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Agudo Castillo B, Mascarenhas M, Martins M, Mendes F, de la Iglesia D, Costa AMMPD, Esteban Fernández-Zarza C, González-Haba Ruiz M. Advancements in biliopancreatic endoscopy: a comprehensive review of artificial intelligence in EUS and ERCP. REVISTA ESPANOLA DE ENFERMEDADES DIGESTIVAS 2024. [PMID: 38832589 DOI: 10.17235/reed.2024.10456/2024] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 06/05/2024]
Abstract
The development and implementation of artificial intelligence (AI), particularly deep learning (DL) models, has generated significant interest across various fields of gastroenterology. While research in luminal endoscopy has seen rapid translation to clinical practice with approved AI devices, its potential extends far beyond, offering promising benefits for biliopancreatic endoscopy like optical characterization of strictures during cholangioscopy or detection and classification of pancreatic lesions during diagnostic endoscopic ultrasound (EUS). This narrative review provides an up-to-date of the latest literature and available studies in this field. Serving as a comprehensive guide to the current landscape of AI in biliopancreatic endoscopy, emphasizing technological advancements, main applications, ethical considerations, and future directions for research and clinical implementation.
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Affiliation(s)
| | | | - Miguel Martins
- Gastroenterology, Centro Hospitalar Universitário de São João
| | - Francisco Mendes
- Gastroenterology, Centro Hospitalar Universitário de São João, Portugal
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Impellizzeri G, Donato G, De Angelis C, Pagano N. Diagnostic Endoscopic Ultrasound (EUS) of the Luminal Gastrointestinal Tract. Diagnostics (Basel) 2024; 14:996. [PMID: 38786295 PMCID: PMC11120241 DOI: 10.3390/diagnostics14100996] [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: 04/10/2024] [Revised: 05/05/2024] [Accepted: 05/08/2024] [Indexed: 05/25/2024] Open
Abstract
The purpose of this review is to focus on the diagnostic endoscopic ultrasound of the gastrointestinal tract. In the last decades, EUS has gained a central role in the staging of epithelial and sub-epithelial lesions of the gastrointestinal tract. With the evolution of imaging, the position of EUS in the diagnostic work-up and the staging flow-chart has continuously changed with two extreme positions: some gastroenterologists think that EUS is absolutely indispensable, and some think it is utterly useless. The truth is, as always, somewhere in between the two extremes. Analyzing the most up-to-date and strong evidence, we will try to give EUS the correct position in our daily practice.
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Affiliation(s)
| | | | | | - Nico Pagano
- Gastroenterology Unit, Department of Oncological and Specialty Medicine, Azienda Ospedaliero-Universitaria Maggiore della Carità, 28100 Novara, Italy; (G.I.); (C.D.A.)
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Kuwahara T, Hara K, Mizuno N, Haba S, Okuno N, Fukui T, Urata M, Yamamoto Y. Current status of artificial intelligence analysis for the treatment of pancreaticobiliary diseases using endoscopic ultrasonography and endoscopic retrograde cholangiopancreatography. DEN OPEN 2024; 4:e267. [PMID: 37397344 PMCID: PMC10312781 DOI: 10.1002/deo2.267] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Figures] [Subscribe] [Scholar Register] [Received: 06/01/2023] [Accepted: 06/18/2023] [Indexed: 07/04/2023]
Abstract
Pancreatic and biliary diseases encompass a range of conditions requiring accurate diagnosis for appropriate treatment strategies. This diagnosis relies heavily on imaging techniques like endoscopic ultrasonography and endoscopic retrograde cholangiopancreatography. Artificial intelligence (AI), including machine learning and deep learning, is becoming integral in medical imaging and diagnostics, such as the detection of colorectal polyps. AI shows great potential in diagnosing pancreatobiliary diseases. Unlike machine learning, which requires feature extraction and selection, deep learning can utilize images directly as input. Accurate evaluation of AI performance is a complex task due to varied terminologies, evaluation methods, and development stages. Essential aspects of AI evaluation involve defining the AI's purpose, choosing appropriate gold standards, deciding on the validation phase, and selecting reliable validation methods. AI, particularly deep learning, is increasingly employed in endoscopic ultrasonography and endoscopic retrograde cholangiopancreatography diagnostics, achieving high accuracy levels in detecting and classifying various pancreatobiliary diseases. The AI often performs better than doctors, even in tasks like differentiating benign from malignant pancreatic tumors, cysts, and subepithelial lesions, identifying gallbladder lesions, assessing endoscopic retrograde cholangiopancreatography difficulty, and evaluating the biliary strictures. The potential for AI in diagnosing pancreatobiliary diseases, especially where other modalities have limitations, is considerable. However, a crucial constraint is the need for extensive, high-quality annotated data for AI training. Future advances in AI, such as large language models, promise further applications in the medical field.
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Affiliation(s)
| | - Kazuo Hara
- Department of GastroenterologyAichi Cancer Center HospitalAichiJapan
| | - Nobumasa Mizuno
- Department of GastroenterologyAichi Cancer Center HospitalAichiJapan
| | - Shin Haba
- Department of GastroenterologyAichi Cancer Center HospitalAichiJapan
| | - Nozomi Okuno
- Department of GastroenterologyAichi Cancer Center HospitalAichiJapan
| | - Toshitaka Fukui
- Department of GastroenterologyAichi Cancer Center HospitalAichiJapan
| | - Minako Urata
- Department of GastroenterologyAichi Cancer Center HospitalAichiJapan
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Barat M, Pellat A, Dohan A, Hoeffel C, Coriat R, Soyer P. CT and MRI of Gastrointestinal Stromal Tumors: New Trends and Perspectives. Can Assoc Radiol J 2024; 75:107-117. [PMID: 37386745 DOI: 10.1177/08465371231180510] [Citation(s) in RCA: 4] [Impact Index Per Article: 4.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 07/01/2023] Open
Abstract
Gastrointestinal stromal tumors (GISTs) are defined as mesenchymal tumors of the gastrointestinal tract that express positivity for CD117, which is a c-KIT proto-oncogene antigen. Expression of the c-KIT protein, a tyrosine kinase growth factor receptor, allows the distinction between GISTs and other mesenchymal tumors such as leiomyoma, leiomyosarcoma, schwannoma and neurofibroma. GISTs can develop anywhere in the gastrointestinal tract, as well as in the mesentery and omentum. Over the years, the management of GISTs has improved due to a better knowledge of their behaviors and risk or recurrence, the identification of specific mutations and the use of targeted therapies. This has resulted in a better prognosis for patients with GISTs. In parallel, imaging of GISTs has been revolutionized by tremendous progress in the field of detection, characterization, survival prediction and monitoring during therapy. Recently, a particular attention has been given to radiomics for the characterization of GISTs using analysis of quantitative imaging features. In addition, radiomics has currently many applications that are developed in conjunction with artificial intelligence with the aim of better characterizing GISTs and providing a more precise assessment of tumor burden. This article sums up recent advances in computed tomography and magnetic resonance imaging of GISTs in the field of image/data acquisition, tumor detection, tumor characterization, treatment response evaluation, and preoperative planning.
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Affiliation(s)
- Maxime Barat
- Department of Radiology, Hopital Cochin, Paris, France
- Université Paris Cité, Faculté de Médecine, Paris, France
| | - Anna Pellat
- Université Paris Cité, Faculté de Médecine, Paris, France
- Department of Gastroenterology and Digestive Oncology, Hôpital Cochin, Paris, France
| | - Anthony Dohan
- Department of Radiology, Hopital Cochin, Paris, France
- Université Paris Cité, Faculté de Médecine, Paris, France
| | - Christine Hoeffel
- Reims Medical School, Department of Radiology, Hopital Robert Debré, CHU Reims, Université Champagne-Ardennes, Reims, France
| | - Romain Coriat
- Université Paris Cité, Faculté de Médecine, Paris, France
- Department of Gastroenterology and Digestive Oncology, Hôpital Cochin, Paris, France
| | - Philippe Soyer
- Department of Radiology, Hopital Cochin, Paris, France
- Université Paris Cité, Faculté de Médecine, Paris, France
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Xie F, Ju J, Zhang T, Wang H, Liu J, Wang J, Zhou Y, Zhao X. A Small Intestinal Stromal Tumor Detection Method Based on an Attention Balance Feature Pyramid. SENSORS (BASEL, SWITZERLAND) 2023; 23:9723. [PMID: 38139569 PMCID: PMC10747994 DOI: 10.3390/s23249723] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Subscribe] [Scholar Register] [Received: 10/10/2023] [Revised: 11/05/2023] [Accepted: 11/15/2023] [Indexed: 12/24/2023]
Abstract
Small intestinal stromal tumor (SIST) is a common gastrointestinal tumor. Currently, SIST diagnosis relies on clinical radiologists reviewing CT images from medical imaging sensors. However, this method is inefficient and greatly affected by subjective factors. The automatic detection method for stromal tumors based on computer vision technology can better solve these problems. However, in CT images, SIST have different shapes and sizes, blurred edge texture, and little difference from surrounding normal tissues, which to a large extent challenges the use of computer vision technology for the automatic detection of stromal tumors. Furthermore, there are the following issues in the research on the detection and recognition of SIST. After analyzing mainstream target detection models on SIST data, it was discovered that there is an imbalance in the features at different levels during the feature fusion stage of the network model. Therefore, this paper proposes an algorithm, based on the attention balance feature pyramid (ABFP), for detecting SIST with unbalanced feature fusion in the target detection model. By combining weighted multi-level feature maps from the backbone network, the algorithm creates a balanced semantic feature map. Spatial attention and channel attention modules are then introduced to enhance this map. In the feature fusion stage, the algorithm scales the enhanced balanced semantic feature map to the size of each level feature map and enhances the original feature information with the original feature map, effectively addressing the imbalance between deep and shallow features. Consequently, the SIST detection model's detection performance is significantly improved, and the method is highly versatile. Experimental results show that the ABFP method can enhance traditional target detection methods, and is compatible with various models and feature fusion strategies.
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Affiliation(s)
- Fei Xie
- Xi’an Key Laboratory of Human–Machine Integration and Control Technology for Intelligent Rehabilitation, Xijing University, Xi’an 710123, China; (F.X.); (J.W.)
- School of AOAIR, Xidian University, Xi’an 710075, China
| | - Jianguo Ju
- School of Information Science and Technology, Northwest University, Xi’an 710126, China; (T.Z.); (J.L.); (Y.Z.)
| | - Tongtong Zhang
- School of Information Science and Technology, Northwest University, Xi’an 710126, China; (T.Z.); (J.L.); (Y.Z.)
| | - Hexu Wang
- Xi’an Key Laboratory of Human–Machine Integration and Control Technology for Intelligent Rehabilitation, Xijing University, Xi’an 710123, China; (F.X.); (J.W.)
| | - Jindong Liu
- School of Information Science and Technology, Northwest University, Xi’an 710126, China; (T.Z.); (J.L.); (Y.Z.)
| | - Juan Wang
- Xi’an Key Laboratory of Human–Machine Integration and Control Technology for Intelligent Rehabilitation, Xijing University, Xi’an 710123, China; (F.X.); (J.W.)
| | - Yang Zhou
- School of Information Science and Technology, Northwest University, Xi’an 710126, China; (T.Z.); (J.L.); (Y.Z.)
| | - Xuesong Zhao
- Departments of Radiology, Ruijin Hospital, Shanghai Jiao Tong University School of Medicine, Shanghai 200025, China
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Huang J, Fan X, Liu W. Applications and Prospects of Artificial Intelligence-Assisted Endoscopic Ultrasound in Digestive System Diseases. Diagnostics (Basel) 2023; 13:2815. [PMID: 37685350 PMCID: PMC10487217 DOI: 10.3390/diagnostics13172815] [Citation(s) in RCA: 3] [Impact Index Per Article: 3.0] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 08/01/2023] [Revised: 08/22/2023] [Accepted: 08/27/2023] [Indexed: 09/10/2023] Open
Abstract
Endoscopic ultrasound (EUS) has emerged as a widely utilized tool in the diagnosis of digestive diseases. In recent years, the potential of artificial intelligence (AI) in healthcare has been gradually recognized, and its superiority in the field of EUS is becoming apparent. Machine learning (ML) and deep learning (DL) are the two main AI algorithms. This paper aims to outline the applications and prospects of artificial intelligence-assisted endoscopic ultrasound (EUS-AI) in digestive diseases over the past decade. The results demonstrated that EUS-AI has shown superiority or at least equivalence to traditional methods in the diagnosis, prognosis, and quality control of subepithelial lesions, early esophageal cancer, early gastric cancer, and pancreatic diseases including pancreatic cystic lesions, autoimmune pancreatitis, and pancreatic cancer. The implementation of EUS-AI has opened up new avenues for individualized precision medicine and has introduced novel diagnostic and treatment approaches for digestive diseases.
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Affiliation(s)
| | | | - Wentian Liu
- Department of Gastroenterology and Hepatology, Tianjin Medical University General Hospital, No. 154, Anshan Road, Heping District, Tianjin 300052, China; (J.H.); (X.F.)
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Li B, Chen H, Yang S, Chen F, Xu L, Li Y, Li M, Zhu C, Shao F, Zhang X, Deng C, Zeng L, He Y, Zhang C. Advances in immunology and immunotherapy for mesenchymal gastrointestinal cancers. Mol Cancer 2023; 22:71. [PMID: 37072770 PMCID: PMC10111719 DOI: 10.1186/s12943-023-01770-6] [Citation(s) in RCA: 12] [Impact Index Per Article: 12.0] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 03/09/2023] [Accepted: 03/29/2023] [Indexed: 04/20/2023] Open
Abstract
Mesenchymal gastrointestinal cancers are represented by the gastrointestinal stromal tumors (GISTs) which occur throughout the whole gastrointestinal tract, and affect human health and economy globally. Curative surgical resections and tyrosine kinase inhibitors (TKIs) are the main managements for localized GISTs and recurrent/metastatic GISTs, respectively. Despite multi-lines of TKIs treatments prolonged the survival time of recurrent/metastatic GISTs by delaying the relapse and metastasis of the tumor, drug resistance developed quickly and inevitably, and became the huge obstacle for stopping disease progression. Immunotherapy, which is typically represented by immune checkpoint inhibitors (ICIs), has achieved great success in several solid tumors by reactivating the host immune system, and been proposed as an alternative choice for GIST treatment. Substantial efforts have been devoted to the research of immunology and immunotherapy for GIST, and great achievements have been made. Generally, the intratumoral immune cell level and the immune-related gene expressions are influenced by metastasis status, anatomical locations, driver gene mutations of the tumor, and modulated by imatinib therapy. Systemic inflammatory biomarkers are regarded as prognostic indicators of GIST and closely associated with its clinicopathological features. The efficacy of immunotherapy strategies for GIST has been widely explored in pre-clinical cell and mouse models and clinical experiments in human, and some patients did benefit from ICIs. This review comprehensively summarizes the up-to-date advancements of immunology, immunotherapy and research models for GIST, and provides new insights and perspectives for future studies.
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Affiliation(s)
- Bo Li
- Guangdong Provincial Key Laboratory of Digestive Cancer Research, Digestive Diseases Center, The Seventh Affiliated Hospital of Sun Yat-Sen University, Shenzhen, 518107, Guangdong, China
| | - Hui Chen
- Shenzhen Key Laboratory of Chinese Medicine Active Substance Screening and Translational Research, Scientific Research Center, The Seventh Affiliated Hospital of Sun Yat-Sen University, Shenzhen, 518107, Guangdong, China
| | - Shaohua Yang
- Guangdong-Hong Kong-Macau University Joint Laboratory of Digestive Cancer Research, Digestive Diseases Center, The Seventh Affiliated Hospital of Sun Yat-Sen University, Shenzhen, 518107, Guangdong, China
| | - Feng Chen
- Guangdong Provincial Key Laboratory of Digestive Cancer Research, Digestive Diseases Center, The Seventh Affiliated Hospital of Sun Yat-Sen University, Shenzhen, 518107, Guangdong, China
| | - Liangliang Xu
- Shenzhen Key Laboratory for Drug Addiction and Medication Safety, Department of Ultrasound, Peking University Shenzhen Hospital, Shenzhen, 518036, China
| | - Yan Li
- Guangdong Provincial Key Laboratory of Digestive Cancer Research, Digestive Diseases Center, The Seventh Affiliated Hospital of Sun Yat-Sen University, Shenzhen, 518107, Guangdong, China
| | - Mingzhe Li
- Guangdong Provincial Key Laboratory of Digestive Cancer Research, Digestive Diseases Center, The Seventh Affiliated Hospital of Sun Yat-Sen University, Shenzhen, 518107, Guangdong, China
| | - Chengming Zhu
- Guangdong Provincial Key Laboratory of Digestive Cancer Research, Digestive Diseases Center, The Seventh Affiliated Hospital of Sun Yat-Sen University, Shenzhen, 518107, Guangdong, China
| | - Fangyuan Shao
- MOE Frontiers Science Center for Precision Oncology, Faculty of Health Sciences, Institute of Translational Medicine, Cancer Center, University of Macau, Macau SAR, 999078, China
| | - Xinhua Zhang
- Department of Gastrointestinal Surgery, The First Affiliated Hospital of Sun Yat-Sen University, No. 58 Zhongshan Road, Guangzhou, 510080, China
| | - Chuxia Deng
- MOE Frontiers Science Center for Precision Oncology, Faculty of Health Sciences, Institute of Translational Medicine, Cancer Center, University of Macau, Macau SAR, 999078, China.
| | - Leli Zeng
- Guangdong Provincial Key Laboratory of Digestive Cancer Research, Digestive Diseases Center, The Seventh Affiliated Hospital of Sun Yat-Sen University, Shenzhen, 518107, Guangdong, China.
| | - Yulong He
- Guangdong Provincial Key Laboratory of Digestive Cancer Research, Digestive Diseases Center, The Seventh Affiliated Hospital of Sun Yat-Sen University, Shenzhen, 518107, Guangdong, China.
| | - Changhua Zhang
- Guangdong Provincial Key Laboratory of Digestive Cancer Research, Digestive Diseases Center, The Seventh Affiliated Hospital of Sun Yat-Sen University, Shenzhen, 518107, Guangdong, China.
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Galati JS, Duve RJ, O'Mara M, Gross SA. Artificial intelligence in gastroenterology: A narrative review. Artif Intell Gastroenterol 2022; 3:117-141. [DOI: 10.35712/aig.v3.i5.117] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 10/09/2022] [Revised: 11/21/2022] [Accepted: 12/21/2022] [Indexed: 12/28/2022] Open
Abstract
Artificial intelligence (AI) is a complex concept, broadly defined in medicine as the development of computer systems to perform tasks that require human intelligence. It has the capacity to revolutionize medicine by increasing efficiency, expediting data and image analysis and identifying patterns, trends and associations in large datasets. Within gastroenterology, recent research efforts have focused on using AI in esophagogastroduodenoscopy, wireless capsule endoscopy (WCE) and colonoscopy to assist in diagnosis, disease monitoring, lesion detection and therapeutic intervention. The main objective of this narrative review is to provide a comprehensive overview of the research being performed within gastroenterology on AI in esophagogastroduodenoscopy, WCE and colonoscopy.
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Affiliation(s)
- Jonathan S Galati
- Department of Medicine, NYU Langone Health, New York, NY 10016, United States
| | - Robert J Duve
- Department of Internal Medicine, Jacobs School of Medicine and Biomedical Sciences, University at Buffalo, Buffalo, NY 14203, United States
| | - Matthew O'Mara
- Division of Gastroenterology, NYU Langone Health, New York, NY 10016, United States
| | - Seth A Gross
- Division of Gastroenterology, NYU Langone Health, New York, NY 10016, United States
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