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Lee JD, Tsai CM. Advancing Barrett's Esophagus Segmentation: A Deep-Learning Ensemble Approach with Data Augmentation and Model Collaboration. Bioengineering (Basel) 2024; 11:47. [PMID: 38247924 PMCID: PMC10813459 DOI: 10.3390/bioengineering11010047] [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: 11/05/2023] [Revised: 11/15/2023] [Accepted: 11/15/2023] [Indexed: 01/23/2024] Open
Abstract
This approach provides a thorough investigation of Barrett's esophagus segmentation using deep-learning methods. This study explores various U-Net model variants with different backbone architectures, focusing on how the choice of backbone influences segmentation accuracy. By employing rigorous data augmentation techniques and ensemble strategies, the goal is to achieve precise and robust segmentation results. Key findings include the superiority of DenseNet backbones, the importance of tailored data augmentation, and the adaptability of training U-Net models from scratch. Ensemble methods are shown to enhance segmentation accuracy, and a grid search is used to fine-tune ensemble weights. A comprehensive comparison with the popular Deeplabv3+ architecture emphasizes the role of dataset characteristics. Insights into training saturation help optimize resource utilization, and efficient ensembles consistently achieve high mean intersection over union (IoU) scores, approaching 0.94. This research marks a significant advancement in Barrett's esophagus segmentation.
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Affiliation(s)
- Jiann-Der Lee
- Department of Electrical Engineering, Chang Gung University, Taoyuan 33302, Taiwan
- Department of Neurosurgery, Chang Gung Memorial Hospital at Linkou, Taoyuan 33305, Taiwan
- Department of Electrical Engineering, Ming Chi University of Technology, New Taipei City 24330, Taiwan
| | - Chih Mao Tsai
- Department of Electrical Engineering, Chang Gung University, Taoyuan 33302, Taiwan
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Cui R, Wang L, Lin L, Li J, Lu R, Liu S, Liu B, Gu Y, Zhang H, Shang Q, Chen L, Tian D. Deep Learning in Barrett's Esophagus Diagnosis: Current Status and Future Directions. Bioengineering (Basel) 2023; 10:1239. [PMID: 38002363 PMCID: PMC10669008 DOI: 10.3390/bioengineering10111239] [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: 08/30/2023] [Revised: 10/13/2023] [Accepted: 10/16/2023] [Indexed: 11/26/2023] Open
Abstract
Barrett's esophagus (BE) represents a pre-malignant condition characterized by abnormal cellular proliferation in the distal esophagus. A timely and accurate diagnosis of BE is imperative to prevent its progression to esophageal adenocarcinoma, a malignancy associated with a significantly reduced survival rate. In this digital age, deep learning (DL) has emerged as a powerful tool for medical image analysis and diagnostic applications, showcasing vast potential across various medical disciplines. In this comprehensive review, we meticulously assess 33 primary studies employing varied DL techniques, predominantly featuring convolutional neural networks (CNNs), for the diagnosis and understanding of BE. Our primary focus revolves around evaluating the current applications of DL in BE diagnosis, encompassing tasks such as image segmentation and classification, as well as their potential impact and implications in real-world clinical settings. While the applications of DL in BE diagnosis exhibit promising results, they are not without challenges, such as dataset issues and the "black box" nature of models. We discuss these challenges in the concluding section. Essentially, while DL holds tremendous potential to revolutionize BE diagnosis, addressing these challenges is paramount to harnessing its full capacity and ensuring its widespread application in clinical practice.
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Affiliation(s)
- Ruichen Cui
- Department of Thoracic Surgery, West China Hospital, Sichuan University, 37 Guoxue Alley, Chengdu 610041, China; (R.C.); (L.W.); (L.L.); (J.L.); (R.L.); (S.L.); (B.L.); (Y.G.); (H.Z.); (Q.S.)
| | - Lei Wang
- Department of Thoracic Surgery, West China Hospital, Sichuan University, 37 Guoxue Alley, Chengdu 610041, China; (R.C.); (L.W.); (L.L.); (J.L.); (R.L.); (S.L.); (B.L.); (Y.G.); (H.Z.); (Q.S.)
- West China School of Nursing, Sichuan University, 37 Guoxue Alley, Chengdu 610041, China
| | - Lin Lin
- Department of Thoracic Surgery, West China Hospital, Sichuan University, 37 Guoxue Alley, Chengdu 610041, China; (R.C.); (L.W.); (L.L.); (J.L.); (R.L.); (S.L.); (B.L.); (Y.G.); (H.Z.); (Q.S.)
- West China School of Nursing, Sichuan University, 37 Guoxue Alley, Chengdu 610041, China
| | - Jie Li
- Department of Thoracic Surgery, West China Hospital, Sichuan University, 37 Guoxue Alley, Chengdu 610041, China; (R.C.); (L.W.); (L.L.); (J.L.); (R.L.); (S.L.); (B.L.); (Y.G.); (H.Z.); (Q.S.)
- West China School of Nursing, Sichuan University, 37 Guoxue Alley, Chengdu 610041, China
| | - Runda Lu
- Department of Thoracic Surgery, West China Hospital, Sichuan University, 37 Guoxue Alley, Chengdu 610041, China; (R.C.); (L.W.); (L.L.); (J.L.); (R.L.); (S.L.); (B.L.); (Y.G.); (H.Z.); (Q.S.)
| | - Shixiang Liu
- Department of Thoracic Surgery, West China Hospital, Sichuan University, 37 Guoxue Alley, Chengdu 610041, China; (R.C.); (L.W.); (L.L.); (J.L.); (R.L.); (S.L.); (B.L.); (Y.G.); (H.Z.); (Q.S.)
| | - Bowei Liu
- Department of Thoracic Surgery, West China Hospital, Sichuan University, 37 Guoxue Alley, Chengdu 610041, China; (R.C.); (L.W.); (L.L.); (J.L.); (R.L.); (S.L.); (B.L.); (Y.G.); (H.Z.); (Q.S.)
| | - Yimin Gu
- Department of Thoracic Surgery, West China Hospital, Sichuan University, 37 Guoxue Alley, Chengdu 610041, China; (R.C.); (L.W.); (L.L.); (J.L.); (R.L.); (S.L.); (B.L.); (Y.G.); (H.Z.); (Q.S.)
| | - Hanlu Zhang
- Department of Thoracic Surgery, West China Hospital, Sichuan University, 37 Guoxue Alley, Chengdu 610041, China; (R.C.); (L.W.); (L.L.); (J.L.); (R.L.); (S.L.); (B.L.); (Y.G.); (H.Z.); (Q.S.)
| | - Qixin Shang
- Department of Thoracic Surgery, West China Hospital, Sichuan University, 37 Guoxue Alley, Chengdu 610041, China; (R.C.); (L.W.); (L.L.); (J.L.); (R.L.); (S.L.); (B.L.); (Y.G.); (H.Z.); (Q.S.)
| | - Longqi Chen
- Department of Thoracic Surgery, West China Hospital, Sichuan University, 37 Guoxue Alley, Chengdu 610041, China; (R.C.); (L.W.); (L.L.); (J.L.); (R.L.); (S.L.); (B.L.); (Y.G.); (H.Z.); (Q.S.)
| | - Dong Tian
- Department of Thoracic Surgery, West China Hospital, Sichuan University, 37 Guoxue Alley, Chengdu 610041, China; (R.C.); (L.W.); (L.L.); (J.L.); (R.L.); (S.L.); (B.L.); (Y.G.); (H.Z.); (Q.S.)
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Mohan A, Asghar Z, Abid R, Subedi R, Kumari K, Kumar S, Majumder K, Bhurgri AI, Tejwaney U, Kumar S. Revolutionizing healthcare by use of artificial intelligence in esophageal carcinoma - a narrative review. Ann Med Surg (Lond) 2023; 85:4920-4927. [PMID: 37811030 PMCID: PMC10553069 DOI: 10.1097/ms9.0000000000001175] [Citation(s) in RCA: 1] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Journal Information] [Subscribe] [Scholar Register] [Received: 05/20/2023] [Accepted: 08/05/2023] [Indexed: 10/10/2023] Open
Abstract
Esophageal cancer is a major cause of cancer-related mortality worldwide, with significant regional disparities. Early detection of precursor lesions is essential to improve patient outcomes. Artificial intelligence (AI) techniques, including deep learning and machine learning, have proved to be of assistance to both gastroenterologists and pathologists in the diagnosis and characterization of upper gastrointestinal malignancies by correlating with the histopathology. The primary diagnostic method in gastroenterology is white light endoscopic evaluation, but conventional endoscopy is partially inefficient in detecting esophageal cancer. However, other endoscopic modalities, such as narrow-band imaging, endocytoscopy, and endomicroscopy, have shown improved visualization of mucosal structures and vasculature, which provides a set of baseline data to develop efficient AI-assisted predictive models for quick interpretation. The main challenges in managing esophageal cancer are identifying high-risk patients and the disease's poor prognosis. Thus, AI techniques can play a vital role in improving the early detection and diagnosis of precursor lesions, assisting gastroenterologists in performing targeted biopsies and real-time decisions of endoscopic mucosal resection or endoscopic submucosal dissection. Combining AI techniques and endoscopic modalities can enhance the diagnosis and management of esophageal cancer, improving patient outcomes and reducing cancer-related mortality rates. The aim of this review is to grasp a better understanding of the application of AI in the diagnosis, treatment, and prognosis of esophageal cancer and how computer-aided diagnosis and computer-aided detection can act as vital tools for clinicians in the long run.
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Affiliation(s)
| | | | - Rabia Abid
- Liaquat College of Medicine and Dentistry
| | - Rasish Subedi
- Universal College of Medical Sciences, Siddharthanagar, Nepal
| | | | | | | | - Aqsa I. Bhurgri
- Shaheed Muhtarma Benazir Bhutto Medical University, Larkana, Pakistan
| | | | - Sarwan Kumar
- Department of Medicine, Chittagong Medical College, Chittagong, Bangladesh
- Wayne State University, Michigan, USA
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Tee CHN, Ravi R, Ang TL, Li JW. Role of artificial intelligence in Barrett’s esophagus. Artif Intell Gastroenterol 2023; 4:28-35. [DOI: 10.35712/aig.v4.i2.28] [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: 03/06/2023] [Revised: 05/17/2023] [Accepted: 06/12/2023] [Indexed: 09/07/2023] Open
Abstract
The application of artificial intelligence (AI) in gastrointestinal endoscopy has gained significant traction over the last decade. One of the more recent applications of AI in this field includes the detection of dysplasia and cancer in Barrett’s esophagus (BE). AI using deep learning methods has shown promise as an adjunct to the endoscopist in detecting dysplasia and cancer. Apart from visual detection and diagnosis, AI may also aid in reducing the considerable interobserver variability in identifying and distinguishing dysplasia on whole slide images from digitized BE histology slides. This review aims to provide a comprehensive summary of the key studies thus far as well as providing an insight into the future role of AI in Barrett’s esophagus.
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Affiliation(s)
- Chin Hock Nicholas Tee
- Department of Gastroenterology and Hepatology, Changi General Hospital, Singapore Health Services, Singapore 529889, Singapore
| | - Rajesh Ravi
- Department of Gastroenterology and Hepatology, Changi General Hospital, Singapore Health Services, Singapore 529889, Singapore
| | - Tiing Leong Ang
- Department of Gastroenterology and Hepatology, Changi General Hospital, Singapore Health Services, Singapore 529889, Singapore
| | - James Weiquan Li
- Department of Gastroenterology and Hepatology, Changi General Hospital, Singapore Health Services, Singapore 529889, Singapore
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Chou CK, Nguyen HT, Wang YK, Chen TH, Wu IC, Huang CW, Wang HC. Preparing Well for Esophageal Endoscopic Detection Using a Hybrid Model and Transfer Learning. Cancers (Basel) 2023; 15:3783. [PMID: 37568599 PMCID: PMC10417640 DOI: 10.3390/cancers15153783] [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: 06/01/2023] [Revised: 07/17/2023] [Accepted: 07/23/2023] [Indexed: 08/13/2023] Open
Abstract
Early detection of esophageal cancer through endoscopic imaging is pivotal for effective treatment. However, the intricacies of endoscopic diagnosis, contingent on the physician's expertise, pose challenges. Esophageal cancer features often manifest ambiguously, leading to potential confusions with other inflammatory esophageal conditions, thereby complicating diagnostic accuracy. In recent times, computer-aided diagnosis has emerged as a promising solution in medical imaging, particularly within the domain of endoscopy. Nonetheless, contemporary AI-based diagnostic models heavily rely on voluminous data sources, limiting their applicability, especially in scenarios with scarce datasets. To address this limitation, our study introduces novel data training strategies based on transfer learning, tailored to optimize performance with limited data. Additionally, we propose a hybrid model integrating EfficientNet and Vision Transformer networks to enhance prediction accuracy. Conducting rigorous evaluations on a carefully curated dataset comprising 1002 endoscopic images (comprising 650 white-light images and 352 narrow-band images), our model achieved exceptional outcomes. Our combined model achieved an accuracy of 96.32%, precision of 96.44%, recall of 95.70%, and f1-score of 96.04%, surpassing state-of-the-art models and individual components, substantiating its potential for precise medical image classification. The AI-based medical image prediction platform presents several advantageous characteristics, encompassing superior prediction accuracy, a compact model size, and adaptability to low-data scenarios. This research heralds a significant stride in the advancement of computer-aided endoscopic imaging for improved esophageal cancer diagnosis.
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Affiliation(s)
- Chu-Kuang Chou
- Division of Gastroenterology and Hepatology, Department of Internal Medicine, Ditmanson Medical Foundation Chia-Yi Christian Hospital, Chiayi 60002, Taiwan;
- Obesity Center, Ditmanson Medical Foundation Chia-Yi Christian Hospital, Chiayi 60002, Taiwan
| | - Hong-Thai Nguyen
- Department of Mechanical Engineering, National Chung Cheng University, Chiayi 62102, Taiwan;
| | - Yao-Kuang Wang
- Division of Gastroenterology, Department of Internal Medicine, Kaohsiung Medical University Hospital, Kaohsiung Medical University, Kaohsiung City 80756, Taiwan;
- Department of Medicine, Faculty of Medicine, College of Medicine, Kaohsiung Medical University, Kaohsiung City 80756, Taiwan;
- Graduate Institute of Clinical Medicine, College of Medicine, Kaohsiung Medical University, Kaohsiung City 80756, Taiwan
| | - Tsung-Hsien Chen
- Department of Internal Medicine, Ditmanson Medical Foundation Chia-Yi Christian Hospital, Chiayi 60002, Taiwan;
| | - I-Chen Wu
- Department of Medicine, Faculty of Medicine, College of Medicine, Kaohsiung Medical University, Kaohsiung City 80756, Taiwan;
- Graduate Institute of Clinical Medicine, College of Medicine, Kaohsiung Medical University, Kaohsiung City 80756, Taiwan
| | - Chien-Wei Huang
- Department of Gastroenterology, Kaohsiung Armed Forces General Hospital, Kaohsiung City 80284, Taiwan
- Department of Nursing, Tajen University, 20, Weixin Rd., Yanpu Township, Pingtung 90741, Taiwan
| | - Hsiang-Chen Wang
- Department of Mechanical Engineering, National Chung Cheng University, Chiayi 62102, Taiwan;
- Hitspectra Intelligent Technology Co., Ltd., Kaohsiung City 80661, Taiwan
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Tang S, Yu X, Cheang CF, Ji X, Yu HH, Choi IC. CLELNet: A continual learning network for esophageal lesion analysis on endoscopic images. COMPUTER METHODS AND PROGRAMS IN BIOMEDICINE 2023; 231:107399. [PMID: 36780717 DOI: 10.1016/j.cmpb.2023.107399] [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: 11/02/2021] [Revised: 01/03/2023] [Accepted: 02/01/2023] [Indexed: 06/18/2023]
Abstract
BACKGROUND AND OBJECTIVE A deep learning-based intelligent diagnosis system can significantly reduce the burden of endoscopists in the daily analysis of esophageal lesions. Considering the need to add new tasks in the diagnosis system, a deep learning model that can train a series of tasks incrementally using endoscopic images is essential for identifying the types and regions of esophageal lesions. METHOD In this paper, we proposed a continual learning-based esophageal lesion network (CLELNet), in which a convolutional autoencoder was designed to extract representation features of endoscopic images among different esophageal lesions. The proposed CLELNet consists of shared layers and task-specific layers. Shared layers are used to extract common features among different lesions while task-specific layers can complete different tasks. The first two tasks trained by the CLELNet are the classification (task 1) and the segmentation (task 2). We collected a dataset of esophageal endoscopic images from Macau Kiang Wu Hospital for training and testing the CLELNet. RESULTS The experimental results showed that the classification accuracy of task 1 was 95.96%, and the Intersection Over Union and the Dice Similarity Coefficient of task 2 were 65.66% and 78.08%, respectively. CONCLUSIONS The proposed CLELNet can realize task-incremental learning without forgetting the previous tasks and thus become a useful computer-aided diagnosis system in esophageal lesions analysis.
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Affiliation(s)
- Suigu Tang
- Faculty of Innovation Engineering-School of Computer Science and Engineering, Macau University of Science and Technology, Avenida Wai Long, Taipa, Macau SAR
| | - Xiaoyuan Yu
- Faculty of Innovation Engineering-School of Computer Science and Engineering, Macau University of Science and Technology, Avenida Wai Long, Taipa, Macau SAR
| | - Chak Fong Cheang
- Faculty of Innovation Engineering-School of Computer Science and Engineering, Macau University of Science and Technology, Avenida Wai Long, Taipa, Macau SAR.
| | - Xiaoyu Ji
- Faculty of Innovation Engineering-School of Computer Science and Engineering, Macau University of Science and Technology, Avenida Wai Long, Taipa, Macau SAR
| | - Hon Ho Yu
- Kiang Wu Hospital, Rua de Coelho do Amaral, Macau SAR
| | - I Cheong Choi
- Kiang Wu Hospital, Rua de Coelho do Amaral, Macau SAR
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Mejza M, Małecka-Wojciesko E. Diagnosis and Management of Barrett's Esophagus. J Clin Med 2023; 12:jcm12062141. [PMID: 36983142 PMCID: PMC10057256 DOI: 10.3390/jcm12062141] [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: 02/06/2023] [Revised: 03/07/2023] [Accepted: 03/07/2023] [Indexed: 03/30/2023] Open
Abstract
Barrett's esophagus is a metaplastic change of esophageal mucosa, which can be characterized by its salmon-colored lining and the presence of columnar epithelium with goblet cells. It is a well-established precancerous state of esophageal adenocarcinoma, a tumor with very poor survival rates, which incidence is rapidly growing. Despite numerous research, the debate about its diagnosis and management is still ongoing. This article aims to provide an overview of the current recommendations and new discoveries regarding the subject.
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Affiliation(s)
- Maja Mejza
- Department of Digestive Tract Diseases, Medical University, 90-153 Lodz, Poland
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Tang S, Yu X, Cheang CF, Liang Y, Zhao P, Yu HH, Choi IC. Transformer-based multi-task learning for classification and segmentation of gastrointestinal tract endoscopic images. Comput Biol Med 2023; 157:106723. [PMID: 36907035 DOI: 10.1016/j.compbiomed.2023.106723] [Citation(s) in RCA: 2] [Impact Index Per Article: 2.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 11/04/2022] [Revised: 02/04/2023] [Accepted: 02/26/2023] [Indexed: 03/07/2023]
Abstract
Despite being widely utilized to help endoscopists identify gastrointestinal (GI) tract diseases using classification and segmentation, models based on convolutional neural network (CNN) have difficulties in distinguishing the similarities among some ambiguous types of lesions presented in endoscopic images, and in the training when lacking labeled datasets. Those will prevent CNN from further improving the accuracy of diagnosis. To address these challenges, we first proposed a Multi-task Network (TransMT-Net) capable of simultaneously learning two tasks (classification and segmentation), which has the transformer designed to learn global features and can combine the advantages of CNN in learning local features so that to achieve a more accurate prediction in identifying the lesion types and regions in GI tract endoscopic images. We further adopted the active learning in TransMT-Net to tackle the labeled image-hungry problem. A dataset was created from the CVC-ClinicDB dataset, Macau Kiang Wu Hospital, and Zhongshan Hospital to evaluate the model performance. Then, the experimental results show that our model not only achieved 96.94% accuracy in the classification task and 77.76% Dice Similarity Coefficient in the segmentation task but also outperformed those of other models on our test set. Meanwhile, active learning also produced positive results for the performance of our model with a small-scale initial training set, and even its performance with 30% of the initial training set was comparable to that of most comparable models with the full training set. Consequently, the proposed TransMT-Net has demonstrated its potential performance in GI tract endoscopic images and it through active learning can alleviate the shortage of labeled images.
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Affiliation(s)
- Suigu Tang
- Faculty of Innovation Engineering-School of Computer Science and Engineering, Macau University of Science and Technology, Macao Special Administrative Region of China
| | - Xiaoyuan Yu
- Faculty of Innovation Engineering-School of Computer Science and Engineering, Macau University of Science and Technology, Macao Special Administrative Region of China
| | - Chak Fong Cheang
- Faculty of Innovation Engineering-School of Computer Science and Engineering, Macau University of Science and Technology, Macao Special Administrative Region of China.
| | - Yanyan Liang
- Faculty of Innovation Engineering-School of Computer Science and Engineering, Macau University of Science and Technology, Macao Special Administrative Region of China
| | - Penghui Zhao
- Faculty of Innovation Engineering-School of Computer Science and Engineering, Macau University of Science and Technology, Macao Special Administrative Region of China
| | - Hon Ho Yu
- Kiang Wu Hospital, Macao Special Administrative Region of China
| | - I Cheong Choi
- Kiang Wu Hospital, Macao Special Administrative Region of China
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Meinikheim M, Messmann H, Ebigbo A. Role of artificial intelligence in diagnosing Barrett's esophagus-related neoplasia. Clin Endosc 2023; 56:14-22. [PMID: 36646423 PMCID: PMC9902686 DOI: 10.5946/ce.2022.247] [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: 09/05/2022] [Accepted: 11/25/2022] [Indexed: 01/18/2023] Open
Abstract
Barrett's esophagus is associated with an increased risk of adenocarcinoma. Thorough screening during endoscopic surveillance is crucial to improve patient prognosis. Detecting and characterizing dysplastic or neoplastic Barrett's esophagus during routine endoscopy are challenging, even for expert endoscopists. Artificial intelligence-based clinical decision support systems have been developed to provide additional assistance to physicians performing diagnostic and therapeutic gastrointestinal endoscopy. In this article, we review the current role of artificial intelligence in the management of Barrett's esophagus and elaborate on potential artificial intelligence in the future.
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Affiliation(s)
- Michael Meinikheim
- Department of Gastroenterology, University Hospital of Augsburg, Augsburg, Germany,Correspondence: Michael Meinikheim Department of Gastroenterology, University Hospital of Augsburg, Stenglinstr. 2, D-86156 Augsburg, Germany E-mail:
| | - Helmut Messmann
- Department of Gastroenterology, University Hospital of Augsburg, Augsburg, Germany
| | - Alanna Ebigbo
- Department of Gastroenterology, University Hospital of Augsburg, Augsburg, Germany
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Deng Y, Chen Y, Xie L, Wang L, Zhan J. The investigation of construction and clinical application of image recognition technology assisted bronchoscopy diagnostic model of lung cancer. Front Oncol 2022; 12:1001840. [PMID: 36387178 PMCID: PMC9647035 DOI: 10.3389/fonc.2022.1001840] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 07/24/2022] [Accepted: 10/07/2022] [Indexed: 12/02/2022] Open
Abstract
Background The incidence and mortality of lung cancer ranks first in China. Bronchoscopy is one of the most common diagnostic methods for lung cancer. In recent years, image recognition technology(IRT) has been more and more widely studied and applied in the medical field. We developed a diagnostic model of lung cancer under bronchoscopy based on deep learning method and tried to classify pathological types. Methods A total of 2238 lesion images were collected retrospectively from 666 cases of lung cancer diagnosed by pathology in the bronchoscopy center of the Third Xiangya Hospital from Oct.01 2017 to Dec.31 2020 and 152 benign cases from Jun.01 2015 to Dec.31 2020. The benign and malignant images were divided into training, verification and test set according to 7:1:2 respectively. The model was trained and tested based on deep learning method. We also tried to classify different pathological types of lung cancer using the model. Furthermore, 9 clinicians with different experience were invited to diagnose the same test images and the results were compared with the model. Results The diagnostic model took a total of 30s to diagnose 467 test images. The overall accuracy, sensitivity, specificity and area under curve (AUC) of the model to differentiate benign and malignant lesions were 0.951, 0.978, 0.833 and 0.940, which were equivalent to the judgment results of 2 doctors in the senior group and higher than those of other doctors. In the classification of squamous cell carcinoma (SCC) and adenocarcinoma (AC), the overall accuracy was 0.745, including 0.790 for SCC, 0.667 for AC and AUC was 0.728. Conclusion The performance of our diagnostic model to distinguish benign and malignant lesions in bronchoscopy is roughly the same as that of experienced clinicians and the efficiency is much higher than manually. Our study verifies the possibility of applying IRT in diagnosis of lung cancer during white light bronchoscopy.
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Affiliation(s)
- Yihong Deng
- Department of Pulmonary and Critical Care Medicine, the Third Xiangya Hospital, Central South University, Changsha, Hunan, China
| | - Yuan Chen
- Department of Computer Science, School of Informatics, Xiamen University, Xiamen, Fujian, China
| | - Lihua Xie
- Department of Pulmonary and Critical Care Medicine, the Third Xiangya Hospital, Central South University, Changsha, Hunan, China
- *Correspondence: Lihua Xie, ; Liansheng Wang, ; Juan Zhan,
| | - Liansheng Wang
- Department of Computer Science, School of Informatics, Xiamen University, Xiamen, Fujian, China
- *Correspondence: Lihua Xie, ; Liansheng Wang, ; Juan Zhan,
| | - Juan Zhan
- Department of Oncology, Zhongshan Hospital affiliated to Xiamen University, Xiamen, Fujian, China
- *Correspondence: Lihua Xie, ; Liansheng Wang, ; Juan Zhan,
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Artificial Intelligence in the Management of Barrett’s Esophagus and Early Esophageal Adenocarcinoma. Cancers (Basel) 2022; 14:cancers14081918. [PMID: 35454824 PMCID: PMC9028107 DOI: 10.3390/cancers14081918] [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: 03/13/2022] [Revised: 04/02/2022] [Accepted: 04/07/2022] [Indexed: 02/06/2023] Open
Abstract
Simple Summary Esophageal adenocarcinoma is increasing in incidence and is the most common subtype of esophageal cancer in Western societies. AI systems are currently under development and validation in many fields of gas-troenterology. Abstract Esophageal adenocarcinoma is increasing in incidence and is the most common subtype of esophageal cancer in Western societies. The stepwise progression of Barrett´s metaplasia to high-grade dysplasia and invasive adenocarcinoma provides an opportunity for screening and surveillance. There are important unresolved issues, which include (i) refining the definition of the screening population in order to avoid unnecessary invasive diagnostics, (ii) a more precise prediction of the (very heterogeneous) individual progression risk from metaplasia to invasive cancer in order to better tailor surveillance recommendations, (iii) improvement of the quality of endoscopy in order to reduce the high miss rate for early neoplastic lesions, and (iv) support for the diagnosis of tumor infiltration depth in order to guide treatment decisions. Artificial intelligence (AI) systems might be useful as a support to better solve the above-mentioned issues.
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