University, 21-2 Maeda, Tanisawa, Aizuwakamatsu, Fukushima, 969-3492, Japan
| | - Yoshikazu Hayashi
- Department of Medicine, Division of Gastroenterology, Jichi Medical University, Shimotsuke, Tochigi, Japan
| | - Takeshi Yamashina
- Gastroenterology and Hepatology, Kansai Medical University Medical Center, Moriguchi, Osaka, Japan
| | - Masato Aizawa
- Department of Coloproctology & Gastroenterology, Aizu Medical Center, Fukushima Medical University, 21-2 Maeda, Tanisawa, Aizuwakamatsu, Fukushima, 969-3492, Japan
| | - Kenichi Utano
- Department of Coloproctology & Gastroenterology, Aizu Medical Center, Fukushima Medical University, 21-2 Maeda, Tanisawa, Aizuwakamatsu, Fukushima, 969-3492, Japan
| | - Alan Kawarai Lefor
- Department of Surgery, Jichi Medical University, Shimotsuke, Tochigi, Japan
| | - Xin Zhu
- Biomedical Information Engineering Lab, The University of Aizu, Aizuwakamatsu, Fukushima, Japan
| | - Kazutomo Togashi
- Department of Coloproctology & Gastroenterology, Aizu Medical Center, Fukushima Medical University, 21-2 Maeda, Tanisawa, Aizuwakamatsu, Fukushima, 969-3492, Japan.
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Kasahara K, Katsumata K, Saito A, Ishizaki T, Enomoto M, Mazaki J, Tago T, Nagakawa Y, Matsubayashi J, Nagao T, Hirano H, Kuroda M, Tsuchida A. Artificial intelligence predicts lymph node metastasis or risk of lymph node metastasis in T1 colorectal cancer. Int J Clin Oncol 2022; 27:1570-1579. [PMID: 35908272 DOI: 10.1007/s10147-022-02209-6] [Citation(s) in RCA: 7] [Impact Index Per Article: 3.5] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 09/16/2021] [Accepted: 06/20/2022] [Indexed: 12/15/2022]
Abstract
BACKGROUND The treatment strategies for colorectal cancer (CRC) must ensure a radical cure of cancer and prevent over/under treatment. Biopsy specimens used for the definitive diagnosis of T1 CRC were analyzed using artificial intelligence (AI) to construct a risk index for lymph node metastasis. METHODS A total of 146 T1 CRC cases were analyzed. The specimens for analysis were mainly biopsy specimens, and in the absence of biopsy specimens, the mucosal layer of the surgical specimens was analyzed. The pathology slides for each case were digitally imaged, and the morphological features of cancer cell nuclei were extracted from the tissue images. First, statistical methods were used to analyze how well these features could predict lymph node metastasis risk. A lymph node metastasis risk model using AI was created based on these morphological features, and accuracy in test cases was verified. RESULTS Each developed model could predict lymph node metastasis risk with a > 90% accuracy in each region of interest of the training cases. Lymph node metastasis risk was predicted with 81.8-86.3% accuracy for randomly validated cases, using a learning model with biopsy data. Moreover, no case with lymph node metastasis or lymph node risk was judged to have no risk using the same model. CONCLUSIONS AI models suggest an association between biopsy specimens and lymph node metastases in T1 CRC and may contribute to increased accuracy of preoperative diagnosis.
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Affiliation(s)
- Kenta Kasahara
- Department of Digestive and Pediatric Surgery, Tokyo Medical University, 6-7-1 Nishi Shinjuku, Shinjuku-ku, Tokyo, 160-0023, Japan.
| | - Kenji Katsumata
- Department of Digestive and Pediatric Surgery, Tokyo Medical University, 6-7-1 Nishi Shinjuku, Shinjuku-ku, Tokyo, 160-0023, Japan
| | - Akira Saito
- Department of AI Applied Quantitative Clinical Science, Tokyo Medical University, 6-7-1 Nishi Shinjuku, Shinjuku-ku, Tokyo, 160-0023, Japan
| | - Tetsuo Ishizaki
- Department of Digestive and Pediatric Surgery, Tokyo Medical University, 6-7-1 Nishi Shinjuku, Shinjuku-ku, Tokyo, 160-0023, Japan
| | - Masanobu Enomoto
- Department of Digestive and Pediatric Surgery, Tokyo Medical University, 6-7-1 Nishi Shinjuku, Shinjuku-ku, Tokyo, 160-0023, Japan
| | - Junichi Mazaki
- Department of Digestive and Pediatric Surgery, Tokyo Medical University, 6-7-1 Nishi Shinjuku, Shinjuku-ku, Tokyo, 160-0023, Japan
| | - Tomoya Tago
- Department of Digestive and Pediatric Surgery, Tokyo Medical University, 6-7-1 Nishi Shinjuku, Shinjuku-ku, Tokyo, 160-0023, Japan
| | - Yuichi Nagakawa
- Department of Digestive and Pediatric Surgery, Tokyo Medical University, 6-7-1 Nishi Shinjuku, Shinjuku-ku, Tokyo, 160-0023, Japan
| | - Jun Matsubayashi
- Department of Anatomic Pathology, Tokyo Medical University, 6-7-1 Nishi Shinjuku, Shinjuku-ku, Tokyo, 160-0023, Japan
| | - Toshitaka Nagao
- Department of Anatomic Pathology, Tokyo Medical University, 6-7-1 Nishi Shinjuku, Shinjuku-ku, Tokyo, 160-0023, Japan
| | - Hiroshi Hirano
- Diagnostic Pathology Division, Tokyo Medical University Hachioji Medical Center, 1163 Tatemachi, Hachioji-shi, 193-0998, Tokyo, Japan
| | - Masahiko Kuroda
- Department of Molecular Pathology, Tokyo Medical University, 6-7-1 Nishi Shinjuku, Shinjuku-ku, Tokyo, 160-0023, Japan
| | - Akihiko Tsuchida
- Department of Digestive and Pediatric Surgery, Tokyo Medical University, 6-7-1 Nishi Shinjuku, Shinjuku-ku, Tokyo, 160-0023, Japan
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Yang LS, Perry E, Shan L, Wilding H, Connell W, Thompson AJ, Taylor ACF, Desmond PV, Holt BA. Clinical application and diagnostic accuracy of artificial intelligence in colonoscopy for inflammatory bowel disease: systematic review. Endosc Int Open 2022; 10:E1004-E1013. [PMID: 35845028 PMCID: PMC9286774 DOI: 10.1055/a-1846-0642] [Citation(s) in RCA: 2] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 11/30/2021] [Accepted: 05/02/2022] [Indexed: 12/15/2022] Open
Abstract
Background and aims Artificial intelligence (AI) technology is being evaluated for its potential to improve colonoscopic assessment of inflammatory bowel disease (IBD), particularly with computer-aided image classifiers. This review evaluates the clinical application and diagnostic test accuracy (DTA) of AI algorithms in colonoscopy for IBD. Methods A systematic review was performed on studies evaluating AI in colonoscopy of adult patients with IBD. MEDLINE, Embase, Emcare, PsycINFO, CINAHL, Cochrane Library and Clinicaltrials.gov databases were searched on 28 th April 2021 for English language articles published between January 1, 2000 and April 28, 2021. Risk of bias and applicability were assessed with the Quality Assessment of Diagnostic Accuracy Studies-2 tool. Diagnostic accuracy was presented as median (interquartile range). Results Of 1029 records screened, nine studies with 7813 patients were included for review. AI was used to predict endoscopic and histologic disease activity in ulcerative colitis, and differentiation of Crohn's disease from Behcet's disease and intestinal tuberculosis. DTA of AI algorithms ranged between 52-91 %. The sensitivity and specificity for AI algorithms predicting endoscopic severity of disease were 78 % (range 72-83, interquartile range 5.5) and 91 % (range 86-96, interquartile range 5), respectively. Conclusions AI has been primarily used to assess disease activity in ulcerative colitis. The diagnostic performance is promising and suggests potential for other clinical application of AI in IBD colonoscopy such as dysplasia detection. However, current evidence is limited by retrospective data and models trained on still images only. Future prospective multicenter studies with full-motion videos are needed to replicate the real-world clinical setting.
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Affiliation(s)
- Linda S. Yang
- Department of Gastroenterology, St. Vincent’s Hospital and the University of Melbourne, Fitzroy, Victoria, Australia
| | - Evelyn Perry
- Department of Gastroenterology, St. Vincent’s Hospital and the University of Melbourne, Fitzroy, Victoria, Australia
| | - Leonard Shan
- Department of Surgery, Faculty of Medicine, Dentistry and Health Sciences, the University of Melbourne, Fitzroy, Victoria, Australia
| | - Helen Wilding
- Library Service, St. Vincent’s Hospital Melbourne, Fitzroy, Victoria, Australia
| | - William Connell
- Department of Gastroenterology, St. Vincent’s Hospital and the University of Melbourne, Fitzroy, Victoria, Australia
| | - Alexander J. Thompson
- Department of Gastroenterology, St. Vincent’s Hospital and the University of Melbourne, Fitzroy, Victoria, Australia
| | - Andrew C. F. Taylor
- Department of Gastroenterology, St. Vincent’s Hospital and the University of Melbourne, Fitzroy, Victoria, Australia
| | - Paul V. Desmond
- Department of Gastroenterology, St. Vincent’s Hospital and the University of Melbourne, Fitzroy, Victoria, Australia
| | - Bronte A. Holt
- Department of Gastroenterology, St. Vincent’s Hospital and the University of Melbourne, Fitzroy, Victoria, Australia
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Zhang P, She Y, Gao J, Feng Z, Tan Q, Min X, Xu S. Development of a Deep Learning System to Detect Esophageal Cancer by Barium Esophagram. Front Oncol 2022; 12:766243. [PMID: 35800062 PMCID: PMC9253273 DOI: 10.3389/fonc.2022.766243] [Citation(s) in RCA: 3] [Impact Index Per Article: 1.5] [Reference Citation Analysis] [Abstract] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 02/28/2022] [Accepted: 05/23/2022] [Indexed: 12/24/2022] Open
Abstract
Background Implementation of deep learning systems (DLSs) for analysis of barium esophagram, a cost-effective diagnostic test for esophageal cancer detection, is expected to reduce the burden to radiologists while ensuring the accuracy of diagnosis. Objective To develop an automated DLS to detect esophageal cancer on barium esophagram. Methods This was a retrospective study using deep learning for esophageal cancer detection. A two-stage DLS (including a Selection network and a Classification network) was developed. Five datasets based on barium esophagram were used for stepwise training, validation, and testing of the DLS. Datasets 1 and 2 were used to respectively train and test the Selection network, while Datasets 3, 4, and 5 were respectively used to train, validate, and test the Classification network. Finally, a positioning box with a probability value was outputted by the DLS. A region of interest delineated by experienced radiologists was selected as the ground truth to evaluate the detection and classification efficiency of the DLS. Standard machine learning metrics (accuracy, recall, precision, sensitivity, and specificity) were calculated. A comparison with the conventional visual inspection approach was also conducted. Results The accuracy, sensitivity, and specificity of our DLS in detecting esophageal cancer were 90.3%, 92.5%, and 88.7%, respectively. With the aid of DLS, the radiologists’ interpretation time was significantly shortened (Reader1, 45.7 s vs. 72.2 s without DLS aid; Reader2, 54.1 s vs. 108.7 s without DLS aid). Respective diagnostic efficiencies for Reader1 with and without DLS aid were 96.8% vs. 89.3% for accuracy, 97.5% vs. 87.5% for sensitivity, 96.2% vs. 90.6% for specificity, and 0.969 vs. 0.890 for AUC. Respective diagnostic efficiencies for Reader2 with and without DLS aid were 95.7% vs. 88.2% for accuracy, 92.5% vs. 77.5% for sensitivity, 98.1% vs. 96.2% for specificity, and 0.953 vs. 0.869 for AUC. Of note, the positioning boxes outputted by the DLS almost overlapped with those manually labeled by the radiologists on Dataset 5. Conclusions The proposed two-stage DLS for detecting esophageal cancer on barium esophagram could effectively shorten the interpretation time with an excellent diagnostic performance. It may well assist radiologists in clinical practice to reduce their burden.
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Affiliation(s)
- Peipei Zhang
- Department of Radiology, Tongji Hospital, Tongji Medical College, Huazhong University of Science and Technology, Wuhan, China
| | - Yifei She
- College of Computer Science, South-Central University for Nationalities, Wuhan, China
| | - Junfeng Gao
- College of Biomedical Engineering, South-Central of University for Nationalities, Wuhan, China
| | - Zhaoyan Feng
- Department of Radiology, Tongji Hospital, Tongji Medical College, Huazhong University of Science and Technology, Wuhan, China
| | - Qinghai Tan
- Department of Gastroenterology, Tongji Hospital, Tongji Medical College, Huazhong University of Science and Technology, Wuhan, China
| | - Xiangde Min
- Department of Radiology, Tongji Hospital, Tongji Medical College, Huazhong University of Science and Technology, Wuhan, China
- *Correspondence: Shengzhou Xu, ; Xiangde Min,
| | - Shengzhou Xu
- College of Computer Science, South-Central University for Nationalities, Wuhan, China
- *Correspondence: Shengzhou Xu, ; Xiangde Min,
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48
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Barua I, Wieszczy P, Kudo SE, Misawa M, Holme Ø, Gulati S, Williams S, Mori K, Itoh H, Takishima K, Mochizuki K, Miyata Y, Mochida K, Akimoto Y, Kuroki T, Morita Y, Shiina O, Kato S, Nemoto T, Hayee B, Patel M, Gunasingam N, Kent A, Emmanuel A, Munck C, Nilsen JA, Hvattum SA, Frigstad SO, Tandberg P, Løberg M, Kalager M, Haji A, Bretthauer M, Mori Y. Real-Time Artificial Intelligence-Based Optical Diagnosis of Neoplastic Polyps during Colonoscopy. NEJM EVIDENCE 2022; 1:EVIDoa2200003. [PMID: 38319238 DOI: 10.1056/evidoa2200003] [Citation(s) in RCA: 33] [Impact Index Per Article: 16.5] [Reference Citation Analysis] [Abstract] [MESH Headings] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 02/07/2024]
Abstract
BACKGROUND: Artificial intelligence using computer-aided diagnosis (CADx) in real time with images acquired during colonoscopy may help colonoscopists distinguish between neoplastic polyps requiring removal and nonneoplastic polyps not requiring removal. In this study, we tested whether CADx analyzed images helped in this decision-making process. METHODS: We performed a multicenter clinical study comparing a novel CADx-system that uses real-time ultra-magnifying polyp visualization during colonoscopy with standard visual inspection of small (≤5 mm in diameter) polyps in the sigmoid colon and the rectum for optical diagnosis of neoplastic histology. After committing to a diagnosis (i.e., neoplastic, uncertain, or nonneoplastic), all imaged polyps were removed. The primary end point was sensitivity for neoplastic polyps by CADx and visual inspection, compared with histopathology. Secondary end points were specificity and colonoscopist confidence level in unaided optical diagnosis. RESULTS: We assessed 1289 individuals for eligibility at colonoscopy centers in Norway, the United Kingdom, and Japan. We detected 892 eligible polyps in 518 patients and included them in analyses: 359 were neoplastic and 533 were nonneoplastic. Sensitivity for the diagnosis of neoplastic polyps with standard visual inspection was 88.4% (95% confidence interval [CI], 84.3 to 91.5) compared with 90.4% (95% CI, 86.8 to 93.1) with CADx (P=0.33). Specificity was 83.1% (95% CI, 79.2 to 86.4) with standard visual inspection and 85.9% (95% CI, 82.3 to 88.8) with CADx. The proportion of polyp assessment with high confidence was 74.2% (95% CI, 70.9 to 77.3) with standard visual inspection versus 92.6% (95% CI, 90.6 to 94.3) with CADx. CONCLUSIONS: Real-time polyp assessment with CADx did not significantly increase the diagnostic sensitivity of neoplastic polyps during a colonoscopy compared with optical evaluation without CADx. (Funded by the Research Council of Norway [Norges Forskningsråd], the Norwegian Cancer Society [Kreftforeningen], and the Japan Society for the Promotion of Science; UMIN number, UMIN000035213.)
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Affiliation(s)
- Ishita Barua
- Clinical Effectiveness Research Group, University of Oslo, Oslo
- Clinical Effectiveness Research Group, Department of Transplantation Medicine, Oslo University Hospital, Oslo
| | - Paulina Wieszczy
- Clinical Effectiveness Research Group, University of Oslo, Oslo
- Clinical Effectiveness Research Group, Department of Transplantation Medicine, Oslo University Hospital, Oslo
- Department of Gastroenterology, Hepatology and Clinical Oncology, Centre of Postgraduate Medical Education, Warsaw, Poland
| | - Shin-Ei Kudo
- Digestive Disease Center, Showa University Northern Yokohama Hospital, Yokohama, Japan
| | - Masashi Misawa
- Digestive Disease Center, Showa University Northern Yokohama Hospital, Yokohama, Japan
| | - Øyvind Holme
- Clinical Effectiveness Research Group, University of Oslo, Oslo
- Clinical Effectiveness Research Group, Department of Transplantation Medicine, Oslo University Hospital, Oslo
- Department of Medicine, Sørlandet Hospital Kristiansand, Kristiansand, Norway
| | - Shraddha Gulati
- King's Institute of Therapeutic Endoscopy, King's College Hospital NHS Foundation Trust, London
| | - Sophie Williams
- King's Institute of Therapeutic Endoscopy, King's College Hospital NHS Foundation Trust, London
| | - Kensaku Mori
- Graduate School of Informatics, Nagoya University, Nagoya, Japan
| | - Hayato Itoh
- Graduate School of Informatics, Nagoya University, Nagoya, Japan
| | - Kazumi Takishima
- Digestive Disease Center, Showa University Northern Yokohama Hospital, Yokohama, Japan
| | - Kenichi Mochizuki
- Digestive Disease Center, Showa University Northern Yokohama Hospital, Yokohama, Japan
| | - Yuki Miyata
- Digestive Disease Center, Showa University Northern Yokohama Hospital, Yokohama, Japan
| | - Kentaro Mochida
- Digestive Disease Center, Showa University Northern Yokohama Hospital, Yokohama, Japan
| | - Yoshika Akimoto
- Digestive Disease Center, Showa University Northern Yokohama Hospital, Yokohama, Japan
| | - Takanori Kuroki
- Digestive Disease Center, Showa University Northern Yokohama Hospital, Yokohama, Japan
| | - Yuriko Morita
- Digestive Disease Center, Showa University Northern Yokohama Hospital, Yokohama, Japan
| | - Osamu Shiina
- Digestive Disease Center, Showa University Northern Yokohama Hospital, Yokohama, Japan
| | - Shun Kato
- Digestive Disease Center, Showa University Northern Yokohama Hospital, Yokohama, Japan
| | - Tetsuo Nemoto
- Department of Diagnostic Pathology, School of Medicine, Showa University Northern Yokohama Hospital, Kanagawa, Japan
| | - Bu Hayee
- King's Institute of Therapeutic Endoscopy, King's College Hospital NHS Foundation Trust, London
| | - Mehul Patel
- King's Institute of Therapeutic Endoscopy, King's College Hospital NHS Foundation Trust, London
| | - Nishmi Gunasingam
- King's Institute of Therapeutic Endoscopy, King's College Hospital NHS Foundation Trust, London
| | - Alexandra Kent
- King's Institute of Therapeutic Endoscopy, King's College Hospital NHS Foundation Trust, London
| | - Andrew Emmanuel
- King's Institute of Therapeutic Endoscopy, King's College Hospital NHS Foundation Trust, London
| | - Carl Munck
- Department of Medicine, Baerum Hospital, Vestre Viken Hospital Trust, Gjettum, Norway
| | - Jens Aksel Nilsen
- Department of Medicine, Baerum Hospital, Vestre Viken Hospital Trust, Gjettum, Norway
| | - Stine Astrup Hvattum
- Department of Medicine, Baerum Hospital, Vestre Viken Hospital Trust, Gjettum, Norway
| | - Svein Oskar Frigstad
- Department of Medicine, Baerum Hospital, Vestre Viken Hospital Trust, Gjettum, Norway
| | - Petter Tandberg
- Department of Medicine, Baerum Hospital, Vestre Viken Hospital Trust, Gjettum, Norway
| | - Magnus Løberg
- Clinical Effectiveness Research Group, University of Oslo, Oslo
- Clinical Effectiveness Research Group, Department of Transplantation Medicine, Oslo University Hospital, Oslo
| | - Mette Kalager
- Clinical Effectiveness Research Group, University of Oslo, Oslo
- Clinical Effectiveness Research Group, Department of Transplantation Medicine, Oslo University Hospital, Oslo
| | - Amyn Haji
- King's Institute of Therapeutic Endoscopy, King's College Hospital NHS Foundation Trust, London
| | - Michael Bretthauer
- Clinical Effectiveness Research Group, University of Oslo, Oslo
- Clinical Effectiveness Research Group, Department of Transplantation Medicine, Oslo University Hospital, Oslo
| | - Yuichi Mori
- Clinical Effectiveness Research Group, University of Oslo, Oslo
- Clinical Effectiveness Research Group, Department of Transplantation Medicine, Oslo University Hospital, Oslo
- Digestive Disease Center, Showa University Northern Yokohama Hospital, Yokohama, Japan
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Yang CB, Kim SH, Lim YJ. Preparation of image databases for artificial intelligence algorithm development in gastrointestinal endoscopy. Clin Endosc 2022; 55:594-604. [PMID: 35636749 PMCID: PMC9539300 DOI: 10.5946/ce.2021.229] [Citation(s) in RCA: 7] [Impact Index Per Article: 3.5] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 09/10/2021] [Accepted: 03/07/2022] [Indexed: 12/09/2022] Open
Abstract
Over the past decade, technological advances in deep learning have led to the introduction of artificial intelligence (AI) in medical imaging. The most commonly used structure in image recognition is the convolutional neural network, which mimics the action of the human visual cortex. The applications of AI in gastrointestinal endoscopy are diverse. Computer-aided diagnosis has achieved remarkable outcomes with recent improvements in machine-learning techniques and advances in computer performance. Despite some hurdles, the implementation of AI-assisted clinical practice is expected to aid endoscopists in real-time decision-making. In this summary, we reviewed state-of-the-art AI in the field of gastrointestinal endoscopy and offered a practical guide for building a learning image dataset for algorithm development.
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Affiliation(s)
- Chang Bong Yang
- Department of Internal Medicine, Dongguk University Ilsan Hospital, Dongguk University College of Medicine, Goyang, Korea
| | - Sang Hoon Kim
- Department of Internal Medicine, Dongguk University Ilsan Hospital, Dongguk University College of Medicine, Goyang, Korea
| | - Yun Jeong Lim
- Department of Internal Medicine, Dongguk University Ilsan Hospital, Dongguk University College of Medicine, Goyang, Korea
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50
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Current Status and Future Perspectives of Artificial Intelligence in Colonoscopy. J Clin Med 2022; 11:jcm11102923. [PMID: 35629049 PMCID: PMC9143862 DOI: 10.3390/jcm11102923] [Citation(s) in RCA: 12] [Impact Index Per Article: 6.0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 03/24/2022] [Revised: 05/18/2022] [Accepted: 05/20/2022] [Indexed: 12/19/2022] Open
Abstract
The early endoscopic identification, resection, and treatment of precancerous adenoma and early-stage cancer has been shown to reduce not only the prevalence of colorectal cancer but also its mortality rate. Recent advances in endoscopic devices and imaging technology have dramatically improved our ability to detect colorectal lesions and predict their pathological diagnosis. In addition to this, rapid advances in artificial intelligence (AI) technology mean that AI-related research and development is now progressing in the diagnostic imaging field, particularly colonoscopy, and AIs (i.e., devices that mimic cognitive abilities, such as learning and problem-solving) already approved as medical devices are now being introduced into everyday clinical practice. Today, there is an increasing expectation that sophisticated AIs will be able to provide high-level diagnostic performance irrespective of the level of skill of the endoscopist. In this paper, we review colonoscopy-related AI research and the AIs that have already been approved and discuss the future prospects of this technology.
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