51
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Jin EH, Seo JY, Bae JH, Lee J, Choi JM, Han YM, Lim JH. Small sessile serrated polyps might not be at a higher risk for future advanced neoplasia than low-risk adenomas or polyp-free groups. Scand J Gastroenterol 2022; 57:99-104. [PMID: 34523359 DOI: 10.1080/00365521.2021.1974933] [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] [Indexed: 02/04/2023]
Abstract
BACKGROUND Polypectomy surveillance colonoscopy is recommended according to the risk stratification of initially removed polyps. This study aimed to evaluate the risk of advanced neoplasia following low-risk SSPs compared with that following LRAs and polyp-free groups. MATERIALS AND METHODS From September 2013 to August 2017, asymptomatic Koreans aged 50-75 years who underwent surveillance colonoscopy post-baseline colonoscopy were enrolled. The 1314 participants who met the study design criteria were stratified into three groups according to the presence of LRAs or low-risk SSPs. The rate of advanced neoplasia was then compared between groups by surveillance colonoscopy. RESULTS A total of 1314 participants were classified according to baseline colonoscopy findings: no polyp (n = 551), LRA (n = 707), and low-risk SSP (n = 56). All participants underwent surveillance colonoscopy after an average of 28.1 ± 8.7 months. The rate of advanced neoplasia at surveillance was not different between groups: no polyp group (13/551, 2.4%), LRA group (27/707, 3.8%), and low-risk SSP group (0/56, 0%). The LRA group exhibited a significantly higher rate of low- and high-risk polyps (47.5, 13.4%) than did the no polyp (35.6, 7.4%, p < .001, p = .001), but no significant differences to the low-risk SSP group (35.7, 7.1%, p = .117, p = .253), respectively. CONCLUSIONS Patients with low-risk SSPs were not at a higher risk of advanced neoplasia than LRA patients, even in the polyp-free group. We suggest that surveillance colonoscopy after the removal of low-risk SSPs is not required more often than for LRAs.
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Affiliation(s)
- Eun Hyo Jin
- Department of Internal Medicine, Healthcare Research Institute, Seoul National University Hospital Healthcare System Gangnam Center, Seoul, South Korea
| | - Ji Yeon Seo
- Department of Internal Medicine, Healthcare Research Institute, Seoul National University Hospital Healthcare System Gangnam Center, Seoul, South Korea
| | - Jung Ho Bae
- Department of Internal Medicine, Healthcare Research Institute, Seoul National University Hospital Healthcare System Gangnam Center, Seoul, South Korea
| | - Jooyoung Lee
- Department of Internal Medicine, Healthcare Research Institute, Seoul National University Hospital Healthcare System Gangnam Center, Seoul, South Korea
| | - Ji Min Choi
- Department of Internal Medicine, Healthcare Research Institute, Seoul National University Hospital Healthcare System Gangnam Center, Seoul, South Korea
| | - Yoo Min Han
- Department of Internal Medicine, Healthcare Research Institute, Seoul National University Hospital Healthcare System Gangnam Center, Seoul, South Korea
| | - Joo Hyun Lim
- Department of Internal Medicine, Healthcare Research Institute, Seoul National University Hospital Healthcare System Gangnam Center, Seoul, South Korea
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52
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Okamoto Y, Yoshida S, Izakura S, Katayama D, Michida R, Koide T, Tamaki T, Kamigaichi Y, Tamari H, Shimohara Y, Nishimura T, Inagaki K, Tanaka H, Yamashita K, Sumimoto K, Oka S, Tanaka S. Development of multi-class computer-aided diagnostic systems using the NICE/JNET classifications for colorectal lesions. J Gastroenterol Hepatol 2022; 37:104-110. [PMID: 34478167 DOI: 10.1111/jgh.15682] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.5] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 05/22/2021] [Revised: 07/22/2021] [Accepted: 08/30/2021] [Indexed: 12/15/2022]
Abstract
BACKGROUND AND AIM Diagnostic support using artificial intelligence may contribute to the equalization of endoscopic diagnosis of colorectal lesions. We developed computer-aided diagnosis (CADx) support system for diagnosing colorectal lesions using the NBI International Colorectal Endoscopic (NICE) classification and the Japan NBI Expert Team (JNET) classification. METHODS Using Residual Network as the classifier and NBI images as training images, we developed a CADx based on the NICE classification (CADx-N) and a CADx based on the JNET classification (CADx-J). For validation, 480 non-magnifying and magnifying NBI images were used for the CADx-N and 320 magnifying NBI images were used for the CADx-J. The diagnostic performance of the CADx-N was evaluated using the magnification rate. RESULTS The accuracy of the CADx-N for Types 1, 2, and 3 was 97.5%, 91.2%, and 93.8%, respectively. The diagnostic performance for each magnification level was good (no statistically significant difference). The sensitivity, specificity, positive predictive value, negative predictive value, and accuracy of the CADx-J were 100%, 96.3%, 82.8%, 100%, and 96.9% for Type 1; 80.3%, 93.7%, 94.1%, 79.2%, and 86.3% for Type 2A; 80.4%, 84.7%, 46.8%, 96.3%, and 84.1% for Type 2B; and 62.5%, 99.6%, 96.8%, 93.8%, and 94.1% for Type 3, respectively. CONCLUSIONS The multi-class CADx systems had good diagnostic performance with both the NICE and JNET classifications and may aid in educating non-expert endoscopists and assist in diagnosing colorectal lesions.
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Affiliation(s)
- Yuki Okamoto
- Department of Gastroenterology and Metabolism, Hiroshima University Hospital, Hiroshima, Japan
| | - Shigeto Yoshida
- Department of Gastroenterology, JR Hiroshima Hospital, Hiroshima, Japan
| | - Seiji Izakura
- Research Institute for Nanodevice and Bio Systems, Hiroshima University, Hiroshima, Japan
| | - Daisuke Katayama
- Research Institute for Nanodevice and Bio Systems, Hiroshima University, Hiroshima, Japan
| | - Ryuichi Michida
- Research Institute for Nanodevice and Bio Systems, Hiroshima University, Hiroshima, Japan
| | - Tetsushi Koide
- Research Institute for Nanodevice and Bio Systems, Hiroshima University, Hiroshima, Japan
| | - Toru Tamaki
- Department of Computer Science, Nagoya Institute of Technology, Nagoya, Japan
| | - Yuki Kamigaichi
- Department of Gastroenterology and Metabolism, Hiroshima University Hospital, Hiroshima, Japan
| | - Hirosato Tamari
- Department of Gastroenterology and Metabolism, Hiroshima University Hospital, Hiroshima, Japan
| | - Yasutsugu Shimohara
- Department of Gastroenterology and Metabolism, Hiroshima University Hospital, Hiroshima, Japan
| | - Tomoyuki Nishimura
- Department of Gastroenterology and Metabolism, Hiroshima University Hospital, Hiroshima, Japan
| | - Katsuaki Inagaki
- Department of Gastroenterology and Metabolism, Hiroshima University Hospital, Hiroshima, Japan
| | - Hidenori Tanaka
- Department of Endoscopy, Hiroshima University Hospital, Hiroshima, Japan
| | - Ken Yamashita
- Department of Endoscopy, Hiroshima University Hospital, Hiroshima, Japan
| | - Kyoku Sumimoto
- Department of Endoscopy, Hiroshima University Hospital, Hiroshima, Japan
| | - Shiro Oka
- Department of Gastroenterology and Metabolism, Hiroshima University Hospital, Hiroshima, Japan
| | - Shinji Tanaka
- Department of Endoscopy, Hiroshima University Hospital, Hiroshima, Japan
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53
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Sánchez-Peralta LF, Pagador JB, Sánchez-Margallo FM. Artificial Intelligence for Colorectal Polyps in Colonoscopy. Artif Intell Med 2022. [DOI: 10.1007/978-3-030-64573-1_308] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 10/19/2022]
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54
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Anastasiou J, Goyal H, Perisetti A, Inamdar S, Tharian B. Artificial intelligence-assisted optical biopsies of colon polyps: Hype or reality? SAUDI JOURNAL OF MEDICINE AND MEDICAL SCIENCES 2022; 10:77-78. [PMID: 35283710 PMCID: PMC8869257 DOI: 10.4103/sjmms.sjmms_524_21] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.5] [Reference Citation Analysis] [Track Full Text] [Download PDF] [Journal Information] [Subscribe] [Scholar Register] [Received: 09/07/2021] [Accepted: 12/27/2021] [Indexed: 11/04/2022] Open
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55
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Oh CK, Kim T, Cho YK, Cheung DY, Lee BI, Cho YS, Kim JI, Choi MG, Lee HH, Lee S. Convolutional neural network-based object detection model to identify gastrointestinal stromal tumors in endoscopic ultrasound images. J Gastroenterol Hepatol 2021; 36:3387-3394. [PMID: 34369001 DOI: 10.1111/jgh.15653] [Citation(s) in RCA: 20] [Impact Index Per Article: 6.7] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 06/16/2021] [Revised: 07/27/2021] [Accepted: 08/02/2021] [Indexed: 12/20/2022]
Abstract
BACKGROUND AND AIM We aimed to develop a convolutional neural network (CNN)-based object detection model for the discrimination of gastric subepithelial tumors, such as gastrointestinal stromal tumors (GISTs), and leiomyomas, in endoscopic ultrasound (EUS) images. METHODS We used 376 images from 114 patients with histologically confirmed gastric GIST or leiomyoma to train the EUS-CNN. We constructed the EUS-CNN using an EfficientNet CNN model for feature extraction and a weighted bi-directional feature pyramid network for object detection. We assessed the performance of our EUS-CNN by calculating its accuracy, sensitivity, specificity, and area under receiver operating characteristic curve (AUC) using a validation set of 170 images from 54 patients. Four EUS experts and 15 EUS trainees were asked to judge the same validation dataset, and the diagnostic yields were compared between the EUS-CNN and human assessments. RESULTS In the per-image analysis, the sensitivity, specificity, accuracy, and AUC of our EUS-CNN were 95.6%, 82.1%, 91.2%, and 0.9234, respectively. In the per-patient analysis, the sensitivity, specificity, accuracy, and AUC for our object detection model were 100.0%, 85.7%, 96.3%, and 0.9929, respectively. The EUS-CNN outperformed human assessment in terms of accuracy, sensitivity, and negative predictive value. CONCLUSIONS We developed the EUS-CNN system, which demonstrated high diagnostic ability for gastric GIST prediction. This EUS-CNN system can be helpful not only for less-experienced endoscopists but also for experienced ones. Additional EUS image accumulation and prospective studies are required alongside validation in a large multicenter trial.
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Affiliation(s)
- Chang Kyo Oh
- Division of Gastroenterology, Department of Internal Medicine, College of Medicine, The Catholic University of Korea, Seoul, South Korea
| | - Taewan Kim
- Department of Mechanical Engineering, Pohang University of Science and Technology (POSTECH), Pohang, South Korea.,Postech-Catholic Biomedical Engineering Institute, Pohang University of Science and Technology (POSTECH), Pohang, South Korea
| | - Yu Kyung Cho
- Division of Gastroenterology, Department of Internal Medicine, College of Medicine, The Catholic University of Korea, Seoul, South Korea
| | - Dae Young Cheung
- Division of Gastroenterology, Department of Internal Medicine, College of Medicine, The Catholic University of Korea, Seoul, South Korea
| | - Bo-In Lee
- Division of Gastroenterology, Department of Internal Medicine, College of Medicine, The Catholic University of Korea, Seoul, South Korea
| | - Young-Seok Cho
- Division of Gastroenterology, Department of Internal Medicine, College of Medicine, The Catholic University of Korea, Seoul, South Korea
| | - Jin Il Kim
- Division of Gastroenterology, Department of Internal Medicine, College of Medicine, The Catholic University of Korea, Seoul, South Korea
| | - Myung-Gyu Choi
- Division of Gastroenterology, Department of Internal Medicine, College of Medicine, The Catholic University of Korea, Seoul, South Korea
| | - Han Hee Lee
- Division of Gastroenterology, Department of Internal Medicine, College of Medicine, The Catholic University of Korea, Seoul, South Korea.,Postech-Catholic Biomedical Engineering Institute, College of Medicine, The Catholic University of Korea, Seoul, South Korea
| | - Seungchul Lee
- Department of Mechanical Engineering, Pohang University of Science and Technology (POSTECH), Pohang, South Korea.,Postech-Catholic Biomedical Engineering Institute, Pohang University of Science and Technology (POSTECH), Pohang, South Korea.,Graduate School of Artificial Intelligence, Pohang University of Science and Technology (POSTECH), Pohang, South Korea.,Institute of Convergence Research and Education in Advanced Technology, Yonsei University, Seoul, South Korea
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56
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Wu L, He X, Liu M, Xie H, An P, Zhang J, Zhang H, Ai Y, Tong Q, Guo M, Huang M, Ge C, Yang Z, Yuan J, Liu J, Zhou W, Jiang X, Huang X, Mu G, Wan X, Li Y, Wang H, Wang Y, Zhang H, Chen D, Gong D, Wang J, Huang L, Li J, Yao L, Zhu Y, Yu H. Evaluation of the effects of an artificial intelligence system on endoscopy quality and preliminary testing of its performance in detecting early gastric cancer: a randomized controlled trial. Endoscopy 2021; 53:1199-1207. [PMID: 33429441 DOI: 10.1055/a-1350-5583] [Citation(s) in RCA: 75] [Impact Index Per Article: 25.0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Indexed: 02/08/2023]
Abstract
BACKGROUND Esophagogastroduodenoscopy (EGD) is a prerequisite for detecting upper gastrointestinal lesions especially early gastric cancer (EGC). An artificial intelligence system has been shown to monitor blind spots during EGD. In this study, we updated the system (ENDOANGEL), verified its effectiveness in improving endoscopy quality, and pretested its performance in detecting EGC in a multicenter randomized controlled trial. METHODS ENDOANGEL was developed using deep convolutional neural networks and deep reinforcement learning. Patients undergoing EGD in five hospitals were randomly assigned to the ENDOANGEL-assisted group or to a control group without use of ENDOANGEL. The primary outcome was the number of blind spots. Secondary outcomes included performance of ENDOANGEL in predicting EGC in a clinical setting. RESULTS 1050 patients were randomized, and 498 and 504 patients in the ENDOANGEL and control groups, respectively, were analyzed. Compared with the control group, the ENDOANGEL group had fewer blind spots (mean 5.38 [standard deviation (SD) 4.32] vs. 9.82 [SD 4.98]; P < 0.001) and longer inspection time (5.40 [SD 3.82] vs. 4.38 [SD 3.91] minutes; P < 0.001). In the ENDOANGEL group, 196 gastric lesions with pathological results were identified. ENDOANGEL correctly predicted all three EGCs (one mucosal carcinoma and two high grade neoplasias) and two advanced gastric cancers, with a per-lesion accuracy of 84.7 %, sensitivity of 100 %, and specificity of 84.3 % for detecting gastric cancer. CONCLUSIONS In this multicenter study, ENDOANGEL was an effective and robust system to improve the quality of EGD and has the potential to detect EGC in real time.
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Affiliation(s)
- Lianlian Wu
- Department of Gastroenterology, Renmin Hospital of Wuhan University, Wuhan, China.,Key Laboratory of Hubei Province for Digestive System Disease, Renmin Hospital of Wuhan University, Wuhan, China.,Hubei Provincial Clinical Research Center for Digestive Disease Minimally Invasive Incision, Renmin Hospital of Wuhan University, Wuhan, China
| | - Xinqi He
- Department of Gastroenterology, Renmin Hospital of Wuhan University, Wuhan, China.,Key Laboratory of Hubei Province for Digestive System Disease, Renmin Hospital of Wuhan University, Wuhan, China.,Hubei Provincial Clinical Research Center for Digestive Disease Minimally Invasive Incision, Renmin Hospital of Wuhan University, Wuhan, China
| | - Mei Liu
- Department of Gastroenterology, Tongji Hospital, Tongji Medical College, Huazhong University of Science and Technology, Wuhan, China
| | - Huaping Xie
- Department of Gastroenterology, Tongji Hospital, Tongji Medical College, Huazhong University of Science and Technology, Wuhan, China
| | - Ping An
- Department of Gastroenterology, Renmin Hospital of Wuhan University, Wuhan, China.,Key Laboratory of Hubei Province for Digestive System Disease, Renmin Hospital of Wuhan University, Wuhan, China.,Hubei Provincial Clinical Research Center for Digestive Disease Minimally Invasive Incision, Renmin Hospital of Wuhan University, Wuhan, China
| | - Jun Zhang
- Department of Gastroenterology, Renmin Hospital of Wuhan University, Wuhan, China.,Key Laboratory of Hubei Province for Digestive System Disease, Renmin Hospital of Wuhan University, Wuhan, China.,Hubei Provincial Clinical Research Center for Digestive Disease Minimally Invasive Incision, Renmin Hospital of Wuhan University, Wuhan, China
| | - Heng Zhang
- Department of Gastroenterology, Central Hospital of Wuhan, Tongji Medical College, Huazhong University of Science and Technology, Wuhan, China
| | - Yaowei Ai
- Department of Gastroenterology, The People's Hospital of China Three Gorges University/The First People's Hospital of Yichang, Yichang, China
| | - Qiaoyun Tong
- Department of Gastroenterology, Yichang Central People's Hospital, China Three Gorges University, Yichang, China
| | - Mingwen Guo
- Department of Gastroenterology, The People's Hospital of China Three Gorges University/The First People's Hospital of Yichang, Yichang, China
| | - Manling Huang
- Department of Gastroenterology, Central Hospital of Wuhan, Tongji Medical College, Huazhong University of Science and Technology, Wuhan, China
| | - Cunjin Ge
- Department of Gastroenterology, Yichang Central People's Hospital, China Three Gorges University, Yichang, China
| | - Zhi Yang
- Department of Gastroenterology, Yichang Central People's Hospital, China Three Gorges University, Yichang, China
| | - Jingping Yuan
- Department of Pathology, Renmin Hospital of Wuhan University, Wuhan, China
| | - Jun Liu
- Department of Gastroenterology, Renmin Hospital of Wuhan University, Wuhan, China.,Hubei Provincial Clinical Research Center for Digestive Disease Minimally Invasive Incision, Renmin Hospital of Wuhan University, Wuhan, China
| | - Wei Zhou
- Department of Gastroenterology, Renmin Hospital of Wuhan University, Wuhan, China.,Key Laboratory of Hubei Province for Digestive System Disease, Renmin Hospital of Wuhan University, Wuhan, China.,Hubei Provincial Clinical Research Center for Digestive Disease Minimally Invasive Incision, Renmin Hospital of Wuhan University, Wuhan, China
| | - Xiaoda Jiang
- Department of Gastroenterology, Renmin Hospital of Wuhan University, Wuhan, China.,Key Laboratory of Hubei Province for Digestive System Disease, Renmin Hospital of Wuhan University, Wuhan, China.,Hubei Provincial Clinical Research Center for Digestive Disease Minimally Invasive Incision, Renmin Hospital of Wuhan University, Wuhan, China
| | - Xu Huang
- Department of Gastroenterology, Renmin Hospital of Wuhan University, Wuhan, China.,Key Laboratory of Hubei Province for Digestive System Disease, Renmin Hospital of Wuhan University, Wuhan, China.,Hubei Provincial Clinical Research Center for Digestive Disease Minimally Invasive Incision, Renmin Hospital of Wuhan University, Wuhan, China
| | - Ganggang Mu
- Department of Gastroenterology, Renmin Hospital of Wuhan University, Wuhan, China.,Key Laboratory of Hubei Province for Digestive System Disease, Renmin Hospital of Wuhan University, Wuhan, China.,Hubei Provincial Clinical Research Center for Digestive Disease Minimally Invasive Incision, Renmin Hospital of Wuhan University, Wuhan, China
| | - Xinyue Wan
- Department of Gastroenterology, Renmin Hospital of Wuhan University, Wuhan, China.,Key Laboratory of Hubei Province for Digestive System Disease, Renmin Hospital of Wuhan University, Wuhan, China.,Hubei Provincial Clinical Research Center for Digestive Disease Minimally Invasive Incision, Renmin Hospital of Wuhan University, Wuhan, China
| | - Yanxia Li
- Department of Gastroenterology, Renmin Hospital of Wuhan University, Wuhan, China.,Key Laboratory of Hubei Province for Digestive System Disease, Renmin Hospital of Wuhan University, Wuhan, China.,Hubei Provincial Clinical Research Center for Digestive Disease Minimally Invasive Incision, Renmin Hospital of Wuhan University, Wuhan, China
| | - Hongguang Wang
- Department of Gastroenterology, Jilin People's Hospital, Jilin, China
| | - Yonggui Wang
- School of Geography and Information Engineering, China University of Geosciences, Wuhan, China
| | - Hongfeng Zhang
- Department of Pathology, Central Hospital of Wuhan, Tongji Medical College, Huazhong University of Science and Technology, Wuhan, China
| | - Di Chen
- Department of Gastroenterology, Renmin Hospital of Wuhan University, Wuhan, China.,Key Laboratory of Hubei Province for Digestive System Disease, Renmin Hospital of Wuhan University, Wuhan, China.,Hubei Provincial Clinical Research Center for Digestive Disease Minimally Invasive Incision, Renmin Hospital of Wuhan University, Wuhan, China
| | - Dexin Gong
- Department of Gastroenterology, Renmin Hospital of Wuhan University, Wuhan, China.,Key Laboratory of Hubei Province for Digestive System Disease, Renmin Hospital of Wuhan University, Wuhan, China.,Hubei Provincial Clinical Research Center for Digestive Disease Minimally Invasive Incision, Renmin Hospital of Wuhan University, Wuhan, China
| | - Jing Wang
- Department of Gastroenterology, Renmin Hospital of Wuhan University, Wuhan, China.,Key Laboratory of Hubei Province for Digestive System Disease, Renmin Hospital of Wuhan University, Wuhan, China.,Hubei Provincial Clinical Research Center for Digestive Disease Minimally Invasive Incision, Renmin Hospital of Wuhan University, Wuhan, China
| | - Li Huang
- Department of Gastroenterology, Renmin Hospital of Wuhan University, Wuhan, China.,Key Laboratory of Hubei Province for Digestive System Disease, Renmin Hospital of Wuhan University, Wuhan, China.,Hubei Provincial Clinical Research Center for Digestive Disease Minimally Invasive Incision, Renmin Hospital of Wuhan University, Wuhan, China
| | - Jia Li
- Department of Gastroenterology, Renmin Hospital of Wuhan University, Wuhan, China.,Key Laboratory of Hubei Province for Digestive System Disease, Renmin Hospital of Wuhan University, Wuhan, China.,Hubei Provincial Clinical Research Center for Digestive Disease Minimally Invasive Incision, Renmin Hospital of Wuhan University, Wuhan, China
| | - Liwen Yao
- Department of Gastroenterology, Renmin Hospital of Wuhan University, Wuhan, China.,Key Laboratory of Hubei Province for Digestive System Disease, Renmin Hospital of Wuhan University, Wuhan, China.,Hubei Provincial Clinical Research Center for Digestive Disease Minimally Invasive Incision, Renmin Hospital of Wuhan University, Wuhan, China
| | - Yijie Zhu
- Department of Gastroenterology, Renmin Hospital of Wuhan University, Wuhan, China.,Key Laboratory of Hubei Province for Digestive System Disease, Renmin Hospital of Wuhan University, Wuhan, China.,Hubei Provincial Clinical Research Center for Digestive Disease Minimally Invasive Incision, Renmin Hospital of Wuhan University, Wuhan, China
| | - Honggang Yu
- Department of Gastroenterology, Renmin Hospital of Wuhan University, Wuhan, China.,Key Laboratory of Hubei Province for Digestive System Disease, Renmin Hospital of Wuhan University, Wuhan, China.,Hubei Provincial Clinical Research Center for Digestive Disease Minimally Invasive Incision, Renmin Hospital of Wuhan University, Wuhan, China
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57
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Fonollà R, van der Zander QEW, Schreuder RM, Subramaniam S, Bhandari P, Masclee AAM, Schoon EJ, van der Sommen F, de With PHN. Automatic image and text-based description for colorectal polyps using BASIC classification. Artif Intell Med 2021; 121:102178. [PMID: 34763800 DOI: 10.1016/j.artmed.2021.102178] [Citation(s) in RCA: 6] [Impact Index Per Article: 2.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 04/29/2021] [Revised: 09/01/2021] [Accepted: 09/21/2021] [Indexed: 12/18/2022]
Abstract
Colorectal polyps (CRP) are precursor lesions of colorectal cancer (CRC). Correct identification of CRPs during in-vivo colonoscopy is supported by the endoscopist's expertise and medical classification models. A recent developed classification model is the Blue light imaging Adenoma Serrated International Classification (BASIC) which describes the differences between non-neoplastic and neoplastic lesions acquired with blue light imaging (BLI). Computer-aided detection (CADe) and diagnosis (CADx) systems are efficient at visually assisting with medical decisions but fall short at translating decisions into relevant clinical information. The communication between machine and medical expert is of crucial importance to improve diagnosis of CRP during in-vivo procedures. In this work, the combination of a polyp image classification model and a language model is proposed to develop a CADx system that automatically generates text comparable to the human language employed by endoscopists. The developed system generates equivalent sentences as the human-reference and describes CRP images acquired with white light (WL), blue light imaging (BLI) and linked color imaging (LCI). An image feature encoder and a BERT module are employed to build the AI model and an external test set is used to evaluate the results and compute the linguistic metrics. The experimental results show the construction of complete sentences with an established metric scores of BLEU-1 = 0.67, ROUGE-L = 0.83 and METEOR = 0.50. The developed CADx system for automatic CRP image captioning facilitates future advances towards automatic reporting and may help reduce time-consuming histology assessment.
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Affiliation(s)
- Roger Fonollà
- Department of Electrical Engineering, Video Coding and Architectures (VCA), Eindhoven University of Technology, Eindhoven, Noord-Brabant, the Netherlands.
| | - Quirine E W van der Zander
- Division of Gastroenterology and Hepatology, Maastricht University Medical Center, Maastricht, the Netherlands; GROW, School for Oncology and Developmental Biology, Maastricht University, Maastricht, the Netherlands
| | - Ramon M Schreuder
- Department of Gastroenterology and Hepatology, Catharina Hospital, Eindhoven, Noord-Brabant, the Netherlands
| | - Sharmila Subramaniam
- Department of Gastroenterology, Portsmouth Hospitals University NHS Trust, Portsmouth, United Kingdom
| | - Pradeep Bhandari
- Department of Gastroenterology, Portsmouth Hospitals University NHS Trust, Portsmouth, United Kingdom
| | - Ad A M Masclee
- Division of Gastroenterology and Hepatology, Maastricht University Medical Center, Maastricht, the Netherlands; NUTRIM, School of Nutrition & Translational Research in Metabolism, Maastricht University, Maastricht, the Netherlands
| | - Erik J Schoon
- Department of Gastroenterology and Hepatology, Catharina Hospital, Eindhoven, Noord-Brabant, the Netherlands
| | - Fons van der Sommen
- Department of Electrical Engineering, Video Coding and Architectures (VCA), Eindhoven University of Technology, Eindhoven, Noord-Brabant, the Netherlands
| | - Peter H N de With
- Department of Electrical Engineering, Video Coding and Architectures (VCA), Eindhoven University of Technology, Eindhoven, Noord-Brabant, the Netherlands
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58
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Goyal H, Sherazi SAA, Mann R, Gandhi Z, Perisetti A, Aziz M, Chandan S, Kopel J, Tharian B, Sharma N, Thosani N. Scope of Artificial Intelligence in Gastrointestinal Oncology. Cancers (Basel) 2021; 13:5494. [PMID: 34771658 PMCID: PMC8582733 DOI: 10.3390/cancers13215494] [Citation(s) in RCA: 3] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Journal Information] [Subscribe] [Scholar Register] [Received: 10/21/2021] [Accepted: 10/27/2021] [Indexed: 12/12/2022] Open
Abstract
Gastrointestinal cancers are among the leading causes of death worldwide, with over 2.8 million deaths annually. Over the last few decades, advancements in artificial intelligence technologies have led to their application in medicine. The use of artificial intelligence in endoscopic procedures is a significant breakthrough in modern medicine. Currently, the diagnosis of various gastrointestinal cancer relies on the manual interpretation of radiographic images by radiologists and various endoscopic images by endoscopists. This can lead to diagnostic variabilities as it requires concentration and clinical experience in the field. Artificial intelligence using machine or deep learning algorithms can provide automatic and accurate image analysis and thus assist in diagnosis. In the field of gastroenterology, the application of artificial intelligence can be vast from diagnosis, predicting tumor histology, polyp characterization, metastatic potential, prognosis, and treatment response. It can also provide accurate prediction models to determine the need for intervention with computer-aided diagnosis. The number of research studies on artificial intelligence in gastrointestinal cancer has been increasing rapidly over the last decade due to immense interest in the field. This review aims to review the impact, limitations, and future potentials of artificial intelligence in screening, diagnosis, tumor staging, treatment modalities, and prediction models for the prognosis of various gastrointestinal cancers.
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Affiliation(s)
- Hemant Goyal
- Department of Internal Medicine, The Wright Center for Graduate Medical Education, 501 S. Washington Avenue, Scranton, PA 18505, USA
| | - Syed A. A. Sherazi
- Department of Medicine, John H Stroger Jr Hospital of Cook County, 1950 W Polk St, Chicago, IL 60612, USA;
| | - Rupinder Mann
- Department of Medicine, Saint Agnes Medical Center, 1303 E. Herndon Ave, Fresno, CA 93720, USA;
| | - Zainab Gandhi
- Department of Medicine, Geisinger Wyoming Valley Medical Center, 1000 E Mountain Dr, Wilkes-Barre, PA 18711, USA;
| | - Abhilash Perisetti
- Division of Interventional Oncology & Surgical Endoscopy (IOSE), Parkview Cancer Institute, 11050 Parkview Circle, Fort Wayne, IN 46845, USA; (A.P.); (N.S.)
| | - Muhammad Aziz
- Department of Gastroenterology and Hepatology, University of Toledo Medical Center, 3000 Arlington Avenue, Toledo, OH 43614, USA;
| | - Saurabh Chandan
- Division of Gastroenterology and Hepatology, CHI Health Creighton University Medical Center, 7500 Mercy Rd, Omaha, NE 68124, USA;
| | - Jonathan Kopel
- Department of Medicine, Texas Tech University Health Sciences Center, 3601 4th St, Lubbock, TX 79430, USA;
| | - Benjamin Tharian
- Department of Gastroenterology and Hepatology, The University of Arkansas for Medical Sciences, 4301 W Markham St, Little Rock, AR 72205, USA;
| | - Neil Sharma
- Division of Interventional Oncology & Surgical Endoscopy (IOSE), Parkview Cancer Institute, 11050 Parkview Circle, Fort Wayne, IN 46845, USA; (A.P.); (N.S.)
| | - Nirav Thosani
- Division of Gastroenterology, Hepatology & Nutrition, McGovern Medical School, UTHealth, 6410 Fannin, St #1014, Houston, TX 77030, USA;
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Kader R, Hadjinicolaou AV, Georgiades F, Stoyanov D, Lovat LB. Optical diagnosis of colorectal polyps using convolutional neural networks. World J Gastroenterol 2021; 27:5908-5918. [PMID: 34629808 PMCID: PMC8475008 DOI: 10.3748/wjg.v27.i35.5908] [Citation(s) in RCA: 4] [Impact Index Per Article: 1.3] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Download PDF] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 02/27/2021] [Revised: 04/29/2021] [Accepted: 08/24/2021] [Indexed: 02/06/2023] Open
Abstract
Colonoscopy remains the gold standard investigation for colorectal cancer screening as it offers the opportunity to both detect and resect pre-malignant and neoplastic polyps. Although technologies for image-enhanced endoscopy are widely available, optical diagnosis has not been incorporated into routine clinical practice, mainly due to significant inter-operator variability. In recent years, there has been a growing number of studies demonstrating the potential of convolutional neural networks (CNN) to enhance optical diagnosis of polyps. Data suggest that the use of CNNs might mitigate the inter-operator variability amongst endoscopists, potentially enabling a “resect and discard“ or ”leave in“ strategy to be adopted in real-time. This would have significant financial benefits for healthcare systems, avoid unnecessary polypectomies of non-neoplastic polyps and improve the efficiency of colonoscopy. Here, we review advances in CNN for the optical diagnosis of colorectal polyps, current limitations and future directions.
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Affiliation(s)
- Rawen Kader
- Wellcome/EPSRC Centre for Interventional and Surgical Sciences, University College London, London W1W 7TY, United Kingdom
- Division of Surgery and Interventional Sciences, University College London, London W1W 7TY, United Kingdom
| | - Andreas V Hadjinicolaou
- MRC Cancer Unit, Department of Gastroenterology, University of Cambridge, Cambridge CB2 0QQ, United Kingdom
| | - Fanourios Georgiades
- Department of Surgery, University of Cambridge, Cambridge CB2 0QQ, United Kingdom
| | - Danail Stoyanov
- Wellcome/EPSRC Centre for Interventional and Surgical Sciences, University College London, London W1W 7TY, United Kingdom
- Department of Computer Science, University College London, London W1W 7TY, United Kingdom
| | - Laurence B Lovat
- Wellcome/EPSRC Centre for Interventional and Surgical Sciences, University College London, London W1W 7TY, United Kingdom
- Division of Surgery and Interventional Sciences, University College London, London W1W 7TY, United Kingdom
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60
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Bang CS, Lee JJ, Baik GH. Computer-Aided Diagnosis of Diminutive Colorectal Polyps in Endoscopic Images: Systematic Review and Meta-analysis of Diagnostic Test Accuracy. J Med Internet Res 2021; 23:e29682. [PMID: 34432643 PMCID: PMC8427459 DOI: 10.2196/29682] [Citation(s) in RCA: 7] [Impact Index Per Article: 2.3] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 04/16/2021] [Revised: 07/04/2021] [Accepted: 07/27/2021] [Indexed: 12/19/2022] Open
Abstract
Background Most colorectal polyps are diminutive and benign, especially those in the rectosigmoid colon, and the resection of these polyps is not cost-effective. Advancements in image-enhanced endoscopy have improved the optical prediction of colorectal polyp histology. However, subjective interpretability and inter- and intraobserver variability prohibits widespread implementation. The number of studies on computer-aided diagnosis (CAD) is increasing; however, their small sample sizes limit statistical significance. Objective This review aims to evaluate the diagnostic test accuracy of CAD models in predicting the histology of diminutive colorectal polyps by using endoscopic images. Methods Core databases were searched for studies that were based on endoscopic imaging, used CAD models for the histologic diagnosis of diminutive colorectal polyps, and presented data on diagnostic performance. A systematic review and diagnostic test accuracy meta-analysis were performed. Results Overall, 13 studies were included. The pooled area under the curve, sensitivity, specificity, and diagnostic odds ratio of CAD models for the diagnosis of diminutive colorectal polyps (adenomatous or neoplastic vs nonadenomatous or nonneoplastic) were 0.96 (95% CI 0.93-0.97), 0.93 (95% CI 0.91-0.95), 0.87 (95% CI 0.76-0.93), and 87 (95% CI 38-201), respectively. The meta-regression analysis showed no heterogeneity, and no publication bias was detected. Subgroup analyses showed robust results. The negative predictive value of CAD models for the diagnosis of adenomatous polyps in the rectosigmoid colon was 0.96 (95% CI 0.95-0.97), and this value exceeded the threshold of the diagnosis and leave strategy. Conclusions CAD models show potential for the optical histological diagnosis of diminutive colorectal polyps via the use of endoscopic images. Trial Registration PROSPERO CRD42021232189; https://www.crd.york.ac.uk/prospero/display_record.php?RecordID=232189
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Affiliation(s)
- Chang Seok Bang
- Department of Internal Medicine, Hallym University College of Medicine, Chuncheon, Republic of Korea.,Institute for Liver and Digestive Diseases, Hallym University College of Medicine, Chuncheon, Republic of Korea.,Institute of New Frontier Research, Hallym University College of Medicine, Chuncheon, Republic of Korea.,Division of Big Data and Artificial Intelligence, Chuncheon Sacred Heart Hospital, Chuncheon, Republic of Korea
| | - Jae Jun Lee
- Institute of New Frontier Research, Hallym University College of Medicine, Chuncheon, Republic of Korea.,Division of Big Data and Artificial Intelligence, Chuncheon Sacred Heart Hospital, Chuncheon, Republic of Korea.,Department of Anesthesiology and Pain Medicine, Hallym University College of Medicine, Chuncheon, Republic of Korea
| | - Gwang Ho Baik
- Department of Internal Medicine, Hallym University College of Medicine, Chuncheon, Republic of Korea.,Institute for Liver and Digestive Diseases, Hallym University College of Medicine, Chuncheon, Republic of Korea
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61
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Parsa N, Byrne MF. Artificial intelligence for identification and characterization of colonic polyps. Ther Adv Gastrointest Endosc 2021; 14:26317745211014698. [PMID: 34263163 PMCID: PMC8252334 DOI: 10.1177/26317745211014698] [Citation(s) in RCA: 6] [Impact Index Per Article: 2.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 01/20/2021] [Accepted: 04/07/2021] [Indexed: 12/27/2022] Open
Abstract
Colonoscopy remains the gold standard exam for colorectal cancer screening due to its ability to detect and resect pre-cancerous lesions in the colon. However, its performance is greatly operator dependent. Studies have shown that up to one-quarter of colorectal polyps can be missed on a single colonoscopy, leading to high rates of interval colorectal cancer. In addition, the American Society for Gastrointestinal Endoscopy has proposed the “resect-and-discard” and “diagnose-and-leave” strategies for diminutive colorectal polyps to reduce the costs of unnecessary polyp resection and pathology evaluation. However, the performance of optical biopsy has been suboptimal in community practice. With recent improvements in machine-learning techniques, artificial intelligence–assisted computer-aided detection and diagnosis have been increasingly utilized by endoscopists. The application of computer-aided design on real-time colonoscopy has been shown to increase the adenoma detection rate while decreasing the withdrawal time and improve endoscopists’ optical biopsy accuracy, while reducing the time to make the diagnosis. These are promising steps toward standardization and improvement of colonoscopy quality, and implementation of “resect-and-discard” and “diagnose-and-leave” strategies. Yet, issues such as real-world applications and regulatory approval need to be addressed before artificial intelligence models can be successfully implemented in clinical practice. In this review, we summarize the recent literature on the application of artificial intelligence for detection and characterization of colorectal polyps and review the limitation of existing artificial intelligence technologies and future directions for this field.
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Affiliation(s)
- Nasim Parsa
- Division of Gastroenterology and Hepatology, Department of Medicine, University of Missouri, Columbia, MO 65211, USA
| | - Michael F Byrne
- Division of Gastroenterology, Department of Medicine, The University of British Columbia, Vancouver, BC, Canada; Satisfai Health, Vancouver, BC, Canada
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62
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Shah N, Jyala A, Patel H, Makker J. Utility of artificial intelligence in colonoscopy. Artif Intell Gastrointest Endosc 2021; 2:79-88. [DOI: 10.37126/aige.v2.i3.79] [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: 06/02/2021] [Revised: 06/20/2021] [Accepted: 06/28/2021] [Indexed: 02/06/2023] Open
Abstract
Colorectal cancer is one of the major causes of death worldwide. Colonoscopy is the most important tool that can identify neoplastic lesion in early stages and resect it in a timely manner which helps in reducing mortality related to colorectal cancer. However, the quality of colonoscopy findings depends on the expertise of the endoscopist and thus the rate of missed adenoma or polyp cannot be controlled. It is desirable to standardize the quality of colonoscopy by reducing the number of missed adenoma/polyps. Introduction of artificial intelligence (AI) in the field of medicine has become popular among physicians nowadays. The application of AI in colonoscopy can help in reducing miss rate and increasing colorectal cancer detection rate as per recent studies. Moreover, AI assistance during colonoscopy has also been utilized in patients with inflammatory bowel disease to improve diagnostic accuracy, assessing disease severity and predicting clinical outcomes. We conducted a literature review on the available evidence on use of AI in colonoscopy. In this review article, we discuss about the principles, application, limitations, and future aspects of AI in colonoscopy.
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Affiliation(s)
- Niel Shah
- Department of Internal Medicine, BronxCare Hospital Center, Bronx, NY 10457, United States
| | - Abhilasha Jyala
- Department of Internal Medicine, BronxCare Hospital Center, Bronx, NY 10457, United States
| | - Harish Patel
- Department of Internal Medicine, Gastroenterology, BronxCare Hospital Center, Bronx, NY 10457, United States
| | - Jasbir Makker
- Department of Internal Medicine, Gastroenterology, BronxCare Hospital Center, Bronx, NY 10457, United States
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63
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Shah N, Jyala A, Patel H, Makker J. Utility of artificial intelligence in colonoscopy. Artif Intell Gastrointest Endosc 2021. [DOI: 10.37126/aige.v2.i3.78] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Indexed: 12/19/2022] Open
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64
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Li JW, Ang TL. Colonoscopy and artificial intelligence: Bridging the gap or a gap needing to be bridged? Artif Intell Gastrointest Endosc 2021; 2:36-49. [DOI: 10.37126/aige.v2.i2.36] [Citation(s) in RCA: 8] [Impact Index Per Article: 2.7] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 03/19/2021] [Revised: 03/27/2021] [Accepted: 04/20/2021] [Indexed: 02/06/2023] Open
Abstract
Research in artificial intelligence (AI) in gastroenterology has increased over the last decade. Colonoscopy represents the most widely published field with regards to its use in gastroenterology. Most studies to date center on polyp detection and characterization, as well as real-time evaluation of adequacy of mucosal exposure for inspection. This review article discusses how advances in AI has bridged certain gaps in colonoscopy. In addition, the gaps formed with the development of AI that currently prevent its routine use in colonoscopy will be explored.
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Affiliation(s)
- James Weiquan Li
- Department of Gastroenterology and Hepatology, Changi General Hospital, Singapore 529889, Singapore
| | - Tiing Leong Ang
- Department of Gastroenterology and Hepatology, Changi General Hospital, Singapore 529889, Singapore
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65
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Mitsala A, Tsalikidis C, Pitiakoudis M, Simopoulos C, Tsaroucha AK. Artificial Intelligence in Colorectal Cancer Screening, Diagnosis and Treatment. A New Era. ACTA ACUST UNITED AC 2021; 28:1581-1607. [PMID: 33922402 PMCID: PMC8161764 DOI: 10.3390/curroncol28030149] [Citation(s) in RCA: 79] [Impact Index Per Article: 26.3] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 03/02/2021] [Revised: 04/09/2021] [Accepted: 04/20/2021] [Indexed: 12/24/2022]
Abstract
The development of artificial intelligence (AI) algorithms has permeated the medical field with great success. The widespread use of AI technology in diagnosing and treating several types of cancer, especially colorectal cancer (CRC), is now attracting substantial attention. CRC, which represents the third most commonly diagnosed malignancy in both men and women, is considered a leading cause of cancer-related deaths globally. Our review herein aims to provide in-depth knowledge and analysis of the AI applications in CRC screening, diagnosis, and treatment based on current literature. We also explore the role of recent advances in AI systems regarding medical diagnosis and therapy, with several promising results. CRC is a highly preventable disease, and AI-assisted techniques in routine screening represent a pivotal step in declining incidence rates of this malignancy. So far, computer-aided detection and characterization systems have been developed to increase the detection rate of adenomas. Furthermore, CRC treatment enters a new era with robotic surgery and novel computer-assisted drug delivery techniques. At the same time, healthcare is rapidly moving toward precision or personalized medicine. Machine learning models have the potential to contribute to individual-based cancer care and transform the future of medicine.
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Affiliation(s)
- Athanasia Mitsala
- Second Department of Surgery, University General Hospital of Alexandroupolis, Democritus University of Thrace Medical School, Dragana, 68100 Alexandroupolis, Greece; (C.T.); (M.P.); (C.S.)
- Correspondence: ; Tel.: +30-6986423707
| | - Christos Tsalikidis
- Second Department of Surgery, University General Hospital of Alexandroupolis, Democritus University of Thrace Medical School, Dragana, 68100 Alexandroupolis, Greece; (C.T.); (M.P.); (C.S.)
| | - Michail Pitiakoudis
- Second Department of Surgery, University General Hospital of Alexandroupolis, Democritus University of Thrace Medical School, Dragana, 68100 Alexandroupolis, Greece; (C.T.); (M.P.); (C.S.)
| | - Constantinos Simopoulos
- Second Department of Surgery, University General Hospital of Alexandroupolis, Democritus University of Thrace Medical School, Dragana, 68100 Alexandroupolis, Greece; (C.T.); (M.P.); (C.S.)
| | - Alexandra K. Tsaroucha
- Laboratory of Experimental Surgery & Surgical Research, Democritus University of Thrace Medical School, Dragana, 68100 Alexandroupolis, Greece;
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66
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Tang D, Zhou J, Wang L, Ni M, Chen M, Hassan S, Luo R, Chen X, He X, Zhang L, Ding X, Yu H, Xu G, Zou X. A Novel Model Based on Deep Convolutional Neural Network Improves Diagnostic Accuracy of Intramucosal Gastric Cancer (With Video). Front Oncol 2021; 11:622827. [PMID: 33959495 PMCID: PMC8095170 DOI: 10.3389/fonc.2021.622827] [Citation(s) in RCA: 16] [Impact Index Per Article: 5.3] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 10/29/2020] [Accepted: 03/23/2021] [Indexed: 12/15/2022] Open
Abstract
Background and Aims Prediction of intramucosal gastric cancer (GC) is a big challenge. It is not clear whether artificial intelligence could assist endoscopists in the diagnosis. Methods A deep convolutional neural networks (DCNN) model was developed via retrospectively collected 3407 endoscopic images from 666 gastric cancer patients from two Endoscopy Centers (training dataset). The DCNN model’s performance was tested with 228 images from 62 independent patients (testing dataset). The endoscopists evaluated the image and video testing dataset with or without the DCNN model’s assistance, respectively. Endoscopists’ diagnostic performance was compared with or without the DCNN model’s assistance and investigated the effects of assistance using correlations and linear regression analyses. Results The DCNN model discriminated intramucosal GC from advanced GC with an AUC of 0.942 (95% CI, 0.915–0.970), a sensitivity of 90.5% (95% CI, 84.1%–95.4%), and a specificity of 85.3% (95% CI, 77.1%–90.9%) in the testing dataset. The diagnostic performance of novice endoscopists was comparable to those of expert endoscopists with the DCNN model’s assistance (accuracy: 84.6% vs. 85.5%, sensitivity: 85.7% vs. 87.4%, specificity: 83.3% vs. 83.0%). The mean pairwise kappa value of endoscopists was increased significantly with the DCNN model’s assistance (0.430–0.629 vs. 0.660–0.861). The diagnostic duration reduced considerably with the assistance of the DCNN model from 4.35s to 3.01s. The correlation between the perseverance of effort and diagnostic accuracy of endoscopists was diminished using the DCNN model (r: 0.470 vs. 0.076). Conclusions An AI-assisted system was established and found useful for novice endoscopists to achieve comparable diagnostic performance with experts.
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Affiliation(s)
- Dehua Tang
- Department of Gastroenterology, Nanjing Drum Tower Hospital, Affiliated Drum Tower Hospital, Medical School of Nanjing University, Nanjing, China
| | - Jie Zhou
- Department of Gastroenterology, Renmin Hospital of Wuhan University, Wuhan, China.,Key Laboratory of Hubei Province for Digestive System Disease, Renmin Hospital of Wuhan University, Wuhan, China.,Hubei Provincial Clinical Research Center for Digestive Disease Minimally Invasive Incision, Renmin Hospital of Wuhan University, Wuhan, China
| | - Lei Wang
- Department of Gastroenterology, Nanjing Drum Tower Hospital, Affiliated Drum Tower Hospital, Medical School of Nanjing University, Nanjing, China
| | - Muhan Ni
- Department of Gastroenterology, Nanjing Drum Tower Hospital, Affiliated Drum Tower Hospital, Medical School of Nanjing University, Nanjing, China
| | - Min Chen
- Department of Gastroenterology, Nanjing Drum Tower Hospital, Affiliated Drum Tower Hospital, Medical School of Nanjing University, Nanjing, China
| | - Shahzeb Hassan
- Northwestern University Feinberg School of Medicine, Chicago, IL, United States
| | - Renquan Luo
- Department of Gastroenterology, Renmin Hospital of Wuhan University, Wuhan, China.,Key Laboratory of Hubei Province for Digestive System Disease, Renmin Hospital of Wuhan University, Wuhan, China.,Hubei Provincial Clinical Research Center for Digestive Disease Minimally Invasive Incision, Renmin Hospital of Wuhan University, Wuhan, China
| | - Xi Chen
- Department of Gastroenterology, Renmin Hospital of Wuhan University, Wuhan, China.,Key Laboratory of Hubei Province for Digestive System Disease, Renmin Hospital of Wuhan University, Wuhan, China.,Hubei Provincial Clinical Research Center for Digestive Disease Minimally Invasive Incision, Renmin Hospital of Wuhan University, Wuhan, China
| | - Xinqi He
- Department of Gastroenterology, Renmin Hospital of Wuhan University, Wuhan, China.,Key Laboratory of Hubei Province for Digestive System Disease, Renmin Hospital of Wuhan University, Wuhan, China.,Hubei Provincial Clinical Research Center for Digestive Disease Minimally Invasive Incision, Renmin Hospital of Wuhan University, Wuhan, China
| | - Lihui Zhang
- Department of Gastroenterology, Renmin Hospital of Wuhan University, Wuhan, China.,Key Laboratory of Hubei Province for Digestive System Disease, Renmin Hospital of Wuhan University, Wuhan, China.,Hubei Provincial Clinical Research Center for Digestive Disease Minimally Invasive Incision, Renmin Hospital of Wuhan University, Wuhan, China
| | - Xiwei Ding
- Department of Gastroenterology, Nanjing Drum Tower Hospital, Affiliated Drum Tower Hospital, Medical School of Nanjing University, Nanjing, China
| | - Honggang Yu
- Department of Gastroenterology, Renmin Hospital of Wuhan University, Wuhan, China.,Key Laboratory of Hubei Province for Digestive System Disease, Renmin Hospital of Wuhan University, Wuhan, China.,Hubei Provincial Clinical Research Center for Digestive Disease Minimally Invasive Incision, Renmin Hospital of Wuhan University, Wuhan, China
| | - Guifang Xu
- Department of Gastroenterology, Nanjing Drum Tower Hospital, Affiliated Drum Tower Hospital, Medical School of Nanjing University, Nanjing, China
| | - Xiaoping Zou
- Department of Gastroenterology, Nanjing Drum Tower Hospital, Affiliated Drum Tower Hospital, Medical School of Nanjing University, Nanjing, China
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Kleppe A, Skrede OJ, De Raedt S, Liestøl K, Kerr DJ, Danielsen HE. Designing deep learning studies in cancer diagnostics. Nat Rev Cancer 2021; 21:199-211. [PMID: 33514930 DOI: 10.1038/s41568-020-00327-9] [Citation(s) in RCA: 138] [Impact Index Per Article: 46.0] [Reference Citation Analysis] [Abstract] [MESH Headings] [Track Full Text] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Accepted: 12/09/2020] [Indexed: 12/16/2022]
Abstract
The number of publications on deep learning for cancer diagnostics is rapidly increasing, and systems are frequently claimed to perform comparable with or better than clinicians. However, few systems have yet demonstrated real-world medical utility. In this Perspective, we discuss reasons for the moderate progress and describe remedies designed to facilitate transition to the clinic. Recent, presumably influential, deep learning studies in cancer diagnostics, of which the vast majority used images as input to the system, are evaluated to reveal the status of the field. By manipulating real data, we then exemplify that much and varied training data facilitate the generalizability of neural networks and thus the ability to use them clinically. To reduce the risk of biased performance estimation of deep learning systems, we advocate evaluation in external cohorts and strongly advise that the planned analyses, including a predefined primary analysis, are described in a protocol preferentially stored in an online repository. Recommended protocol items should be established for the field, and we present our suggestions.
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Affiliation(s)
- Andreas Kleppe
- Institute for Cancer Genetics and Informatics, Oslo University Hospital, Oslo, Norway
- Department of Informatics, University of Oslo, Oslo, Norway
| | - Ole-Johan Skrede
- Institute for Cancer Genetics and Informatics, Oslo University Hospital, Oslo, Norway
- Department of Informatics, University of Oslo, Oslo, Norway
| | - Sepp De Raedt
- Institute for Cancer Genetics and Informatics, Oslo University Hospital, Oslo, Norway
- Department of Informatics, University of Oslo, Oslo, Norway
| | - Knut Liestøl
- Institute for Cancer Genetics and Informatics, Oslo University Hospital, Oslo, Norway
- Department of Informatics, University of Oslo, Oslo, Norway
| | - David J Kerr
- Nuffield Division of Clinical Laboratory Sciences, University of Oxford, Oxford, UK
| | - Håvard E Danielsen
- Institute for Cancer Genetics and Informatics, Oslo University Hospital, Oslo, Norway.
- Department of Informatics, University of Oslo, Oslo, Norway.
- Nuffield Division of Clinical Laboratory Sciences, University of Oxford, Oxford, UK.
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68
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Parsa N, Rex DK, Byrne MF. Colorectal polyp characterization with standard endoscopy: Will Artificial Intelligence succeed where human eyes failed? Best Pract Res Clin Gastroenterol 2021; 52-53:101736. [PMID: 34172255 DOI: 10.1016/j.bpg.2021.101736] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.3] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 11/10/2020] [Revised: 02/16/2021] [Accepted: 02/18/2021] [Indexed: 01/31/2023]
Abstract
The American Society for Gastrointestinal Endoscopy (ASGE) has proposed the "resect-and-discard" and "diagnose-and-leave" strategies for diminutive colorectal polyps to reduce the costs of unnecessary polyp resection and pathology evaluation. However, the diagnostic thresholds set by these guidelines are not always met in community practice. To overcome this sub-optimal performance, artificial intelligence (AI) has been applied to the field of endoscopy. The incorporation of deep learning algorithms with AI models resulted in highly accurate systems that match the expert endoscopists' optical biopsy and exceed the ASGE recommended thresholds. Recent studies have demonstrated that the integration of AI in clinical practice results in significant improvement in endoscopists' diagnostic accuracy while reducing the time to make a diagnosis. Yet, several points need to be addressed before AI models can be successfully implemented in clinical practice. In this review, we summarize the recent literature on the application of AI for characterization of colorectal polyps, and review the current limitation and future directions for this field.
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Affiliation(s)
- Nasim Parsa
- University of Missouri, Department of Medicine, Division of Gastroenterology and Hepatology, Columbia, MO, United States
| | - Douglas K Rex
- Indiana University School of Medicine, Department of Medicine, Division of Gastroenterology and Hepatology, Indianapolis, IN, United States
| | - Michael F Byrne
- University of British Columbia, Department of Medicine, Division of Gastroenterology and Hepatology Vancouver, British Columbia, Canada; Satisfai Health and AI4GI Joint Venture, Vancouver, British Columbia, Canada.
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69
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Jheng YC, Wang YP, Lin HE, Sung KY, Chu YC, Wang HS, Jiang JK, Hou MC, Lee FY, Lu CL. A novel machine learning-based algorithm to identify and classify lesions and anatomical landmarks in colonoscopy images. Surg Endosc 2021; 36:640-650. [PMID: 33591447 DOI: 10.1007/s00464-021-08331-2] [Citation(s) in RCA: 4] [Impact Index Per Article: 1.3] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 05/07/2020] [Accepted: 01/13/2021] [Indexed: 02/06/2023]
Abstract
OBJECTIVES Computer-aided diagnosis (CAD)-based artificial intelligence (AI) has been shown to be highly accurate for detecting and characterizing colon polyps. However, the application of AI to identify normal colon landmarks and differentiate multiple colon diseases has not yet been established. We aimed to develop a convolutional neural network (CNN)-based algorithm (GUTAID) to recognize different colon lesions and anatomical landmarks. METHODS Colonoscopic images were obtained to train and validate the AI classifiers. An independent dataset was collected for verification. The architecture of GUTAID contains two major sub-models: the Normal, Polyp, Diverticulum, Cecum and CAncer (NPDCCA) and Narrow-Band Imaging for Adenomatous/Hyperplastic polyps (NBI-AH) models. The development of GUTAID was based on the 16-layer Visual Geometry Group (VGG16) architecture and implemented on Google Cloud Platform. RESULTS In total, 7838 colonoscopy images were used for developing and validating the AI model. An additional 1273 images were independently applied to verify the GUTAID. The accuracy for GUTAID in detecting various colon lesions/landmarks is 93.3% for polyps, 93.9% for diverticula, 91.7% for cecum, 97.5% for cancer, and 83.5% for adenomatous/hyperplastic polyps. CONCLUSIONS A CNN-based algorithm (GUTAID) to identify colonic abnormalities and landmarks was successfully established with high accuracy. This GUTAID system can further characterize polyps for optical diagnosis. We demonstrated that AI classification methodology is feasible to identify multiple and different colon diseases.
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Affiliation(s)
- Ying-Chun Jheng
- Endoscopy Center for Diagnosis and Treatment, Taipei Veterans General Hospital, Taipei, Taiwan.,Division of Gastroenterology, Department of Medicine, Taipei Veterans General Hospital, Taipei, Taiwan.,Department of Medical Research, Taipei Veterans General Hospital, Taipei, Taiwan.,Faculty of Medicine, National Yang-Ming University School of Medicine, Taipei, Taiwan
| | - Yen-Po Wang
- Endoscopy Center for Diagnosis and Treatment, Taipei Veterans General Hospital, Taipei, Taiwan.,Division of Gastroenterology, Department of Medicine, Taipei Veterans General Hospital, Taipei, Taiwan.,Institute of Brain Science, National Yang-Ming University School of Medicine, Taipei, Taiwan.,Faculty of Medicine, National Yang-Ming University School of Medicine, Taipei, Taiwan
| | - Hung-En Lin
- Endoscopy Center for Diagnosis and Treatment, Taipei Veterans General Hospital, Taipei, Taiwan.,Division of Gastroenterology, Department of Medicine, Taipei Veterans General Hospital, Taipei, Taiwan.,Faculty of Medicine, National Yang-Ming University School of Medicine, Taipei, Taiwan
| | - Kuang-Yi Sung
- Endoscopy Center for Diagnosis and Treatment, Taipei Veterans General Hospital, Taipei, Taiwan.,Division of Gastroenterology, Department of Medicine, Taipei Veterans General Hospital, Taipei, Taiwan.,Faculty of Medicine, National Yang-Ming University School of Medicine, Taipei, Taiwan
| | - Yuan-Chia Chu
- Information Management Office, Taipei Veterans General Hospital, Taipei, Taiwan.,Faculty of Medicine, National Yang-Ming University School of Medicine, Taipei, Taiwan
| | - Huann-Sheng Wang
- Endoscopy Center for Diagnosis and Treatment, Taipei Veterans General Hospital, Taipei, Taiwan.,Division of Colon and Rectum Surgery, Department of Surgery, Taipei Veterans General Hospital, Taipei, Taiwan.,Faculty of Medicine, National Yang-Ming University School of Medicine, Taipei, Taiwan
| | - Jeng-Kai Jiang
- Division of Colon and Rectum Surgery, Department of Surgery, Taipei Veterans General Hospital, Taipei, Taiwan.,Faculty of Medicine, National Yang-Ming University School of Medicine, Taipei, Taiwan
| | - Ming-Chih Hou
- Endoscopy Center for Diagnosis and Treatment, Taipei Veterans General Hospital, Taipei, Taiwan.,Division of Gastroenterology, Department of Medicine, Taipei Veterans General Hospital, Taipei, Taiwan.,Faculty of Medicine, National Yang-Ming University School of Medicine, Taipei, Taiwan
| | - Fa-Yauh Lee
- Division of Gastroenterology, Department of Medicine, Taipei Veterans General Hospital, Taipei, Taiwan.,Faculty of Medicine, National Yang-Ming University School of Medicine, Taipei, Taiwan
| | - Ching-Liang Lu
- Endoscopy Center for Diagnosis and Treatment, Taipei Veterans General Hospital, Taipei, Taiwan. .,Division of Gastroenterology, Department of Medicine, Taipei Veterans General Hospital, Taipei, Taiwan. .,Institute of Brain Science, National Yang-Ming University School of Medicine, Taipei, Taiwan. .,Faculty of Medicine, National Yang-Ming University School of Medicine, Taipei, Taiwan.
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Sánchez-Peralta LF, Pagador JB, Sánchez-Margallo FM. Artificial Intelligence for Colorectal Polyps in Colonoscopy. Artif Intell Med 2021. [DOI: 10.1007/978-3-030-58080-3_308-1] [Citation(s) in RCA: 2] [Impact Index Per Article: 0.7] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 12/17/2022]
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71
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Rogers B, Samanta S, Ghobadi K, Patel A, Savarino E, Roman S, Sifrim D, Gyawali CP. Artificial intelligence automates and augments baseline impedance measurements from pH-impedance studies in gastroesophageal reflux disease. J Gastroenterol 2021; 56:34-41. [PMID: 33151406 DOI: 10.1007/s00535-020-01743-2] [Citation(s) in RCA: 20] [Impact Index Per Article: 6.7] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 08/31/2020] [Accepted: 10/19/2020] [Indexed: 02/04/2023]
Abstract
BACKGROUND Artificial intelligence (AI) has potential to streamline interpretation of pH-impedance studies. In this exploratory observational cohort study, we determined feasibility of automated AI extraction of baseline impedance (AIBI) and evaluated clinical value of novel AI metrics. METHODS pH-impedance data from a convenience sample of symptomatic patients studied off (n = 117, 53.1 ± 1.2 years, 66% F) and on (n = 93, 53.8 ± 1.3 years, 74% F) anti-secretory therapy and from asymptomatic volunteers (n = 115, 29.3 ± 0.8 years, 47% F) were uploaded into dedicated prototypical AI software designed to automatically extract AIBI. Acid exposure time (AET) and manually extracted mean nocturnal baseline impedance (MNBI) were compared to corresponding total, upright, and recumbent AIBI and upright:recumbent AIBI ratio. AI metrics were compared to AET and MNBI in predicting ≥ 50% symptom improvement in GERD patients. RESULTS Recumbent, but not upright AIBI, correlated with MNBI. Upright:recumbent AIBI ratio was higher when AET > 6% (median 1.18, IQR 1.0-1.5), compared to < 4% (0.95, IQR 0.84-1.1), 4-6% (0.89, IQR 0.72-0.98), and controls (0.93, IQR 0.80-1.09, p ≤ 0.04). While MNBI, total AIBI, and the AIBI ratio off PPI were significantly different between those with and without symptom improvement (p < 0.05 for each comparison), only AIBI ratio segregated management responders from other cohorts. On ROC analysis, off therapy AIBI ratio outperformed AET in predicting GERD symptom improvement when AET was > 6% (AUC 0.766 vs. 0.606) and 4-6% (AUC 0.563 vs. 0.516) and outperformed MNBI overall (AUC 0.661 vs. 0.313). CONCLUSIONS BI calculation can be automated using AI. Novel AI metrics show potential in predicting GERD treatment outcome.
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Affiliation(s)
- Benjamin Rogers
- Division of Gastroenterology, Washington University School of Medicine, 660 South Euclid Ave., Campus Box 8124, Saint Louis, MO, 63110, USA
| | | | | | - Amit Patel
- Division of Gastroenterology, Duke University School of Medicine, The Durham Veterans Affairs Medical Center, Durham, NC, USA
| | - Edoardo Savarino
- Division of Gastroenterology, Department of Surgery, Oncology and Gastroenterology, University of Padua, Padua, Italy
| | - Sabine Roman
- Digestive Physiology, Hospices Civils de Lyon, Hopital E Herriot, Université de Lyon, 69437, Lyon, France.,Digestive Physiology, Université de Lyon, Lyon I University, 69008, Lyon, France.,Université de Lyon, Inserm U1032, LabTAU, 69008, Lyon, France
| | - Daniel Sifrim
- Barts and The London School of Medicine and Dentistry Queen Mary, University of London, London, UK
| | - C Prakash Gyawali
- Division of Gastroenterology, Washington University School of Medicine, 660 South Euclid Ave., Campus Box 8124, Saint Louis, MO, 63110, USA.
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72
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Senbekov M, Saliev T, Bukeyeva Z, Almabayeva A, Zhanaliyeva M, Aitenova N, Toishibekov Y, Fakhradiyev I. The Recent Progress and Applications of Digital Technologies in Healthcare: A Review. Int J Telemed Appl 2020; 2020:8830200. [PMID: 33343657 PMCID: PMC7732404 DOI: 10.1155/2020/8830200] [Citation(s) in RCA: 37] [Impact Index Per Article: 9.3] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 08/14/2020] [Revised: 11/16/2020] [Accepted: 11/17/2020] [Indexed: 02/06/2023] Open
Abstract
BACKGROUND The implementation of medical digital technologies can provide better accessibility and flexibility of healthcare for the public. It encompasses the availability of open information on the health, treatment, complications, and recent progress on biomedical research. At present, even in low-income countries, diagnostic and medical services are becoming more accessible and available. However, many issues related to digital health technologies remain unmet, including the reliability, safety, testing, and ethical aspects. PURPOSE The aim of the review is to discuss and analyze the recent progress on the application of big data, artificial intelligence, telemedicine, block-chain platforms, smart devices in healthcare, and medical education. Basic Design. The publication search was carried out using Google Scholar, PubMed, Web of Sciences, Medline, Wiley Online Library, and CrossRef databases. The review highlights the applications of artificial intelligence, "big data," telemedicine and block-chain technologies, and smart devices (internet of things) for solving the real problems in healthcare and medical education. Major Findings. We identified 252 papers related to the digital health area. However, the number of papers discussed in the review was limited to 152 due to the exclusion criteria. The literature search demonstrated that digital health technologies became highly sought due to recent pandemics, including COVID-19. The disastrous dissemination of COVID-19 through all continents triggered the need for fast and effective solutions to localize, manage, and treat the viral infection. In this regard, the use of telemedicine and other e-health technologies might help to lessen the pressure on healthcare systems. Summary. Digital platforms can help optimize diagnosis, consulting, and treatment of patients. However, due to the lack of official regulations and recommendations, the stakeholders, including private and governmental organizations, are facing the problem with adequate validation and approbation of novel digital health technologies. In this regard, proper scientific research is required before a digital product is deployed for the healthcare sector.
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Affiliation(s)
- Maksut Senbekov
- S.D. Asfendiyarov Kazakh National Medical University, Almaty, Kazakhstan
| | - Timur Saliev
- S.D. Asfendiyarov Kazakh National Medical University, Almaty, Kazakhstan
| | | | | | | | - Nazym Aitenova
- NJSC “Astana Medical University”, Nur-Sultan, Kazakhstan
| | | | - Ildar Fakhradiyev
- S.D. Asfendiyarov Kazakh National Medical University, Almaty, Kazakhstan
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Pannala R, Krishnan K, Melson J, Parsi MA, Schulman AR, Sullivan S, Trikudanathan G, Trindade AJ, Watson RR, Maple JT, Lichtenstein DR. Artificial intelligence in gastrointestinal endoscopy. VIDEOGIE : AN OFFICIAL VIDEO JOURNAL OF THE AMERICAN SOCIETY FOR GASTROINTESTINAL ENDOSCOPY 2020; 5:598-613. [PMID: 33319126 PMCID: PMC7732722 DOI: 10.1016/j.vgie.2020.08.013] [Citation(s) in RCA: 38] [Impact Index Per Article: 9.5] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Subscribe] [Scholar Register] [Indexed: 02/08/2023]
Abstract
BACKGROUND AND AIMS Artificial intelligence (AI)-based applications have transformed several industries and are widely used in various consumer products and services. In medicine, AI is primarily being used for image classification and natural language processing and has great potential to affect image-based specialties such as radiology, pathology, and gastroenterology (GE). This document reviews the reported applications of AI in GE, focusing on endoscopic image analysis. METHODS The MEDLINE database was searched through May 2020 for relevant articles by using key words such as machine learning, deep learning, artificial intelligence, computer-aided diagnosis, convolutional neural networks, GI endoscopy, and endoscopic image analysis. References and citations of the retrieved articles were also evaluated to identify pertinent studies. The manuscript was drafted by 2 authors and reviewed in person by members of the American Society for Gastrointestinal Endoscopy Technology Committee and subsequently by the American Society for Gastrointestinal Endoscopy Governing Board. RESULTS Deep learning techniques such as convolutional neural networks have been used in several areas of GI endoscopy, including colorectal polyp detection and classification, analysis of endoscopic images for diagnosis of Helicobacter pylori infection, detection and depth assessment of early gastric cancer, dysplasia in Barrett's esophagus, and detection of various abnormalities in wireless capsule endoscopy images. CONCLUSIONS The implementation of AI technologies across multiple GI endoscopic applications has the potential to transform clinical practice favorably and improve the efficiency and accuracy of current diagnostic methods.
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Key Words
- ADR, adenoma detection rate
- AI, artificial intelligence
- AMR, adenoma miss rate
- ANN, artificial neural network
- BE, Barrett’s esophagus
- CAD, computer-aided diagnosis
- CADe, CAD studies for colon polyp detection
- CADx, CAD studies for colon polyp classification
- CI, confidence interval
- CNN, convolutional neural network
- CRC, colorectal cancer
- DL, deep learning
- GI, gastroenterology
- HD-WLE, high-definition white light endoscopy
- HDWL, high-definition white light
- ML, machine learning
- NBI, narrow-band imaging
- NPV, negative predictive value
- PIVI, preservation and Incorporation of Valuable Endoscopic Innovations
- SVM, support vector machine
- VLE, volumetric laser endomicroscopy
- WCE, wireless capsule endoscopy
- WL, white light
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Affiliation(s)
- Rahul Pannala
- Department of Gastroenterology and Hepatology, Mayo Clinic, Scottsdale, Arizona
| | - Kumar Krishnan
- Division of Gastroenterology, Department of Internal Medicine, Harvard Medical School and Massachusetts General Hospital, Boston, Massachusetts
| | - Joshua Melson
- Division of Digestive Diseases, Department of Internal Medicine, Rush University Medical Center, Chicago, Illinois
| | - Mansour A Parsi
- Section for Gastroenterology and Hepatology, Tulane University Health Sciences Center, New Orleans, Louisiana
| | - Allison R Schulman
- Department of Gastroenterology, Michigan Medicine, University of Michigan, Ann Arbor, Michigan
| | - Shelby Sullivan
- Division of Gastroenterology and Hepatology, University of Colorado School of Medicine, Aurora, Colorado
| | - Guru Trikudanathan
- Department of Gastroenterology, Hepatology and Nutrition, University of Minnesota, Minneapolis, Minnesota
| | - Arvind J Trindade
- Department of Gastroenterology, Zucker School of Medicine at Hofstra/Northwell, Long Island Jewish Medical Center, New Hyde Park, New York
| | - Rabindra R Watson
- Department of Gastroenterology, Interventional Endoscopy Services, California Pacific Medical Center, San Francisco, California
| | - John T Maple
- Division of Digestive Diseases and Nutrition, University of Oklahoma Health Sciences Center, Oklahoma City, Oklahoma
| | - David R Lichtenstein
- Division of Gastroenterology, Boston Medical Center, Boston University School of Medicine, Boston, Massachusetts
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Kudou M, Kosuga T, Otsuji E. Artificial intelligence in gastrointestinal cancer: Recent advances and future perspectives. Artif Intell Gastroenterol 2020; 1:71-85. [DOI: 10.35712/aig.v1.i4.71] [Citation(s) in RCA: 3] [Impact Index Per Article: 0.8] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 09/19/2020] [Revised: 10/28/2020] [Accepted: 11/12/2020] [Indexed: 02/06/2023] Open
Abstract
Artificial intelligence (AI) using machine or deep learning algorithms is attracting increasing attention because of its more accurate image recognition ability and prediction performance than human-aid analyses. The application of AI models to gastrointestinal (GI) clinical oncology has been investigated for the past decade. AI has the capacity to automatically detect and diagnose GI tumors with similar diagnostic accuracy to expert clinicians. AI may also predict malignant potential, such as tumor histology, metastasis, patient survival, resistance to cancer treatments and the molecular biology of tumors, through image analyses of radiological or pathological imaging data using complex deep learning models beyond human cognition. The introduction of AI-assisted diagnostic systems into clinical settings is expected in the near future. However, limitations associated with the evaluation of GI tumors by AI models have yet to be resolved. Recent studies on AI-assisted diagnostic models of gastric and colorectal cancers in the endoscopic, pathological, and radiological fields were herein reviewed. The limitations and future perspectives for the application of AI systems in clinical settings have also been discussed. With the establishment of a multidisciplinary team containing AI experts in each medical institution and prospective studies, AI-assisted medical systems will become a promising tool for GI cancer.
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Affiliation(s)
- Michihiro Kudou
- Division of Digestive Surgery, Department of Surgery, Kyoto Prefectural University of Medicine, Kyoto 602-8566, Japan
- Department of Surgery, Kyoto Okamoto Memorial Hospital, Kyoto 613-0034, Japan
| | - Toshiyuki Kosuga
- Division of Digestive Surgery, Department of Surgery, Kyoto Prefectural University of Medicine, Kyoto 602-8566, Japan
- Department of Surgery, Saiseikai Shiga Hospital, Ritto 520-3046, Japan
| | - Eigo Otsuji
- Division of Digestive Surgery, Department of Surgery, Kyoto Prefectural University of Medicine, Kyoto 602-8566, Japan
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Hollenbach M, Feisthammel J, Hoffmeister A. [Endoscopic diagnosis, treatment, and follow-up of polyps of the lower gastrointestinal tract]. Internist (Berl) 2020; 62:151-162. [PMID: 33237438 DOI: 10.1007/s00108-020-00902-0] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.3] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/30/2022]
Abstract
BACKGROUND The endoscopic management of polyps of the lower gastrointestinal tract (l-GIT) has emerged in recent years as a result of numerous technological innovations. However, proven expertise and experience are essential. OBJECTIVES Presentation of novel and standard techniques and best-practice recommendations for the characterization and resection of l‑GIT polyps. METHODS Recent specialist literature and current guidelines. RESULTS High-definition endoscopy should be the standard when performing colonoscopy. The (virtual) chromoendoscopy can improve detection and characterization of polyps, but always requires special expertise and experience of the endoscopist in advanced endoscopic imaging. In this regard, computer-aided-diagnosis (CAD) systems have the potential to support endoscopists in the future. Pedunculated polyps should be removed with a hot snare. Small flat polyps can be resected by cold snare or large forceps. Large, non-pedunculated polyps should be treated in an interdisciplinary approach at a referral center with long-standing experience depending on its malignancy potential. After complete resection of small adenoma without high grade dysplasia, surveillance endoscopy is recommended after 5-10 years. Patients with large adenoma or high grade dysplasia should undergo endoscopy after 3 years and patients with multiple adenoma earlier than 3 years. After incomplete or piecemeal resection or insufficient bowel preparation, near-term endoscopy is recommended. CONCLUSIONS Adequate characterization and treatment are essential for the appropriate management of l‑GIT polyps.
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Affiliation(s)
- M Hollenbach
- Bereich Gastroenterologie der Klinik für Onkologie, Gastroenterologie, Hepatologie, Pneumologie und Infektiologie, Department für Innere Medizin, Neurologie und Dermatologie, Universitätsklinikum Leipzig AöR, Liebigstraße 20, 04103, Leipzig, Deutschland.
| | - J Feisthammel
- Bereich Gastroenterologie der Klinik für Onkologie, Gastroenterologie, Hepatologie, Pneumologie und Infektiologie, Department für Innere Medizin, Neurologie und Dermatologie, Universitätsklinikum Leipzig AöR, Liebigstraße 20, 04103, Leipzig, Deutschland
| | - A Hoffmeister
- Bereich Gastroenterologie der Klinik für Onkologie, Gastroenterologie, Hepatologie, Pneumologie und Infektiologie, Department für Innere Medizin, Neurologie und Dermatologie, Universitätsklinikum Leipzig AöR, Liebigstraße 20, 04103, Leipzig, Deutschland
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Pacal I, Karaboga D, Basturk A, Akay B, Nalbantoglu U. A comprehensive review of deep learning in colon cancer. Comput Biol Med 2020; 126:104003. [DOI: 10.1016/j.compbiomed.2020.104003] [Citation(s) in RCA: 28] [Impact Index Per Article: 7.0] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 04/03/2020] [Revised: 08/28/2020] [Accepted: 08/28/2020] [Indexed: 12/17/2022]
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Wang KW, Dong M. Potential applications of artificial intelligence in colorectal polyps and cancer: Recent advances and prospects. World J Gastroenterol 2020; 26:5090-5100. [PMID: 32982111 PMCID: PMC7495038 DOI: 10.3748/wjg.v26.i34.5090] [Citation(s) in RCA: 20] [Impact Index Per Article: 5.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Download PDF] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 05/22/2020] [Revised: 07/01/2020] [Accepted: 08/12/2020] [Indexed: 02/06/2023] Open
Abstract
Since the advent of artificial intelligence (AI) technology, it has been constantly studied and has achieved rapid development. The AI assistant system is expected to improve the quality of automatic polyp detection and classification. It could also help prevent endoscopists from missing polyps and make an accurate optical diagnosis. These functions provided by AI could result in a higher adenoma detection rate and decrease the cost of polypectomy for hyperplastic polyps. In addition, AI has good performance in the staging, diagnosis, and segmentation of colorectal cancer. This article provides an overview of recent research focusing on the application of AI in colorectal polyps and cancer and highlights the advances achieved.
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Affiliation(s)
- Ke-Wei Wang
- Department of Gastrointestinal Surgery, the First Affiliated Hospital of China Medical University, Shenyang 110001, Liaoning Province, China
| | - Ming Dong
- Department of Gastrointestinal Surgery, the First Affiliated Hospital of China Medical University, Shenyang 110001, Liaoning Province, China
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Masuzaki R, Kanda T, Sasaki R, Matsumoto N, Nirei K, Ogawa M, Moriyama M. Application of artificial intelligence in hepatology: Minireview. Artif Intell Gastroenterol 2020; 1:5-11. [DOI: 10.35712/aig.v1.i1.5] [Citation(s) in RCA: 7] [Impact Index Per Article: 1.8] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 05/28/2020] [Revised: 06/23/2020] [Accepted: 07/16/2020] [Indexed: 02/06/2023] Open
Abstract
With the rapid advancements in computer science, artificial intelligence (AI) has become an intrinsic part of our daily life and clinical practices. The concepts of AI, such as machine learning, deep learning, and big data, are extensively used in clinical and basic research. In this review, we searched for the articles in PubMed and summarized recent developments of AI concerning hepatology while focusing on the diagnosis and risk assessment of liver diseases. Ultrasound is widely conducted for the routine surveillance of hepatocellular carcinoma along with tumor markers. Computer-aided diagnosis is useful in the detection of tumors and characterization of space-occupying lesions. The prognosis of hepatocellular carcinoma can be estimated via AI using large-scale and high-quality training datasets. The prevalence of nonalcoholic fatty liver disease is increasing worldwide and pivotal concern in the field is who will progress and develop hepatocellular carcinoma. Most AI studies require a large dataset, including laboratory or radiological findings and outcome data. AI will be useful in reducing medical errors, supporting clinical decisions, and predicting clinical outcomes. Thus, cooperation between AI and humans is expected to improve healthcare.
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Affiliation(s)
- Ryota Masuzaki
- Division of Gastroenterology and Hepatology, Department of Medicine, Nihon University School of Medicine, Tokyo 173-8610, Japan
| | - Tatsuo Kanda
- Division of Gastroenterology and Hepatology, Department of Medicine, Nihon University School of Medicine, Tokyo 173-8610, Japan
| | - Reina Sasaki
- Division of Gastroenterology and Hepatology, Department of Medicine, Nihon University School of Medicine, Tokyo 173-8610, Japan
| | - Naoki Matsumoto
- Division of Gastroenterology and Hepatology, Department of Medicine, Nihon University School of Medicine, Tokyo 173-8610, Japan
| | - Kazushige Nirei
- Division of Gastroenterology and Hepatology, Department of Medicine, Nihon University School of Medicine, Tokyo 173-8610, Japan
| | - Masahiro Ogawa
- Division of Gastroenterology and Hepatology, Department of Medicine, Nihon University School of Medicine, Tokyo 173-8610, Japan
| | - Mitsuhiko Moriyama
- Division of Gastroenterology and Hepatology, Department of Medicine, Nihon University School of Medicine, Tokyo 173-8610, Japan
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Byrne MF. Hype or Reality? Will Artificial Intelligence Actually Make Us Better at Performing Optical Biopsy of Colon Polyps? Gastroenterology 2020; 158:2049-2051. [PMID: 32222397 DOI: 10.1053/j.gastro.2020.03.038] [Citation(s) in RCA: 3] [Impact Index Per Article: 0.8] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 03/11/2020] [Accepted: 03/20/2020] [Indexed: 12/27/2022]
Affiliation(s)
- Michael F Byrne
- Division of Gastroenterology, Vancouver General Hospital, University of British Columbia, Vancouver, British Columbia, Canada.
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