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Wang Y, Shi T, Gao F, Tian S, Yu L. Celiac disease diagnosis from endoscopic images based on multi-scale adaptive hybrid architecture model. Phys Med Biol 2024; 69:075014. [PMID: 38306971 DOI: 10.1088/1361-6560/ad25c1] [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: 08/31/2023] [Accepted: 02/02/2024] [Indexed: 02/04/2024]
Abstract
Objective. Celiac disease (CD) has emerged as a significant global public health concern, exhibiting an estimated worldwide prevalence of approximately 1%. However, existing research pertaining to domestic occurrences of CD is confined mainly to case reports and limited case analyses. Furthermore, there is a substantial population of undiagnosed patients in the Xinjiang region. This study endeavors to create a novel, high-performance, lightweight deep learning model utilizing endoscopic images from CD patients in Xinjiang as a dataset, with the intention of enhancing the accuracy of CD diagnosis.Approach. In this study, we propose a novel CNN-Transformer hybrid architecture for deep learning, tailored to the diagnosis of CD using endoscopic images. Within this architecture, a multi-scale spatial adaptive selective kernel convolution feature attention module demonstrates remarkable efficacy in diagnosing CD. Within this module, we dynamically capture salient features within the local channel feature map that correspond to distinct manifestations of endoscopic image lesions in the CD-affected areas such as the duodenal bulb, duodenal descending segment, and terminal ileum. This process serves to extract and fortify the spatial information specific to different lesions. This strategic approach facilitates not only the extraction of diverse lesion characteristics but also the attentive consideration of their spatial distribution. Additionally, we integrate the global representation of the feature map obtained from the Transformer with the locally extracted information via convolutional layers. This integration achieves a harmonious synergy that optimizes the diagnostic prowess of the model.Main results. Overall, the accuracy, specificity, F1-Score, and precision in the experimental results were 98.38%, 99.04%, 98.66% and 99.38%, respectively.Significance. This study introduces a deep learning network equipped with both global feature response and local feature extraction capabilities. This innovative architecture holds significant promise for the accurate diagnosis of CD by leveraging endoscopic images captured from diverse anatomical sites.
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Affiliation(s)
- Yilei Wang
- College of Software, Xinjiang University, Urumqi, Xinjiang, People's Republic of China
- Key Laboratory of Software Engineering Technology, College of Software, Xin Jiang University, Urumqi, People's Republic of China
| | - Tian Shi
- Department of Gastroenterologys, People's Hospital of Xinjiang Uyghur Autonomous Region, Urumqi, Xinjiang Uyghur Autonomous Region, People's Republic of China
- Xinjiang Clinical Research Center for Digestive Diseases, Urumqi, People's Republic of China
| | - Feng Gao
- Department of Gastroenterologys, People's Hospital of Xinjiang Uyghur Autonomous Region, Urumqi, Xinjiang Uyghur Autonomous Region, People's Republic of China
- Xinjiang Clinical Research Center for Digestive Diseases, Urumqi, People's Republic of China
| | - Shengwei Tian
- College of Software, Xinjiang University, Urumqi, Xinjiang, People's Republic of China
- Key Laboratory of Software Engineering Technology, College of Software, Xin Jiang University, Urumqi, People's Republic of China
| | - Long Yu
- College of Network Center, Xinjiang University, Urumqi, People's Republic of China
- Signal and Signal Processing Laboratory, College of Information Science and Engineering, Xinjiang University, Urumqi, People's Republic of China
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2
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Mota J, Almeida MJ, Mendes F, Martins M, Ribeiro T, Afonso J, Cardoso P, Cardoso H, Andrade P, Ferreira J, Mascarenhas M, Macedo G. From Data to Insights: How Is AI Revolutionizing Small-Bowel Endoscopy? Diagnostics (Basel) 2024; 14:291. [PMID: 38337807 PMCID: PMC10855436 DOI: 10.3390/diagnostics14030291] [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: 11/13/2023] [Revised: 01/09/2024] [Accepted: 01/16/2024] [Indexed: 02/12/2024] Open
Abstract
The role of capsule endoscopy and enteroscopy in managing various small-bowel pathologies is well-established. However, their broader application has been hampered mainly by their lengthy reading times. As a result, there is a growing interest in employing artificial intelligence (AI) in these diagnostic and therapeutic procedures, driven by the prospect of overcoming some major limitations and enhancing healthcare efficiency, while maintaining high accuracy levels. In the past two decades, the applicability of AI to gastroenterology has been increasing, mainly because of the strong imaging component. Nowadays, there are a multitude of studies using AI, specifically using convolutional neural networks, that prove the potential applications of AI to these endoscopic techniques, achieving remarkable results. These findings suggest that there is ample opportunity for AI to expand its presence in the management of gastroenterology diseases and, in the future, catalyze a game-changing transformation in clinical activities. This review provides an overview of the current state-of-the-art of AI in the scope of small-bowel study, with a particular focus on capsule endoscopy and enteroscopy.
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Affiliation(s)
- Joana Mota
- Precision Medicine Unit, Department of Gastroenterology, São João University Hospital, Alameda Professor Hernâni Monteiro, 4200-427 Porto, Portugal (G.M.)
- WGO Gastroenterology and Hepatology Training Center, Alameda Professor Hernâni Monteiro, 4200-427 Porto, Portugal
| | - Maria João Almeida
- Precision Medicine Unit, Department of Gastroenterology, São João University Hospital, Alameda Professor Hernâni Monteiro, 4200-427 Porto, Portugal (G.M.)
- WGO Gastroenterology and Hepatology Training Center, Alameda Professor Hernâni Monteiro, 4200-427 Porto, Portugal
| | - Francisco Mendes
- Precision Medicine Unit, Department of Gastroenterology, São João University Hospital, Alameda Professor Hernâni Monteiro, 4200-427 Porto, Portugal (G.M.)
- WGO Gastroenterology and Hepatology Training Center, Alameda Professor Hernâni Monteiro, 4200-427 Porto, Portugal
| | - Miguel Martins
- Precision Medicine Unit, Department of Gastroenterology, São João University Hospital, Alameda Professor Hernâni Monteiro, 4200-427 Porto, Portugal (G.M.)
- WGO Gastroenterology and Hepatology Training Center, Alameda Professor Hernâni Monteiro, 4200-427 Porto, Portugal
| | - Tiago Ribeiro
- Precision Medicine Unit, Department of Gastroenterology, São João University Hospital, Alameda Professor Hernâni Monteiro, 4200-427 Porto, Portugal (G.M.)
- WGO Gastroenterology and Hepatology Training Center, Alameda Professor Hernâni Monteiro, 4200-427 Porto, Portugal
| | - João Afonso
- Precision Medicine Unit, Department of Gastroenterology, São João University Hospital, Alameda Professor Hernâni Monteiro, 4200-427 Porto, Portugal (G.M.)
- WGO Gastroenterology and Hepatology Training Center, Alameda Professor Hernâni Monteiro, 4200-427 Porto, Portugal
| | - Pedro Cardoso
- Precision Medicine Unit, Department of Gastroenterology, São João University Hospital, Alameda Professor Hernâni Monteiro, 4200-427 Porto, Portugal (G.M.)
- WGO Gastroenterology and Hepatology Training Center, Alameda Professor Hernâni Monteiro, 4200-427 Porto, Portugal
| | - Helder Cardoso
- Precision Medicine Unit, Department of Gastroenterology, São João University Hospital, Alameda Professor Hernâni Monteiro, 4200-427 Porto, Portugal (G.M.)
- WGO Gastroenterology and Hepatology Training Center, Alameda Professor Hernâni Monteiro, 4200-427 Porto, Portugal
- Faculty of Medicine, University of Porto, Alameda Professor Hernâni Monteiro, 4200-427 Porto, Portugal
| | - Patrícia Andrade
- Precision Medicine Unit, Department of Gastroenterology, São João University Hospital, Alameda Professor Hernâni Monteiro, 4200-427 Porto, Portugal (G.M.)
- WGO Gastroenterology and Hepatology Training Center, Alameda Professor Hernâni Monteiro, 4200-427 Porto, Portugal
- Faculty of Medicine, University of Porto, Alameda Professor Hernâni Monteiro, 4200-427 Porto, Portugal
| | - João Ferreira
- Department of Mechanical Engineering, Faculty of Engineering, University of Porto, R. Dr. Roberto Frias, 4200-465 Porto, Portugal;
- Digestive Artificial Intelligence Development, R. Alfredo Allen 455-461, 4200-135 Porto, Portugal
| | - Miguel Mascarenhas
- Precision Medicine Unit, Department of Gastroenterology, São João University Hospital, Alameda Professor Hernâni Monteiro, 4200-427 Porto, Portugal (G.M.)
- WGO Gastroenterology and Hepatology Training Center, Alameda Professor Hernâni Monteiro, 4200-427 Porto, Portugal
- Faculty of Medicine, University of Porto, Alameda Professor Hernâni Monteiro, 4200-427 Porto, Portugal
- ManopH Gastroenterology Clinic, R. de Sá da Bandeira 752, 4000-432 Porto, Portugal
| | - Guilherme Macedo
- Precision Medicine Unit, Department of Gastroenterology, São João University Hospital, Alameda Professor Hernâni Monteiro, 4200-427 Porto, Portugal (G.M.)
- WGO Gastroenterology and Hepatology Training Center, Alameda Professor Hernâni Monteiro, 4200-427 Porto, Portugal
- Faculty of Medicine, University of Porto, Alameda Professor Hernâni Monteiro, 4200-427 Porto, Portugal
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Wu S, Zhang R, Yan J, Li C, Liu Q, Wang L, Wang H. High-Speed and Accurate Diagnosis of Gastrointestinal Disease: Learning on Endoscopy Images Using Lightweight Transformer with Local Feature Attention. Bioengineering (Basel) 2023; 10:1416. [PMID: 38136007 PMCID: PMC10741161 DOI: 10.3390/bioengineering10121416] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 11/18/2023] [Revised: 12/04/2023] [Accepted: 12/10/2023] [Indexed: 12/24/2023] Open
Abstract
In response to the pressing need for robust disease diagnosis from gastrointestinal tract (GIT) endoscopic images, we proposed FLATer, a fast, lightweight, and highly accurate transformer-based model. FLATer consists of a residual block, a vision transformer module, and a spatial attention block, which concurrently focuses on local features and global attention. It can leverage the capabilities of both convolutional neural networks (CNNs) and vision transformers (ViT). We decomposed the classification of endoscopic images into two subtasks: a binary classification to discern between normal and pathological images and a further multi-class classification to categorize images into specific diseases, namely ulcerative colitis, polyps, and esophagitis. FLATer has exhibited exceptional prowess in these tasks, achieving 96.4% accuracy in binary classification and 99.7% accuracy in ternary classification, surpassing most existing models. Notably, FLATer could maintain impressive performance when trained from scratch, underscoring its robustness. In addition to the high precision, FLATer boasted remarkable efficiency, reaching a notable throughput of 16.4k images per second, which positions FLATer as a compelling candidate for rapid disease identification in clinical practice.
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Affiliation(s)
- Shibin Wu
- Shenzhen International Graduate School, Tsinghua University, Shenzhen 518055, China; (S.W.); (R.Z.); (J.Y.)
| | - Ruxin Zhang
- Shenzhen International Graduate School, Tsinghua University, Shenzhen 518055, China; (S.W.); (R.Z.); (J.Y.)
| | - Jiayi Yan
- Shenzhen International Graduate School, Tsinghua University, Shenzhen 518055, China; (S.W.); (R.Z.); (J.Y.)
| | - Chengquan Li
- School of Clinical Medicine, Tsinghua University, Beijing 100084, China;
| | - Qicai Liu
- Vanke School of Public Health, Tsinghua University, Beijing 100084, China;
| | - Liyang Wang
- School of Clinical Medicine, Tsinghua University, Beijing 100084, China;
| | - Haoqian Wang
- Shenzhen International Graduate School, Tsinghua University, Shenzhen 518055, China; (S.W.); (R.Z.); (J.Y.)
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4
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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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5
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Dhaliwal J, Walsh CM. Artificial Intelligence in Pediatric Endoscopy: Current Status and Future Applications. Gastrointest Endosc Clin N Am 2023; 33:291-308. [PMID: 36948747 DOI: 10.1016/j.giec.2022.12.001] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 03/24/2023]
Abstract
The application of artificial intelligence (AI) has great promise for improving pediatric endoscopy. The majority of preclinical studies have been undertaken in adults, with the greatest progress being made in the context of colorectal cancer screening and surveillance. This development has only been possible with advances in deep learning, like the convolutional neural network model, which has enabled real-time detection of pathology. Comparatively, the majority of deep learning systems developed in inflammatory bowel disease have focused on predicting disease severity and were developed using still images rather than videos. The application of AI to pediatric endoscopy is in its infancy, thus providing an opportunity to develop clinically meaningful and fair systems that do not perpetuate societal biases. In this review, we provide an overview of AI, summarize the advances of AI in endoscopy, and describe its potential application to pediatric endoscopic practice and education.
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Affiliation(s)
- Jasbir Dhaliwal
- Division of Pediatric Gastroenterology, Hepatology and Nutrition, Cincinnati Children's Hospital Medictal Center, University of Cincinnati, OH, USA.
| | - Catharine M Walsh
- Division of Gastroenterology, Hepatology, and Nutrition, and the SickKids Research and Learning Institutes, The Hospital for Sick Children, Toronto, ON, Canada; Department of Paediatrics and The Wilson Centre, University of Toronto, Temerty Faculty of Medicine, University of Toronto, Toronto, ON, Canada
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6
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Sivari E, Bostanci E, Guzel MS, Acici K, Asuroglu T, Ercelebi Ayyildiz T. A New Approach for Gastrointestinal Tract Findings Detection and Classification: Deep Learning-Based Hybrid Stacking Ensemble Models. Diagnostics (Basel) 2023; 13:diagnostics13040720. [PMID: 36832205 PMCID: PMC9954881 DOI: 10.3390/diagnostics13040720] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 01/27/2023] [Revised: 02/06/2023] [Accepted: 02/10/2023] [Indexed: 02/17/2023] Open
Abstract
Endoscopic procedures for diagnosing gastrointestinal tract findings depend on specialist experience and inter-observer variability. This variability can cause minor lesions to be missed and prevent early diagnosis. In this study, deep learning-based hybrid stacking ensemble modeling has been proposed for detecting and classifying gastrointestinal system findings, aiming at early diagnosis with high accuracy and sensitive measurements and saving workload to help the specialist and objectivity in endoscopic diagnosis. In the first level of the proposed bi-level stacking ensemble approach, predictions are obtained by applying 5-fold cross-validation to three new CNN models. A machine learning classifier selected at the second level is trained according to the obtained predictions, and the final classification result is reached. The performances of the stacking models were compared with the performances of the deep learning models, and McNemar's statistical test was applied to support the results. According to the experimental results, stacking ensemble models performed with a significant difference with 98.42% ACC and 98.19% MCC in the KvasirV2 dataset and 98.53% ACC and 98.39% MCC in the HyperKvasir dataset. This study is the first to offer a new learning-oriented approach that efficiently evaluates CNN features and provides objective and reliable results with statistical testing compared to state-of-the-art studies on the subject. The proposed approach improves the performance of deep learning models and outperforms the state-of-the-art studies in the literature.
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Affiliation(s)
- Esra Sivari
- Department of Computer Engineering, Cankiri Karatekin University, Cankiri 18100, Turkey
| | - Erkan Bostanci
- Department of Computer Engineering, Ankara University, Ankara 06830, Turkey
| | | | - Koray Acici
- Department of Artificial Intelligence and Data Engineering, Ankara University, Ankara 06830, Turkey
| | - Tunc Asuroglu
- Faculty of Medicine and Health Technology, Tampere University, 33720 Tampere, Finland
- Correspondence:
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Scheppach MW, Rauber D, Stallhofer J, Muzalyova A, Otten V, Manzeneder C, Schwamberger T, Wanzl J, Schlottmann J, Tadic V, Probst A, Schnoy E, Römmele C, Fleischmann C, Meinikheim M, Miller S, Märkl B, Stallmach A, Palm C, Messmann H, Ebigbo A. Detection of duodenal villous atrophy on endoscopic images using a deep learning algorithm. Gastrointest Endosc 2023; 97:911-916. [PMID: 36646146 DOI: 10.1016/j.gie.2023.01.006] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 09/28/2022] [Revised: 12/16/2022] [Accepted: 01/01/2023] [Indexed: 01/18/2023]
Abstract
BACKGROUND AND AIMS Celiac disease with its endoscopic manifestation of villous atrophy (VA) is underdiagnosed worldwide. The application of artificial intelligence (AI) for the macroscopic detection of VA at routine EGD may improve diagnostic performance. METHODS A dataset of 858 endoscopic images of 182 patients with VA and 846 images from 323 patients with normal duodenal mucosa was collected and used to train a ResNet18 deep learning model to detect VA. An external dataset was used to test the algorithm, in addition to 6 fellows and 4 board-certified gastroenterologists. Fellows could consult the AI algorithm's result during the test. From their consultation distribution, a stratification of test images into "easy" and "difficult" was performed and used for classified performance measurement. RESULTS External validation of the AI algorithm yielded values of 90%, 76%, and 84% for sensitivity, specificity, and accuracy, respectively. Fellows scored corresponding values of 63%, 72%, and 67% and experts scored 72%, 69%, and 71%, respectively. AI consultation significantly improved all trainee performance statistics. Although fellows and experts showed significantly lower performance for difficult images, the performance of the AI algorithm was stable. CONCLUSIONS In this study, an AI algorithm outperformed endoscopy fellows and experts in the detection of VA on endoscopic still images. AI decision support significantly improved the performance of nonexpert endoscopists. The stable performance on difficult images suggests a further positive add-on effect in challenging cases.
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Affiliation(s)
- Markus W Scheppach
- Internal Medicine III-Gastroenterology, University Hospital of Augsburg, Augsburg, Germany
| | - David Rauber
- Regensburg Medical Image Computing (ReMIC), Ostbayerische Technische Hochschule Regensburg, Regensburg, Germany; Regensburg Center of Biomedical Engineering, Ostbayerische Technische Hochschule Regensburg, Regensburg, Germany
| | - Johannes Stallhofer
- Department of Internal Medicine IV (Gastroenterology, Hepatology, and Infectious Diseases), Jena University Hospital, Jena, Germany
| | - Anna Muzalyova
- Internal Medicine III-Gastroenterology, University Hospital of Augsburg, Augsburg, Germany
| | - Vera Otten
- Internal Medicine III-Gastroenterology, University Hospital of Augsburg, Augsburg, Germany
| | - Carolin Manzeneder
- Internal Medicine III-Gastroenterology, University Hospital of Augsburg, Augsburg, Germany
| | - Tanja Schwamberger
- Internal Medicine III-Gastroenterology, University Hospital of Augsburg, Augsburg, Germany
| | - Julia Wanzl
- Internal Medicine III-Gastroenterology, University Hospital of Augsburg, Augsburg, Germany
| | - Jakob Schlottmann
- Internal Medicine III-Gastroenterology, University Hospital of Augsburg, Augsburg, Germany
| | - Vidan Tadic
- Internal Medicine III-Gastroenterology, University Hospital of Augsburg, Augsburg, Germany
| | - Andreas Probst
- Internal Medicine III-Gastroenterology, University Hospital of Augsburg, Augsburg, Germany
| | - Elisabeth Schnoy
- Internal Medicine III-Gastroenterology, University Hospital of Augsburg, Augsburg, Germany
| | - Christoph Römmele
- Internal Medicine III-Gastroenterology, University Hospital of Augsburg, Augsburg, Germany
| | - Carola Fleischmann
- Internal Medicine III-Gastroenterology, University Hospital of Augsburg, Augsburg, Germany
| | - Michael Meinikheim
- Internal Medicine III-Gastroenterology, University Hospital of Augsburg, Augsburg, Germany
| | - Silvia Miller
- Department of Pathology, University Hospital of Augsburg, Augsburg, Germany
| | - Bruno Märkl
- Department of Pathology, University Hospital of Augsburg, Augsburg, Germany
| | - Andreas Stallmach
- Department of Internal Medicine IV (Gastroenterology, Hepatology, and Infectious Diseases), Jena University Hospital, Jena, Germany
| | - Christoph Palm
- Regensburg Medical Image Computing (ReMIC), Ostbayerische Technische Hochschule Regensburg, Regensburg, Germany; Regensburg Center of Biomedical Engineering, Ostbayerische Technische Hochschule Regensburg, Regensburg, Germany
| | - Helmut Messmann
- Internal Medicine III-Gastroenterology, University Hospital of Augsburg, Augsburg, Germany
| | - Alanna Ebigbo
- Internal Medicine III-Gastroenterology, University Hospital of Augsburg, Augsburg, Germany
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Pennazio M, Rondonotti E, Despott EJ, Dray X, Keuchel M, Moreels T, Sanders DS, Spada C, Carretero C, Cortegoso Valdivia P, Elli L, Fuccio L, Gonzalez Suarez B, Koulaouzidis A, Kunovsky L, McNamara D, Neumann H, Perez-Cuadrado-Martinez E, Perez-Cuadrado-Robles E, Piccirelli S, Rosa B, Saurin JC, Sidhu R, Tacheci I, Vlachou E, Triantafyllou K. Small-bowel capsule endoscopy and device-assisted enteroscopy for diagnosis and treatment of small-bowel disorders: European Society of Gastrointestinal Endoscopy (ESGE) Guideline - Update 2022. Endoscopy 2023; 55:58-95. [PMID: 36423618 DOI: 10.1055/a-1973-3796] [Citation(s) in RCA: 65] [Impact Index Per Article: 65.0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Indexed: 11/26/2022]
Abstract
MR1: ESGE recommends small-bowel capsule endoscopy as the first-line examination, before consideration of other endoscopic and radiological diagnostic tests for suspected small-bowel bleeding, given the excellent safety profile of capsule endoscopy, its patient tolerability, and its potential to visualize the entire small-bowel mucosa.Strong recommendation, moderate quality evidence. MR2: ESGE recommends small-bowel capsule endoscopy in patients with overt suspected small-bowel bleeding as soon as possible after the bleeding episode, ideally within 48 hours, to maximize the diagnostic and subsequent therapeutic yield.Strong recommendation, high quality evidence. MR3: ESGE does not recommend routine second-look endoscopy prior to small-bowel capsule endoscopy in patients with suspected small-bowel bleeding or iron-deficiency anemia.Strong recommendation, low quality evidence. MR4: ESGE recommends conservative management in those patients with suspected small-bowel bleeding and high quality negative small-bowel capsule endoscopy.Strong recommendation, moderate quality evidence. MR5: ESGE recommends device-assisted enteroscopy to confirm and possibly treat lesions identified by small-bowel capsule endoscopy.Strong recommendation, high quality evidence. MR6: ESGE recommends the performance of small-bowel capsule endoscopy as a first-line examination in patients with iron-deficiency anemia when small bowel evaluation is indicated.Strong recommendation, high quality evidence. MR7: ESGE recommends small-bowel capsule endoscopy in patients with suspected Crohn's disease and negative ileocolonoscopy findings as the initial diagnostic modality for investigating the small bowel, in the absence of obstructive symptoms or known bowel stenosis.Strong recommendation, high quality evidence. MR8: ESGE recommends, in patients with unremarkable or nondiagnostic findings from dedicated small-bowel cross-sectional imaging, small-bowel capsule endoscopy as a subsequent investigation if deemed likely to influence patient management.Strong recommendation, low quality evidence. MR9: ESGE recommends, in patients with established Crohn's disease, the use of a patency capsule before small-bowel capsule endoscopy to decrease the capsule retention rate.Strong recommendation, moderate quality evidence. MR10: ESGE recommends device-assisted enteroscopy (DAE) as an alternative to surgery for foreign bodies retained in the small bowel requiring retrieval in patients without acute intestinal obstruction.Strong recommendation, moderate quality evidence. MR11: ESGE recommends DAE-endoscopic retrograde cholangiopancreatography (DAE-ERCP) as a first-line endoscopic approach to treat pancreaticobiliary diseases in patients with surgically altered anatomy (except for Billroth II patients).Strong recommendation, moderate quality evidence.
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Affiliation(s)
- Marco Pennazio
- University Division of Gastroenterology, City of Health and Science University Hospital, University of Turin, Turin, Italy
| | | | - Edward J Despott
- Royal Free Unit for Endoscopy, The Royal Free Hospital and UCL Institute for Liver and Digestive Health, London, UK
| | - Xavier Dray
- Sorbonne University, Endoscopy Unit, AP-HP, Hôpital Saint-Antoine, Paris, France
| | - Martin Keuchel
- Clinic for Internal Medicine, Agaplesion Bethesda Krankenhaus Bergedorf, Hamburg, Germany
| | - Tom Moreels
- Division of Gastroenterology and Hepatology, University Hospital Saint-Luc, Brussels, Belgium
| | - David S Sanders
- Sheffield Teaching Hospitals NHS Foundation Trust, Gastroenterology Sheffield, Sheffield, UK
| | - Cristiano Spada
- Digestive Endoscopy Unit and Gastroenterology, Fondazione Poliambulanza, Brescia, Italy.,Università Cattolica del Sacro Cuore, Rome, Italy
| | - Cristina Carretero
- Department of Gastroenterology. University of Navarre Clinic, Healthcare Research Institute of Navarre, Pamplona, Spain
| | - Pablo Cortegoso Valdivia
- Gastroenterology and Endoscopy Unit, University Hospital of Parma, University of Parma, Parma, Italy
| | - Luca Elli
- Gastroenterology and Endoscopy Unit, Fondazione IRCCS Ca' Granda Ospedale Maggiore Policlinico, Milan, Italy
| | - Lorenzo Fuccio
- IRCCS Azienda Ospedaliero-Universitaria di Bologna, Department of Medical and Surgical Sciences, Gastroenterology Unit, University of Bologna, Bologna, Italy
| | - Begona Gonzalez Suarez
- Gastroenterology Department - ICMDiM, Hospital Clínic of Barcelona, DIBAPS, CiBERHED, Barcelona, Spain
| | - Anastasios Koulaouzidis
- Centre for Clinical Implementation of Capsule Endoscopy, Store Adenomer Tidlige Cancere Center, Svendborg, University of Southern Denmark, Denmark
| | - Lumir Kunovsky
- 2nd Department of Internal Medicine - Gastroenterology and Geriatrics, University Hospital Olomouc, Faculty of Medicine and Dentistry, Palacky University Olomouc, Olomouc, Czech Republic.,Department of Surgery, University Hospital Brno, Faculty of Medicine, Masaryk University, Brno, Czech Republic.,Department of Gastroenterology and Digestive Endoscopy, Masaryk Memorial Cancer Institute, Brno, Czech Republic
| | - Deirdre McNamara
- TAGG Research Centre, Department of Clinical Medicine, Trinity Centre, Tallaght Hospital, Dublin, Ireland
| | - Helmut Neumann
- Department of Medicine I, University Medical Center Mainz, Mainz, Germany
| | | | | | - Stefania Piccirelli
- Digestive Endoscopy Unit and Gastroenterology, Fondazione Poliambulanza, Brescia, Italy
| | - Bruno Rosa
- Department of Gastroenterology, Hospital da Senhora da Oliveira, Guimarães, Portugal.,Life and Health Sciences Research Institute, School of Medicine, University of Minho, Braga/Guimarães, Portugal.,ICVS/3B's, PT Government Associate Laboratory, Braga/Guimarães, Portugal
| | - Jean-Christophe Saurin
- Gastroenterology and Endoscopy Unit, Hospices Civils de Lyon, Hôpital E. Herriot, Lyon, France
| | - Reena Sidhu
- Academic Department of Gastroenterology and Hepatology, Sheffield Teaching Hospitals, Sheffield, United Kingdom.,Department of Infection, Immunity and Cardiovascular Diseases, University of Sheffield, United Kingdom
| | - Ilja Tacheci
- 2nd Department of Internal Medicine - Gastroenterology, University Hospital Hradec Králové, Charles University, Faculty of Medicine in Hradec Králové, Czech Republic
| | | | - Konstantinos Triantafyllou
- Hepatogastroenterology Unit, Second Department of Internal Medicine - Propaedeutic, Research Institute and Diabetes Center, Medical School, National and Kapodistrian University of Athens, Attikon University General Hospital, Athens, Greece
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9
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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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10
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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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11
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Parkash O, Siddiqui ATS, Jiwani U, Rind F, Padhani ZA, Rizvi A, Hoodbhoy Z, Das JK. Diagnostic accuracy of artificial intelligence for detecting gastrointestinal luminal pathologies: A systematic review and meta-analysis. Front Med (Lausanne) 2022; 9:1018937. [PMID: 36405592 PMCID: PMC9672666 DOI: 10.3389/fmed.2022.1018937] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.5] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 08/15/2022] [Accepted: 10/03/2022] [Indexed: 11/06/2022] Open
Abstract
Background Artificial Intelligence (AI) holds considerable promise for diagnostics in the field of gastroenterology. This systematic review and meta-analysis aims to assess the diagnostic accuracy of AI models compared with the gold standard of experts and histopathology for the diagnosis of various gastrointestinal (GI) luminal pathologies including polyps, neoplasms, and inflammatory bowel disease. Methods We searched PubMed, CINAHL, Wiley Cochrane Library, and Web of Science electronic databases to identify studies assessing the diagnostic performance of AI models for GI luminal pathologies. We extracted binary diagnostic accuracy data and constructed contingency tables to derive the outcomes of interest: sensitivity and specificity. We performed a meta-analysis and hierarchical summary receiver operating characteristic curves (HSROC). The risk of bias was assessed using Quality Assessment for Diagnostic Accuracy Studies-2 (QUADAS-2) tool. Subgroup analyses were conducted based on the type of GI luminal disease, AI model, reference standard, and type of data used for analysis. This study is registered with PROSPERO (CRD42021288360). Findings We included 73 studies, of which 31 were externally validated and provided sufficient information for inclusion in the meta-analysis. The overall sensitivity of AI for detecting GI luminal pathologies was 91.9% (95% CI: 89.0–94.1) and specificity was 91.7% (95% CI: 87.4–94.7). Deep learning models (sensitivity: 89.8%, specificity: 91.9%) and ensemble methods (sensitivity: 95.4%, specificity: 90.9%) were the most commonly used models in the included studies. Majority of studies (n = 56, 76.7%) had a high risk of selection bias while 74% (n = 54) studies were low risk on reference standard and 67% (n = 49) were low risk for flow and timing bias. Interpretation The review suggests high sensitivity and specificity of AI models for the detection of GI luminal pathologies. There is a need for large, multi-center trials in both high income countries and low- and middle- income countries to assess the performance of these AI models in real clinical settings and its impact on diagnosis and prognosis. Systematic review registration [https://www.crd.york.ac.uk/prospero/display_record.php?RecordID=288360], identifier [CRD42021288360].
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Affiliation(s)
- Om Parkash
- Department of Medicine, Aga Khan University, Karachi, Pakistan
| | | | - Uswa Jiwani
- Center of Excellence in Women and Child Health, Aga Khan University, Karachi, Pakistan
| | - Fahad Rind
- Head and Neck Oncology, The Ohio State University, Columbus, OH, United States
| | - Zahra Ali Padhani
- Institute for Global Health and Development, Aga Khan University, Karachi, Pakistan
| | - Arjumand Rizvi
- Center of Excellence in Women and Child Health, Aga Khan University, Karachi, Pakistan
| | - Zahra Hoodbhoy
- Department of Pediatrics and Child Health, Aga Khan University, Karachi, Pakistan
| | - Jai K. Das
- Institute for Global Health and Development, Aga Khan University, Karachi, Pakistan
- Department of Pediatrics and Child Health, Aga Khan University, Karachi, Pakistan
- *Correspondence: Jai K. Das,
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12
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Deep Learning Multi-Domain Model Provides Accurate Detection and Grading of Mucosal Ulcers in Different Capsule Endoscopy Types. Diagnostics (Basel) 2022; 12:diagnostics12102490. [PMID: 36292178 PMCID: PMC9600959 DOI: 10.3390/diagnostics12102490] [Citation(s) in RCA: 5] [Impact Index Per Article: 2.5] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 08/31/2022] [Revised: 10/06/2022] [Accepted: 10/08/2022] [Indexed: 11/17/2022] Open
Abstract
Background and Aims: The aim of our study was to create an accurate patient-level combined algorithm for the identification of ulcers on CE images from two different capsules. Methods: We retrospectively collected CE images from PillCam-SB3′s capsule and PillCam-Crohn’s capsule. ML algorithms were trained to classify small bowel CE images into either normal or ulcerated mucosa: a separate model for each capsule type, a cross-domain model (training the model on one capsule type and testing on the other), and a combined model. Results: The dataset included 33,100 CE images: 20,621 PillCam-SB3 images and 12,479 PillCam-Crohn’s images, of which 3582 were colonic images. There were 15,684 normal mucosa images and 17,416 ulcerated mucosa images. While the separate model for each capsule type achieved excellent accuracy (average AUC 0.95 and 0.98, respectively), the cross-domain model achieved a wide range of accuracies (0.569–0.88) with an AUC of 0.93. The combined model achieved the best results with an average AUC of 0.99 and average mean patient accuracy of 0.974. Conclusions: A combined model for two different capsules provided high and consistent diagnostic accuracy. Creating a holistic AI model for automated capsule reading is an essential part of the refinement required in ML models on the way to adapting them to clinical practice.
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13
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Time-based self-supervised learning for Wireless Capsule Endoscopy. Comput Biol Med 2022; 146:105631. [DOI: 10.1016/j.compbiomed.2022.105631] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 12/15/2021] [Revised: 04/17/2022] [Accepted: 04/17/2022] [Indexed: 11/18/2022]
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14
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Higuchi N, Hiraga H, Sasaki Y, Hiraga N, Igarashi S, Hasui K, Ogasawara K, Maeda T, Murai Y, Tatsuta T, Kikuchi H, Chinda D, Mikami T, Matsuzaka M, Sakuraba H, Fukuda S. Automated evaluation of colon capsule endoscopic severity of ulcerative colitis using ResNet50. PLoS One 2022; 17:e0269728. [PMID: 35687553 PMCID: PMC9187078 DOI: 10.1371/journal.pone.0269728] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.5] [Reference Citation Analysis] [Abstract] [MESH Headings] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 01/26/2022] [Accepted: 05/26/2022] [Indexed: 12/19/2022] Open
Abstract
Capsule endoscopy has been widely used as a non-invasive diagnostic tool for small or large intestinal lesions. In recent years, automated lesion detection systems using machine learning have been devised. This study aimed to develop an automated system for capsule endoscopic severity in patients with ulcerative colitis along the entire length of the colon using ResNet50. Capsule endoscopy videos from patients with ulcerative colitis were collected prospectively. Each single examination video file was partitioned into four segments: the cecum and ascending colon, transverse colon, descending and sigmoid colon, and rectum. Fifty still pictures (576 × 576 pixels) were extracted from each partitioned video. A patch (128 × 128 pixels) was trimmed from the still picture at every 32-pixel-strides. A total of 739,021 patch images were manually classified into six categories: 0) Mayo endoscopic subscore (MES) 0, 1) MES1, 2) MES2, 3) MES3, 4) inadequate quality for evaluation, and 5) ileal mucosa. ResNet50, a deep learning framework, was trained using 483,644 datasets and validated using 255,377 independent datasets. In total, 31 capsule endoscopy videos from 22 patients were collected. The accuracy rates of the training and validation datasets were 0.992 and 0.973, respectively. An automated evaluation system for the capsule endoscopic severity of ulcerative colitis was developed. This could be a useful tool for assessing topographic disease activity, thus decreasing the burden of image interpretation on endoscopists.
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Affiliation(s)
- Naoki Higuchi
- Department of Gastroenterology and Hematology, Hirosaki University Graduate School of Medicine, Hirosaki, Japan
| | - Hiroto Hiraga
- Department of Gastroenterology and Hematology, Hirosaki University Graduate School of Medicine, Hirosaki, Japan
- * E-mail:
| | - Yoshihiro Sasaki
- Department of Medical Informatics, Hirosaki University Hospital, Hirosaki, Japan
| | - Noriko Hiraga
- Department of Gastroenterology and Hematology, Hirosaki University Graduate School of Medicine, Hirosaki, Japan
| | - Shohei Igarashi
- Department of Gastroenterology and Hematology, Hirosaki University Graduate School of Medicine, Hirosaki, Japan
| | - Keisuke Hasui
- Department of Gastroenterology and Hematology, Hirosaki University Graduate School of Medicine, Hirosaki, Japan
| | - Kohei Ogasawara
- Department of Gastroenterology and Hematology, Hirosaki University Graduate School of Medicine, Hirosaki, Japan
| | - Takato Maeda
- Department of Gastroenterology and Hematology, Hirosaki University Graduate School of Medicine, Hirosaki, Japan
| | - Yasuhisa Murai
- Department of Gastroenterology and Hematology, Hirosaki University Graduate School of Medicine, Hirosaki, Japan
| | - Tetsuya Tatsuta
- Department of Gastroenterology and Hematology, Hirosaki University Graduate School of Medicine, Hirosaki, Japan
| | - Hidezumi Kikuchi
- Department of Gastroenterology and Hematology, Hirosaki University Graduate School of Medicine, Hirosaki, Japan
| | - Daisuke Chinda
- Department of Gastroenterology and Hematology, Hirosaki University Graduate School of Medicine, Hirosaki, Japan
| | - Tatsuya Mikami
- Department of Gastroenterology and Hematology, Hirosaki University Graduate School of Medicine, Hirosaki, Japan
| | - Masashi Matsuzaka
- Department of Medical Informatics, Hirosaki University Hospital, Hirosaki, Japan
| | - Hirotake Sakuraba
- Department of Gastroenterology and Hematology, Hirosaki University Graduate School of Medicine, Hirosaki, Japan
| | - Shinsaku Fukuda
- Department of Gastroenterology and Hematology, Hirosaki University Graduate School of Medicine, Hirosaki, Japan
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15
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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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16
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Gnodi E, Meneveri R, Barisani D. Celiac disease: From genetics to epigenetics. World J Gastroenterol 2022; 28:449-463. [PMID: 35125829 PMCID: PMC8790554 DOI: 10.3748/wjg.v28.i4.449] [Citation(s) in RCA: 10] [Impact Index Per Article: 5.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Download PDF] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 04/18/2021] [Revised: 06/16/2021] [Accepted: 01/11/2022] [Indexed: 02/06/2023] Open
Abstract
Celiac disease (CeD) is a multifactorial autoimmune disorder spread worldwide. The exposure to gluten, a protein found in cereals like wheat, barley and rye, is the main environmental factor involved in its pathogenesis. Even if the genetic predisposition represented by HLA-DQ2 or HLA-DQ8 haplotypes is widely recognised as mandatory for CeD development, it is not enough to explain the total predisposition for the disease. Furthermore, the onset of CeD comprehend a wide spectrum of symptoms, that often leads to a delay in CeD diagnosis. To overcome this deficiency and help detecting people with increased risk for CeD, also clarifying CeD traits linked to disease familiarity, different studies have tried to make light on other predisposing elements. These were in many cases genetic variants shared with other autoimmune diseases. Since inherited traits can be regulated by epigenetic modifications, also induced by environmental factors, the most recent studies focused on the potential involvement of epigenetics in CeD. Epigenetic factors can in fact modulate gene expression with many mechanisms, generating more or less stable changes in gene expression without affecting the DNA sequence. Here we analyze the different epigenetic modifications in CeD, in particular DNA methylation, histone modifications, non-coding RNAs and RNA methylation. Special attention is dedicated to the additional predispositions to CeD, the involvement of epigenetics in developing CeD complications, the pathogenic pathways modulated by epigenetic factors such as microRNAs and the potential use of epigenetic profiling as biomarker to discriminate different classes of patients.
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Affiliation(s)
- Elisa Gnodi
- School of Medicine and Surgery, University of Milano-Bicocca, Monza 20900, Italy
| | - Raffaella Meneveri
- School of Medicine and Surgery, University of Milano-Bicocca, Monza 20900, Italy
| | - Donatella Barisani
- School of Medicine and Surgery, University of Milano-Bicocca, Monza 20900, Italy
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17
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Ribeiro T, Saraiva MM, Ferreira JPS, Cardoso H, Afonso J, Andrade P, Parente M, Jorge RN, Macedo G. Artificial intelligence and capsule endoscopy: automatic detection of vascular lesions using a convolutional neural network. Ann Gastroenterol 2021; 34:820-828. [PMID: 34815648 PMCID: PMC8596215 DOI: 10.20524/aog.2021.0653] [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: 01/30/2021] [Accepted: 04/07/2021] [Indexed: 12/09/2022] Open
Abstract
Background Capsule endoscopy (CE) is the first line for evaluation of patients with obscure gastrointestinal bleeding. A wide range of small intestinal vascular lesions with different hemorrhagic potential are frequently found in these patients. Nevertheless, reading CE exams is time-consuming and prone to errors. Convolutional neural networks (CNN) are artificial intelligence tools with high performance levels in image analysis. This study aimed to develop a CNN-based model for identification and differentiation of vascular lesions with distinct hemorrhagic potential in CE images. Methods The development of the CNN was based on a database of CE images. This database included images of normal small intestinal mucosa, red spots, and angiectasia/varices. The hemorrhagic risk was assessed by Saurin's classification. For CNN development, 11,588 images (9525 normal mucosa, 1026 red spots, and 1037 angiectasia/varices) were ultimately extracted. Two image datasets were created for CNN training and testing. Results The network was 91.8% sensitive and 95.9% specific for detection of vascular lesions, providing accurate predictions in 94.4% of cases. In particular, the CNN had a sensitivity and specificity of 97.1% and 95.3%, respectively, for detection of red spots. Detection of angiectasia/varices occurred with a sensitivity of 94.1% and a specificity of 95.1%. The CNN had a frame reading rate of 145 frames/sec. Conclusions The developed algorithm is the first CNN-based model to accurately detect and distinguish enteric vascular lesions with different hemorrhagic risk. CNN-assisted CE reading may improve the diagnosis of these lesions and overall CE efficiency.
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Affiliation(s)
- Tiago Ribeiro
- Department of Gastroenterology, São João University Hospital, Alameda Professor Hernâni Monteiro (Tiago Ribeiro, Miguel Mascarenhas Saraiva, Hélder Cardoso, João Afonso, Patrícia Andrade, Guilherme Macedo).,WGO Gastroenterology and Hepatology Training Center (Tiago Ribeiro, Miguel Mascarenhas Saraiva, Hélder Cardoso, João Afonso, Patrícia Andrade, Guilherme Macedo)
| | - Miguel Mascarenhas Saraiva
- Department of Gastroenterology, São João University Hospital, Alameda Professor Hernâni Monteiro (Tiago Ribeiro, Miguel Mascarenhas Saraiva, Hélder Cardoso, João Afonso, Patrícia Andrade, Guilherme Macedo).,WGO Gastroenterology and Hepatology Training Center (Tiago Ribeiro, Miguel Mascarenhas Saraiva, Hélder Cardoso, João Afonso, Patrícia Andrade, Guilherme Macedo).,Faculty of Medicine of the University of Porto, Alameda Professor Hernâni Monteiro (Miguel Mascarenhas Saraiva, Hélder Cardoso, Patrícia Andrade, Guilherme Macedo)
| | - João P S Ferreira
- Faculty of Engineering of the University of Porto (João P.S. Ferreira, Marco Parente, Renato Natal Jorge).,INEGI - Institute of Science and Innovation in Mechanical and Industrial Engineering (João P.S. Ferreira, Marco Parente, Renato Natal Jorge), Porto, Portugal
| | - Hélder Cardoso
- Department of Gastroenterology, São João University Hospital, Alameda Professor Hernâni Monteiro (Tiago Ribeiro, Miguel Mascarenhas Saraiva, Hélder Cardoso, João Afonso, Patrícia Andrade, Guilherme Macedo).,WGO Gastroenterology and Hepatology Training Center (Tiago Ribeiro, Miguel Mascarenhas Saraiva, Hélder Cardoso, João Afonso, Patrícia Andrade, Guilherme Macedo).,Faculty of Medicine of the University of Porto, Alameda Professor Hernâni Monteiro (Miguel Mascarenhas Saraiva, Hélder Cardoso, Patrícia Andrade, Guilherme Macedo)
| | - João Afonso
- Department of Gastroenterology, São João University Hospital, Alameda Professor Hernâni Monteiro (Tiago Ribeiro, Miguel Mascarenhas Saraiva, Hélder Cardoso, João Afonso, Patrícia Andrade, Guilherme Macedo).,WGO Gastroenterology and Hepatology Training Center (Tiago Ribeiro, Miguel Mascarenhas Saraiva, Hélder Cardoso, João Afonso, Patrícia Andrade, Guilherme Macedo)
| | - Patrícia Andrade
- Department of Gastroenterology, São João University Hospital, Alameda Professor Hernâni Monteiro (Tiago Ribeiro, Miguel Mascarenhas Saraiva, Hélder Cardoso, João Afonso, Patrícia Andrade, Guilherme Macedo).,WGO Gastroenterology and Hepatology Training Center (Tiago Ribeiro, Miguel Mascarenhas Saraiva, Hélder Cardoso, João Afonso, Patrícia Andrade, Guilherme Macedo).,Faculty of Medicine of the University of Porto, Alameda Professor Hernâni Monteiro (Miguel Mascarenhas Saraiva, Hélder Cardoso, Patrícia Andrade, Guilherme Macedo)
| | - Marco Parente
- Faculty of Engineering of the University of Porto (João P.S. Ferreira, Marco Parente, Renato Natal Jorge).,INEGI - Institute of Science and Innovation in Mechanical and Industrial Engineering (João P.S. Ferreira, Marco Parente, Renato Natal Jorge), Porto, Portugal
| | - Renato Natal Jorge
- Faculty of Engineering of the University of Porto (João P.S. Ferreira, Marco Parente, Renato Natal Jorge).,INEGI - Institute of Science and Innovation in Mechanical and Industrial Engineering (João P.S. Ferreira, Marco Parente, Renato Natal Jorge), Porto, Portugal
| | - Guilherme Macedo
- Department of Gastroenterology, São João University Hospital, Alameda Professor Hernâni Monteiro (Tiago Ribeiro, Miguel Mascarenhas Saraiva, Hélder Cardoso, João Afonso, Patrícia Andrade, Guilherme Macedo).,WGO Gastroenterology and Hepatology Training Center (Tiago Ribeiro, Miguel Mascarenhas Saraiva, Hélder Cardoso, João Afonso, Patrícia Andrade, Guilherme Macedo).,Faculty of Medicine of the University of Porto, Alameda Professor Hernâni Monteiro (Miguel Mascarenhas Saraiva, Hélder Cardoso, Patrícia Andrade, Guilherme Macedo)
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18
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Kröner PT, Engels MML, Glicksberg BS, Johnson KW, Mzaik O, van Hooft JE, Wallace MB, El-Serag HB, Krittanawong C. Artificial intelligence in gastroenterology: A state-of-the-art review. World J Gastroenterol 2021; 27:6794-6824. [PMID: 34790008 PMCID: PMC8567482 DOI: 10.3748/wjg.v27.i40.6794] [Citation(s) in RCA: 39] [Impact Index Per Article: 13.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Download PDF] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 05/11/2021] [Revised: 06/15/2021] [Accepted: 09/16/2021] [Indexed: 02/06/2023] Open
Abstract
The development of artificial intelligence (AI) has increased dramatically in the last 20 years, with clinical applications progressively being explored for most of the medical specialties. The field of gastroenterology and hepatology, substantially reliant on vast amounts of imaging studies, is not an exception. The clinical applications of AI systems in this field include the identification of premalignant or malignant lesions (e.g., identification of dysplasia or esophageal adenocarcinoma in Barrett’s esophagus, pancreatic malignancies), detection of lesions (e.g., polyp identification and classification, small-bowel bleeding lesion on capsule endoscopy, pancreatic cystic lesions), development of objective scoring systems for risk stratification, predicting disease prognosis or treatment response [e.g., determining survival in patients post-resection of hepatocellular carcinoma), determining which patients with inflammatory bowel disease (IBD) will benefit from biologic therapy], or evaluation of metrics such as bowel preparation score or quality of endoscopic examination. The objective of this comprehensive review is to analyze the available AI-related studies pertaining to the entirety of the gastrointestinal tract, including the upper, middle and lower tracts; IBD; the hepatobiliary system; and the pancreas, discussing the findings and clinical applications, as well as outlining the current limitations and future directions in this field.
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Affiliation(s)
- Paul T Kröner
- Division of Gastroenterology and Hepatology, Mayo Clinic, Jacksonville, FL 32224, United States
| | - Megan ML Engels
- Division of Gastroenterology and Hepatology, Mayo Clinic, Jacksonville, FL 32224, United States
- Cancer Center Amsterdam, Department of Gastroenterology and Hepatology, Amsterdam UMC, Location AMC, Amsterdam 1105, The Netherlands
| | - Benjamin S Glicksberg
- The Hasso Plattner Institute for Digital Health, Icahn School of Medicine at Mount Sinai, New York, NY 10029, United States
| | - Kipp W Johnson
- The Hasso Plattner Institute for Digital Health, Icahn School of Medicine at Mount Sinai, New York, NY 10029, United States
| | - Obaie Mzaik
- Division of Gastroenterology and Hepatology, Mayo Clinic, Jacksonville, FL 32224, United States
| | - Jeanin E van Hooft
- Department of Gastroenterology and Hepatology, Leiden University Medical Center, Amsterdam 2300, The Netherlands
| | - Michael B Wallace
- Division of Gastroenterology and Hepatology, Mayo Clinic, Jacksonville, FL 32224, United States
- Division of Gastroenterology and Hepatology, Sheikh Shakhbout Medical City, Abu Dhabi 11001, United Arab Emirates
| | - Hashem B El-Serag
- Section of Gastroenterology and Hepatology, Michael E. DeBakey VA Medical Center and Baylor College of Medicine, Houston, TX 77030, United States
- Section of Health Services Research, Michael E. DeBakey VA Medical Center and Baylor College of Medicine, Houston, TX 77030, United States
| | - Chayakrit Krittanawong
- Section of Health Services Research, Michael E. DeBakey VA Medical Center and Baylor College of Medicine, Houston, TX 77030, United States
- Section of Cardiology, Michael E. DeBakey VA Medical Center, Houston, TX 77030, United States
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19
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Eroglu Y, Yildirim K, Çinar A, Yildirim M. Diagnosis and grading of vesicoureteral reflux on voiding cystourethrography images in children using a deep hybrid model. COMPUTER METHODS AND PROGRAMS IN BIOMEDICINE 2021; 210:106369. [PMID: 34474195 DOI: 10.1016/j.cmpb.2021.106369] [Citation(s) in RCA: 2] [Impact Index Per Article: 0.7] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Subscribe] [Scholar Register] [Received: 05/31/2021] [Accepted: 08/17/2021] [Indexed: 06/13/2023]
Abstract
BACKGROUND AND OBJECTIVE Vesicoureteral reflux is the leakage of urine from the bladder into the ureter. As a result, urinary tract infections and kidney scarring can occur in children. Voiding cystourethrography is the primary radiological imaging method used to diagnose vesicoureteral reflux in children with a history of recurrent urinary tract infection. Besides the diagnosis of reflux, it is graded with voiding cystourethrography. In this study, we aimed to diagnose and grade vesicoureteral reflux in Voiding cystourethrography images using hybrid CNN in deep learning methods. METHODS Images of pediatric patients diagnosed with VUR between 2016 and 2021 in our hospital (Firat University Hospital) were graded according to the international vesicoureteral reflux radiographic grading system. VCUG images of 236 normal and 992 with vesicoureteral reflux pediatric patients were available. A total of 6 classes were created as normal and graded 1-5 patients. RESULTS In this study, a hybrid-based mRMR (Minimum Redundancy Maximum Relevance) using CNN (Convolutional Neural Networks) model is developed for the diagnosis and grading of vesicoureteral reflux on voiding cystourethrography images. Googlenet, MobilenetV2, and Densenet201 models are used as a part of the hybrid architecture. The obtained features from these architectures are examined in concatenating process. Then, these features are classified in machine learning classifiers after optimizing with the mRMR method. Among the models used in the study, the highest accuracy value was obtained in the proposed model with an accuracy rate of 96.9%. CONCLUSIONS It shows that the hybrid model developed according to the findings of our study can be used in the diagnosis and grading of vesicoureteral reflux in voiding cystourethrography images.
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Affiliation(s)
- Yesim Eroglu
- Department of Radiology, Firat University School of Medicine, Elazig, Turkey.
| | - Kadir Yildirim
- Department of Urology, Turgut Ozal University, Malatya, Turkey.
| | - Ahmet Çinar
- Department of Computer Engineering, Firat University, Elazig, Turkey.
| | - Muhammed Yildirim
- Department of Computer Engineering, Firat University, Elazig, Turkey.
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20
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Eroğlu Y, Yildirim M, Çinar A. Convolutional Neural Networks based classification of breast ultrasonography images by hybrid method with respect to benign, malignant, and normal using mRMR. Comput Biol Med 2021; 133:104407. [PMID: 33901712 DOI: 10.1016/j.compbiomed.2021.104407] [Citation(s) in RCA: 27] [Impact Index Per Article: 9.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 02/23/2021] [Revised: 04/08/2021] [Accepted: 04/13/2021] [Indexed: 12/25/2022]
Abstract
Early diagnosis of breast lesions and differentiation of malignant lesions from benign lesions are important for the prognosis of breast cancer. In the diagnosis of this disease ultrasound is an extremely important radiological imaging method because it enables biopsy as well as lesion characterization. Since ultrasonographic diagnosis depends on the expert, the knowledge level and experience of the user is very important. In addition, the contribution of computer aided systems is quite high, as these systems can reduce the workload of radiologists and reinforce their knowledge and experience when considered together with a dense patient population in hospital conditions. In this paper, a hybrid based CNN system is developed for diagnosing breast cancer lesions with respect to benign, malignant and normal. Alexnet, MobilenetV2, and Resnet50 models are used as the base for the Hybrid structure. The features of these models used are obtained and concatenated separately. Thus, the number of features used are increased. Later, the most valuable of these features are selected by the mRMR (Minimum Redundancy Maximum Relevance) feature selection method and classified with machine learning classifiers such as SVM, KNN. The highest rate is obtained in the SVM classifier with 95.6% in accuracy.
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Affiliation(s)
- Yeşim Eroğlu
- Department of Radiology, Firat University School of Medicine, Elazig, Turkey.
| | | | - Ahmet Çinar
- Computer Engineering Department, Firat University, Elazig, Turkey.
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21
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Mascarenhas M, Afonso J, Andrade P, Cardoso H, Macedo G. Artificial intelligence and capsule endoscopy: unravelling the future. Ann Gastroenterol 2021; 34:300-309. [PMID: 33948053 PMCID: PMC8079882 DOI: 10.20524/aog.2021.0606] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.3] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 10/26/2020] [Accepted: 12/20/2020] [Indexed: 12/22/2022] Open
Abstract
The applicability of artificial intelligence (AI) in gastroenterology is a hot topic because of its disruptive nature. Capsule endoscopy plays an important role in several areas of digestive pathology, namely in the investigation of obscure hemorrhagic lesions and the management of inflammatory bowel disease. Therefore, there is growing interest in the use of AI in capsule endoscopy. Several studies have demonstrated the enormous potential of using convolutional neural networks in various areas of capsule endoscopy. The exponential development of the usefulness of AI in capsule endoscopy requires consideration of its medium- and long-term impact on clinical practice. Indeed, the advent of deep learning in the field of capsule endoscopy, with its evolutionary character, could lead to a paradigm shift in clinical activity in this setting. In this review, we aim to illustrate the state of the art of AI in the field of capsule endoscopy.
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Affiliation(s)
| | - João Afonso
- Gastroenterology Department, Hospital de São João, Porto, Portugal
| | - Patrícia Andrade
- Gastroenterology Department, Hospital de São João, Porto, Portugal
| | - Hélder Cardoso
- Gastroenterology Department, Hospital de São João, Porto, Portugal
| | - Guilherme Macedo
- Gastroenterology Department, Hospital de São João, Porto, Portugal
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22
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Sana MK, Hussain ZM, Shah PA, Maqsood MH. Artificial intelligence in celiac disease. Comput Biol Med 2020; 125:103996. [PMID: 32979542 DOI: 10.1016/j.compbiomed.2020.103996] [Citation(s) in RCA: 5] [Impact Index Per Article: 1.3] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 08/05/2020] [Revised: 09/01/2020] [Accepted: 09/07/2020] [Indexed: 12/14/2022]
Abstract
Celiac disease (CD) has been on the rise in the world and a large part of it remains undiagnosed. Novel methods are required to address the gaps in prompt detection and management. Artificial intelligence (AI) has seen an exponential surge in the last decade worldwide. With the advent of big data and powerful computational ability, we now have self-driving cars and smart devices in our daily lives. Huge databases in the form of electronic medical records and images have rendered healthcare a lucrative sector where AI can prove revolutionary. It is being used extensively to overcome the barriers in clinical workflows. From the perspective of a disease, it can be deployed in multiple steps i.e. screening tools, diagnosis, developing novel therapeutic agents, proposing management plans, and defining prognostic indicators, etc. We review the areas where it may augment physicians in the delivery of better healthcare by summarizing current literature on the use of AI in healthcare using CD as a model. We further outline major barriers to its large-scale implementations and prospects from the healthcare point of view.
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Affiliation(s)
- Muhammad Khawar Sana
- Department of Internal Medicine, King Edward Medical University, Mayo Hospital Lahore, Lahore, Punjab, 54000, Pakistan.
| | - Zeshan M Hussain
- Computer Science & Artificial Intelligence Laboratory, Massachusetts Institute of Technology, 32 Vassar St, Cambridge, MA, 02139, United States.
| | - Pir Ahmad Shah
- Department of Internal Medicine, University of Texas Health Science Center, San Antonio, TX, 78229, United States.
| | - Muhammad Haisum Maqsood
- Department of Internal Medicine, King Edward Medical University, Mayo Hospital Lahore, Lahore, Punjab, 54000, Pakistan.
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23
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Rahimzadeh M, Attar A. A modified deep convolutional neural network for detecting COVID-19 and pneumonia from chest X-ray images based on the concatenation of Xception and ResNet50V2. INFORMATICS IN MEDICINE UNLOCKED 2020; 19:100360. [PMID: 32501424 PMCID: PMC7255267 DOI: 10.1016/j.imu.2020.100360] [Citation(s) in RCA: 195] [Impact Index Per Article: 48.8] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 04/21/2020] [Revised: 05/19/2020] [Accepted: 05/21/2020] [Indexed: 12/17/2022] Open
Abstract
In this paper, we have trained several deep convolutional networks with introduced training techniques for classifying X-ray images into three classes: normal, pneumonia, and COVID-19, based on two open-source datasets. Our data contains 180 X-ray images that belong to persons infected with COVID-19, and we attempted to apply methods to achieve the best possible results. In this research, we introduce some training techniques that help the network learn better when we have an unbalanced dataset (fewer cases of COVID-19 along with more cases from other classes). We also propose a neural network that is a concatenation of the Xception and ResNet50V2 networks. This network achieved the best accuracy by utilizing multiple features extracted by two robust networks. For evaluating our network, we have tested it on 11302 images to report the actual accuracy achievable in real circumstances. The average accuracy of the proposed network for detecting COVID-19 cases is 99.50%, and the overall average accuracy for all classes is 91.4%.
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Affiliation(s)
- Mohammad Rahimzadeh
- School of Computer Engineering, Iran University of Science and Technology, Iran
| | - Abolfazl Attar
- Department of Electrical Engineering Sharif University of Technology, Iran
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