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Kraus M, Anteby R, Konen E, Eshed I, Klang E. Artificial intelligence for X-ray scaphoid fracture detection: a systematic review and diagnostic test accuracy meta-analysis. Eur Radiol 2024; 34:4341-4351. [PMID: 38097728 PMCID: PMC11213739 DOI: 10.1007/s00330-023-10473-x] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 07/27/2023] [Revised: 09/22/2023] [Accepted: 09/28/2023] [Indexed: 06/29/2024]
Abstract
OBJECTIVES Scaphoid fractures are usually diagnosed using X-rays, a low-sensitivity modality. Artificial intelligence (AI) using Convolutional Neural Networks (CNNs) has been explored for diagnosing scaphoid fractures in X-rays. The aim of this systematic review and meta-analysis is to evaluate the use of AI for detecting scaphoid fractures on X-rays and analyze its accuracy and usefulness. MATERIALS AND METHODS This study followed the guidelines of Preferred Reporting Items for Systematic Reviews and Meta-analyses (PRISMA) and PRISMA-Diagnostic Test Accuracy. A literature search was conducted in the PubMed database for original articles published until July 2023. The risk of bias and applicability were evaluated using the QUADAS-2 tool. A bivariate diagnostic random-effects meta-analysis was conducted, and the results were analyzed using the Summary Receiver Operating Characteristic (SROC) curve. RESULTS Ten studies met the inclusion criteria and were all retrospective. The AI's diagnostic performance for detecting scaphoid fractures ranged from AUC 0.77 to 0.96. Seven studies were included in the meta-analysis, with a total of 3373 images. The meta-analysis pooled sensitivity and specificity were 0.80 and 0.89, respectively. The meta-analysis overall AUC was 0.88. The QUADAS-2 tool found high risk of bias and concerns about applicability in 9 out of 10 studies. CONCLUSIONS The current results of AI's diagnostic performance for detecting scaphoid fractures in X-rays show promise. The results show high overall sensitivity and specificity and a high SROC result. Further research is needed to compare AI's diagnostic performance to human diagnostic performance in a clinical setting. CLINICAL RELEVANCE STATEMENT Scaphoid fractures are prone to be missed secondary to assessment with a low sensitivity modality and a high occult fracture rate. AI systems can be beneficial for clinicians and radiologists to facilitate early diagnosis, and avoid missed injuries. KEY POINTS • Scaphoid fractures are common and some can be easily missed in X-rays. • Artificial intelligence (AI) systems demonstrate high diagnostic performance for the diagnosis of scaphoid fractures in X-rays. • AI systems can be beneficial in diagnosing both obvious and occult scaphoid fractures.
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Affiliation(s)
- Matan Kraus
- Department of Diagnostic Imaging, Sheba Medical Center, 2 Sheba Road, 5262000, Ramat Gan, Israel.
- Sackler School of Medicine, Tel Aviv University, Tel Aviv, Israel.
| | - Roi Anteby
- Sackler School of Medicine, Tel Aviv University, Tel Aviv, Israel
- Department of General Surgery, Sheba Medical Center, 2 Sheba Road, 5262000, Ramat Gan, Israel
| | - Eli Konen
- Department of Diagnostic Imaging, Sheba Medical Center, 2 Sheba Road, 5262000, Ramat Gan, Israel
- Sackler School of Medicine, Tel Aviv University, Tel Aviv, Israel
| | - Iris Eshed
- Department of Diagnostic Imaging, Sheba Medical Center, 2 Sheba Road, 5262000, Ramat Gan, Israel
- Sackler School of Medicine, Tel Aviv University, Tel Aviv, Israel
| | - Eyal Klang
- Department of Diagnostic Imaging, Sheba Medical Center, 2 Sheba Road, 5262000, Ramat Gan, Israel
- Sackler School of Medicine, Tel Aviv University, Tel Aviv, Israel
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Cocca S, Pontillo G, Grande G, Conigliaro R. Artificial intelligence in detection of small bowel lesions and their bleeding risk: A new step forward. World J Gastroenterol 2024; 30:2482-2484. [PMID: 38764765 PMCID: PMC11099393 DOI: 10.3748/wjg.v30.i18.2482] [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: 02/03/2024] [Revised: 03/09/2024] [Accepted: 04/17/2024] [Indexed: 05/11/2024] Open
Abstract
The present letter to the editor is related to the study with the title "Automatic detection of small bowel (SB) lesions with different bleeding risk based on deep learning models". Capsule endoscopy (CE) is the main tool to assess SB diseases but it is a time-consuming procedure with a significant error rate. The development of artificial intelligence (AI) in CE could simplify physicians' tasks. The novel deep learning model by Zhang et al seems to be able to identify various SB lesions and their bleeding risk, and it could pave the way to next perspective studies to better enhance the diagnostic support of AI in the detection of different types of SB lesions in clinical practice.
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Affiliation(s)
- Silvia Cocca
- Gastroenterology and Endoscopy Unit, Azienda Ospedaliero Universitaria Policlinico di Modena, Modena 41121, Italy
| | - Giuseppina Pontillo
- Gastroenterology and Endoscopy Unit, Presidio Ospedaliero San Giuseppe Moscati (Aversa, CE) - ASL Caserta, Caserta 81100, Italy
| | - Giuseppe Grande
- Department of Gastroenterology and Digestive Endoscopy, Azienda Ospedaliero Universitaria di Modena, Modena 41121, Italy
| | - Rita Conigliaro
- Gastroenterology and Endoscopy Unit, Azienda Ospedaliero Universitaria Policlinico di Modena, Modena 41121, Italy
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Brem O, Elisha D, Konen E, Amitai M, Klang E. Deep learning in magnetic resonance enterography for Crohn's disease assessment: a systematic review. Abdom Radiol (NY) 2024:10.1007/s00261-024-04326-4. [PMID: 38693270 DOI: 10.1007/s00261-024-04326-4] [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: 03/15/2024] [Revised: 03/15/2024] [Accepted: 04/01/2024] [Indexed: 05/03/2024]
Abstract
Crohn's disease (CD) poses significant morbidity, underscoring the need for effective, non-invasive inflammatory assessment using magnetic resonance enterography (MRE). This literature review evaluates recent publications on the role of deep learning in improving MRE for CD assessment. We searched MEDLINE/PUBMED for studies that reported the use of deep learning algorithms for assessment of CD activity. The study was conducted according to the PRISMA guidelines. The risk of bias was evaluated using the QUADAS-2 tool. Five eligible studies, encompassing 468 subjects, were identified. Our study suggests that diverse deep learning applications, including image quality enhancement, bowel segmentation for disease burden quantification, and 3D reconstruction for surgical planning are useful and promising for CD assessment. However, most of the studies are preliminary, retrospective studies, and have a high risk of bias in at least one category. Future research is needed to assess how deep learning can impact CD patient diagnostics, particularly when considering the increasing integration of such models into hospital systems.
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Affiliation(s)
- Ofir Brem
- Faculty of Medicine, Tel Aviv University, Tel Aviv, Israel.
- Arrow Program for Research Education, Sheba Medical Center, Tel-Hashomer, Israel.
| | - David Elisha
- Faculty of Medicine, Tel Aviv University, Tel Aviv, Israel
- Arrow Program for Research Education, Sheba Medical Center, Tel-Hashomer, Israel
| | - Eli Konen
- Division of Diagnostic Imaging, The Chaim Sheba Medical Center, Affiliated to the Faculty of Medicine, Tel Aviv University, Tel Aviv, Israel
| | - Michal Amitai
- Arrow Program for Research Education, Sheba Medical Center, Tel-Hashomer, Israel
- Division of Diagnostic Imaging, The Chaim Sheba Medical Center, Affiliated to the Faculty of Medicine, Tel Aviv University, Tel Aviv, Israel
| | - Eyal Klang
- Arrow Program for Research Education, Sheba Medical Center, Tel-Hashomer, Israel
- The Division of Data Driven and Digital Medicine (D3M), Icahn School of Medicine at Mount Sinai, New York, NY, USA
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Li L, Yang L, Zhang B, Yan G, Bao Y, Zhu R, Li S, Wang H, Chen M, Jin C, Chen Y, Yu C. Automated detection of small bowel lesions based on capsule endoscopy using deep learning algorithm. Clin Res Hepatol Gastroenterol 2024; 48:102334. [PMID: 38582328 DOI: 10.1016/j.clinre.2024.102334] [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: 09/29/2023] [Revised: 03/20/2024] [Accepted: 04/04/2024] [Indexed: 04/08/2024]
Abstract
BACKGROUND In order to overcome the challenges of lesion detection in capsule endoscopy (CE), we improved the YOLOv5-based deep learning algorithm and established the CE-YOLOv5 algorithm to identify small bowel lesions captured by CE. METHODS A total of 124,678 typical abnormal images from 1,452 patients were enrolled to train the CE-YOLOv5 model. Then 298 patients with suspected small bowel lesions detected by CE were prospectively enrolled in the testing phase of the study. Small bowel images and videos from the above 298 patients were interpreted by the experts, non-experts and CE-YOLOv5, respectively. RESULTS The sensitivity of CE-YOLOv5 in diagnosing vascular lesions, ulcerated/erosive lesions, protruding lesions, parasite, diverticulum, active bleeding and villous lesions based on CE videos was 91.9 %, 92.2 %, 91.4 %, 93.1 %, 93.3 %, 95.1 %, and 100 % respectively. Furthermore, CE-YOLOv5 achieved specificity and accuracy of more than 90 % for all lesions. Compared with experts, the CE-YOLOv5 showed comparable overall sensitivity, specificity and accuracy (all P > 0.05). Compared with non-experts, the CE-YOLOv5 showed significantly higher overall sensitivity (P < 0.0001) and overall accuracy (P < 0.0001), and a moderately higher overall specificity (P = 0.0351). Furthermore, the time for AI-reading (5.62 ± 2.81 min) was significantly shorter than that for the other two groups (both P < 0.0001). CONCLUSIONS CE-YOLOv5 diagnosed small bowel lesions in CE videos with high sensitivity, specificity and accuracy, providing a reliable approach for automated lesion detection in real-world clinical practice.
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Affiliation(s)
- Lan Li
- Department of Gastroenterology, The First Affiliated Hospital, Zhejiang University School of Medicine, 79 Qingchun Road, Hangzhou, Zhejiang 310003, China.
| | - Liping Yang
- Department of Gastroenterology, The First Affiliated Hospital, Zhejiang University School of Medicine, 79 Qingchun Road, Hangzhou, Zhejiang 310003, China
| | - Bingling Zhang
- Department of Gastroenterology, The First Affiliated Hospital, Zhejiang University School of Medicine, 79 Qingchun Road, Hangzhou, Zhejiang 310003, China
| | - Guofei Yan
- Zhejiang Center for Medical Device Evaluation, Hangzhou, China
| | - Yaqing Bao
- GBA Center for Medical Device Evaluation and Inspection, National Medical Products Administration, Shenzhen, China
| | - Renke Zhu
- Department of Gastroenterology, The First Affiliated Hospital, Zhejiang University School of Medicine, 79 Qingchun Road, Hangzhou, Zhejiang 310003, China
| | - Shengjie Li
- Department of Gastroenterology, The First Affiliated Hospital, Zhejiang University School of Medicine, 79 Qingchun Road, Hangzhou, Zhejiang 310003, China
| | - Huogen Wang
- Zhejiang Herymed Technology Co., Ltd, Hangzhou, China; Hithink RoyalFlush Information Network Co., Ltd, Hangzhou, China
| | - Ming Chen
- Hithink RoyalFlush Information Network Co., Ltd, Hangzhou, China
| | - Chaohui Jin
- Zhejiang Herymed Technology Co., Ltd, Hangzhou, China; Hithink RoyalFlush Information Network Co., Ltd, Hangzhou, China
| | - Yishu Chen
- Department of Gastroenterology, The First Affiliated Hospital, Zhejiang University School of Medicine, 79 Qingchun Road, Hangzhou, Zhejiang 310003, China
| | - Chaohui Yu
- Department of Gastroenterology, The First Affiliated Hospital, Zhejiang University School of Medicine, 79 Qingchun Road, Hangzhou, Zhejiang 310003, China
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Luo X, Wang J, Tan C, Dou Q, Han Z, Wang Z, Tasnim F, Wang X, Zhan Q, Li X, Zhou Q, Cheng J, Liao F, Yip HC, Jiang J, Tan RT, Liu S, Yu H. Rapid Endoscopic Diagnosis of Benign Ulcerative Colorectal Diseases With an Artificial Intelligence Contextual Framework. Gastroenterology 2024:S0016-5085(24)00365-2. [PMID: 38583724 DOI: 10.1053/j.gastro.2024.03.039] [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: 05/24/2023] [Revised: 03/22/2024] [Accepted: 03/28/2024] [Indexed: 04/09/2024]
Abstract
BACKGROUND & AIMS Benign ulcerative colorectal diseases (UCDs) such as ulcerative colitis, Crohn's disease, ischemic colitis, and intestinal tuberculosis share similar phenotypes with different etiologies and treatment strategies. To accurately diagnose closely related diseases like UCDs, we hypothesize that contextual learning is critical in enhancing the ability of the artificial intelligence models to differentiate the subtle differences in lesions amidst the vastly divergent spatial contexts. METHODS White-light colonoscopy datasets of patients with confirmed UCDs and healthy controls were retrospectively collected. We developed a Multiclass Contextual Classification (MCC) model that can differentiate among the mentioned UCDs and healthy controls by incorporating the tissue object contexts surrounding the individual lesion region in a scene and spatial information from other endoscopic frames (video-level) into a unified framework. Internal and external datasets were used to validate the model's performance. RESULTS Training datasets included 762 patients, and the internal and external testing cohorts included 257 patients and 293 patients, respectively. Our MCC model provided a rapid reference diagnosis on internal test sets with a high averaged area under the receiver operating characteristic curve (image-level: 0.950 and video-level: 0.973) and balanced accuracy (image-level: 76.1% and video-level: 80.8%), which was superior to junior endoscopists (accuracy: 71.8%, P < .0001) and similar to experts (accuracy: 79.7%, P = .732). The MCC model achieved an area under the receiver operating characteristic curve of 0.988 and balanced accuracy of 85.8% using external testing datasets. CONCLUSIONS These results enable this model to fit in the routine endoscopic workflow, and the contextual framework to be adopted for diagnosing other closely related diseases.
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Affiliation(s)
- Xiaobei Luo
- Guangdong Provincial Key Laboratory of Gastroenterology, Department of Gastroenterology, Nanfang Hospital, Southern Medical University, Guangzhou, Guangdong, China; Department of Gastroenterology, Zhuhai People's Hospital (Zhuhai Clinical Medical College of Jinan University), Zhuhai, Guangdong, China.
| | - Jiahao Wang
- Mechanobiology Institute, National University of Singapore, Singapore; Institute of Bioengineering and Bioimaging, Agency for Science, Technology and Research (A∗STAR), Singapore
| | - Chuanchuan Tan
- The First Hospital of Hunan University of Chinese Medicine, Hunan, China
| | - Qi Dou
- Department of Computer Science and Engineering, The Chinese University of Hong Kong, Hong Kong
| | - Zelong Han
- Guangdong Provincial Key Laboratory of Gastroenterology, Department of Gastroenterology, Nanfang Hospital, Southern Medical University, Guangzhou, Guangdong, China
| | - Zhenjiang Wang
- Department of Gastroenterology, Zhuhai People's Hospital (Zhuhai Clinical Medical College of Jinan University), Zhuhai, Guangdong, China
| | - Farah Tasnim
- Institute of Bioengineering and Bioimaging, Agency for Science, Technology and Research (A∗STAR), Singapore
| | - Xiyu Wang
- Guangdong Provincial Key Laboratory of Gastroenterology, Department of Gastroenterology, Nanfang Hospital, Southern Medical University, Guangzhou, Guangdong, China
| | - Qiang Zhan
- Department of Gastroenterology, The Affiliated Wuxi People's Hospital of Nanjing Medical University, Wuxi People's Hospital, Wuxi Medical Center, Nanjing Medical University, Wuxi, Jiangsu, China
| | - Xiang Li
- Digestive Department of The Second Affiliated Hospital, School of Medicine, The Chinese University of Hong Kong, Shenzhen & Longgang District People's Hospital of Shenzhen, Shenzhen, China
| | - Qunyan Zhou
- Department of Gastroenterology, The Affiliated Wuxi People's Hospital of Nanjing Medical University, Wuxi People's Hospital, Wuxi Medical Center, Nanjing Medical University, Wuxi, Jiangsu, China
| | - Jianbin Cheng
- Department of Gastroenterology, Zhuhai People's Hospital (Zhuhai Clinical Medical College of Jinan University), Zhuhai, Guangdong, China
| | - Fabiao Liao
- Digestive Department of The Second Affiliated Hospital, School of Medicine, The Chinese University of Hong Kong, Shenzhen & Longgang District People's Hospital of Shenzhen, Shenzhen, China
| | - Hon Chi Yip
- Division of Upper Gastrointestinal and Metabolic Surgery, Department of Surgery, Faculty of Medicine, The Chinese University of Hong Kong, Hong Kong
| | - Jiayi Jiang
- Guangdong Provincial Key Laboratory of Gastroenterology, Department of Gastroenterology, Nanfang Hospital, Southern Medical University, Guangzhou, Guangdong, China
| | - Robby T Tan
- Department of Electrical and Computer Engineering, National University of Singapore, Singapore
| | - Side Liu
- Guangdong Provincial Key Laboratory of Gastroenterology, Department of Gastroenterology, Nanfang Hospital, Southern Medical University, Guangzhou, Guangdong, China; Department of Gastroenterology, Zhuhai People's Hospital (Zhuhai Clinical Medical College of Jinan University), Zhuhai, Guangdong, China.
| | - Hanry Yu
- Guangdong Provincial Key Laboratory of Gastroenterology, Department of Gastroenterology, Nanfang Hospital, Southern Medical University, Guangzhou, Guangdong, China; Mechanobiology Institute, National University of Singapore, Singapore; Institute of Bioengineering and Bioimaging, Agency for Science, Technology and Research (A∗STAR), Singapore; CAMP, Singapore-MIT Alliance for Research and Technology, Singapore; Department of Physiology, The Institute for Digital Medicine (WisDM), Yong Loo Lin School of Medicine, Singapore.
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6
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Choi KS, Park D, Kim JS, Cheung DY, Lee BI, Cho YS, Kim JI, Lee S, Lee HH. Deep learning in negative small-bowel capsule endoscopy improves small-bowel lesion detection and diagnostic yield. Dig Endosc 2024; 36:437-445. [PMID: 37612137 DOI: 10.1111/den.14670] [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: 04/26/2023] [Accepted: 08/20/2023] [Indexed: 08/25/2023]
Abstract
OBJECTIVES Although several studies have shown the usefulness of artificial intelligence to identify abnormalities in small-bowel capsule endoscopy (SBCE) images, few studies have proven its actual clinical usefulness. Thus, the aim of this study was to examine whether meaningful findings could be obtained when negative SBCE videos were reanalyzed with a deep convolutional neural network (CNN) model. METHODS Clinical data of patients who received SBCE for suspected small-bowel bleeding at two academic hospitals between February 2018 and July 2020 were retrospectively collected. All SBCE videos read as negative were reanalyzed with the CNN algorithm developed in our previous study. Meaningful findings such as angioectasias and ulcers were finally decided after reviewing CNN-selected images by two gastroenterologists. RESULTS Among 202 SBCE videos, 103 (51.0%) were read as negative by humans. Meaningful findings were detected in 63 (61.2%) of these 103 videos after reanalyzing them with the CNN model. There were 79 red spots or angioectasias in 40 videos and 66 erosions or ulcers in 35 videos. After reanalysis, the diagnosis was changed for 10 (10.3%) patients who had initially negative SBCE results. During a mean follow-up of 16.5 months, rebleeding occurred in 19 (18.4%) patients. The rebleeding rate was 23.6% (13/55) for patients with meaningful findings and 16.1% (5/31) for patients without meaningful findings (P = 0.411). CONCLUSION Our CNN algorithm detected meaningful findings in negative SBCE videos that were missed by humans. The use of deep CNN for SBCE image reading is expected to compensate for human error.
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Affiliation(s)
- Kyung Seok Choi
- Division of Gastroenterology, Department of Internal Medicine, College of Medicine, The Catholic University of Korea, Seoul, Korea
| | - DoGyeom Park
- Department of Mechanical Engineering, Pohang University of Science and Technology, Pohang, Korea
| | - Jin Su Kim
- Division of Gastroenterology, Department of Internal Medicine, College of Medicine, The Catholic University of Korea, Seoul, Korea
| | - Dae Young Cheung
- Division of Gastroenterology, Department of Internal Medicine, College of Medicine, The Catholic University of Korea, Seoul, Korea
| | - Bo-In Lee
- Division of Gastroenterology, Department of Internal Medicine, College of Medicine, The Catholic University of Korea, Seoul, Korea
| | - Young-Seok Cho
- Division of Gastroenterology, Department of Internal Medicine, College of Medicine, The Catholic University of Korea, Seoul, Korea
| | - Jin Il Kim
- Division of Gastroenterology, Department of Internal Medicine, College of Medicine, The Catholic University of Korea, Seoul, Korea
| | - Seungchul Lee
- Institute for Convergence Research and Education in Advanced Technology, Yonsei University, Seoul, Korea
- Department of Mechanical Engineering, Pohang University of Science and Technology, Pohang, Korea
- Graduate School of Artificial Intelligence, Pohang University of Science and Technology, Pohang, Korea
| | - Han Hee Lee
- Division of Gastroenterology, Department of Internal Medicine, College of Medicine, The Catholic University of Korea, Seoul, Korea
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Wu R, Qin K, Fang Y, Xu Y, Zhang H, Li W, Luo X, Han Z, Liu S, Li Q. Application of the convolution neural network in determining the depth of invasion of gastrointestinal cancer: a systematic review and meta-analysis. J Gastrointest Surg 2024; 28:538-547. [PMID: 38583908 DOI: 10.1016/j.gassur.2023.12.029] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 10/22/2023] [Revised: 12/16/2023] [Accepted: 12/30/2023] [Indexed: 04/09/2024]
Abstract
BACKGROUND With the development of endoscopic technology, endoscopic submucosal dissection (ESD) has been widely used in the treatment of gastrointestinal tumors. It is necessary to evaluate the depth of tumor invasion before the application of ESD. The convolution neural network (CNN) is a type of artificial intelligence that has the potential to assist in the classification of the depth of invasion in endoscopic images. This meta-analysis aimed to evaluate the performance of CNN in determining the depth of invasion of gastrointestinal tumors. METHODS A search on PubMed, Web of Science, and SinoMed was performed to collect the original publications about the use of CNN in determining the depth of invasion of gastrointestinal neoplasms. Pooled sensitivity and specificity were calculated using an exact binominal rendition of the bivariate mixed-effects regression model. I2 was used for the evaluation of heterogeneity. RESULTS A total of 17 articles were included; the pooled sensitivity was 84% (95% CI, 0.81-0.88), specificity was 91% (95% CI, 0.85-0.94), and the area under the curve (AUC) was 0.93 (95% CI, 0.90-0.95). The performance of CNN was significantly better than that of endoscopists (AUC: 0.93 vs 0.83, respectively; P = .0005). CONCLUSION Our review revealed that CNN is one of the most effective methods of endoscopy to evaluate the depth of invasion of early gastrointestinal tumors, which has the potential to work as a remarkable tool for clinical endoscopists to make decisions on whether the lesion is feasible for endoscopic treatment.
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Affiliation(s)
- Ruo Wu
- Nanfang Hospital (The First School of Clinical Medicine), Southern Medical University, Guangzhou, Guangdong, China
| | - Kaiwen Qin
- Department of Gastroenterology, Guangdong Provincial Key Laboratory of Gastroenterology, Nanfang Hospital, Southern Medical University, Guangzhou, Guangdong, China
| | - Yuxin Fang
- Department of Gastroenterology, Guangdong Provincial Key Laboratory of Gastroenterology, Nanfang Hospital, Southern Medical University, Guangzhou, Guangdong, China
| | - Yuyuan Xu
- Department of Hepatology Unit and Infectious Diseases, State Key Laboratory of Organ Failure Research, Guangdong Provincial Key Laboratory of Viral Hepatitis Research, Nanfang Hospital, Southern Medical University, Guangzhou, Guangdong, China
| | - Haonan Zhang
- Department of Gastroenterology, Guangdong Provincial Key Laboratory of Gastroenterology, Nanfang Hospital, Southern Medical University, Guangzhou, Guangdong, China
| | - Wenhua Li
- Nanfang Hospital (The First School of Clinical Medicine), Southern Medical University, Guangzhou, Guangdong, China
| | - Xiaobei Luo
- Department of Gastroenterology, Guangdong Provincial Key Laboratory of Gastroenterology, Nanfang Hospital, Southern Medical University, Guangzhou, Guangdong, China
| | - Zelong Han
- Department of Gastroenterology, Guangdong Provincial Key Laboratory of Gastroenterology, Nanfang Hospital, Southern Medical University, Guangzhou, Guangdong, China
| | - Side Liu
- Department of Gastroenterology, Guangdong Provincial Key Laboratory of Gastroenterology, Nanfang Hospital, Southern Medical University, Guangzhou, Guangdong, China; Pazhou Lab, Guangzhou, Guangdong, China
| | - Qingyuan Li
- Department of Gastroenterology, Guangdong Provincial Key Laboratory of Gastroenterology, Nanfang Hospital, Southern Medical University, Guangzhou, Guangdong, China.
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Carter D, Bykhovsky D, Hasky A, Mamistvalov I, Zimmer Y, Ram E, Hoffer O. Convolutional neural network deep learning model accurately detects rectal cancer in endoanal ultrasounds. Tech Coloproctol 2024; 28:44. [PMID: 38561492 PMCID: PMC10984882 DOI: 10.1007/s10151-024-02917-3] [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: 11/22/2023] [Accepted: 03/06/2024] [Indexed: 04/04/2024]
Abstract
BACKGROUND Imaging is vital for assessing rectal cancer, with endoanal ultrasound (EAUS) being highly accurate in large tertiary medical centers. However, EAUS accuracy drops outside such settings, possibly due to varied examiner experience and fewer examinations. This underscores the need for an AI-based system to enhance accuracy in non-specialized centers. This study aimed to develop and validate deep learning (DL) models to differentiate rectal cancer in standard EAUS images. METHODS A transfer learning approach with fine-tuned DL architectures was employed, utilizing a dataset of 294 images. The performance of DL models was assessed through a tenfold cross-validation. RESULTS The DL diagnostics model exhibited a sensitivity and accuracy of 0.78 each. In the identification phase, the automatic diagnostic platform achieved an area under the curve performance of 0.85 for diagnosing rectal cancer. CONCLUSIONS This research demonstrates the potential of DL models in enhancing rectal cancer detection during EAUS, especially in settings with lower examiner experience. The achieved sensitivity and accuracy suggest the viability of incorporating AI support for improved diagnostic outcomes in non-specialized medical centers.
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Affiliation(s)
- D Carter
- Department of Gastroenterology, Chaim Sheba Medical Center, Ramat Gan, Israel.
- Faculty of Medicine, Tel Aviv University, Tel Aviv, Israel.
| | - D Bykhovsky
- Electrical and Electronics Engineering Department, Shamoon College of Engineering, Beer-Sheba, Israel
| | - A Hasky
- School of Electrical Engineering, Afeka College of Engineering, Tel Aviv, Israel
| | - I Mamistvalov
- School of Electrical Engineering, Afeka College of Engineering, Tel Aviv, Israel
| | - Y Zimmer
- School of Medical Engineering, Afeka College of Engineering, Tel Aviv, Israel
| | - E Ram
- Department of Gastroenterology, Chaim Sheba Medical Center, Ramat Gan, Israel
- Faculty of Medicine, Tel Aviv University, Tel Aviv, Israel
| | - O Hoffer
- School of Electrical Engineering, Afeka College of Engineering, Tel Aviv, Israel
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9
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Brodersen JB, Jensen MD, Leenhardt R, Kjeldsen J, Histace A, Knudsen T, Dray X. Artificial Intelligence-assisted Analysis of Pan-enteric Capsule Endoscopy in Patients with Suspected Crohn's Disease: A Study on Diagnostic Performance. J Crohns Colitis 2024; 18:75-81. [PMID: 37527554 DOI: 10.1093/ecco-jcc/jjad131] [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: 05/30/2023] [Indexed: 08/03/2023]
Abstract
BACKGROUND AND AIM Pan-enteric capsule endoscopy [PCE] is a highly sensitive but time-consuming tool for detecting pathology. Artificial intelligence [AI] algorithms might offer a possibility to assist in the review and reduce the analysis time of PCE. This study examines the agreement between PCE assessments aided by AI technology and standard evaluations, in patients suspected of Crohn's disease [CD]. METHOD PCEs from a prospective, blinded, multicentre study, including patients suspected of CD, were processed by the deep learning solution AXARO® [Augmented Endoscopy, Paris, France]. Based on the image output, two observers classified the patient's PCE as normal or suggestive of CD, ulcerative colitis, or cancer. The primary outcome was per-patient sensitivities and specificities for detecting CD and inflammatory bowel disease [IBD]. Complete reading of PCE served as the reference standard. RESULTS A total of 131 patients' PCEs were analysed, with a median recording time of 303 min. The AXARO® framework reduced output to a median of 470 images [2.1%] per patient, and the pooled median review time was 3.2 min per patient. For detecting CD, the observers had a sensitivity of 96% and 92% and a specificity of 93% and 90%, respectively. For the detection of IBD, both observers had a sensitivity of 97% and had a specificity of 91% and 90%, respectively. The negative predictive value was 95% for CD and 97% for IBD. CONCLUSIONS Using the AXARO® framework reduced the initial review time substantially while maintaining high diagnostic accuracy-suggesting its use as a rapid tool to rule out IBD in PCEs of patients suspected of Crohn's disease.
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Affiliation(s)
- Jacob Broder Brodersen
- Department of Internal Medicine, Section of Gastroenterology, Hospital of South West Jutland, Esbjerg, Denmark
- Department of Regional Health Research, University of Southern Denmark, Odense, Denmark
| | - Michael Dam Jensen
- Department of Regional Health Research, University of Southern Denmark, Odense, Denmark
- Department of Internal Medicine, Section of Gastroenterology, Lillebaelt Hospital, Vejle, Denmark
| | - Romain Leenhardt
- Équipes Traitement de l'Information et Systèmes, ETIS UMR 8051, CY Paris Cergy University, ENSEA, CNRS, Cergy, France
- Sorbonne University, Center for Digestive Endoscopy, Saint-Antoine Hospital, APHP, Paris, France
| | - Jens Kjeldsen
- Department of Medical Gastroenterology, Odense University Hospital, Odense, Denmark
- Department of Clinical Research, University of Southern Denmark, Odense, Denmark
- OPEN Odense Patient Data Explorative Network, Odense University Hospital, Odense, Denmark
| | - Aymeric Histace
- Équipes Traitement de l'Information et Systèmes, ETIS UMR 8051, CY Paris Cergy University, ENSEA, CNRS, Cergy, France
| | - Torben Knudsen
- Department of Internal Medicine, Section of Gastroenterology, Hospital of South West Jutland, Esbjerg, Denmark
- Department of Regional Health Research, University of Southern Denmark, Odense, Denmark
| | - Xavier Dray
- Équipes Traitement de l'Information et Systèmes, ETIS UMR 8051, CY Paris Cergy University, ENSEA, CNRS, Cergy, France
- Sorbonne University, Center for Digestive Endoscopy, Saint-Antoine Hospital, APHP, Paris, France
- Department of Clinical Research, University of Southern Denmark, Odense, Denmark
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Mendes F, Mascarenhas M, Ribeiro T, Afonso J, Cardoso P, Martins M, Cardoso H, Andrade P, Ferreira JPS, Mascarenhas Saraiva M, Macedo G. Artificial Intelligence and Panendoscopy-Automatic Detection of Clinically Relevant Lesions in Multibrand Device-Assisted Enteroscopy. Cancers (Basel) 2024; 16:208. [PMID: 38201634 PMCID: PMC10778030 DOI: 10.3390/cancers16010208] [Citation(s) in RCA: 1] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 12/05/2023] [Revised: 12/27/2023] [Accepted: 12/28/2023] [Indexed: 01/12/2024] Open
Abstract
Device-assisted enteroscopy (DAE) is capable of evaluating the entire gastrointestinal tract, identifying multiple lesions. Nevertheless, DAE's diagnostic yield is suboptimal. Convolutional neural networks (CNN) are multi-layer architecture artificial intelligence models suitable for image analysis, but there is a lack of studies about their application in DAE. Our group aimed to develop a multidevice CNN for panendoscopic detection of clinically relevant lesions during DAE. In total, 338 exams performed in two specialized centers were retrospectively evaluated, with 152 single-balloon enteroscopies (Fujifilm®, Porto, Portugal), 172 double-balloon enteroscopies (Olympus®, Porto, Portugal) and 14 motorized spiral enteroscopies (Olympus®, Porto, Portugal); then, 40,655 images were divided in a training dataset (90% of the images, n = 36,599) and testing dataset (10% of the images, n = 4066) used to evaluate the model. The CNN's output was compared to an expert consensus classification. The model was evaluated by its sensitivity, specificity, positive (PPV) and negative predictive values (NPV), accuracy and area under the precision recall curve (AUC-PR). The CNN had an 88.9% sensitivity, 98.9% specificity, 95.8% PPV, 97.1% NPV, 96.8% accuracy and an AUC-PR of 0.97. Our group developed the first multidevice CNN for panendoscopic detection of clinically relevant lesions during DAE. The development of accurate deep learning models is of utmost importance for increasing the diagnostic yield of DAE-based panendoscopy.
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Affiliation(s)
- Francisco Mendes
- Alameda Professor Hernâni Monteiro, Department of Gastroenterology, São João University Hospital, 4200-427 Porto, Portugal; (F.M.); (T.R.); (P.C.); (M.M.); (P.A.); (G.M.)
- WGO Gastroenterology and Hepatology Training Center, 4050-345 Porto, Portugal
| | - Miguel Mascarenhas
- Alameda Professor Hernâni Monteiro, Department of Gastroenterology, São João University Hospital, 4200-427 Porto, Portugal; (F.M.); (T.R.); (P.C.); (M.M.); (P.A.); (G.M.)
- WGO Gastroenterology and Hepatology Training Center, 4050-345 Porto, Portugal
- Faculty of Medicine, University of Porto, Alameda Professor Hernâni Monteiro, 4200-427 Porto, Portugal
| | - Tiago Ribeiro
- Alameda Professor Hernâni Monteiro, Department of Gastroenterology, São João University Hospital, 4200-427 Porto, Portugal; (F.M.); (T.R.); (P.C.); (M.M.); (P.A.); (G.M.)
- WGO Gastroenterology and Hepatology Training Center, 4050-345 Porto, Portugal
- Faculty of Medicine, University of Porto, Alameda Professor Hernâni Monteiro, 4200-427 Porto, Portugal
| | - João Afonso
- Alameda Professor Hernâni Monteiro, Department of Gastroenterology, São João University Hospital, 4200-427 Porto, Portugal; (F.M.); (T.R.); (P.C.); (M.M.); (P.A.); (G.M.)
- WGO Gastroenterology and Hepatology Training Center, 4050-345 Porto, Portugal
- Faculty of Medicine, University of Porto, Alameda Professor Hernâni Monteiro, 4200-427 Porto, Portugal
| | - Pedro Cardoso
- Alameda Professor Hernâni Monteiro, Department of Gastroenterology, São João University Hospital, 4200-427 Porto, Portugal; (F.M.); (T.R.); (P.C.); (M.M.); (P.A.); (G.M.)
- WGO Gastroenterology and Hepatology Training Center, 4050-345 Porto, Portugal
- Faculty of Medicine, University of Porto, Alameda Professor Hernâni Monteiro, 4200-427 Porto, Portugal
| | - Miguel Martins
- Alameda Professor Hernâni Monteiro, Department of Gastroenterology, São João University Hospital, 4200-427 Porto, Portugal; (F.M.); (T.R.); (P.C.); (M.M.); (P.A.); (G.M.)
- WGO Gastroenterology and Hepatology Training Center, 4050-345 Porto, Portugal
| | - Hélder Cardoso
- Alameda Professor Hernâni Monteiro, Department of Gastroenterology, São João University Hospital, 4200-427 Porto, Portugal; (F.M.); (T.R.); (P.C.); (M.M.); (P.A.); (G.M.)
- WGO Gastroenterology and Hepatology Training Center, 4050-345 Porto, Portugal
- Faculty of Medicine, University of Porto, Alameda Professor Hernâni Monteiro, 4200-427 Porto, Portugal
| | - Patrícia Andrade
- Alameda Professor Hernâni Monteiro, Department of Gastroenterology, São João University Hospital, 4200-427 Porto, Portugal; (F.M.); (T.R.); (P.C.); (M.M.); (P.A.); (G.M.)
- WGO Gastroenterology and Hepatology Training Center, 4050-345 Porto, Portugal
- Faculty of Medicine, University of Porto, Alameda Professor Hernâni Monteiro, 4200-427 Porto, Portugal
| | - João P. S. Ferreira
- Department of Mechanical Engineering, Faculty of Engineering, University of Porto, Rua Dr. Roberto Frias, 4200-465 Porto, Portugal;
- DigestAID—Digestive Artificial Intelligence Development, R. Alfredo Allen n°. 455/461, 4200-135 Porto, Portugal
| | | | - Guilherme Macedo
- Alameda Professor Hernâni Monteiro, Department of Gastroenterology, São João University Hospital, 4200-427 Porto, Portugal; (F.M.); (T.R.); (P.C.); (M.M.); (P.A.); (G.M.)
- WGO Gastroenterology and Hepatology Training Center, 4050-345 Porto, Portugal
- Faculty of Medicine, University of Porto, Alameda Professor Hernâni Monteiro, 4200-427 Porto, Portugal
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11
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Brzeski A, Dziubich T, Krawczyk H. Visual Features for Improving Endoscopic Bleeding Detection Using Convolutional Neural Networks. SENSORS (BASEL, SWITZERLAND) 2023; 23:9717. [PMID: 38139563 PMCID: PMC10748269 DOI: 10.3390/s23249717] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Subscribe] [Scholar Register] [Received: 10/16/2023] [Revised: 11/19/2023] [Accepted: 12/04/2023] [Indexed: 12/24/2023]
Abstract
The presented paper investigates the problem of endoscopic bleeding detection in endoscopic videos in the form of a binary image classification task. A set of definitions of high-level visual features of endoscopic bleeding is introduced, which incorporates domain knowledge from the field. The high-level features are coupled with respective feature descriptors, enabling automatic capture of the features using image processing methods. Each of the proposed feature descriptors outputs a feature activation map in the form of a grayscale image. Acquired feature maps can be appended in a straightforward way to the original color channels of the input image and passed to the input of a convolutional neural network during the training and inference steps. An experimental evaluation is conducted to compare the classification ROC AUC of feature-extended convolutional neural network models with baseline models using regular color image inputs. The advantage of feature-extended models is demonstrated for the Resnet and VGG convolutional neural network architectures.
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Affiliation(s)
- Adam Brzeski
- Faculty of Electronics, Telecommunications and Informatics, Gdańsk University of Technology, 80-233 Gdańsk, Poland; (T.D.); (H.K.)
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12
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Turck D, Dratsch T, Schröder L, Lorenz F, Dinter J, Bürger M, Schiffmann L, Kasper P, Allo G, Goeser T, Chon SH, Nierhoff D. A convolutional neural network for bleeding detection in capsule endoscopy using real clinical data. MINIM INVASIV THER 2023; 32:335-340. [PMID: 37640056 DOI: 10.1080/13645706.2023.2250445] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 12/03/2022] [Accepted: 08/14/2023] [Indexed: 08/31/2023]
Abstract
BACKGROUND The goal of the present study was to develop a convolutional neural network for the detection of bleedings in capsule endoscopy videos using realistic clinical data from one single-centre. METHODS Capsule endoscopy videos from all 133 patients (79 male, 54 female; meanage = 53.73 years, SDage = 26.13) who underwent capsule endoscopy at our institution between January 2014 and August 2018 were screened for pathology. All videos were screened for pathology by two independent capsule experts and confirmed findings were checked again by a third capsule expert. From these videos, 125 pathological findings (individual episodes of bleeding spanning a total of 5696 images) and 103 non-pathological findings (sections of normal mucosal tissue without pathologies spanning a total of 7420 images) were used to develop and validate a neural network (Inception V3) using transfer learning. RESULTS The overall accuracy of the model for the detection of bleedings was 90.6% [95%CI: 89.4%-91.7%], with a sensitivity of 89.4% [95%CI: 87.6%-91.2%] and a specificity of 91.7% [95%CI: 90.1%-93.2%]. CONCLUSION Our results show that neural networks can detect bleedings in capsule endoscopy videos under realistic, clinical conditions with an accuracy of 90.6%, potentially reducing reading time per capsule and helping to improve diagnostic accuracy.
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Affiliation(s)
- Dorothee Turck
- Department of Medicine, University of Cologne, Cologne, Germany
| | - Thomas Dratsch
- Institute of Diagnostic and Interventional Radiology, University Hospital Cologne, Cologne, Germany
| | - Lorenz Schröder
- Department of Medicine, University of Cologne, Cologne, Germany
| | - Florian Lorenz
- Department of Gastroenterology and Hepatology, University Hospital Cologne, Cologne, Germany
| | - Johanna Dinter
- Gastroenterologische Schwerpunktpraxis Stähler, Cologne, Germany
| | - Martin Bürger
- Department of Gastroenterology and Hepatology, University Hospital Cologne, Cologne, Germany
| | - Lars Schiffmann
- Department of General, Visceral, Cancer, and Transplant Surgery, University Hospital Cologne, Cologne, Germany
| | - Philipp Kasper
- Department of Gastroenterology and Hepatology, University Hospital Cologne, Cologne, Germany
| | - Gabriel Allo
- Department of Gastroenterology and Hepatology, University Hospital Cologne, Cologne, Germany
| | - Tobias Goeser
- Department of Gastroenterology and Hepatology, University Hospital Cologne, Cologne, Germany
| | - Seung-Hun Chon
- Department of Gastroenterology and Hepatology, University Hospital Cologne, Cologne, Germany
- Department of General, Visceral, Cancer, and Transplant Surgery, University Hospital Cologne, Cologne, Germany
| | - Dirk Nierhoff
- Department of Gastroenterology and Hepatology, University Hospital Cologne, Cologne, Germany
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13
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Chen Y, Wu G, Qu C, Ye Z, Kang Y, Tian X. A multifaceted comparative analysis of image and video technologies in gastrointestinal endoscope and their clinical applications. Front Med (Lausanne) 2023; 10:1226748. [PMID: 37881626 PMCID: PMC10595015 DOI: 10.3389/fmed.2023.1226748] [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: 05/22/2023] [Accepted: 09/25/2023] [Indexed: 10/27/2023] Open
Abstract
This paper presents a comprehensive exploration of endoscopic technologies in clinical applications across seven tables, each focusing on a unique facet of the medical field. The discourse begins with a detailed analysis of pediatric endoscopes, highlighting their diagnostic capabilities in various conditions. It then delves into the specifications and applications of globally recognized capsule endoscopy devices. Additionally, the paper incorporates an analysis of advanced imaging techniques, such as Narrow Band Imaging (NBI), Flexible Spectral Imaging Color Enhancement (FICE), and i-scan, which are increasingly being integrated into ultrathin gastrointestinal (GI) endoscopes. Factors like technological capabilities, light source, camera technology, and computational constraints are evaluated to understand their compatibility with these advanced imaging techniques, each offering unique advantages and challenges in clinical settings. NBI, for instance, is lauded for its user-friendly, real-time enhanced imaging capabilities, making it effective for early detection of conditions like colorectal cancer and Barrett's esophagus. Conversely, FICE and i-scan offer high customizability and are compatible with a broader range of endoscope models. The paper further delves into innovative advances in movement control for Nasojejunal (NJ) feeding tube endoscopy, elucidating the potential of AI and other novel strategies. A review of the technologies and methodologies enhancing endoscopic procedure control and diagnostic precision follows, emphasizing image and video technologies in pediatric endoscopy, capsule endoscopes, ultrathin endoscopes, and their clinical applications. Finally, a comparative analysis of leading real-time video monitoring endoscopes in clinical practices underscores the continuous advancements in the field of endoscopy, ensuring improved diagnostics and precision in surgical procedures. Collectively, the comparative analysis presented in this paper highlights the remarkable diversity and continuous evolution of endoscopic technologies, underlining their crucial role in diagnosing and treating an array of medical conditions, thereby fostering advancements in patient care and clinical outcomes.
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Affiliation(s)
| | | | | | | | | | - Xin Tian
- Department of Intensive Care Unit, Fifth Affiliated Hospital of Wenzhou Medical University, Lishui Municipal Central Hospital, Lishui, China
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14
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Oh S, Oh D, Kim D, Song W, Hwang Y, Cho N, Lim YJ. Video Analysis of Small Bowel Capsule Endoscopy Using a Transformer Network. Diagnostics (Basel) 2023; 13:3133. [PMID: 37835876 PMCID: PMC10572266 DOI: 10.3390/diagnostics13193133] [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: 08/01/2023] [Revised: 09/19/2023] [Accepted: 09/30/2023] [Indexed: 10/15/2023] Open
Abstract
Although wireless capsule endoscopy (WCE) detects small bowel diseases effectively, it has some limitations. For example, the reading process can be time consuming due to the numerous images generated per case and the lesion detection accuracy may rely on the operators' skills and experiences. Hence, many researchers have recently developed deep-learning-based methods to address these limitations. However, they tend to select only a portion of the images from a given WCE video and analyze each image individually. In this study, we note that more information can be extracted from the unused frames and the temporal relations of sequential frames. Specifically, to increase the accuracy of lesion detection without depending on experts' frame selection skills, we suggest using whole video frames as the input to the deep learning system. Thus, we propose a new Transformer-architecture-based neural encoder that takes the entire video as the input, exploiting the power of the Transformer architecture to extract long-term global correlation within and between the input frames. Subsequently, we can capture the temporal context of the input frames and the attentional features within a frame. Tests on benchmark datasets of four WCE videos showed 95.1% sensitivity and 83.4% specificity. These results may significantly advance automated lesion detection techniques for WCE images.
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Affiliation(s)
- SangYup Oh
- School of Electrical and Computer Engineering, Seoul National University, 1 Gwanak-ro, Kwanak-gu, Seoul 08826, Republic of Korea; (S.O.); (W.S.)
| | - DongJun Oh
- Department of Internal Medicine, Dongguk University Ilsan Hospital, Dongguk University College of Medicine, Goyang 10326, Republic of Korea;
| | - Dongmin Kim
- JLK TOWER, Gangnam-gu, Seoul 06141, Republic of Korea;
| | - Woohyuk Song
- School of Electrical and Computer Engineering, Seoul National University, 1 Gwanak-ro, Kwanak-gu, Seoul 08826, Republic of Korea; (S.O.); (W.S.)
| | - Youngbae Hwang
- Department of Electronics Engineering, Chungbuk National University, Cheongju 28644, Republic of Korea;
| | - Namik Cho
- School of Electrical and Computer Engineering, Seoul National University, 1 Gwanak-ro, Kwanak-gu, Seoul 08826, Republic of Korea; (S.O.); (W.S.)
| | - Yun Jeong Lim
- Department of Internal Medicine, Dongguk University Ilsan Hospital, Dongguk University College of Medicine, Goyang 10326, Republic of Korea;
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15
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O'Hara FJ, Mc Namara D. Capsule endoscopy with artificial intelligence-assisted technology: Real-world usage of a validated AI model for capsule image review. Endosc Int Open 2023; 11:E970-E975. [PMID: 37828977 PMCID: PMC10567136 DOI: 10.1055/a-2161-1816] [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: 01/31/2023] [Accepted: 08/25/2023] [Indexed: 10/14/2023] Open
Abstract
Background and study aims Capsule endoscopy is a time-consuming procedure with a significance error rate. Artificial intelligence (AI) can potentially reduce reading time significantly by reducing the number of images that need human review. An OMOM Artificial Intelligence-enabled small bowel capsule has been recently trained and validated for small bowel capsule endoscopy video review. This study aimed to assess its performance in a real-world setting in comparison with standard reading methods. Patients and methods In this single-center retrospective study, 40 patient studies performed using the OMOM capsule were analyzed first with standard reading methods and later using AI-assisted reading. Reading time, pathology identified, intestinal landmark identification and bowel preparation assessment (Brotz Score) were compared. Results Overall diagnosis correlated 100% between the two reading methods. In a per-lesion analysis, 1293 images of significant lesions were identified combining standard and AI-assisted reading methods. AI-assisted reading captured 1268 (98.1%, 95% CI 97.15-98.7) of these findings while standard reading mode captured 1114 (86.2%, 95% confidence interval 84.2-87.9), P < 0.001. Mean reading time went from 29.7 minutes with standard reading to 2.3 minutes with AI-assisted reading ( P < 0.001), for an average time saving of 27.4 minutes per study. Time of first cecal image showed a wide discrepancy between AI and standard reading of 99.2 minutes (r = 0.085, P = 0.68). Bowel cleansing evaluation agreed in 97.4% (r = 0.805 P < 0.001). Conclusions AI-assisted reading has shown significant time savings without reducing sensitivity in this study. Limitations remain in the evaluation of other indicators.
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Affiliation(s)
- Fintan John O'Hara
- Gastroenterology, Tallaght University Hospital, Dublin, Ireland
- Medicine, Trinity College Dublin School of Medicine, Dublin, Ireland
| | - Deirdre Mc Namara
- Gastroenterology, Tallaght University Hospital, Dublin, Ireland
- Medicine, Trinity College Dublin School of Medicine, Dublin, Ireland
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Musha A, Hasnat R, Mamun AA, Ping EP, Ghosh T. Computer-Aided Bleeding Detection Algorithms for Capsule Endoscopy: A Systematic Review. SENSORS (BASEL, SWITZERLAND) 2023; 23:7170. [PMID: 37631707 PMCID: PMC10459126 DOI: 10.3390/s23167170] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Subscribe] [Scholar Register] [Received: 05/27/2023] [Revised: 08/08/2023] [Accepted: 08/10/2023] [Indexed: 08/27/2023]
Abstract
Capsule endoscopy (CE) is a widely used medical imaging tool for the diagnosis of gastrointestinal tract abnormalities like bleeding. However, CE captures a huge number of image frames, constituting a time-consuming and tedious task for medical experts to manually inspect. To address this issue, researchers have focused on computer-aided bleeding detection systems to automatically identify bleeding in real time. This paper presents a systematic review of the available state-of-the-art computer-aided bleeding detection algorithms for capsule endoscopy. The review was carried out by searching five different repositories (Scopus, PubMed, IEEE Xplore, ACM Digital Library, and ScienceDirect) for all original publications on computer-aided bleeding detection published between 2001 and 2023. The Preferred Reporting Items for Systematic Review and Meta-Analyses (PRISMA) methodology was used to perform the review, and 147 full texts of scientific papers were reviewed. The contributions of this paper are: (I) a taxonomy for computer-aided bleeding detection algorithms for capsule endoscopy is identified; (II) the available state-of-the-art computer-aided bleeding detection algorithms, including various color spaces (RGB, HSV, etc.), feature extraction techniques, and classifiers, are discussed; and (III) the most effective algorithms for practical use are identified. Finally, the paper is concluded by providing future direction for computer-aided bleeding detection research.
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Affiliation(s)
- Ahmmad Musha
- Department of Electrical and Electronic Engineering, Pabna University of Science and Technology, Pabna 6600, Bangladesh; (A.M.); (R.H.)
| | - Rehnuma Hasnat
- Department of Electrical and Electronic Engineering, Pabna University of Science and Technology, Pabna 6600, Bangladesh; (A.M.); (R.H.)
| | - Abdullah Al Mamun
- Faculty of Engineering and Technology, Multimedia University, Melaka 75450, Malaysia;
| | - Em Poh Ping
- Faculty of Engineering and Technology, Multimedia University, Melaka 75450, Malaysia;
| | - Tonmoy Ghosh
- Department of Electrical and Computer Engineering, The University of Alabama, Tuscaloosa, AL 35487, USA;
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Liu W, Choi SJ, George D, Li L, Zhong Z, Zhang R, Choi SY, Selaru FM, Gracias DH. Untethered shape-changing devices in the gastrointestinal tract. Expert Opin Drug Deliv 2023; 20:1801-1822. [PMID: 38044866 PMCID: PMC10872387 DOI: 10.1080/17425247.2023.2291450] [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: 09/30/2023] [Accepted: 12/01/2023] [Indexed: 12/05/2023]
Abstract
INTRODUCTION Advances in microfabrication, automation, and computer engineering seek to revolutionize small-scale devices and machines. Emerging trends in medicine point to smart devices that emulate the motility, biosensing abilities, and intelligence of cells and pathogens that inhabit the human body. Two important characteristics of smart medical devices are the capability to be deployed in small conduits, which necessitates being untethered, and the capacity to perform mechanized functions, which requires autonomous shape-changing. AREAS COVERED We motivate the need for untethered shape-changing devices in the gastrointestinal tract for drug delivery, diagnosis, and targeted treatment. We survey existing structures and devices designed and utilized across length scales from the macro to the sub-millimeter. These devices range from triggerable pre-stressed thin film microgrippers and spring-loaded devices to shape-memory and differentially swelling structures. EXPERT OPINION Recent studies demonstrate that when fully enabled, tether-free and shape-changing devices, especially at sub-mm scales, could significantly advance the diagnosis and treatment of GI diseases ranging from cancer and inflammatory bowel disease (IBD) to irritable bowel syndrome (IBS) by improving treatment efficacy, reducing costs, and increasing medication compliance. We discuss the challenges and possibilities associated with ensuring safe, reliable, and autonomous operation of these smart devices.
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Affiliation(s)
- Wangqu Liu
- Department of Chemical and Biomolecular Engineering, Johns Hopkins University, Baltimore, MD 21218, USA
| | - Soo Jin Choi
- Department of Chemical and Biomolecular Engineering, Johns Hopkins University, Baltimore, MD 21218, USA
| | - Derosh George
- Department of Chemical and Biomolecular Engineering, Johns Hopkins University, Baltimore, MD 21218, USA
| | - Ling Li
- Gastroenterology and Hepatology, Department of Medicine, Johns Hopkins University School of Medicine, Baltimore, MD 21287, USA
| | - Zijian Zhong
- Department of Chemical and Biomolecular Engineering, Johns Hopkins University, Baltimore, MD 21218, USA
| | - Ruili Zhang
- Department of Chemical and Biomolecular Engineering, Johns Hopkins University, Baltimore, MD 21218, USA
| | - Si Young Choi
- Department of Chemical and Biomolecular Engineering, Johns Hopkins University, Baltimore, MD 21218, USA
| | - Florin M. Selaru
- Gastroenterology and Hepatology, Department of Medicine, Johns Hopkins University School of Medicine, Baltimore, MD 21287, USA
| | - David H. Gracias
- Department of Chemical and Biomolecular Engineering, Johns Hopkins University, Baltimore, MD 21218, USA
- Department of Chemistry, Johns Hopkins University, Baltimore, MD 21218, USA
- Department of Materials Science and Engineering, Johns Hopkins University, Baltimore, MD 21218, USA
- Department of Oncology, Johns Hopkins University School of Medicine, Baltimore, MD 21205, USA
- Laboratory for Computational Sensing and Robotics (LCSR), Johns Hopkins University, Baltimore, MD 21218, USA
- Sidney Kimmel Comprehensive Cancer Center (SKCCC), Johns Hopkins School of Medicine, Baltimore, MD 21205, USA
- Center for MicroPhysiological Systems (MPS), Johns Hopkins School of Medicine, Baltimore, MD 21205, USA
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Varkey J, Jonsson V, Hessman E, De Lange T, Hedenström P, Oltean M. Diagnostic yield for video capsule endoscopy in gastrointestinal graft- versus -host disease: a systematic review and metaanalysis. Scand J Gastroenterol 2023; 58:945-952. [PMID: 36740843 DOI: 10.1080/00365521.2023.2175621] [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: 11/29/2022] [Revised: 01/24/2023] [Accepted: 01/30/2023] [Indexed: 02/07/2023]
Abstract
BACKGROUND The gastrointestinal tract is the second most involved organ for graft-versus-host disease where involvement of the small intestine is present in 50% of the cases. Therefore, the use of a non-invasive investigation i.e., video capsule endoscopy (VCE) seems ideal in the diagnostic work-up, but this has never been systematically evaluated before. OBJECTIVE The aim of this systematic review was to determine the efficacy and safety of VCE, in comparison with conventional endoscopy in patients who received hematopoietic stem cell transplantation. METHOD Databases searched were PubMed, Scopus, EMBASE, and Cochrane CENTRAL. All databases were searched from their inception date until June 17, 2022. The search identified 792 publications, of which 8 studies were included in our analysis comprising of 232 unique patients. Efficacy was calculated in comparison with the golden standard i.e., histology. Risk of bias assessment was performed using the Quality Assessment of Diagnostic Accuracy Studies-2 tool. RESULTS The pooled sensitivity was higher for VCE at 0.77 (95% CI: 0.60-0.89) compared to conventional endoscopy 0.62 (95% CI: 0.47-0.75) but the difference was not statistically significant (p = 0.155, Q = 2.02). Similarly, the pooled specificity was higher for VCE at 0.68 (95% CI: 0.46-0.84) than for conventional endoscopy at 0.58 (95% CI: 0.40-0.74) but not statistically significant (p = 0.457, Q = 0.55). Moreover, concern for adverse events such as intestinal obstruction or perforation was not justified since none of the capsules were retained in the small bowel and no perforations occurred in relation to VCE. A limitation to the study is the retrospective approach seen in 50% of the studies. CONCLUSION The role of video capsule endoscopy in diagnosing or dismissing graft-versus-host disease is not yet established and requires further studies. However, the modality appears safe in this cohort.
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Affiliation(s)
- Jonas Varkey
- Department of Molecular and Clinical Medicine, Institute of Medicine, University of Gothenburg, Gothenburg, Sweden
- Sahlgrenska Intestinal Failure and Transplant Centre, Gothenburg, Sweden
- Division of Gastroenterology, Department of Medicine, Sahlgrenska University Hospital, Gothenburg, Sweden
| | - Viktor Jonsson
- Department of Public Health and Community Medicine, Institute of Medicine, Sahlgrenska Academy, University of Gothenburg, Gothenburg, Sweden
| | - Eva Hessman
- Biomedical Library, Gothenburg University Library, University of Gothenburg, Gothenburg, Sweden
| | - Thomas De Lange
- Department of Molecular and Clinical Medicine, Institute of Medicine, University of Gothenburg, Gothenburg, Sweden
- Medical Department, Sahlgrenska University Hospital-Möndal, Gothenburg, Sweden
| | - Per Hedenström
- Department of Molecular and Clinical Medicine, Institute of Medicine, University of Gothenburg, Gothenburg, Sweden
- Division of Gastroenterology, Department of Medicine, Sahlgrenska University Hospital, Gothenburg, Sweden
| | - Mihai Oltean
- Department of Surgery, Institute for Clinical Sciences, University of Gothenburg, Gothenburg, Sweden
- Transplant Institute, Sahlgrenska University Hospital, Gothenburg, Sweden
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19
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Carretero C, Bojorquez A, Eliakim R, Lazaridis N. Updates in the diagnosis and management of small-bowel Crohn's disease. Best Pract Res Clin Gastroenterol 2023; 64-65:101855. [PMID: 37652654 DOI: 10.1016/j.bpg.2023.101855] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [MESH Headings] [Track Full Text] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 04/16/2023] [Revised: 07/06/2023] [Accepted: 08/01/2023] [Indexed: 09/02/2023]
Affiliation(s)
- Cristina Carretero
- Gastroenterology Department, Clínica Universidad de Navarra, Instituto de Investigación Sanitaria de Navarra (IDISNA), Clínica Universidad de Navarra. Pio XII 36, 31004, Pamplona, Spain.
| | - Alejandro Bojorquez
- Gastroenterology Department, Clínica Universidad de Navarra, Clínica Universidad de Navarra. Pio XII 36, 31004, Pamplona, Spain.
| | - Rami Eliakim
- Department of Gastroenterology, Sheba Medical Center, Sackler School of Medicine, Tel Aviv University, Tek-Aviv, Israel.
| | - Nikolaos Lazaridis
- Royal Free Unit for Endoscopy, The Royal Free Hospital and University College London (UCL) Institute for Liver and Digestive Health, London, United Kingdom; Saint Luke's Hospital, Small Bowel Service, Agias Sofias 18, 54622, Thessaloniki, Greece.
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20
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Qi J, Ruan G, Ping Y, Xiao Z, Liu K, Cheng Y, Liu R, Zhang B, Zhi M, Chen J, Xiao F, Zhao T, Li J, Zhang Z, Zou Y, Cao Q, Nian Y, Wei Y. Development and validation of a deep learning-based approach to predict the Mayo endoscopic score of ulcerative colitis. Therap Adv Gastroenterol 2023; 16:17562848231170945. [PMID: 37251086 PMCID: PMC10214058 DOI: 10.1177/17562848231170945] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 11/16/2022] [Accepted: 04/04/2023] [Indexed: 05/31/2023] Open
Abstract
Background The ulcerative colitis (UC) Mayo endoscopy score is a useful tool for evaluating the severity of UC in patients in clinical practice. Objectives We aimed to develop and validate a deep learning-based approach to automatically predict the Mayo endoscopic score using UC endoscopic images. Design A multicenter, diagnostic retrospective study. Methods We collected 15120 colonoscopy images of 768 UC patients from two hospitals in China and developed a deep model based on a vision transformer named the UC-former. The performance of the UC-former was compared with that of six endoscopists on the internal test set. Furthermore, multicenter validation from three hospitals was also carried out to evaluate UC-former's generalization performance. Results On the internal test set, the areas under the curve of Mayo 0, Mayo 1, Mayo 2, and Mayo 3 achieved by the UC-former were 0.998, 0.984, 0.973, and 0.990, respectively. The accuracy (ACC) achieved by the UC-former was 90.8%, which is higher than that achieved by the best senior endoscopist. For three multicenter external validations, the ACC was 82.4%, 85.0%, and 83.6%, respectively. Conclusions The developed UC-former could achieve high ACC, fidelity, and stability to evaluate the severity of UC, which may provide potential application in clinical practice. Registration This clinical trial was registered at the ClinicalTrials.gov (trial registration number: NCT05336773).
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Affiliation(s)
- Jing Qi
- Department of Digital Medicine, School of
Biomedical Engineering and Imaging Medicine, Army Medical University,
Chongqing, China
| | - Guangcong Ruan
- Department of Gastroenterology, Chongqing Key
Laboratory of Digestive Malignancies, Daping Hospital, Army Medical
University (Third Military Medical University), Chongqing, China
| | - Yi Ping
- Department of Gastroenterology, Chongqing Key
Laboratory of Digestive Malignancies, Daping Hospital, Army Medical
University (Third Military Medical University), Chongqing, China
| | - Zhifeng Xiao
- Department of Gastroenterology, Chongqing Key
Laboratory of Digestive Malignancies, Daping Hospital, Army Medical
University (Third Military Medical University), Chongqing, China
| | - Kaijun Liu
- Department of Gastroenterology, Chongqing Key
Laboratory of Digestive Malignancies, Daping Hospital, Army Medical
University (Third Military Medical University), Chongqing, China
| | - Yi Cheng
- Department of Gastroenterology, Chongqing Key
Laboratory of Digestive Malignancies, Daping Hospital, Army Medical
University (Third Military Medical University), Chongqing, China
| | - Rongbei Liu
- Department of Gastroenterology, Sir Run Run
Shaw Hospital, Zhejiang University School of Medicine, Hangzhou, China
| | - Bingqiang Zhang
- Department of Gastroenterology, The First
Affiliated Hospital of Chongqing Medical University, Chongqing, China
| | - Min Zhi
- Department of Gastroenterology, Guangdong
Provincial Key Laboratory of Colorectal and Pelvic Floor Diseases, The Sixth
Affiliated Hospital of Sun Yat-Sen University, Guangzhou, China
| | - Junrong Chen
- Department of Gastroenterology, Guangdong
Provincial Key Laboratory of Colorectal and Pelvic Floor Diseases, The Sixth
Affiliated Hospital of Sun Yat-Sen University, Guangzhou, China
| | - Fang Xiao
- Department of Gastroenterology, Tongji
Hospital of Tongji Medical College, Huazhong University of Science and
Technology, Wuhan, China
| | - Tingting Zhao
- School of Basic Medicine, Army Medical
University (Third Military Medical University), Chongqing, China
| | - Jiaxing Li
- School of Basic Medicine, Army Medical
University (Third Military Medical University), Chongqing, China
| | - Zhou Zhang
- Department of Gastroenterology, Sir Run Run
Shaw Hospital, Zhejiang University School of Medicine, Hangzhou, China
| | - Yuxin Zou
- Department of Digital Medicine, School of
Biomedical Engineering and Imaging Medicine, Army Medical University,
Chongqing, China
| | - Qian Cao
- Department of Gastroenterology, Sir Run Run
Shaw Hospital, Zhejiang University School of Medicine, Hangzhou, 310016,
China
| | - Yongjian Nian
- Department of Digital Medicine, School of
Biomedical Engineering and Imaging Medicine, Army Medical University (Third
Military Medical University), Chongqing, 400038, China
| | - Yanling Wei
- Department of Gastroenterology, Chongqing Key
Laboratory of Digestive Malignancies, Daping Hospital, Army Medical
University (Third Military Medical University), 10 Changjiang Branch Road,
Chongqing, 400042, China
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21
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Oka P, Ray M, Sidhu R. Small Bowel Bleeding: Clinical Diagnosis and Management in the Elderly. Expert Rev Gastroenterol Hepatol 2023:1-8. [PMID: 37184832 DOI: 10.1080/17474124.2023.2214726] [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: 05/16/2023]
Abstract
INTRODUCTION With the global increase in life expectancy, there is an increase in gastrointestinal presentations in the elderly. Small bowel bleeding (SBB) is a cause of significant morbidity in the elderly requiring multiple hospital visits, investigations and potentially expensive therapy. AREAS COVERED In this review we will outline the different modalities which are used for the diagnosis and management of SBB. We will also discuss the common causes of SBB in the elderly. EXPERT OPINION SBB in elderly has a significant impact on the quality of life of the elderly. Larger randomized studies in the elderly are urgently required to help guide clinicians on the best and most cost-effective treatment algorithm in this challenging cohort.
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Affiliation(s)
- Priya Oka
- Department of Gastroenterology, University of Sheffield, Sheffield, UK
- Department of Infection, Immunity and Cardiovascular Disease, Royal Hallamshire Hospital, Sheffield, UK
| | - Meghna Ray
- Department of Gastroenterology, University of Sheffield, Sheffield, UK
- Department of Infection, Immunity and Cardiovascular Disease, Royal Hallamshire Hospital, Sheffield, UK
| | - Reena Sidhu
- Department of Gastroenterology, University of Sheffield, Sheffield, UK
- Department of Infection, Immunity and Cardiovascular Disease, Royal Hallamshire Hospital, Sheffield, UK
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22
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Lei II, Tompkins K, White E, Watson A, Parsons N, Noufaily A, Segui S, Wenzek H, Badreldin R, Conlin A, Arasaradnam RP. Study of capsule endoscopy delivery at scale through enhanced artificial intelligence-enabled analysis (the CESCAIL study). Colorectal Dis 2023. [PMID: 37272471 DOI: 10.1111/codi.16575] [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: 11/10/2022] [Revised: 03/05/2023] [Accepted: 03/21/2023] [Indexed: 06/06/2023]
Abstract
AIM Lower gastrointestinal (GI) diagnostics have been facing relentless capacity constraints for many years, even before the COVID-19 era. Restrictions from the COVID pandemic have resulted in a significant backlog in lower GI diagnostics. Given recent developments in deep neural networks (DNNs) and the application of artificial intelligence (AI) in endoscopy, automating capsule video analysis is now within reach. Comparable to the efficiency and accuracy of AI applications in small bowel capsule endoscopy, AI in colon capsule analysis will also improve the efficiency of video reading and address the relentless demand on lower GI services. The aim of the CESCAIL study is to determine the feasibility, accuracy and productivity of AI-enabled analysis tools (AiSPEED) for polyp detection compared with the 'gold standard': a conventional care pathway with clinician analysis. METHOD This multi-centre, diagnostic accuracy study aims to recruit 674 participants retrospectively and prospectively from centres conducting colon capsule endoscopy (CCE) as part of their standard care pathway. After the study participants have undergone CCE, the colon capsule videos will be uploaded onto two different pathways: AI-enabled video analysis and the gold standard conventional clinician analysis pathway. The reports generated from both pathways will be compared for accuracy (sensitivity and specificity). The reading time can only be compared in the prospective cohort. In addition to validating the AI tool, this study will also provide observational data concerning its use to assess the pathway execution in real-world performance. RESULTS The study is currently recruiting participants at multiple centres within the United Kingdom and is at the stage of collecting data. CONCLUSION This standard diagnostic accuracy study carries no additional risk to patients as it does not affect the standard care pathway, and hence patient care remains unaffected.
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Affiliation(s)
- Ian Io Lei
- Department of Gastroenterology, University Hospitals Coventry and Warwickshire NHS Trust, Coventry, UK
| | - Katie Tompkins
- Department of Gastroenterology, University Hospitals Coventry and Warwickshire NHS Trust, Coventry, UK
| | | | - Angus Watson
- Institute of Applied Health Sciences, University of Aberdeen, Aberdeen, UK
| | | | | | - Santi Segui
- Department of Maths and Computer Science, University of Barcelona, Barcelona, Spain
| | - Hagen Wenzek
- Institute of Applied Health Sciences, University of Aberdeen, Aberdeen, UK
| | - Rawya Badreldin
- Department of Gastroenterology, James Paget University Hospitals NHS Foundation Trust, Lowestoft, UK
| | - Abby Conlin
- Department of Gastroenterology, Northern Care Alliance NHS Foundation Trust, Salford, UK
| | - Ramesh P Arasaradnam
- Department of Gastroenterology, University Hospitals Coventry and Warwickshire NHS Trust, Coventry, UK
- Leicester Cancer Centre, University of Leicester, Leicester, UK
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23
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Ribeiro T, Mascarenhas Saraiva MJ, Afonso J, Cardoso P, Mendes F, Martins M, Andrade AP, Cardoso H, Mascarenhas Saraiva M, Ferreira J, Macedo G. Design of a Convolutional Neural Network as a Deep Learning Tool for the Automatic Classification of Small-Bowel Cleansing in Capsule Endoscopy. MEDICINA (KAUNAS, LITHUANIA) 2023; 59:medicina59040810. [PMID: 37109768 PMCID: PMC10145655 DOI: 10.3390/medicina59040810] [Citation(s) in RCA: 3] [Impact Index Per Article: 3.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Subscribe] [Scholar Register] [Received: 02/05/2023] [Revised: 04/03/2023] [Accepted: 04/06/2023] [Indexed: 04/29/2023]
Abstract
Background and objectives: Capsule endoscopy (CE) is a non-invasive method to inspect the small bowel that, like other enteroscopy methods, requires adequate small-bowel cleansing to obtain conclusive results. Artificial intelligence (AI) algorithms have been seen to offer important benefits in the field of medical imaging over recent years, particularly through the adaptation of convolutional neural networks (CNNs) to achieve more efficient image analysis. Here, we aimed to develop a deep learning model that uses a CNN to automatically classify the quality of intestinal preparation in CE. Methods: A CNN was designed based on 12,950 CE images obtained at two clinical centers in Porto (Portugal). The quality of the intestinal preparation was classified for each image as: excellent, ≥90% of the image surface with visible mucosa; satisfactory, 50-90% of the mucosa visible; and unsatisfactory, <50% of the mucosa visible. The total set of images was divided in an 80:20 ratio to establish training and validation datasets, respectively. The CNN prediction was compared with the classification established by consensus of a group of three experts in CE, currently considered the gold standard to evaluate cleanliness. Subsequently, how the CNN performed in diagnostic terms was evaluated using an independent validation dataset. Results: Among the images obtained, 3633 were designated as unsatisfactory preparation, 6005 satisfactory preparation, and 3312 with excellent preparation. When differentiating the classes of small-bowel preparation, the algorithm developed here achieved an overall accuracy of 92.1%, with a sensitivity of 88.4%, a specificity of 93.6%, a positive predictive value of 88.5%, and a negative predictive value of 93.4%. The area under the curve for the detection of excellent, satisfactory, and unsatisfactory classes was 0.98, 0.95, and 0.99, respectively. Conclusions: A CNN-based tool was developed to automatically classify small-bowel preparation for CE, and it was seen to accurately classify intestinal preparation for CE. The development of such a system could enhance the reproducibility of the scales used for such purposes.
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Affiliation(s)
- Tiago Ribeiro
- Department of Gasteroenterology, São João University Hospital, Alameda Professor Hernâni Monteiro, 4200-427 Porto, Portugal
- Gastroenterology and Hepatology, WGO Gastroenterology and Hepatology Training Centre, 4050-345 Porto, Portugal
| | - Miguel José Mascarenhas Saraiva
- Department of Gasteroenterology, São João University Hospital, Alameda Professor Hernâni Monteiro, 4200-427 Porto, Portugal
- Gastroenterology and Hepatology, WGO Gastroenterology and Hepatology Training Centre, 4050-345 Porto, Portugal
- Faculty of Medicine, University of Porto, 4200-319 Porto, Portugal
| | - João Afonso
- Department of Gasteroenterology, São João University Hospital, Alameda Professor Hernâni Monteiro, 4200-427 Porto, Portugal
- Gastroenterology and Hepatology, WGO Gastroenterology and Hepatology Training Centre, 4050-345 Porto, Portugal
| | - Pedro Cardoso
- Department of Gasteroenterology, São João University Hospital, Alameda Professor Hernâni Monteiro, 4200-427 Porto, Portugal
- Gastroenterology and Hepatology, WGO Gastroenterology and Hepatology Training Centre, 4050-345 Porto, Portugal
| | - Francisco Mendes
- Department of Gasteroenterology, São João University Hospital, Alameda Professor Hernâni Monteiro, 4200-427 Porto, Portugal
- Gastroenterology and Hepatology, WGO Gastroenterology and Hepatology Training Centre, 4050-345 Porto, Portugal
| | - Miguel Martins
- Department of Gasteroenterology, São João University Hospital, Alameda Professor Hernâni Monteiro, 4200-427 Porto, Portugal
- Gastroenterology and Hepatology, WGO Gastroenterology and Hepatology Training Centre, 4050-345 Porto, Portugal
| | - Ana Patrícia Andrade
- Department of Gasteroenterology, São João University Hospital, Alameda Professor Hernâni Monteiro, 4200-427 Porto, Portugal
- Gastroenterology and Hepatology, WGO Gastroenterology and Hepatology Training Centre, 4050-345 Porto, Portugal
- Faculty of Medicine, University of Porto, 4200-319 Porto, Portugal
| | - Hélder Cardoso
- Department of Gasteroenterology, São João University Hospital, Alameda Professor Hernâni Monteiro, 4200-427 Porto, Portugal
- Gastroenterology and Hepatology, WGO Gastroenterology and Hepatology Training Centre, 4050-345 Porto, Portugal
- Faculty of Medicine, University of Porto, 4200-319 Porto, Portugal
| | | | - João Ferreira
- Department of Mechanical Engineering, Faculty of Engineering, University of Porto, Rua Dr. Roberto Frias, 4200-465 Porto, Portugal
- INEGI-Institute of Science and Innovation in Mechanical and Industrial Engineering, 4200-465 Porto, Portugal
| | - Guilherme Macedo
- Department of Gasteroenterology, São João University Hospital, Alameda Professor Hernâni Monteiro, 4200-427 Porto, Portugal
- Gastroenterology and Hepatology, WGO Gastroenterology and Hepatology Training Centre, 4050-345 Porto, Portugal
- Faculty of Medicine, University of Porto, 4200-319 Porto, Portugal
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Levartovsky A, Eliakim R. Video Capsule Endoscopy Plays an Important Role in the Management of Crohn's Disease. Diagnostics (Basel) 2023; 13:diagnostics13081507. [PMID: 37189607 DOI: 10.3390/diagnostics13081507] [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: 03/23/2023] [Revised: 04/15/2023] [Accepted: 04/18/2023] [Indexed: 05/17/2023] Open
Abstract
Crohn's disease (CD) is a chronic inflammatory disorder characterized by a transmural inflammation that may involve any part of the gastrointestinal tract. An evaluation of small bowel involvement, allowing recognition of disease extent and severity, is important for disease management. Current guidelines recommend the use of capsule endoscopy (CE) as a first-line diagnosis method for suspected small bowel CD. CE has an essential role in monitoring disease activity in established CD patients, as it can assess response to treatment and identify high-risk patients for disease exacerbation and post-operative relapse. Moreover, several studies have shown that CE is the best tool to assess mucosal healing as part of the treat-to-target strategy in CD patients. The PillCam Crohn's capsule is a novel pan-enteric capsule which enables visualization of the whole gastrointestinal tract. It is useful to monitor pan-enteric disease activity, mucosal healing and accordingly allows for the prediction of relapse and response using a single procedure. In addition, the integration of artificial intelligence algorithms has showed improved accuracy rates for automatic ulcer detection and the ability to shorten reading times. In this review, we summarize the main indications and virtue for using CE for the evaluation of CD, as well as its implementation in clinical practice.
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Affiliation(s)
- Asaf Levartovsky
- Department of Gastroenterology, Sheba Medical Center, Sackler School of Medicine, Tel Aviv University, Tel-Aviv 69978, Israel
| | - Rami Eliakim
- Department of Gastroenterology, Sheba Medical Center, Sackler School of Medicine, Tel Aviv University, Tel-Aviv 69978, Israel
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25
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Shen MH, Huang CC, Chen YT, Tsai YJ, Liou FM, Chang SC, Phan NN. Deep Learning Empowers Endoscopic Detection and Polyps Classification: A Multiple-Hospital Study. Diagnostics (Basel) 2023; 13:diagnostics13081473. [PMID: 37189575 DOI: 10.3390/diagnostics13081473] [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: 11/21/2022] [Revised: 04/03/2023] [Accepted: 04/12/2023] [Indexed: 05/17/2023] Open
Abstract
The present study aimed to develop an AI-based system for the detection and classification of polyps using colonoscopy images. A total of about 256,220 colonoscopy images from 5000 colorectal cancer patients were collected and processed. We used the CNN model for polyp detection and the EfficientNet-b0 model for polyp classification. Data were partitioned into training, validation and testing sets, with a 70%, 15% and 15% ratio, respectively. After the model was trained/validated/tested, to evaluate its performance rigorously, we conducted a further external validation using both prospective (n = 150) and retrospective (n = 385) approaches for data collection from 3 hospitals. The deep learning model performance with the testing set reached a state-of-the-art sensitivity and specificity of 0.9709 (95% CI: 0.9646-0.9757) and 0.9701 (95% CI: 0.9663-0.9749), respectively, for polyp detection. The polyp classification model attained an AUC of 0.9989 (95% CI: 0.9954-1.00). The external validation from 3 hospital results achieved 0.9516 (95% CI: 0.9295-0.9670) with the lesion-based sensitivity and a frame-based specificity of 0.9720 (95% CI: 0.9713-0.9726) for polyp detection. The model achieved an AUC of 0.9521 (95% CI: 0.9308-0.9734) for polyp classification. The high-performance, deep-learning-based system could be used in clinical practice to facilitate rapid, efficient and reliable decisions by physicians and endoscopists.
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Affiliation(s)
- Ming-Hung Shen
- Department of Surgery, Fu Jen Catholic University Hospital, Fu Jen Catholic University, New Taipei City 24205, Taiwan
- School of Medicine, College of Medicine, Fu Jen Catholic University, New Taipei City 24205, Taiwan
| | - Chi-Cheng Huang
- Department of Surgery, Taipei Veterans General Hospital, Taipei City 11217, Taiwan
- Institute of Epidemiology and Preventive Medicine, College of Public Health, National Taiwan University, Taipei City 10663, Taiwan
| | - Yu-Tsung Chen
- Department of Internal Medicine, Fu Jen Catholic University Hospital, New Taipei City 24205, Taiwan
| | - Yi-Jian Tsai
- Division of Colorectal Surgery, Department of Surgery, Fu Jen Catholic University Hospital, New Taipei City 24205, Taiwan
- Graduate Institute of Biomedical Electronics and Bioinformatics, Department of Electrical Engineering, National Taiwan University, Taipei City 10663, Taiwan
| | | | - Shih-Chang Chang
- Division of Colorectal Surgery, Department of Surgery, Cathay General Hospital, Taipei City 106443, Taiwan
| | - Nam Nhut Phan
- Bioinformatics and Biostatistics Core, Centre of Genomic and Precision Medicine, National Taiwan University, Taipei City 10055, Taiwan
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Chung J, Oh DJ, Park J, Kim SH, Lim YJ. Automatic Classification of GI Organs in Wireless Capsule Endoscopy Using a No-Code Platform-Based Deep Learning Model. Diagnostics (Basel) 2023; 13:diagnostics13081389. [PMID: 37189489 DOI: 10.3390/diagnostics13081389] [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: 02/22/2023] [Revised: 04/03/2023] [Accepted: 04/10/2023] [Indexed: 05/17/2023] Open
Abstract
The first step in reading a capsule endoscopy (CE) is determining the gastrointestinal (GI) organ. Because CE produces too many inappropriate and repetitive images, automatic organ classification cannot be directly applied to CE videos. In this study, we developed a deep learning algorithm to classify GI organs (the esophagus, stomach, small bowel, and colon) using a no-code platform, applied it to CE videos, and proposed a novel method to visualize the transitional area of each GI organ. We used training data (37,307 images from 24 CE videos) and test data (39,781 images from 30 CE videos) for model development. This model was validated using 100 CE videos that included "normal", "blood", "inflamed", "vascular", and "polypoid" lesions. Our model achieved an overall accuracy of 0.98, precision of 0.89, recall of 0.97, and F1 score of 0.92. When we validated this model relative to the 100 CE videos, it produced average accuracies for the esophagus, stomach, small bowel, and colon of 0.98, 0.96, 0.87, and 0.87, respectively. Increasing the AI score's cut-off improved most performance metrics in each organ (p < 0.05). To locate a transitional area, we visualized the predicted results over time, and setting the cut-off of the AI score to 99.9% resulted in a better intuitive presentation than the baseline. In conclusion, the GI organ classification AI model demonstrated high accuracy on CE videos. The transitional area could be more easily located by adjusting the cut-off of the AI score and visualization of its result over time.
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Affiliation(s)
- Joowon Chung
- Department of Internal Medicine, Nowon Eulji Medical Center, Eulji University School of Medicine, Seoul 01830, Republic of Korea
| | - Dong Jun Oh
- Department of Internal Medicine, Dongguk University Ilsan Hospital, Dongguk University College of Medicine, Goyang 10326, Republic of Korea
| | - Junseok Park
- Department of Internal Medicine, Digestive Disease Center, Institute for Digestive Research, Soonchunhyang University College of Medicine, Seoul 04401, Republic of Korea
| | - Su Hwan Kim
- Department of Internal Medicine, Seoul Metropolitan Government Seoul National University Boramae Medical Center, Seoul 07061, Republic of Korea
| | - Yun Jeong Lim
- Department of Internal Medicine, Dongguk University Ilsan Hospital, Dongguk University College of Medicine, Goyang 10326, Republic of Korea
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Clinicians’ Guide to Artificial Intelligence in Colon Capsule Endoscopy—Technology Made Simple. Diagnostics (Basel) 2023; 13:diagnostics13061038. [PMID: 36980347 PMCID: PMC10047552 DOI: 10.3390/diagnostics13061038] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 12/29/2022] [Revised: 02/07/2023] [Accepted: 02/21/2023] [Indexed: 03/12/2023] Open
Abstract
Artificial intelligence (AI) applications have become widely popular across the healthcare ecosystem. Colon capsule endoscopy (CCE) was adopted in the NHS England pilot project following the recent COVID pandemic’s impact. It demonstrated its capability to relieve the national backlog in endoscopy. As a result, AI-assisted colon capsule video analysis has become gastroenterology’s most active research area. However, with rapid AI advances, mastering these complex machine learning concepts remains challenging for healthcare professionals. This forms a barrier for clinicians to take on this new technology and embrace the new era of big data. This paper aims to bridge the knowledge gap between the current CCE system and the future, fully integrated AI system. The primary focus is on simplifying the technical terms and concepts in machine learning. This will hopefully address the general “fear of the unknown in AI” by helping healthcare professionals understand the basic principle of machine learning in capsule endoscopy and apply this knowledge in their future interactions and adaptation to AI technology. It also summarises the evidence of AI in CCE and its impact on diagnostic pathways. Finally, it discusses the unintended consequences of using AI, ethical challenges, potential flaws, and bias within clinical settings.
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Kim HJ, Sritandi W, Xiong Z, Ho JS. Bioelectronic devices for light-based diagnostics and therapies. BIOPHYSICS REVIEWS 2023; 4:011304. [PMID: 38505817 PMCID: PMC10903427 DOI: 10.1063/5.0102811] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Subscribe] [Scholar Register] [Received: 06/13/2022] [Accepted: 12/28/2022] [Indexed: 03/21/2024]
Abstract
Light has broad applications in medicine as a tool for diagnosis and therapy. Recent advances in optical technology and bioelectronics have opened opportunities for wearable, ingestible, and implantable devices that use light to continuously monitor health and precisely treat diseases. In this review, we discuss recent progress in the development and application of light-based bioelectronic devices. We summarize the key features of the technologies underlying these devices, including light sources, light detectors, energy storage and harvesting, and wireless power and communications. We investigate the current state of bioelectronic devices for the continuous measurement of health and on-demand delivery of therapy. Finally, we highlight major challenges and opportunities associated with light-based bioelectronic devices and discuss their promise for enabling digital forms of health care.
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Affiliation(s)
| | - Weni Sritandi
- Department of Electrical and Computer Engineering, National University of Singapore, Singapore
| | | | - John S. Ho
- Author to whom correspondence should be addressed:
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29
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Xia H, Peng S, Huang S, Jiang J, Zeng X, Zhang H, Pu X, Zou K, Lü Y, Xu H, Peng Y, Lü M, Tang X. A systematic evaluation of methodological and reporting quality of meta-analysis published in the field of gastrointestinal endoscopy. Surg Endosc 2023; 37:807-816. [PMID: 36050611 DOI: 10.1007/s00464-022-09570-7] [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: 03/12/2022] [Accepted: 08/15/2022] [Indexed: 02/08/2023]
Abstract
BACKGROUND To evaluate the methodological and reporting quality of published meta-analyses (MAs) in four major gastrointestinal endoscopic journals, and identify the predicted factors for high quality. METHODS A systematic search was performed in PubMed to identify MAs from 1, January, 2016 to 31, December, 2020 in four major gastrointestinal endoscopic journals (including Digestive Endoscopy, Gastrointestinal Endoscopy, Surgical Endoscopy, and Endoscopy). We collected the characteristics of MAs after filtering unqualified articles, and assessed methodological and reporting qualities for eligible articles by AMSTAR tool and PRISMA checklist, respectively. Logistic regression was used for identifying predictive factors for high quality. RESULTS A total of 289 MAs were identified after screening by predefined inclusion and exclusion criteria. The scores (mean ± SD) of AMSTAR and PRISMA were 7.73 ± 1.11 and 22.90 ± 1.85, respectively. In PRISMA checklist, some items had less than 50% complete adherence, including item 2 (structured summary), items 5 (protocol and registration), items 12 and 19 (risk of bias in studies), item 27 (funding support). Item 1 (a priori design), item 4 (gray literature research), item 5 (list of included and excluded) were inferior to 50% adherence in AMSTAR tool. We found the predictive factors for high quality through logistic regression analysis: a priori design and funding support were associated with methodological quality. Protocol and registration influenced the methodological and reporting quality closely. CONCLUSION In general, qualities on the methodology and the reporting of MAs published in the gastrointestinal endoscopic journals are good, but both of which still potentially need further improvement.
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Affiliation(s)
- Huifang Xia
- Department of Gastroenterology, The Affiliated Hospital of Southwest Medical University, Street Taiping No.25, Region Jiangyang, Luzhou, 646099, Sichuan Province, China
| | - Shicheng Peng
- Department of Gastroenterology, The Affiliated Hospital of Southwest Medical University, Street Taiping No.25, Region Jiangyang, Luzhou, 646099, Sichuan Province, China
| | - Shu Huang
- Department of Gastroenterology, The People's Hospital of Lianshui, Huaian, China
| | - Jiao Jiang
- Department of Gastroenterology, The Affiliated Hospital of Southwest Medical University, Street Taiping No.25, Region Jiangyang, Luzhou, 646099, Sichuan Province, China
| | - Xinyi Zeng
- Department of Gastroenterology, The Affiliated Hospital of Southwest Medical University, Street Taiping No.25, Region Jiangyang, Luzhou, 646099, Sichuan Province, China
| | - Han Zhang
- Department of Gastroenterology, The Affiliated Hospital of Southwest Medical University, Street Taiping No.25, Region Jiangyang, Luzhou, 646099, Sichuan Province, China
| | - Xinxin Pu
- Department of Gastroenterology, The Affiliated Hospital of Southwest Medical University, Street Taiping No.25, Region Jiangyang, Luzhou, 646099, Sichuan Province, China
| | - Kang Zou
- Department of Gastroenterology, The Affiliated Hospital of Southwest Medical University, Street Taiping No.25, Region Jiangyang, Luzhou, 646099, Sichuan Province, China
| | - Yingqin Lü
- Department of Gastroenterology, The Affiliated Hospital of Southwest Medical University, Street Taiping No.25, Region Jiangyang, Luzhou, 646099, Sichuan Province, China
| | - Huan Xu
- Department of Gastroenterology, The Affiliated Hospital of Southwest Medical University, Street Taiping No.25, Region Jiangyang, Luzhou, 646099, Sichuan Province, China
| | - Yan Peng
- Department of Gastroenterology, The Affiliated Hospital of Southwest Medical University, Street Taiping No.25, Region Jiangyang, Luzhou, 646099, Sichuan Province, China
| | - Muhan Lü
- Department of Gastroenterology, The Affiliated Hospital of Southwest Medical University, Street Taiping No.25, Region Jiangyang, Luzhou, 646099, Sichuan Province, China
| | - Xiaowei Tang
- Department of Gastroenterology, The Affiliated Hospital of Southwest Medical University, Street Taiping No.25, Region Jiangyang, Luzhou, 646099, Sichuan Province, China.
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30
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Stroke mortality prediction using machine learning: systematic review. J Neurol Sci 2023; 444:120529. [PMID: 36580703 DOI: 10.1016/j.jns.2022.120529] [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: 07/21/2022] [Revised: 11/30/2022] [Accepted: 12/18/2022] [Indexed: 12/24/2022]
Abstract
BACKGROUND AND AIMS Accurate prognostication of stroke may help in appropriate therapy and rehabilitation planning. In the past few years, several machine learning (ML) algorithms were applied for prediction of stroke outcomes. We aimed to examine the performance of machine learning-based models for the prediction of mortality after stroke, as well as to identify the most prominent factors for mortality. MATERIALS AND METHODS We searched MEDLINE/PubMed and Web of Science databases for original publications on machine learning applications in stroke mortality prediction, published between January 1, 2011, and October 27, 2022. Risk of bias and applicability were evaluated using the tailored QUADAS-2 tool. RESULTS Of the 1015 studies retrieved, 28 studies were included. Twenty-Five studies were retrospective. The ML models demonstrated a favorable range of AUC for mortality prediction (0.67-0.98). In most of the articles, the models were applied for short-term post stroke mortality. The number of explanatory features used in the models to predict mortality ranged from 5 to 200, with substantial overlap in the variables included. Age, high BMI and high NIHSS score were identified as important predictors for mortality. Almost all studies had a high risk of bias in at least one category and concerns regarding applicability. CONCLUSION Using machine learning, data available at the time of admission may aid in stroke mortality prediction. Notwithstanding, current research is based on few preliminary works with high risk of bias and high heterogeneity. Thus, future prospective, multicenter studies with standardized reports are crucial to firmly establish the usefulness of the algorithms in stroke prognostication.
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Ribeiro T, Mascarenhas M, Afonso J, Cardoso H, Andrade P, Lopes S, Ferreira J, Mascarenhas Saraiva M, Macedo G. Artificial intelligence and colon capsule endoscopy: Automatic detection of ulcers and erosions using a convolutional neural network. J Gastroenterol Hepatol 2022; 37:2282-2288. [PMID: 36181257 DOI: 10.1111/jgh.16011] [Citation(s) in RCA: 7] [Impact Index Per Article: 3.5] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 07/22/2022] [Revised: 09/12/2022] [Accepted: 09/25/2022] [Indexed: 12/13/2022]
Abstract
BACKGROUND AND AIM Colon capsule endoscopy (CCE) has become a minimally invasive alternative for conventional colonoscopy. Nevertheless, each CCE exam produces between 50 000 and 100 000 frames, making its analysis time-consuming and prone to errors. Convolutional neural networks (CNNs) are a type of artificial intelligence (AI) architecture with high performance in image analysis. This study aims to develop a CNN model for the identification of colonic ulcers and erosions in CCE images. METHODS A CNN model was designed using a database of CCE images. A total of 124 CCE exams performed between 2010 and 2020 in two centers were reviewed. For CNN development, a total of 37 319 images were extracted, 33 749 showing normal colonic mucosa and 3570 showing colonic ulcers and erosions. Datasets for CNN training, validation, and testing were created. The performance of the algorithm was evaluated regarding its sensitivity, specificity, positive and negative predictive values, accuracy, and area under the curve. RESULTS The network had a sensitivity of 96.9% and a specificity of 99.9% specific for the detection of colonic ulcers and erosions. The algorithm had an overall accuracy of 99.6%. The area under the curve was 1.00. The CNN had an image processing capacity of 90 frames per second. CONCLUSIONS The developed algorithm is the first CNN-based model to accurately detect ulcers and erosions in CCE images, also providing a good image processing performance. The development of these AI systems may contribute to improve both the diagnostic and time efficiency of CCE exams, facilitating CCE adoption to routine clinical practice.
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Affiliation(s)
- Tiago Ribeiro
- Department of Gastroenterology, São João University Hospital, Porto, Portugal.,World Gastroenterology Organisation Gastroenterology and Hepatology Training Center, Porto, Portugal
| | - Miguel Mascarenhas
- Department of Gastroenterology, São João University Hospital, Porto, Portugal.,World Gastroenterology Organisation Gastroenterology and Hepatology Training Center, Porto, Portugal.,Faculty of Medicine of the University of Porto, Porto, Portugal
| | - João Afonso
- Department of Gastroenterology, São João University Hospital, Porto, Portugal.,World Gastroenterology Organisation Gastroenterology and Hepatology Training Center, Porto, Portugal
| | - Hélder Cardoso
- Department of Gastroenterology, São João University Hospital, Porto, Portugal.,World Gastroenterology Organisation Gastroenterology and Hepatology Training Center, Porto, Portugal.,Faculty of Medicine of the University of Porto, Porto, Portugal
| | - Patrícia Andrade
- Department of Gastroenterology, São João University Hospital, Porto, Portugal.,World Gastroenterology Organisation Gastroenterology and Hepatology Training Center, Porto, Portugal.,Faculty of Medicine of the University of Porto, Porto, Portugal
| | - Susana Lopes
- Department of Gastroenterology, São João University Hospital, Porto, Portugal.,World Gastroenterology Organisation Gastroenterology and Hepatology Training Center, Porto, Portugal.,Faculty of Medicine of the University of Porto, Porto, Portugal
| | - João Ferreira
- Faculty of Engineering of the University of Porto, Porto, Portugal.,INEGI-Institute of Science and Innovation in Mechanical and Industrial Engineering, Porto, Portugal
| | | | - Guilherme Macedo
- Department of Gastroenterology, São João University Hospital, Porto, Portugal.,World Gastroenterology Organisation Gastroenterology and Hepatology Training Center, Porto, Portugal.,Faculty of Medicine of the University of Porto, Porto, Portugal
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Yang W, Li Z, Liu R, Tong X, Wang W, Xu D, Gao S. Application of capsule endoscopy in patients with chronic and recurrent abdominal pain: Abbreviated running title: capsule endoscopy in abdominal pain. Med Eng Phys 2022; 110:103901. [PMID: 36241495 DOI: 10.1016/j.medengphy.2022.103901] [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: 04/24/2022] [Revised: 08/15/2022] [Accepted: 10/02/2022] [Indexed: 12/24/2022]
Abstract
OBJECTIVE The incidence of chronic and recurrent abdominal pain increases every year, while the diagnosis is still unsatisfactory even after a number of check-ups. This study aimed to evaluate the diagnosis value of capsule endoscopy in patients suffering from chronic and recurrent abdominal pain. METHODS A retrospective case study was performed in 80 chronic and recurrent abdominal pain patients at Xiangyang Central Hospital from January 2013 to November 2017. Meanwhile, diagnoses by capsule endoscopy were collected for analysis. RESULTS Abnormal findings were found in 54 of 80 (67.5%) patients. The findings in chronic and recurrent abdominal pain patients include small intestinal erosion and congestion, small intestinal ulcers, small intestinal parasites, small intestinal vascular malformations, small intestinal polyps, small intestinal diverticulum, and small intestinal lymphangiectasia. There were no immediate significant side effects without being reported up to 1 month after ingestion of the capsule. The capsule was evacuated by all patients. CONCLUSIONS Capsule endoscopy has a great value in the diagnosis of chronic and recurrent abdominal pain with satisfactory safety and less pain for patients. Inflammatory lesions and ulcers in the small intestine account for the majority of positive findings in these patients.
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Affiliation(s)
- Wei Yang
- Department of Gastroenterology, Xiangyang Central Hospital, Affiliated Hospital of Hubei University of Arts and Science, Xiangyang, Hubei Province, 441021, China
| | - Zheng Li
- Department of Gastroenterology, Xiangyang Central Hospital, Affiliated Hospital of Hubei University of Arts and Science, Xiangyang, Hubei Province, 441021, China
| | - Rui Liu
- Medical School of Xiangyang Vocational and Technical College
| | - Xudong Tong
- Department of Gastroenterology, Xiangyang Central Hospital, Affiliated Hospital of Hubei University of Arts and Science, Xiangyang, Hubei Province, 441021, China
| | - Wei Wang
- Department of Gastroenterology, Xiangyang Central Hospital, Affiliated Hospital of Hubei University of Arts and Science, Xiangyang, Hubei Province, 441021, China
| | - Dongqiang Xu
- Department of Gastroenterology, Xiangyang Central Hospital, Affiliated Hospital of Hubei University of Arts and Science, Xiangyang, Hubei Province, 441021, China
| | - Shan Gao
- Department of Gastroenterology, Xiangyang Central Hospital, Affiliated Hospital of Hubei University of Arts and Science, Xiangyang, Hubei Province, 441021, China.
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Endoscopy, video capsule endoscopy, and biopsy for automated celiac disease detection: A review. Biocybern Biomed Eng 2022. [DOI: 10.1016/j.bbe.2022.12.002] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 12/23/2022]
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34
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Messmann H, Bisschops R, Antonelli G, Libânio D, Sinonquel P, Abdelrahim M, Ahmad OF, Areia M, Bergman JJGHM, Bhandari P, Boskoski I, Dekker E, Domagk D, Ebigbo A, Eelbode T, Eliakim R, Häfner M, Haidry RJ, Jover R, Kaminski MF, Kuvaev R, Mori Y, Palazzo M, Repici A, Rondonotti E, Rutter MD, Saito Y, Sharma P, Spada C, Spadaccini M, Veitch A, Gralnek IM, Hassan C, Dinis-Ribeiro M. Expected value of artificial intelligence in gastrointestinal endoscopy: European Society of Gastrointestinal Endoscopy (ESGE) Position Statement. Endoscopy 2022; 54:1211-1231. [PMID: 36270318 DOI: 10.1055/a-1950-5694] [Citation(s) in RCA: 40] [Impact Index Per Article: 20.0] [Reference Citation Analysis] [Abstract] [MESH Headings] [Track Full Text] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Indexed: 01/10/2023]
Abstract
This ESGE Position Statement defines the expected value of artificial intelligence (AI) for the diagnosis and management of gastrointestinal neoplasia within the framework of the performance measures already defined by ESGE. This is based on the clinical relevance of the expected task and the preliminary evidence regarding artificial intelligence in artificial or clinical settings. MAIN RECOMMENDATIONS:: (1) For acceptance of AI in assessment of completeness of upper GI endoscopy, the adequate level of mucosal inspection with AI should be comparable to that assessed by experienced endoscopists. (2) For acceptance of AI in assessment of completeness of upper GI endoscopy, automated recognition and photodocumentation of relevant anatomical landmarks should be obtained in ≥90% of the procedures. (3) For acceptance of AI in the detection of Barrett's high grade intraepithelial neoplasia or cancer, the AI-assisted detection rate for suspicious lesions for targeted biopsies should be comparable to that of experienced endoscopists with or without advanced imaging techniques. (4) For acceptance of AI in the management of Barrett's neoplasia, AI-assisted selection of lesions amenable to endoscopic resection should be comparable to that of experienced endoscopists. (5) For acceptance of AI in the diagnosis of gastric precancerous conditions, AI-assisted diagnosis of atrophy and intestinal metaplasia should be comparable to that provided by the established biopsy protocol, including the estimation of extent, and consequent allocation to the correct endoscopic surveillance interval. (6) For acceptance of artificial intelligence for automated lesion detection in small-bowel capsule endoscopy (SBCE), the performance of AI-assisted reading should be comparable to that of experienced endoscopists for lesion detection, without increasing but possibly reducing the reading time of the operator. (7) For acceptance of AI in the detection of colorectal polyps, the AI-assisted adenoma detection rate should be comparable to that of experienced endoscopists. (8) For acceptance of AI optical diagnosis (computer-aided diagnosis [CADx]) of diminutive polyps (≤5 mm), AI-assisted characterization should match performance standards for implementing resect-and-discard and diagnose-and-leave strategies. (9) For acceptance of AI in the management of polyps ≥ 6 mm, AI-assisted characterization should be comparable to that of experienced endoscopists in selecting lesions amenable to endoscopic resection.
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Affiliation(s)
- Helmut Messmann
- III Medizinische Klinik, Universitatsklinikum Augsburg, Augsburg, Germany
| | - Raf Bisschops
- Department of Gastroenterology and Hepatology, Catholic University of Leuven (KUL), TARGID, University Hospital Leuven, Leuven, Belgium
| | - Giulio Antonelli
- Gastroenterology and Digestive Endoscopy Unit, Ospedale dei Castelli Hospital, Ariccia, Rome, Italy
- Department of Anatomical, Histological, Forensic Medicine and Orthopedics Sciences, Sapienza University of Rome, Italy
| | - Diogo Libânio
- Department of Gastroenterology, Porto Comprehensive Cancer Center, and RISE@CI-IPOP (Health Research Network), Porto, Portugal
- MEDCIDS, Faculty of Medicine, University of Porto, Porto, Portugal
| | - Pieter Sinonquel
- Department of Gastroenterology and Hepatology, Catholic University of Leuven (KUL), TARGID, University Hospital Leuven, Leuven, Belgium
| | - Mohamed Abdelrahim
- Endoscopy Department, Portsmouth Hospitals University NHS Trust, Portsmouth, UK
| | - Omer F Ahmad
- Wellcome/EPSRC Centre for Interventional and Surgical Sciences, University College London Hospital, London, UK
- Division of Surgery and Interventional Sciences, University College London Hospital, London, UK
- Gastrointestinal Services, University College London Hospital, London, UK
| | - Miguel Areia
- Gastroenterology Department, Portuguese Oncology Institute of Coimbra, Coimbra, Portugal
| | | | - Pradeep Bhandari
- Endoscopy Department, Portsmouth Hospitals University NHS Trust, Portsmouth, UK
| | - Ivo Boskoski
- Digestive Endoscopy Unit, Fondazione Policlinico Universitario Agostino Gemelli IRCCS, Rome, Italy
| | - Evelien Dekker
- Department of Gastroenterology and Hepatology, Amsterdam UMC, Amsterdam, The Netherlands
| | - Dirk Domagk
- Department of Medicine I, Josephs-Hospital Warendorf, Academic Teaching Hospital, University of Muenster, Warendorf, Germany
| | - Alanna Ebigbo
- III Medizinische Klinik, Universitatsklinikum Augsburg, Augsburg, Germany
| | - Tom Eelbode
- Department of Electrical Engineering (ESAT/PSI), Medical Imaging Research Center, KU Leuven, Leuven, Belgium
| | - Rami Eliakim
- Department of Gastroenterology, Sheba Medical Center Tel Hashomer & Sackler School of Medicine, Tel-Aviv University, Ramat Gan, Israel
| | - Michael Häfner
- 2nd Medical Department, Barmherzige Schwestern Krankenhaus, Vienna, Austria
| | - Rehan J Haidry
- Wellcome/EPSRC Centre for Interventional and Surgical Sciences, University College London Hospital, London, UK
- Division of Surgery and Interventional Sciences, University College London Hospital, London, UK
| | - Rodrigo Jover
- Servicio de Gastroenterología, Hospital General Universitario Dr. Balmis, Instituto de Investigación Biomédica de Alicante ISABIAL, Departamento de Medicina Clínica, Universidad Miguel Hernández, Alicante, Spain
| | - Michal F Kaminski
- Clinical Effectiveness Research Group, University of Oslo, Oslo, Norway
- Department of Gastroenterology, Hepatology and Clinical Oncology, Centre of Postgraduate Medical Education, Warsaw, Poland
- Department of Oncological Gastroenterology and Department of Cancer Prevention, Maria Sklodowska-Curie National Research Institute of Oncology, Warsaw, Poland
| | - Roman Kuvaev
- Endoscopy Department, Yaroslavl Regional Cancer Hospital, Yaroslavl, Russian Federation
- Department of Gastroenterology, Faculty of Additional Professional Education, N.A. Pirogov Russian National Research Medical University, Moscow, Russian Federation
| | - Yuichi Mori
- Clinical Effectiveness Research Group, University of Oslo, Oslo, Norway
- Digestive Disease Center, Showa University Northern Yokohama Hospital, Yokohama, Japan
| | | | - Alessandro Repici
- Department of Biomedical Sciences, Humanitas University, Rozzano, Milan, Italy
- IRCCS Humanitas Research Hospital, Rozzano, Milan, Italy
| | | | - Matthew D Rutter
- North Tees and Hartlepool NHS Foundation Trust, Stockton-on-Tees, UK
- Population Health Sciences Institute, Newcastle University, Newcastle, UK
| | - Yutaka Saito
- Endoscopy Division, National Cancer Center Hospital, Tokyo, Japan
| | - Prateek Sharma
- Gastroenterology and Hepatology Division, University of Kansas School of Medicine, Kansas, USA
- Kansas City VA Medical Center, Kansas City, USA
| | - Cristiano Spada
- Digestive Endoscopy Unit, Fondazione Policlinico Universitario Agostino Gemelli IRCCS, Rome, Italy
- Digestive Endoscopy, Fondazione Poliambulanza Istituto Ospedaliero, Brescia, Italy
| | - Marco Spadaccini
- Department of Biomedical Sciences, Humanitas University, Rozzano, Milan, Italy
- IRCCS Humanitas Research Hospital, Rozzano, Milan, Italy
| | - Andrew Veitch
- Department of Gastroenterology, Royal Wolverhampton Hospitals NHS Trust, Wolverhampton, UK
| | - Ian M Gralnek
- Ellen and Pinchas Mamber Institute of Gastroenterology and Hepatology, Emek Medical Center, Afula, Israel
- Rappaport Faculty of Medicine, Technion Israel Institute of Technology, Haifa, Israel
| | - Cesare Hassan
- Department of Biomedical Sciences, Humanitas University, Rozzano, Milan, Italy
- IRCCS Humanitas Research Hospital, Rozzano, Milan, Italy
| | - Mario Dinis-Ribeiro
- Department of Gastroenterology, Porto Comprehensive Cancer Center, and RISE@CI-IPOP (Health Research Network), Porto, Portugal
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Su Q, Wang F, Chen D, Chen G, Li C, Wei L. Deep convolutional neural networks with ensemble learning and transfer learning for automated detection of gastrointestinal diseases. Comput Biol Med 2022; 150:106054. [PMID: 36244302 DOI: 10.1016/j.compbiomed.2022.106054] [Citation(s) in RCA: 15] [Impact Index Per Article: 7.5] [Reference Citation Analysis] [Abstract] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 07/25/2022] [Revised: 08/12/2022] [Accepted: 08/27/2022] [Indexed: 11/22/2022]
Abstract
Gastrointestinal (GI) diseases are serious health threats to human health, and the related detection and treatment of gastrointestinal diseases place a huge burden on medical institutions. Imaging-based methods are one of the most important approaches for automated detection of gastrointestinal diseases. Although deep neural networks have shown impressive performance in a number of imaging tasks, its application to detection of gastrointestinal diseases has not been sufficiently explored. In this study, we propose a novel and practical method to detect gastrointestinal disease from wireless capsule endoscopy (WCE) images by convolutional neural networks. The proposed method utilizes three backbone networks modified and fine-tuned by transfer learning as the feature extractors, and an integrated classifier using ensemble learning is trained to detection of gastrointestinal diseases. The proposed method outperforms existing computational methods on the benchmark dataset. The case study results show that the proposed method captures discriminative information of wireless capsule endoscopy images. This work shows the potential of using deep learning-based computer vision models for effective GI disease screening.
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Affiliation(s)
- Qiaosen Su
- School of Software, Shandong University, Jinan, China; Joint SDU-NTU Centre for Artificial Intelligence Research (C-FAIR), Shandong University, Jinan, China
| | - Fengsheng Wang
- School of Software, Shandong University, Jinan, China; Joint SDU-NTU Centre for Artificial Intelligence Research (C-FAIR), Shandong University, Jinan, China
| | | | | | - Chao Li
- Beidahuang Industry Group General Hospital, Harbin, China.
| | - Leyi Wei
- School of Software, Shandong University, Jinan, China; Joint SDU-NTU Centre for Artificial Intelligence Research (C-FAIR), Shandong University, Jinan, China.
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Ortiz Zúñiga O, Fernández Esparrach MG, Daca M, Pellisé M. Artificial intelligence in gastrointestinal endoscopy - Evolution to a new era. REVISTA ESPANOLA DE ENFERMEDADES DIGESTIVAS 2022; 114:605-615. [PMID: 35770604 DOI: 10.17235/reed.2022.8961/2022] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [MESH Headings] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 06/15/2023]
Abstract
Artificial intelligence (AI) systems based on machine learning have evolved in the last few years with an increasing applicability in gastrointestinal endoscopy. Thanks to AI, an image (input) can be transformed into a clinical decision (output). Although AI systems have been initially studied to improve detection (CADe) and characterization of colorectal lesions (CADx), other indications are being currently investigated as detection of blind spots, scope guidance, or delineation/measurement of lesions. The objective of these review is to summarize the current evidence on applicability of AI systems in gastrointestinal endoscopy, highlight strengths and limitations of the technology and review regulatory and ethical aspects for its general implementation in gastrointestinal endoscopy.
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Affiliation(s)
| | | | - María Daca
- Gastroenterología, Hospital Clínic Barcelona, España
| | - María Pellisé
- Gastroenterología, Hospital Clínic Barcelona, España
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B S, P A. Recent developments in wireless capsule endoscopy imaging: Compression and summarization techniques. Comput Biol Med 2022; 149:106087. [PMID: 36115301 DOI: 10.1016/j.compbiomed.2022.106087] [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: 05/11/2022] [Revised: 08/10/2022] [Accepted: 09/03/2022] [Indexed: 11/03/2022]
Abstract
Wireless capsule endoscopy (WCE) can be viewed as an innovative technology introduced in the medical domain to directly visualize the digestive system using a battery-powered electronic capsule. It is considered a desirable substitute for conventional digestive tract diagnostic methods for a comfortable and painless inspection. Despite many benefits, WCE results in poor video quality due to low frame resolution and diagnostic accuracy. Many research groups have presented diversified, low-complexity compression techniques to economize battery power consumed in the radio-frequency transmission of the captured video, which allows for capturing the images at high resolution. Many vision-based computational methods have been developed to improve the diagnostic yield. These methods include approaches for automatically detecting abnormalities and reducing the amount of time needed for video analysis. Though various research works have been put forth in the WCE imaging field, there is still a wide gap between the existing techniques and the current needs. Hence, this article systematically reviews recent WCE video compression and summarization techniques. The review's objectives are as follows: First, to provide the details of the requirement, challenges and design percepts for the low complexity WCE video compressor. Second, to discuss the most recent compression methods, emphasizing simple distributed video coding methods. Next, to review the most recent summarization techniques and the significance of using deep neural networks. Further, this review aims to provide a quantitative analysis of the state-of-the-art methods along with their advantages and drawbacks. At last, to discuss existing problems and possible future directions for building a robust WCE imaging framework.
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Affiliation(s)
- Sushma B
- Image Processing and Analysis Lab (iPAL), Department of Electronics and Communication Engineering, National Institute of Technology Karnataka-Surathkal, Mangalore 575025, Karnataka, India; Department of Electronics and Communication Engineering, CMR Institute of Technology, Bengaluru 560037, Karnataka, India.
| | - Aparna P
- Image Processing and Analysis Lab (iPAL), Department of Electronics and Communication Engineering, National Institute of Technology Karnataka-Surathkal, Mangalore 575025, Karnataka, India
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Moen S, Vuik FER, Kuipers EJ, Spaander MCW. Artificial Intelligence in Colon Capsule Endoscopy—A Systematic Review. Diagnostics (Basel) 2022; 12:diagnostics12081994. [PMID: 36010345 PMCID: PMC9407289 DOI: 10.3390/diagnostics12081994] [Citation(s) in RCA: 7] [Impact Index Per Article: 3.5] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 07/06/2022] [Revised: 08/05/2022] [Accepted: 08/10/2022] [Indexed: 12/17/2022] Open
Abstract
Background and aims: The applicability of colon capsule endoscopy in daily practice is limited by the accompanying labor-intensive reviewing time and the risk of inter-observer variability. Automated reviewing of colon capsule endoscopy images using artificial intelligence could be timesaving while providing an objective and reproducible outcome. This systematic review aims to provide an overview of the available literature on artificial intelligence for reviewing colonic mucosa by colon capsule endoscopy and to assess the necessary action points for its use in clinical practice. Methods: A systematic literature search of literature published up to January 2022 was conducted using Embase, Web of Science, OVID MEDLINE and Cochrane CENTRAL. Studies reporting on the use of artificial intelligence to review second-generation colon capsule endoscopy colonic images were included. Results: 1017 studies were evaluated for eligibility, of which nine were included. Two studies reported on computed bowel cleansing assessment, five studies reported on computed polyp or colorectal neoplasia detection and two studies reported on other implications. Overall, the sensitivity of the proposed artificial intelligence models were 86.5–95.5% for bowel cleansing and 47.4–98.1% for the detection of polyps and colorectal neoplasia. Two studies performed per-lesion analysis, in addition to per-frame analysis, which improved the sensitivity of polyp or colorectal neoplasia detection to 81.3–98.1%. By applying a convolutional neural network, the highest sensitivity of 98.1% for polyp detection was found. Conclusion: The use of artificial intelligence for reviewing second-generation colon capsule endoscopy images is promising. The highest sensitivity of 98.1% for polyp detection was achieved by deep learning with a convolutional neural network. Convolutional neural network algorithms should be optimized and tested with more data, possibly requiring the set-up of a large international colon capsule endoscopy database. Finally, the accuracy of the optimized convolutional neural network models need to be confirmed in a prospective setting.
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Martinez-Millana A, Saez-Saez A, Tornero-Costa R, Azzopardi-Muscat N, Traver V, Novillo-Ortiz D. Artificial intelligence and its impact on the domains of universal health coverage, health emergencies and health promotion: An overview of systematic reviews. Int J Med Inform 2022; 166:104855. [PMID: 35998421 PMCID: PMC9551134 DOI: 10.1016/j.ijmedinf.2022.104855] [Citation(s) in RCA: 4] [Impact Index Per Article: 2.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 05/27/2022] [Revised: 08/01/2022] [Accepted: 08/11/2022] [Indexed: 12/04/2022]
Abstract
An overview of systematic reviews on the application of AI including 129 studies. AI use is prominent in Universal Health Coverage, featuring image analysis in neoplasms. Half of the reviews did not evaluate validation procedures nor reporting guidelines. Risk of bias was only included un a third of the reviews. There is not sufficient evidence to transfer AI to actual healthcare delivery.
Background Artificial intelligence is fueling a new revolution in medicine and in the healthcare sector. Despite the growing evidence on the benefits of artificial intelligence there are several aspects that limit the measure of its impact in people’s health. It is necessary to assess the current status on the application of AI towards the improvement of people’s health in the domains defined by WHO’s Thirteenth General Programme of Work (GPW13) and the European Programme of Work (EPW), to inform about trends, gaps, opportunities, and challenges. Objective To perform a systematic overview of systematic reviews on the application of artificial intelligence in the people’s health domains as defined in the GPW13 and provide a comprehensive and updated map on the application specialties of artificial intelligence in terms of methodologies, algorithms, data sources, outcomes, predictors, performance, and methodological quality. Methods A systematic search in MEDLINE, EMBASE, Cochrane and IEEEXplore was conducted between January 2015 and June 2021 to collect systematic reviews using a combination of keywords related to the domains of universal health coverage, health emergencies protection, and better health and wellbeing as defined by the WHO’s PGW13 and EPW. Eligibility criteria was based on methodological quality and the inclusion of practical implementation of artificial intelligence. Records were classified and labeled using ICD-11 categories into the domains of the GPW13. Descriptors related to the area of implementation, type of modeling, data entities, outcomes and implementation on care delivery were extracted using a structured form and methodological aspects of the included reviews studies was assessed using the AMSTAR checklist. Results The search strategy resulted in the screening of 815 systematic reviews from which 203 were assessed for eligibility and 129 were included in the review. The most predominant domain for artificial intelligence applications was Universal Health Coverage (N = 98) followed by Health Emergencies (N = 16) and Better Health and Wellbeing (N = 15). Neoplasms area on Universal Health Coverage was the disease area featuring most of the applications (21.7 %, N = 28). The reviews featured analytics primarily over both public and private data sources (67.44 %, N = 87). The most used type of data was medical imaging (31.8 %, N = 41) and predictors based on regions of interest and clinical data. The most prominent subdomain of Artificial Intelligence was Machine Learning (43.4 %, N = 56), in which Support Vector Machine method was predominant (20.9 %, N = 27). Regarding the purpose, the application of Artificial Intelligence I is focused on the prediction of the diseases (36.4 %, N = 47). With respect to the validation, more than a half of the reviews (54.3 %, N = 70) did not report a validation procedure and, whenever available, the main performance indicator was the accuracy (28.7 %, N = 37). According to the methodological quality assessment, a third of the reviews (34.9 %, N = 45) implemented methods for analysis the risk of bias and the overall AMSTAR score below was 5 (4.01 ± 1.93) on all the included systematic reviews. Conclusion Artificial intelligence is being used for disease modelling, diagnose, classification and prediction in the three domains of GPW13. However, the evidence is often limited to laboratory and the level of adoption is largely unbalanced between ICD-11 categoriesand diseases. Data availability is a determinant factor on the developmental stage of artificial intelligence applications. Most of the reviewed studies show a poor methodological quality and are at high risk of bias, which limits the reproducibility of the results and the reliability of translating these applications to real clinical scenarios. The analyzed papers show results only in laboratory and testing scenarios and not in clinical trials nor case studies, limiting the supporting evidence to transfer artificial intelligence to actual care delivery.
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Affiliation(s)
- Antonio Martinez-Millana
- Instituto Universitario de Investigación de Aplicaciones de las Tecnologías de la Información y de las Comunicaciones Avanzadas (ITACA), Universitat Politècnica de València, Camino de Vera S/N, Valencia 46022, Spain
| | - Aida Saez-Saez
- Instituto Universitario de Investigación de Aplicaciones de las Tecnologías de la Información y de las Comunicaciones Avanzadas (ITACA), Universitat Politècnica de València, Camino de Vera S/N, Valencia 46022, Spain
| | - Roberto Tornero-Costa
- Instituto Universitario de Investigación de Aplicaciones de las Tecnologías de la Información y de las Comunicaciones Avanzadas (ITACA), Universitat Politècnica de València, Camino de Vera S/N, Valencia 46022, Spain
| | - Natasha Azzopardi-Muscat
- Division of Country Health Policies and Systems, World Health Organization, Regional Office for Europe, Copenhagen, Denmark
| | - Vicente Traver
- Instituto Universitario de Investigación de Aplicaciones de las Tecnologías de la Información y de las Comunicaciones Avanzadas (ITACA), Universitat Politècnica de València, Camino de Vera S/N, Valencia 46022, Spain
| | - David Novillo-Ortiz
- Division of Country Health Policies and Systems, World Health Organization, Regional Office for Europe, Copenhagen, Denmark.
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Son G, Eo T, An J, Oh DJ, Shin Y, Rha H, Kim YJ, Lim YJ, Hwang D. Small Bowel Detection for Wireless Capsule Endoscopy Using Convolutional Neural Networks with Temporal Filtering. Diagnostics (Basel) 2022; 12:diagnostics12081858. [PMID: 36010210 PMCID: PMC9406835 DOI: 10.3390/diagnostics12081858] [Citation(s) in RCA: 2] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 06/29/2022] [Revised: 07/28/2022] [Accepted: 07/28/2022] [Indexed: 12/22/2022] Open
Abstract
By automatically classifying the stomach, small bowel, and colon, the reading time of the wireless capsule endoscopy (WCE) can be reduced. In addition, it is an essential first preprocessing step to localize the small bowel in order to apply automated small bowel lesion detection algorithms based on deep learning. The purpose of the study was to develop an automated small bowel detection method from long untrimmed videos captured from WCE. Through this, the stomach and colon can also be distinguished. The proposed method is based on a convolutional neural network (CNN) with a temporal filtering on the predicted probabilities from the CNN. For CNN, we use a ResNet50 model to classify three organs including stomach, small bowel, and colon. The hybrid temporal filter consisting of a Savitzky–Golay filter and a median filter is applied to the temporal probabilities for the “small bowel” class. After filtering, the small bowel and the other two organs are differentiated with thresholding. The study was conducted on dataset of 200 patients (100 normal and 100 abnormal WCE cases), which was divided into a training set of 140 cases, a validation set of 20 cases, and a test set of 40 cases. For the test set of 40 patients (20 normal and 20 abnormal WCE cases), the proposed method showed accuracy of 99.8% in binary classification for the small bowel. Transition time errors for gastrointestinal tracts were only 38.8 ± 25.8 seconds for the transition between stomach and small bowel and 32.0 ± 19.1 seconds for the transition between small bowel and colon, compared to the ground truth organ transition points marked by two experienced gastroenterologists.
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Affiliation(s)
- Geonhui Son
- School of Electrical and Electronic Engineering, Yonsei University, Seoul 03722, Korea; (G.S.); (T.E.); (J.A.); (Y.S.); (H.R.)
| | - Taejoon Eo
- School of Electrical and Electronic Engineering, Yonsei University, Seoul 03722, Korea; (G.S.); (T.E.); (J.A.); (Y.S.); (H.R.)
| | - Jiwoong An
- School of Electrical and Electronic Engineering, Yonsei University, Seoul 03722, Korea; (G.S.); (T.E.); (J.A.); (Y.S.); (H.R.)
| | - Dong Jun Oh
- Department of Internal Medicine, Dongguk University Ilsan Hospital, Dongguk University College of Medicine, Goyang 10326, Korea;
| | - Yejee Shin
- School of Electrical and Electronic Engineering, Yonsei University, Seoul 03722, Korea; (G.S.); (T.E.); (J.A.); (Y.S.); (H.R.)
| | - Hyenogseop Rha
- School of Electrical and Electronic Engineering, Yonsei University, Seoul 03722, Korea; (G.S.); (T.E.); (J.A.); (Y.S.); (H.R.)
| | - You Jin Kim
- IntroMedic, Capsule Endoscopy Medical Device Manufacturer, Seoul 08375, Korea;
| | - Yun Jeong Lim
- Department of Internal Medicine, Dongguk University Ilsan Hospital, Dongguk University College of Medicine, Goyang 10326, Korea;
- Correspondence: (Y.J.L.); (D.H.)
| | - Dosik Hwang
- School of Electrical and Electronic Engineering, Yonsei University, Seoul 03722, Korea; (G.S.); (T.E.); (J.A.); (Y.S.); (H.R.)
- Center for Healthcare Robotics, Korea Institute of Science and Technology, 5, Hwarang-ro 14-gil, Seongbuk-gu, Seoul 02792, Korea
- Department of Oral and Maxillofacial Radiology, Yonsei University College of Dentistry, Seoul 03722, Korea
- Department of Radiology and Center for Clinical Imaging Data Science (CCIDS), Yonsei University College of Medicine, Seoul 03722, Korea
- Correspondence: (Y.J.L.); (D.H.)
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Alemanni LV, Fabbri S, Rondonotti E, Mussetto A. Recent developments in small bowel endoscopy: the "black box" is now open! Clin Endosc 2022; 55:473-479. [PMID: 35831981 PMCID: PMC9329645 DOI: 10.5946/ce.2022.113] [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: 03/28/2022] [Accepted: 05/11/2022] [Indexed: 12/09/2022] Open
Abstract
Over the last few years, capsule endoscopy has been established as a fundamental device in the practicing gastroenterologist’s toolbox. Its utilization in diagnostic algorithms for suspected small bowel bleeding, Crohn’s disease, and small bowel tumors has been approved by several guidelines. The advent of double-balloon enteroscopy has significantly increased the therapeutic possibilities and release of multiple devices (single-balloon enteroscopy and spiral enteroscopy) aimed at improving the performance of small bowel enteroscopy. Recently, some important innovations have appeared in the small bowel endoscopy scene, providing further improvement to its evolution. Artificial intelligence in capsule endoscopy should increase diagnostic accuracy and reading efficiency, and the introduction of motorized spiral enteroscopy into clinical practice could also improve the therapeutic yield. This review focuses on the most recent studies on artificial-intelligence-assisted capsule endoscopy and motorized spiral enteroscopy.
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Affiliation(s)
- Luigina Vanessa Alemanni
- Gastroenterology Unit, Santa Maria delle Croci Hospital, Ravenna, Italy.,Department of Medical and Surgical Sciences, S. Orsola-Malpighi Hospital, Bologna, Italy
| | - Stefano Fabbri
- Gastroenterology Unit, Santa Maria delle Croci Hospital, Ravenna, Italy.,Department of Medical and Surgical Sciences, S. Orsola-Malpighi Hospital, Bologna, Italy
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Development of a Deep-Learning Algorithm for Small Bowel-Lesion Detection and a Study of the Improvement in the False-Positive Rate. J Clin Med 2022; 11:jcm11133682. [PMID: 35806969 PMCID: PMC9267395 DOI: 10.3390/jcm11133682] [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: 05/02/2022] [Revised: 06/23/2022] [Accepted: 06/24/2022] [Indexed: 02/04/2023] Open
Abstract
Deep learning has recently been gaining attention as a promising technology to improve the identification of lesions, and deep-learning algorithms for lesion detection have been actively developed in small-bowel capsule endoscopy (SBCE). We developed a detection algorithm for abnormal findings by deep learning (convolutional neural network) the SBCE imaging data of 30 cases with abnormal findings. To enable the detection of a wide variety of abnormal findings, the training data were balanced to include all major findings identified in SBCE (bleeding, angiodysplasia, ulceration, and neoplastic lesions). To reduce the false-positive rate, “findings that may be responsible for hemorrhage” and “findings that may require therapeutic intervention” were extracted from the images of abnormal findings and added to the training dataset. For the performance evaluation, the sensitivity and the specificity were calculated using 271 detectable findings in 35 cases. The sensitivity was calculated using 68,494 images of non-abnormal findings. The sensitivity and specificity were 93.4% and 97.8%, respectively. The average number of images detected by the algorithm as having abnormal findings was 7514. We developed an image-reading support system using deep learning for SBCE and obtained a good detection performance.
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Chetcuti Zammit S, Sidhu R. Artificial intelligence within the small bowel: are we lagging behind? Curr Opin Gastroenterol 2022; 38:307-317. [PMID: 35645023 DOI: 10.1097/mog.0000000000000827] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [MESH Headings] [Track Full Text] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Indexed: 12/10/2022]
Abstract
PURPOSE OF REVIEW The use of artificial intelligence in small bowel capsule endoscopy is expanding. This review focusses on the use of artificial intelligence for small bowel pathology compared with human data and developments to date. RECENT FINDINGS The diagnosis and management of small bowel disease has been revolutionized with the advent of capsule endoscopy. Reading of capsule endoscopy videos however is time consuming with an average reading time of 40 min. Furthermore, the fatigued human eye may miss subtle lesions including indiscreet mucosal bulges. In recent years, artificial intelligence has made significant progress in the field of medicine including gastroenterology. Machine learning has enabled feature extraction and in combination with deep neural networks, image classification has now materialized for routine endoscopy for the clinician. SUMMARY Artificial intelligence is in built within the Navicam-Ankon capsule endoscopy reading system. This development will no doubt expand to other capsule endoscopy platforms and capsule endoscopies that are used to visualize other parts of the gastrointestinal tract as a standard. This wireless and patient friendly technique combined with rapid reading platforms with the help of artificial intelligence will become an attractive and viable choice to alter how patients are investigated in the future.
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Affiliation(s)
| | - Reena Sidhu
- Academic Department of Gastroenterology, Royal Hallamshire Hospital
- Academic Unit of Gastroenterology, Department of Infection, Immunity and Cardiovascular Disease, University of Sheffield, Sheffield, United Kingdom
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Estevinho MM, Pinho R, Fernandes C, Rodrigues A, Ponte A, Gomes AC, Afecto E, Correia J, Carvalho J. Diagnostic and therapeutic yields of early capsule endoscopy and device-assisted enteroscopy in the setting of overt GI bleeding: a systematic review with meta-analysis. Gastrointest Endosc 2022; 95:610-625.e9. [PMID: 34952093 DOI: 10.1016/j.gie.2021.12.009] [Citation(s) in RCA: 6] [Impact Index Per Article: 3.0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 09/06/2021] [Accepted: 12/11/2021] [Indexed: 02/08/2023]
Abstract
BACKGROUND AND AIMS Small-bowel capsule endoscopy (SBCE) and device-assisted enteroscopy (DAE) are essential in obscure GI bleeding (OGIB) management. However, the best timing for such procedures remains unknown. This meta-analysis aimed to compare, for the first time, diagnostic and therapeutic yields, detection of active bleeding and vascular lesions, recurrent bleeding, and mortality of "early" versus "nonearly" SBCE and DAE. METHODS MEDLINE, ScienceDirect, and Cochrane Central Register of Controlled Trials were searched to identify studies comparing early versus nonearly SBCE and DAE. Random-effects meta-analysis was performed; reporting quality was assessed. RESULTS From 1974 records, 39 were included (4825 patients). Time intervals for the early approach varied, within 14 days in SBCE and 72 hours in DAE. The pooled diagnostic and therapeutic yields of early DAE were superior to those of SBCE (7.97% and 20.89%, respectively; P < .05). The odds for active bleeding (odds ratio [OR], 5.09; I2 = 53%), positive diagnosis (OR, 3.99; I2 = 45%), and therapeutic intervention (OR, 3.86; I2 = 67%) were higher in the early group for SBCE and DAE (P < .01). Subgroup effects in diagnostic yield were only identified for the early group sample size. Our study failed to identify differences when studies were classified according to time intervals for early DAE (I2 < 5%), but the analysis was limited because of a lack of data availability. Lower recurrent bleeding in early SBCE and DAE was observed (OR, .40; P < .01; I2 = 0%). CONCLUSIONS The role of small-bowel studies in the early evaluation of OGIB is unquestionable, impacting diagnosis, therapeutic intervention, and prognosis. Comparative studies are still needed to identify optimal timing.
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Affiliation(s)
- Maria Manuela Estevinho
- Department of Gastroenterology, Vila Nova de Gaia/Espinho Hospital Center, Vila Nova de Gaia, Portugal; Department of Biomedicine, Unit of Pharmacology and Therapeutics, Faculty of Medicine, University of Porto, Porto, Portugal
| | - Rolando Pinho
- Department of Gastroenterology, Vila Nova de Gaia/Espinho Hospital Center, Vila Nova de Gaia, Portugal
| | - Carlos Fernandes
- Department of Gastroenterology, Vila Nova de Gaia/Espinho Hospital Center, Vila Nova de Gaia, Portugal
| | - Adélia Rodrigues
- Department of Gastroenterology, Vila Nova de Gaia/Espinho Hospital Center, Vila Nova de Gaia, Portugal
| | - Ana Ponte
- Department of Gastroenterology, Vila Nova de Gaia/Espinho Hospital Center, Vila Nova de Gaia, Portugal
| | - Ana Catarina Gomes
- Department of Gastroenterology, Vila Nova de Gaia/Espinho Hospital Center, Vila Nova de Gaia, Portugal
| | - Edgar Afecto
- Department of Gastroenterology, Vila Nova de Gaia/Espinho Hospital Center, Vila Nova de Gaia, Portugal
| | - João Correia
- Department of Gastroenterology, Vila Nova de Gaia/Espinho Hospital Center, Vila Nova de Gaia, Portugal
| | - João Carvalho
- Department of Gastroenterology, Vila Nova de Gaia/Espinho Hospital Center, Vila Nova de Gaia, Portugal
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Abstract
The health system can reap significant benefits by adopting and implementing innovative measures, as was recently demonstrated and emphasized during the COVID-19 pandemic. Herein, we present our bird's eye view of gastroenterology's innovative technologies via utilizing a text-mining technique. We analyzed five research fields that comply with innovation: artificial intelligence (AI), virtual reality (VR), telemedicine, the microbiome, and advanced endoscopy. According to gastroenterology literature, the two most innovative fields were the microbiome and advanced endoscopy. Though artificial intelligence (AI), virtual reality (VR), and telemedicine trailed behind, the number of AI publications in gastroenterology has shown an exponential trend in the last couple of years. While VR and telemedicine are neglected compared to other fields, their implementation could improve physician and patient training, patient access to care, cost reduction, and patient outcomes.
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Klang E, Soffer S, Tsur A, Shachar E, Lahat A. Innovation in Gastroenterology—Can We Do Better? Biomimetics (Basel) 2022; 7:biomimetics7010033. [PMID: 35323190 PMCID: PMC8945015 DOI: 10.3390/biomimetics7010033] [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: 01/31/2022] [Revised: 03/14/2022] [Accepted: 03/15/2022] [Indexed: 02/04/2023] Open
Abstract
The health system can reap significant benefits by adopting and implementing innovative measures, as was recently demonstrated and emphasized during the COVID-19 pandemic. Herein, we present our bird’s eye view of gastroenterology’s innovative technologies via utilizing a text-mining technique. We analyzed five research fields that comply with innovation: artificial intelligence (AI), virtual reality (VR), telemedicine, the microbiome, and advanced endoscopy. According to gastroenterology literature, the two most innovative fields were the microbiome and advanced endoscopy. Though artificial intelligence (AI), virtual reality (VR), and telemedicine trailed behind, the number of AI publications in gastroenterology has shown an exponential trend in the last couple of years. While VR and telemedicine are neglected compared to other fields, their implementation could improve physician and patient training, patient access to care, cost reduction, and patient outcomes.
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Affiliation(s)
- Eyal Klang
- Department of Diagnostic Imaging, Sheba Medical Center, Tel Hashomer, Israel, and Sackler Medical School, Tel Aviv University, Tel Aviv 6997801, Israel;
- Sheba Talpiot Medical Leadership Program, Tel Hashomer, Israel, and Sackler Medical School, Tel Aviv University, Tel Aviv 6997801, Israel
- DeepVision Lab, Sheba Medical Center, Tel Aviv 6997801, Israel
- Department of Population Health Science and Policy, Institute for Healthcare Delivery Science, Mount Sinai Health System, New York, NY 10016, USA
| | - Shelly Soffer
- DeepVision Lab, Sheba Medical Center, Tel Aviv 6997801, Israel
- Internal Medicine B, Assuta Medical Center, Ashdod, Israel, and Ben-Gurion University of the Negev, Be’er Sheva 8410501, Israel
- Samson Assuta Ashdod University Hospital, Ha-Refu’a St 7, Ashdod 7747629, Israel
- Correspondence: ; Tel.: +973-8-300-4100; Fax: +972-3-5357315
| | - Abraham Tsur
- Department of Obstetrics and Gynecology, Sheba Medical Center, Tel Hashomer, Israel, and Sackler Medical School, Tel Aviv University, Tel Aviv 6997801, Israel;
| | - Eyal Shachar
- Department of Gastroenterology, Sheba Medical Center, Tel Hashomer, Israel, and Sackler Medical School, Tel Aviv University, Tel Aviv 6997801, Israel; (E.S.); (A.L.)
| | - Adi Lahat
- Department of Gastroenterology, Sheba Medical Center, Tel Hashomer, Israel, and Sackler Medical School, Tel Aviv University, Tel Aviv 6997801, Israel; (E.S.); (A.L.)
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Mascarenhas M, Ribeiro T, Afonso J, Ferreira JP, Cardoso H, Andrade P, Parente MP, Jorge RN, Mascarenhas Saraiva M, Macedo G. Deep learning and colon capsule endoscopy: automatic detection of blood and colonic mucosal lesions using a convolutional neural network. Endosc Int Open 2022; 10:E171-E177. [PMID: 35186665 PMCID: PMC8850002 DOI: 10.1055/a-1675-1941] [Citation(s) in RCA: 11] [Impact Index Per Article: 5.5] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 04/15/2021] [Accepted: 09/21/2021] [Indexed: 10/31/2022] Open
Abstract
Background and study aims Colon capsule endoscopy (CCE) is a minimally invasive alternative to conventional colonoscopy. However, CCE produces long videos, making its analysis time-consuming and prone to errors. Convolutional neural networks (CNN) are artificial intelligence (AI) algorithms with high performance levels in image analysis. We aimed to develop a deep learning model for automatic identification and differentiation of significant colonic mucosal lesions and blood in CCE images. Patients and methods A retrospective multicenter study including 124 CCE examinations was conducted for development of a CNN model, using a database of CCE images including anonymized images of patients with normal colon mucosa, several mucosal lesions (erosions, ulcers, vascular lesions and protruding lesions) and luminal blood. For CNN development, 9005 images (3,075 normal mucosa, 3,115 blood and 2,815 mucosal lesions) were ultimately extracted. Two image datasets were created and used for CNN training and validation. Results The mean (standard deviation) sensitivity and specificity of the CNN were 96.3 % (3.9 %) and 98.2 % (1.8 %) Mucosal lesions were detected with a sensitivity of 92.0 % and a specificity of 98.5 %. Blood was detected with a sensitivity and specificity of 97.2 % and 99.9 %, respectively. The algorithm was 99.2 % sensitive and 99.6 % specific in distinguishing blood from mucosal lesions. The CNN processed 65 frames per second. Conclusions This is the first CNN-based algorithm to accurately detect and distinguish colonic mucosal lesions and luminal blood in CCE images. AI may improve diagnostic and time efficiency of CCE exams, thus facilitating CCE adoption to routine clinical practice.
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Affiliation(s)
- Miguel Mascarenhas
- Department of Gastroenterology, São João University Hospital, Porto, Portugal,WGO Gastroenterology and Hepatology Training Center, Porto, Portugal,Faculty of Medicine of the University of Porto Porto, Portugal
| | - Tiago Ribeiro
- Department of Gastroenterology, São João University Hospital, Porto, Portugal,WGO Gastroenterology and Hepatology Training Center, Porto, Portugal
| | - João Afonso
- Department of Gastroenterology, São João University Hospital, Porto, Portugal,WGO Gastroenterology and Hepatology Training Center, Porto, Portugal
| | - João P.S. Ferreira
- Department of Mechanical Engineering, Faculty of Engineering of the University of Porto, Porto, Portugal,INEGI – Institute of Science and Innovation in Mechanical and Industrial Engineering, Porto, Portugal.
| | - Hélder Cardoso
- Department of Gastroenterology, São João University Hospital, Porto, Portugal,WGO Gastroenterology and Hepatology Training Center, Porto, Portugal,Faculty of Medicine of the University of Porto Porto, Portugal
| | - Patrícia Andrade
- Department of Gastroenterology, São João University Hospital, Porto, Portugal,WGO Gastroenterology and Hepatology Training Center, Porto, Portugal,Faculty of Medicine of the University of Porto Porto, Portugal
| | - Marco P.L. Parente
- Department of Mechanical Engineering, Faculty of Engineering of the University of Porto, Porto, Portugal,INEGI – Institute of Science and Innovation in Mechanical and Industrial Engineering, Porto, Portugal.
| | - Renato N. Jorge
- Department of Mechanical Engineering, Faculty of Engineering of the University of Porto, Porto, Portugal,INEGI – Institute of Science and Innovation in Mechanical and Industrial Engineering, Porto, Portugal.
| | | | - Guilherme Macedo
- Department of Gastroenterology, São João University Hospital, Porto, Portugal,WGO Gastroenterology and Hepatology Training Center, Porto, Portugal,Faculty of Medicine of the University of Porto Porto, Portugal
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Muruganantham P, Balakrishnan SM. Attention Aware Deep Learning Model for Wireless Capsule Endoscopy Lesion Classification and Localization. J Med Biol Eng 2022. [DOI: 10.1007/s40846-022-00686-8] [Citation(s) in RCA: 6] [Impact Index Per Article: 3.0] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 01/18/2023]
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Ionescu A, Glodeanu A, Ionescu M, Zaharie S, Ciurea A, Golli A, Mavritsakis N, Popa D, Vere C. Clinical impact of wireless capsule endoscopy for small bowel investigation (Review). Exp Ther Med 2022; 23:262. [PMID: 35251328 PMCID: PMC8892621 DOI: 10.3892/etm.2022.11188] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Journal Information] [Subscribe] [Scholar Register] [Received: 10/13/2021] [Accepted: 11/12/2021] [Indexed: 11/06/2022] Open
Abstract
Wireless capsule endoscopy is currently considered the gold standard in the investigation of the small bowel. It is both practical for physicians and easily accepted by patients. Prior to its development, two types of imaging investigations of the small bowel were available: radiologic and endoscopic. The first category is less invasive and comfortable for patients; it presents the ensemble of the small bowel, but it may imply radiation exposure. Images are constructed based on signals emitted by various equipment and require special interpretation. Endoscopic techniques provide real-time colored images acquired by miniature cameras from inside the small bowel, require interpretation only from a medical point of view, may allow the possibility to perform biopsies, but the investigation only covers a part of the small bowel and are more difficult to accept by patients. Wireless capsule endoscopy is the current solution that overcomes a part of the previous drawbacks: it covers the entire small bowel, it provides real-time images acquired by cameras, it is painless for patients, and it represents an abundant source of information for physicians. Yet, it lacks motion control and the possibility to perform biopsies or administer drugs. However, significant effort has been oriented in these directions by technical and medical teams, and more advanced capsules will surely be available in the following years.
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Affiliation(s)
- Alin Ionescu
- Department of Medical History, University of Medicine and Pharmacy of Craiova, 200349 Craiova, Romania
| | - Adina Glodeanu
- Department of Internal Medicine, University of Medicine and Pharmacy of Craiova, 200349 Craiova, Romania
| | - Mihaela Ionescu
- Department of Medical Informatics and Biostatistics, University of Medicine and Pharmacy of Craiova, 200349 Craiova, Romania
| | - Sorin Zaharie
- Department of Nephrology, University of Medicine and Pharmacy of Craiova, 200349 Craiova, Romania
| | - Ana Ciurea
- Department of Oncology, University of Medicine and Pharmacy of Craiova, 200349 Craiova, Romania
| | - Andreea Golli
- Department of Public Health Management, University of Medicine and Pharmacy of Craiova, 200349 Craiova, Romania
| | - Nikolaos Mavritsakis
- Department of Physical Education and Sport, ‘1 Decembrie 1918’ University, 510009 Alba Iulia, Romania
| | - Didi Popa
- Department of Information and Communication Technology, University of Craiova, 200585 Craiova, Romania
| | - Cristin Vere
- Department of Gastroenterology, University of Medicine and Pharmacy of Craiova, 200349 Craiova, Romania
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Soffer S, Morgenthau AS, Shimon O, Barash Y, Konen E, Glicksberg BS, Klang E. Artificial Intelligence for Interstitial Lung Disease Analysis on Chest Computed Tomography: A Systematic Review. Acad Radiol 2022; 29 Suppl 2:S226-S235. [PMID: 34219012 DOI: 10.1016/j.acra.2021.05.014] [Citation(s) in RCA: 20] [Impact Index Per Article: 10.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 03/24/2021] [Revised: 05/10/2021] [Accepted: 05/11/2021] [Indexed: 12/22/2022]
Abstract
RATIONALE AND OBJECTIVES High-resolution computed tomography (HRCT) is paramount in the assessment of interstitial lung disease (ILD). Yet, HRCT interpretation of ILDs may be hampered by inter- and intra-observer variability. Recently, artificial intelligence (AI) has revolutionized medical image analysis. This technology has the potential to advance patient care in ILD. We aimed to systematically evaluate the application of AI for the analysis of ILD in HRCT. MATERIALS AND METHODS We searched MEDLINE/PubMed databases for original publications of deep learning for ILD analysis on chest CT. The search included studies published up to March 1, 2021. The risk of bias evaluation included tailored Quality Assessment of Diagnostic Accuracy Studies and the modified Joanna Briggs Institute Critical Appraisal checklist. RESULTS Data was extracted from 19 retrospective studies. Deep learning techniques included detection, segmentation, and classification of ILD on HRCT. Most studies focused on the classification of ILD into different morphological patterns. Accuracies of 78%-91% were achieved. Two studies demonstrated near-expert performance for the diagnosis of idiopathic pulmonary fibrosis (IPF). The Quality Assessment of Diagnostic Accuracy Studies tool identified a high risk of bias in 15/19 (78.9%) of the studies. CONCLUSION AI has the potential to contribute to the radiologic diagnosis and classification of ILD. However, the accuracy performance is still not satisfactory, and research is limited by a small number of retrospective studies. Hence, the existing published data may not be sufficiently reliable. Only well-designed prospective controlled studies can accurately assess the value of existing AI tools for ILD evaluation.
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