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Duan R, Duan L, Chen X, Liu M, Song X, Wei L. An artificial intelligence model utilizing endoscopic ultrasonography for differentiating small and micro gastric stromal tumors from gastric leiomyomas. BMC Gastroenterol 2025; 25:237. [PMID: 40205374 PMCID: PMC11983923 DOI: 10.1186/s12876-025-03825-y] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 10/23/2024] [Accepted: 03/27/2025] [Indexed: 04/11/2025] Open
Abstract
BACKGROUND Gastric stromal tumors (GSTs) and gastric leiomyomas (GLs) represent the primary subtypes of gastric submucosal tumors (SMTs) characterized by distinct biological characteristics and treatment modalities. The accurate differentiation between GSTs and GLs poses a significant clinical challenge. Recent advancements in artificial intelligence (AI) leveraging endoscopic ultrasonography (EUS) have demonstrated promising results in the categorization of larger-diameter SMTs (> 2.0 cm). However, the diagnostic capacity of AI models for micro-diameter SMTs (< 1.0 cm) remains uncertain due to limited imaging features. This study seeks to develop a specialized diagnostic model utilizing EUS images to differentiate small and micro GSTs from GLs effectively. METHODS In this study, a dataset comprising 358 EUS images of GSTs or GLs was utilized for training the EUS-AI model. Subsequently, 216 EUS images were allocated for validation purposes, with 159 images in validation set 1 (micro SMTs: tumor diameter < 1.0 cm) and 216 images in validation set 2 (small SMTs: tumor diameter < 2.0 cm). The diagnostic performance of the EUS-AI model for individual tumors was assessed by consolidating the diagnostic outcomes of the corresponding images. Comparative analyses were conducted between the diagnostic outcomes of endoscopists, clinical signatures, and those of the EUS-AI models. RESULTS The EUS-AI models were developed using DenseNet201, ResNet50, and VGG19 architectures. Among the three models, the ResNet50 model demonstrated superior performance on EUS images, achieving area under the curve (AUC) values of 0.938, 0.832, and 0.841 in the training set, validation set 1, and validation set 2, respectively. By combining predictions from multiple images for each tumor, the diagnostic efficacy of ResNet50 was further enhanced, resulting in AUCs of 0.994, 0.911, and 0.915 in the aforementioned sets. In comparison, both clinical signatures and endoscopists exhibited notably lower AUC values than those obtained with the EUS-AI model. CONCLUSIONS The EUS-AI model utilizing ResNet50 architecture effectively discriminates between micro GSTs and GLs from both image-centric and tumor-centric perspectives. Demonstrating superior diagnostic efficiency compared to clinical models and assessments by endoscopists, the EUS-AI model serves as a valuable tool for clinicians in precisely distinguishing small and micro GSTs from GLs before surgery.
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Affiliation(s)
- Ruifeng Duan
- Department of Gastroenterology and Digestive Endoscopy Center, The Second Hospital of Jilin University, Chang Chun, Jilin, China
| | - Liwei Duan
- Department of Gastroenterology and Digestive Endoscopy Center, The Second Hospital of Jilin University, Chang Chun, Jilin, China
| | - Xin Chen
- Department of Gastroenterology and Digestive Endoscopy Center, The Second Hospital of Jilin University, Chang Chun, Jilin, China
| | - Min Liu
- Department of Gastroenterology and Digestive Endoscopy Center, The Second Hospital of Jilin University, Chang Chun, Jilin, China
| | - Xiangyi Song
- Department of Gastroenterology and Digestive Endoscopy Center, The Second Hospital of Jilin University, Chang Chun, Jilin, China
| | - Lijuan Wei
- Department of Gastroenterology and Digestive Endoscopy Center, The Second Hospital of Jilin University, Chang Chun, Jilin, China.
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Sun X, Mo X, Shi J, Zhou X, Niu Y, Zhang XD, Li M, Li Y. A Lightweight Machine Learning Model for High Precision Gastrointestinal Stromal Tumors Identification. Bioengineering (Basel) 2025; 12:381. [PMID: 40281741 PMCID: PMC12024531 DOI: 10.3390/bioengineering12040381] [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: 02/04/2025] [Revised: 03/08/2025] [Accepted: 03/13/2025] [Indexed: 04/29/2025] Open
Abstract
Gastrointestinal stromal tumors (GISTs), which usually develop with a significant malignant potential, are a serious challenge in stromal health. With Endoscopic ultrasound (EUS), GISTs can appear similar to other tumors. This study introduces a lightweight convolutional neural network model optimized for the classification of GISTs and leiomyomas using EUS images only. Models are constructed based on a dataset that comprises 13277 augmented grayscale images derived from 703 patients, ensuring a balanced representation between GIST and leiomyoma cases. The optimized model architecture includes seven convolutional units followed by fully connected layers. After being trained and evaluated with a 5-fold cross-validation, the optimized model achieves an average validation accuracy of 96.2%. The model achieved a sensitivity, specificity, positive predictive value, and negative predictive value of 97.7%, 94.7%, 94.6%, and 97.7%, respectively, and significantly outperformed endoscopists' assessments. The study highlights the model's robustness and consistency. Our results suggest that instead of using developed deep models with fine-tuning, lightweight models with their simpler designs may grasp the essence and drop speckle noise. A lightweight model as a hypothesis with fewer model parameters is preferable to a deeper model with 10 times the model parameters according to Occam's razor statement.
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Affiliation(s)
- Xin Sun
- Haihe Hospital, Tianjin University, Tianjin 300350, China;
- Tianjin Union Medical Center, Nankai University, Tianjin 300071, China
- Tianjin Key Laboratory of Brain Science and Neural Engineering, Academy of Medical Engineering and Translational Medicine, Tianjin University, Tianjin 300072, China; (X.M.); (J.S.); (X.Z.); (X.-D.Z.)
| | - Xiwen Mo
- Tianjin Key Laboratory of Brain Science and Neural Engineering, Academy of Medical Engineering and Translational Medicine, Tianjin University, Tianjin 300072, China; (X.M.); (J.S.); (X.Z.); (X.-D.Z.)
| | - Jing Shi
- Tianjin Key Laboratory of Brain Science and Neural Engineering, Academy of Medical Engineering and Translational Medicine, Tianjin University, Tianjin 300072, China; (X.M.); (J.S.); (X.Z.); (X.-D.Z.)
| | - Xinran Zhou
- Tianjin Key Laboratory of Brain Science and Neural Engineering, Academy of Medical Engineering and Translational Medicine, Tianjin University, Tianjin 300072, China; (X.M.); (J.S.); (X.Z.); (X.-D.Z.)
| | - Yanqing Niu
- Department of Clinical Medicine, Tianjin Medical University, Tianjin 300203, China;
| | - Xiao-Dong Zhang
- Tianjin Key Laboratory of Brain Science and Neural Engineering, Academy of Medical Engineering and Translational Medicine, Tianjin University, Tianjin 300072, China; (X.M.); (J.S.); (X.Z.); (X.-D.Z.)
- Department of Physics and Tianjin Key Laboratory of Low Dimensional Materials Physics and Preparing Technology, School of Sciences, Tianjin University, Tianjin 300350, China
| | - Man Li
- Gastroenterology, The Second Hospital of Tianjin Medical University, Tianjin 300211, China
| | - Yonghui Li
- Department of Physics and Tianjin Key Laboratory of Low Dimensional Materials Physics and Preparing Technology, School of Sciences, Tianjin University, Tianjin 300350, China
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Sreedharan JK, Saleh F, Alqahtani A, Albalawi IA, Gopalakrishnan GK, Alahmed HA, Alsultan BA, Alalharith DM, Alnasser M, Alahmari AD, Karthika M. Applications of artificial intelligence in emergency and critical care diagnostics: a systematic review and meta-analysis. Front Artif Intell 2024; 7:1422551. [PMID: 39430618 PMCID: PMC11487586 DOI: 10.3389/frai.2024.1422551] [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: 04/24/2024] [Accepted: 09/23/2024] [Indexed: 10/22/2024] Open
Abstract
Introduction Artificial intelligence has come to be the highlight in almost all fields of science. It uses various models and algorithms to detect patterns and specific findings to diagnose a disease with utmost accuracy. With the increasing need for accurate and precise diagnosis of disease, employing artificial intelligence models and concepts in healthcare setup can be beneficial. Methodology The search engines and databases employed in this study are PubMed, ScienceDirect and Medline. Studies published between 1st January 2013 to 1st February 2023 were included in this analysis. The selected articles were screened preliminarily using the Rayyan web tool, after which investigators screened the selected articles individually. The risk of bias for the selected studies was assessed using QUADAS-2 tool specially designed to test bias among studies related to diagnostic test reviews. Results In this review, 17 studies were included from a total of 12,173 studies. These studies were analysed for their sensitivity, accuracy, positive predictive value, specificity and negative predictive value in diagnosing barrette's neoplasia, cardiac arrest, esophageal adenocarcinoma, sepsis and gastrointestinal stromal tumors. All the studies reported heterogeneity with p-value <0.05 at confidence interval 95%. Conclusion The existing evidential data suggests that artificial intelligence can be highly helpful in the field of diagnosis providing maximum precision and early detection. This helps to prevent disease progression and also helps to provide treatment at the earliest. Employing artificial intelligence in diagnosis will define the advancement of health care environment and also be beneficial in every aspect concerned with treatment to illnesses.
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Affiliation(s)
- Jithin K. Sreedharan
- Department of Respiratory Therapy, College of Health Sciences, University of Doha for Science and Technology, Doha, Qatar
| | - Fred Saleh
- Deanship—College of Health Sciences, University of Doha for Science and Technology, Doha, Qatar
| | - Abdullah Alqahtani
- Department of Respiratory Care, Prince Sultan Military College of Health Sciences, Dammam, Saudi Arabia
| | - Ibrahim Ahmed Albalawi
- Department of Respiratory Care, Prince Sultan Military College of Health Sciences, Dammam, Saudi Arabia
| | | | | | | | | | - Musallam Alnasser
- Department of Respiratory Care, Prince Sultan Military College of Health Sciences, Dammam, Saudi Arabia
| | - Ayedh Dafer Alahmari
- Department of Rehabilitation Science, College of Applied Medical Sciences, King Saud University, Riyadh, Saudi Arabia
| | - Manjush Karthika
- Faculty of Medical and Health Sciences, Liwa College, Abu Dhabi, United Arab Emirates
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Yuan Y, Pan B, Mo H, Wu X, Long Z, Yang Z, Zhu J, Ming J, Qiu L, Sun Y, Yin S, Zhang F. Deep learning-based computer-aided diagnosis system for the automatic detection and classification of lateral cervical lymph nodes on original ultrasound images of papillary thyroid carcinoma: a prospective diagnostic study. Endocrine 2024; 85:1289-1299. [PMID: 38570388 DOI: 10.1007/s12020-024-03808-1] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 11/25/2023] [Accepted: 03/26/2024] [Indexed: 04/05/2024]
Abstract
PURPOSE This study aims to develop a deep learning-based computer-aided diagnosis (CAD) system for the automatic detection and classification of lateral cervical lymph nodes (LNs) on original ultrasound images of papillary thyroid carcinoma (PTC) patients. METHODS A retrospective data set of 1801 cervical LN ultrasound images from 1675 patients with PTC and a prospective test set including 185 images from 160 patients were collected. Four different deep leaning models were trained and validated in the retrospective data set. The best model was selected for CAD system development and compared with three sonographers in the retrospective and prospective test sets. RESULTS The Deformable Detection Transformer (DETR) model showed the highest diagnostic efficacy, with a mean average precision score of 86.3% in the retrospective test set, and was therefore used in constructing the CAD system. The detection performance of the CAD system was superior to the junior sonographer and intermediate sonographer with accuracies of 86.3% and 92.4% in the retrospective and prospective test sets, respectively. The classification performance of the CAD system was better than all sonographers with the areas under the curve (AUCs) of 94.4% and 95.2% in the retrospective and prospective test sets, respectively. CONCLUSIONS This study developed a Deformable DETR model-based CAD system for automatically detecting and classifying lateral cervical LNs on original ultrasound images, which showed excellent diagnostic efficacy and clinical utility. It can be an important tool for assisting sonographers in the diagnosis process.
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Affiliation(s)
- Yuquan Yuan
- Department of Breast and Thyroid Surgery, Chongqing General Hospital, Chongqing, China
- Graduate School of Medicine, Chongqing Medical University, Chongqing, China
| | - Bin Pan
- Department of Breast and Thyroid Surgery, Chongqing General Hospital, Chongqing, China
- Graduate School of Medicine, Chongqing Medical University, Chongqing, China
| | - Hongbiao Mo
- Department of Breast and Thyroid Surgery, Chongqing General Hospital, Chongqing, China
| | - Xing Wu
- College of Computer Science, Chongqing University, Chongqing, China
| | - Zhaoxin Long
- College of Computer Science, Chongqing University, Chongqing, China
| | - Zeyu Yang
- Department of Breast and Thyroid Surgery, Chongqing General Hospital, Chongqing, China
- Graduate School of Medicine, Chongqing Medical University, Chongqing, China
| | - Junping Zhu
- Department of Breast and Thyroid Surgery, Chongqing General Hospital, Chongqing, China
| | - Jing Ming
- Department of Breast and Thyroid Surgery, Chongqing General Hospital, Chongqing, China
| | - Lin Qiu
- Department of Breast and Thyroid Surgery, Chongqing General Hospital, Chongqing, China
| | - Yiceng Sun
- Department of Breast and Thyroid Surgery, Chongqing General Hospital, Chongqing, China
| | - Supeng Yin
- Department of Breast and Thyroid Surgery, Chongqing General Hospital, Chongqing, China.
- Chongqing Hospital of Traditional Chinese Medicine, Chongqing, China.
| | - Fan Zhang
- Department of Breast and Thyroid Surgery, Chongqing General Hospital, Chongqing, China.
- Graduate School of Medicine, Chongqing Medical University, Chongqing, China.
- Chongqing Hospital of Traditional Chinese Medicine, Chongqing, China.
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Yuan H, Kido T, Hirata M, Ueno K, Imai Y, Chen K, Ren W, Yang L, Chen K, Qu L, Wu Y. New vision of HookEfficientNet deep neural network: Intelligent histopathological recognition system of non-small cell lung cancer. Comput Biol Med 2024; 178:108710. [PMID: 38843570 DOI: 10.1016/j.compbiomed.2024.108710] [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: 11/02/2023] [Revised: 05/24/2024] [Accepted: 06/03/2024] [Indexed: 07/24/2024]
Abstract
BACKGROUND Efficient and precise diagnosis of non-small cell lung cancer (NSCLC) is quite critical for subsequent targeted therapy and immunotherapy. Since the advent of whole slide images (WSIs), the transition from traditional histopathology to digital pathology has aroused the application of convolutional neural networks (CNNs) in histopathological recognition and diagnosis. HookNet can make full use of macroscopic and microscopic information for pathological diagnosis, but it cannot integrate other excellent CNN structures. The new version of HookEfficientNet is based on a combination of HookNet structure and EfficientNet that performs well in the recognition of general objects. Here, a high-precision artificial intelligence-guided histopathological recognition system was established by HookEfficientNet to provide a basis for the intelligent differential diagnosis of NSCLC. METHODS A total of 216 WSIs of lung adenocarcinoma (LUAD) and 192 WSIs of lung squamous cell carcinoma (LUSC) were recruited from the First Affiliated Hospital of Zhengzhou University. Deep learning methods based on HookEfficientNet, HookNet and EfficientNet B4-B6 were developed and compared with each other using area under the curve (AUC) and the Youden index. Temperature scaling was used to calibrate the heatmap and highlight the cancer region of interest. Four pathologists of different levels blindly reviewed 108 WSIs of LUAD and LUSC, and the diagnostic results were compared with the various deep learning models. RESULTS The HookEfficientNet model outperformed HookNet and EfficientNet B4-B6. After temperature scaling, the HookEfficientNet model achieved AUCs of 0.973, 0.980, and 0.989 and Youden index values of 0.863, 0.899, and 0.922 for LUAD, LUSC and normal lung tissue, respectively, in the testing set. The accuracy of the model was better than the average accuracy from experienced pathologists, and the model was superior to pathologists in the diagnosis of LUSC. CONCLUSIONS HookEfficientNet can effectively recognize LUAD and LUSC with performance superior to that of senior pathologists, especially for LUSC. The model has great potential to facilitate the application of deep learning-assisted histopathological diagnosis for LUAD and LUSC in the future.
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Affiliation(s)
- Huijie Yuan
- College of Public Health, Zhengzhou University, Zhengzhou, 450001, China
| | | | | | - Kengo Ueno
- KYOCERA Communication Systems Co., Ltd, Kyoto, Japan
| | - Yuji Imai
- KYOCERA Communication Systems (Shanghai) Co., Ltd, Shanghai, China
| | - Kangxuan Chen
- KYOCERA Communication Systems (Shanghai) Co., Ltd, Shanghai, China
| | - Wujie Ren
- Henan 863 Software Co., Ltd, Zhengzhou, China
| | - Liang Yang
- Henan 863 Software Co., Ltd, Zhengzhou, China
| | - Kuisheng Chen
- The First Affiliated Hospital of Zhengzhou University, Zhengzhou, 450052, China
| | - Lingbo Qu
- Henan Joint International Research Laboratory of Green Construction of Functional Molecules and Their Bioanalytical Applications, Zhengzhou University, Zhengzhou, 450001, China
| | - Yongjun Wu
- College of Public Health, Zhengzhou University, Zhengzhou, 450001, China.
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Dong Z, Zhao X, Zheng H, Zheng H, Chen D, Cao J, Xiao Z, Sun Y, Zhuang Q, Wu S, Xia J, Ning M, Qin B, Zhou H, Bao J, Wan X. Efficacy of real-time artificial intelligence-aid endoscopic ultrasonography diagnostic system in discriminating gastrointestinal stromal tumors and leiomyomas: a multicenter diagnostic study. EClinicalMedicine 2024; 73:102656. [PMID: 38828130 PMCID: PMC11137341 DOI: 10.1016/j.eclinm.2024.102656] [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: 02/24/2024] [Revised: 05/03/2024] [Accepted: 05/07/2024] [Indexed: 06/05/2024] Open
Abstract
Background Gastrointestinal stromal tumors (GISTs) represent the most prevalent type of subepithelial lesions (SELs) with malignant potential. Current imaging tools struggle to differentiate GISTs from leiomyomas. This study aimed to create and assess a real-time artificial intelligence (AI) system using endoscopic ultrasonography (EUS) images to differentiate between GISTs and leiomyomas. Methods The AI system underwent development and evaluation using EUS images from 5 endoscopic centers in China between January 2020 and August 2023. EUS images of 1101 participants with SELs were retrospectively collected for AI system development. A cohort of 241 participants with SELs was recruited for external AI system evaluation. Another cohort of 59 participants with SELs was prospectively enrolled to assess the real-time clinical application of the AI system. The AI system's performance was compared to that of endoscopists. This study is registered with Chictr.org.cn, Number ChiCT2000035787. Findings The AI system displayed an area under the curve (AUC) of 0.948 (95% CI: 0.921-0.969) for discriminating GISTs and leiomyomas. The AI system's accuracy (ACC), sensitivity, specificity, positive predictive value (PPV), and negative predictive value (NPV) reached 91.7% (95% CI 87.5%-94.6%), 90.3% (95% CI 83.4%-94.5%), 93.0% (95% CI 87.2%-96.3%), 91.9% (95% CI 85.3%-95.7%), and 91.5% (95% CI 85.5%-95.2%), respectively. Moreover, the AI system exhibited excellent performance in diagnosing ≤20 mm SELs (ACC 93.5%, 95% CI 0.900-0.969). In a prospective real-time clinical application trial, the AI system achieved an AUC of 0.865 (95% CI 0.764-0.966) and 0.864 (95% CI 0.762-0.966) for GISTs and leiomyomas diagnosis, respectively, markedly surpassing endoscopists [AUC 0.698 (95% CI 0.562-0.834) for GISTs and AUC 0.695 (95% CI 0.546-0.825) for leiomyomas]. Interpretation We successfully developed a real-time AI-assisted EUS diagnostic system. The incorporation of the real-time AI system during EUS examinations can assist endoscopists in rapidly and accurately differentiating various types of SELs in clinical practice, facilitating improved diagnostic and therapeutic decision-making. Funding Science and Technology Commission Foundation of Shanghai Municipality, Science and Technology Commission Foundation of the Xuhui District, the Interdisciplinary Program of Shanghai Jiao Tong University and the Research Funds of Shanghai Sixth people's Hospital.
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Affiliation(s)
- Zhixia Dong
- Digestive Endoscopic Center, Shanghai Sixth People's Hospital Affiliated to Shanghai Jiao Tong University School of Medicine, Shanghai, China
| | - Xiangyun Zhao
- Digestive Endoscopic Center, Shanghai Sixth People's Hospital Affiliated to Shanghai Jiao Tong University School of Medicine, Shanghai, China
| | - Hangbin Zheng
- College of Mechanical Engineering, Dong Hua University, Shanghai, China
| | - HanYao Zheng
- College of Mechanical Engineering, Dong Hua University, Shanghai, China
| | - Dafan Chen
- Digestive Endoscopic Center, Shanghai Sixth People's Hospital Affiliated to Shanghai Jiao Tong University School of Medicine, Shanghai, China
| | - Jia Cao
- Endoscopy Center, Department of Gastroenterology, Shanghai East Hospital, Tongji University School of Medicine, Shanghai, China
| | - Zili Xiao
- Digestive Endoscopic Department, Huadong Hospital Affiliated to Fudan University, Shanghai, China
| | - Yunwei Sun
- Department of Gastroenterology, Shanghai Ruijin Hospital, Shanghai Jiao Tong University School of Medicine, Shanghai, China
| | - Qian Zhuang
- Digestive Endoscopic Center, Shanghai Sixth People's Hospital Affiliated to Shanghai Jiao Tong University School of Medicine, Shanghai, China
| | - Shan Wu
- Digestive Endoscopic Center, Shanghai Sixth People's Hospital Affiliated to Shanghai Jiao Tong University School of Medicine, Shanghai, China
| | - Jie Xia
- Digestive Endoscopic Center, Shanghai Sixth People's Hospital Affiliated to Shanghai Jiao Tong University School of Medicine, Shanghai, China
| | - Min Ning
- Digestive Endoscopic Center, Shanghai Sixth People's Hospital Affiliated to Shanghai Jiao Tong University School of Medicine, Shanghai, China
| | - Binjie Qin
- School of Biomedical Engineering, Shanghai Jiao Tong University, China
| | - Hui Zhou
- Department of Gastroenterology, Shanghai General Hospital, Shanghai Jiao Tong University School of Medicine, Shanghai, China
| | - Jinsong Bao
- College of Mechanical Engineering, Dong Hua University, Shanghai, China
| | - Xinjian Wan
- Digestive Endoscopic Center, Shanghai Sixth People's Hospital Affiliated to Shanghai Jiao Tong University School of Medicine, Shanghai, China
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Tai J, Han M, Choi BY, Kang SH, Kim H, Kwak J, Lee D, Lee TH, Cho Y, Kim TH. Deep learning model for differentiating nasal cavity masses based on nasal endoscopy images. BMC Med Inform Decis Mak 2024; 24:145. [PMID: 38811961 PMCID: PMC11138030 DOI: 10.1186/s12911-024-02517-z] [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: 09/03/2023] [Accepted: 04/17/2024] [Indexed: 05/31/2024] Open
Abstract
BACKGROUND Nasal polyps and inverted papillomas often look similar. Clinically, it is difficult to distinguish the masses by endoscopic examination. Therefore, in this study, we aimed to develop a deep learning algorithm for computer-aided diagnosis of nasal endoscopic images, which may provide a more accurate clinical diagnosis before pathologic confirmation of the nasal masses. METHODS By performing deep learning of nasal endoscope images, we evaluated our computer-aided diagnosis system's assessment ability for nasal polyps and inverted papilloma and the feasibility of their clinical application. We used curriculum learning pre-trained with patches of nasal endoscopic images and full-sized images. The proposed model's performance for classifying nasal polyps, inverted papilloma, and normal tissue was analyzed using five-fold cross-validation. RESULTS The normal scores for our best-performing network were 0.9520 for recall, 0.7900 for precision, 0.8648 for F1-score, 0.97 for the area under the curve, and 0.8273 for accuracy. For nasal polyps, the best performance was 0.8162, 0.8496, 0.8409, 0.89, and 0.8273, respectively, for recall, precision, F1-score, area under the curve, and accuracy. Finally, for inverted papilloma, the best performance was obtained for recall, precision, F1-score, area under the curve, and accuracy values of 0.5172, 0.8125, 0.6122, 0.83, and 0.8273, respectively. CONCLUSION Although there were some misclassifications, the results of gradient-weighted class activation mapping were generally consistent with the areas under the curve determined by otolaryngologists. These results suggest that the convolutional neural network is highly reliable in resolving lesion locations in nasal endoscopic images.
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Affiliation(s)
- Junhu Tai
- Department of Otorhinolaryngology-Head & Neck Surgery, College of Medicine, Korea University, Seoul, Republic of Korea
| | - Munsoo Han
- Department of Otorhinolaryngology-Head & Neck Surgery, College of Medicine, Korea University, Seoul, Republic of Korea
- Mucosal Immunology Institute, College of Medicine, Korea University, Seoul, Republic of Korea
| | - Bo Yoon Choi
- Department of Otorhinolaryngology-Head & Neck Surgery, College of Medicine, Korea University, Seoul, Republic of Korea
| | - Sung Hoon Kang
- Department of Otorhinolaryngology-Head & Neck Surgery, College of Medicine, Korea University, Seoul, Republic of Korea
| | - Hyeongeun Kim
- Department of Otorhinolaryngology-Head & Neck Surgery, College of Medicine, Korea University, Seoul, Republic of Korea
| | - Jiwon Kwak
- Department of Otorhinolaryngology-Head & Neck Surgery, College of Medicine, Korea University, Seoul, Republic of Korea
| | - Dabin Lee
- Department of Otorhinolaryngology-Head & Neck Surgery, College of Medicine, Korea University, Seoul, Republic of Korea
| | - Tae Hoon Lee
- Department of Otorhinolaryngology-Head & Neck Surgery, College of Medicine, Korea University, Seoul, Republic of Korea
| | - Yongwon Cho
- Department of Radiology and AI center, College of Medicine, Korea University, Seoul, Republic of Korea.
- Department of Computer Science and Engineering, Soonchunhyang University, Cheonan-Asan, Republic of Korea.
| | - Tae Hoon Kim
- Department of Otorhinolaryngology-Head & Neck Surgery, College of Medicine, Korea University, Seoul, Republic of Korea.
- Mucosal Immunology Institute, College of Medicine, Korea University, Seoul, Republic of Korea.
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Kuwahara T, Hara K, Mizuno N, Haba S, Okuno N, Fukui T, Urata M, Yamamoto Y. Current status of artificial intelligence analysis for the treatment of pancreaticobiliary diseases using endoscopic ultrasonography and endoscopic retrograde cholangiopancreatography. DEN OPEN 2024; 4:e267. [PMID: 37397344 PMCID: PMC10312781 DOI: 10.1002/deo2.267] [Citation(s) in RCA: 6] [Impact Index Per Article: 6.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Figures] [Subscribe] [Scholar Register] [Received: 06/01/2023] [Accepted: 06/18/2023] [Indexed: 07/04/2023]
Abstract
Pancreatic and biliary diseases encompass a range of conditions requiring accurate diagnosis for appropriate treatment strategies. This diagnosis relies heavily on imaging techniques like endoscopic ultrasonography and endoscopic retrograde cholangiopancreatography. Artificial intelligence (AI), including machine learning and deep learning, is becoming integral in medical imaging and diagnostics, such as the detection of colorectal polyps. AI shows great potential in diagnosing pancreatobiliary diseases. Unlike machine learning, which requires feature extraction and selection, deep learning can utilize images directly as input. Accurate evaluation of AI performance is a complex task due to varied terminologies, evaluation methods, and development stages. Essential aspects of AI evaluation involve defining the AI's purpose, choosing appropriate gold standards, deciding on the validation phase, and selecting reliable validation methods. AI, particularly deep learning, is increasingly employed in endoscopic ultrasonography and endoscopic retrograde cholangiopancreatography diagnostics, achieving high accuracy levels in detecting and classifying various pancreatobiliary diseases. The AI often performs better than doctors, even in tasks like differentiating benign from malignant pancreatic tumors, cysts, and subepithelial lesions, identifying gallbladder lesions, assessing endoscopic retrograde cholangiopancreatography difficulty, and evaluating the biliary strictures. The potential for AI in diagnosing pancreatobiliary diseases, especially where other modalities have limitations, is considerable. However, a crucial constraint is the need for extensive, high-quality annotated data for AI training. Future advances in AI, such as large language models, promise further applications in the medical field.
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Affiliation(s)
| | - Kazuo Hara
- Department of GastroenterologyAichi Cancer Center HospitalAichiJapan
| | - Nobumasa Mizuno
- Department of GastroenterologyAichi Cancer Center HospitalAichiJapan
| | - Shin Haba
- Department of GastroenterologyAichi Cancer Center HospitalAichiJapan
| | - Nozomi Okuno
- Department of GastroenterologyAichi Cancer Center HospitalAichiJapan
| | - Toshitaka Fukui
- Department of GastroenterologyAichi Cancer Center HospitalAichiJapan
| | - Minako Urata
- Department of GastroenterologyAichi Cancer Center HospitalAichiJapan
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Wang J, Shao M, Hu H, Xiao W, Cheng G, Yang G, Ji H, Yu S, Wan J, Xie Z, Xu M. Convolutional neural network applied to preoperative venous-phase CT images predicts risk category in patients with gastric gastrointestinal stromal tumors. BMC Cancer 2024; 24:280. [PMID: 38429653 PMCID: PMC10908217 DOI: 10.1186/s12885-024-11962-y] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 03/23/2023] [Accepted: 02/05/2024] [Indexed: 03/03/2024] Open
Abstract
OBJECTIVE The risk category of gastric gastrointestinal stromal tumors (GISTs) are closely related to the surgical method, the scope of resection, and the need for preoperative chemotherapy. We aimed to develop and validate convolutional neural network (CNN) models based on preoperative venous-phase CT images to predict the risk category of gastric GISTs. METHOD A total of 425 patients pathologically diagnosed with gastric GISTs at the authors' medical centers between January 2012 and July 2021 were split into a training set (154, 84, and 59 with very low/low, intermediate, and high-risk, respectively) and a validation set (67, 35, and 26, respectively). Three CNN models were constructed by obtaining the upper and lower 1, 4, and 7 layers of the maximum tumour mask slice based on venous-phase CT Images and models of CNN_layer3, CNN_layer9, and CNN_layer15 established, respectively. The area under the receiver operating characteristics curve (AUROC) and the Obuchowski index were calculated to compare the diagnostic performance of the CNN models. RESULTS In the validation set, CNN_layer3, CNN_layer9, and CNN_layer15 had AUROCs of 0.89, 0.90, and 0.90, respectively, for low-risk gastric GISTs; 0.82, 0.83, and 0.83 for intermediate-risk gastric GISTs; and 0.86, 0.86, and 0.85 for high-risk gastric GISTs. In the validation dataset, CNN_layer3 (Obuchowski index, 0.871) provided similar performance than CNN_layer9 and CNN_layer15 (Obuchowski index, 0.875 and 0.873, respectively) in prediction of the gastric GIST risk category (All P >.05). CONCLUSIONS The CNN based on preoperative venous-phase CT images showed good performance for predicting the risk category of gastric GISTs.
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Affiliation(s)
- Jian Wang
- Department of Radiology, Tongde Hospital of Zhejiang Province, Hangzhou, Zhejiang, China
- Department of radiology, The First Affiliated Hospital of Zhejiang Chinese Medical University (Zhejiang Provincial Hospital of Chinese Medicine), Hangzhou, Zhejiang, China
| | - Meihua Shao
- Department of Radiology, Tongde Hospital of Zhejiang Province, Hangzhou, Zhejiang, China
| | - Hongjie Hu
- Department of Radiology, The Sir Run Shaw Hospital, Zhejiang University School of Medicine, Hangzhou, Zhejiang, China
| | - Wenbo Xiao
- Department of radiology,The First Affiliated Hospital, Zhejiang University School of Medicine, Hangzhou, Zhejiang, China
| | | | - Guangzhao Yang
- Department of Radiology, Tongde Hospital of Zhejiang Province, Hangzhou, Zhejiang, China
| | - Hongli Ji
- Jianpei Technology, Hangzhou, Zhejiang, China
| | - Susu Yu
- Department of radiology,The First Affiliated Hospital, Zhejiang University School of Medicine, Hangzhou, Zhejiang, China
| | - Jie Wan
- Jianpei Technology, Hangzhou, Zhejiang, China
| | - Zongyu Xie
- Department of Radiology, The First Affliated Hospital of Bengbu Medical University, Bengbu, Anhui, China
| | - Maosheng Xu
- Department of radiology, The First Affiliated Hospital of Zhejiang Chinese Medical University (Zhejiang Provincial Hospital of Chinese Medicine), Hangzhou, Zhejiang, China.
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10
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Chen X, Zhou J, Wang P, Wang P, Wang L, Mu L, Lang C, Mu Y, Wang X, Shang R, Li Q, Lv H, Wu K, Shi N, Jia X, Lai Y, Zhang Y, Li Z, Zhong N. Endoscopic ultrasound-based application system for predicting endoscopic resection-related outcomes and diagnosing subepithelial lesions: Multicenter prospective study. Dig Endosc 2024; 36:141-151. [PMID: 37059698 DOI: 10.1111/den.14568] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 02/17/2023] [Accepted: 04/13/2023] [Indexed: 04/16/2023]
Abstract
OBJECTIVES Subepithelial lesions (SELs) are associated with various endoscopic resection (ER) outcomes and diagnostic challenges. We aimed to establish a tool for predicting ER-related outcomes and diagnosing SELs and to investigate the predictive value of endoscopic ultrasound (EUS). METHODS Phase 1 (system development) was performed in a retrospective cohort (n = 837) who underwent EUS before ER for SELs at eight hospitals. Prediction models for five key outcomes were developed using logistic regression. Models with satisfactory internal validation performance were included in a mobile application system, SEL endoscopic resection predictor (SELERP). In Phase 2, the models were externally validated in a prospective cohort of 200 patients. RESULTS An SELERP was developed using EUS characteristics, which included 10 models for five key outcomes: post-ER ulcer management, short procedure time, long hospital stay, high medication costs, and diagnosis of SELs. In Phase 1, 10 models were derived and validated (C-statistics, 0.67-0.99; calibration-in-the-large, -0.14-0.10; calibration slopes, 0.92-1.08). In Phase 2, the derived risk prediction models showed convincing discrimination (C-statistics, 0.64-0.73) and calibration (calibration-in-the-large, -0.02-0.05; calibration slopes, 1.01-1.09) in the prospective cohort. The sensitivities and specificities of the five diagnostic models were 68.3-95.7% and 64.1-83.3%, respectively. CONCLUSION We developed and prospectively validated an application system for the prediction of ER outcomes and diagnosis of SELs, which could aid clinical decision-making and facilitate patient-physician consultation. EUS features significantly contributed to the prediction. TRIAL REGISTRATION Chinese Clinical Trial Registry, http://www.chictr.org.cn (ChiCTR2000040118).
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Affiliation(s)
- Xinyu Chen
- Department of Gastroenterology, Qilu Hospital of Shandong University, Jinan, China
- Laboratory of Translational Gastroenterology, Qilu Hospital of Shandong University, Jinan, China
- Shandong Provincial Clinical Research Center for Digestive Disease, Qilu Hospital of Shandong University, Jinan, China
| | - Jiawei Zhou
- Department of Gastroenterology, Qilu Hospital of Shandong University, Jinan, China
- Laboratory of Translational Gastroenterology, Qilu Hospital of Shandong University, Jinan, China
- Shandong Provincial Clinical Research Center for Digestive Disease, Qilu Hospital of Shandong University, Jinan, China
| | - Peizhu Wang
- Department of Gastroenterology, Qilu Hospital of Shandong University, Jinan, China
- Laboratory of Translational Gastroenterology, Qilu Hospital of Shandong University, Jinan, China
- Shandong Provincial Clinical Research Center for Digestive Disease, Qilu Hospital of Shandong University, Jinan, China
| | - Peng Wang
- Department of Gastroenterology, Qilu Hospital of Shandong University, Jinan, China
| | - Limei Wang
- Department of Gastroenterology, Qilu Hospital of Shandong University, Jinan, China
| | - Linjun Mu
- Department of Gastroenterology, Weifang People's Hospital, Weifang, China
| | - Cuicui Lang
- Department of Gastroenterology, Liaocheng People's Hospital, Liaocheng, China
| | - Ying Mu
- Department of Gastroenterology, Liaocheng People's Hospital, Liaocheng, China
| | - Xiaohong Wang
- Department of Gastroenterology, The Affiliated Taian City Centeral Hospital of Qingdao University, Taian, China
| | - Ruilian Shang
- Department of Gastroenterology, The 960th Hospital of the PLA Joint Logistics Support Force, Jinan, China
| | - Qun Li
- Department of Gastroenterology, The 960th Hospital of the PLA Joint Logistics Support Force, Jinan, China
| | - Hongna Lv
- Department of Gastroenterology and Hepatology, Binzhou People's Hospital, Binzhou, China
| | - Kangkang Wu
- Department of Gastroenterology, Qilu Hospital of Shandong University (Qingdao), Qingdao, China
| | - Ning Shi
- Department of Gastroenterology, Binzhou Medical University Hospital, Binzhou, China
| | - Xingfang Jia
- Department of Gastroenterology, Binzhou Medical University Hospital, Binzhou, China
| | - Yonghang Lai
- Qingdao Medicon Digital Engineering Co., Ltd., Qingdao, China
| | - Yiyan Zhang
- Qingdao Medicon Digital Engineering Co., Ltd., Qingdao, China
| | - Zhen Li
- Department of Gastroenterology, Qilu Hospital of Shandong University, Jinan, China
- Laboratory of Translational Gastroenterology, Qilu Hospital of Shandong University, Jinan, China
| | - Ning Zhong
- Department of Gastroenterology, Qilu Hospital of Shandong University, Jinan, China
- Laboratory of Translational Gastroenterology, Qilu Hospital of Shandong University, Jinan, China
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11
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Fan L, Gong X, Zheng C, Li J. Data pyramid structure for optimizing EUS-based GISTs diagnosis in multi-center analysis with missing label. Comput Biol Med 2024; 169:107897. [PMID: 38171262 DOI: 10.1016/j.compbiomed.2023.107897] [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: 11/01/2023] [Revised: 12/04/2023] [Accepted: 12/23/2023] [Indexed: 01/05/2024]
Abstract
This study introduces the Data Pyramid Structure (DPS) to address data sparsity and missing labels in medical image analysis. The DPS optimizes multi-task learning and enables sustainable expansion of multi-center data analysis. Specifically, It facilitates attribute prediction and malignant tumor diagnosis tasks by implementing a segmentation and aggregation strategy on data with absent attribute labels. To leverage multi-center data, we propose the Unified Ensemble Learning Framework (UELF) and the Unified Federated Learning Framework (UFLF), which incorporate strategies for data transfer and incremental learning in scenarios with missing labels. The proposed method was evaluated on a challenging EUS patient dataset from five centers, achieving promising diagnostic performance. The average accuracy was 0.984 with an AUC of 0.927 for multi-center analysis, surpassing state-of-the-art approaches. The interpretability of the predictions further highlights the potential clinical relevance of our method.
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Affiliation(s)
- Lin Fan
- School of Computing and Artificial Intelligence, Southwest Jiaotong University, Chengdu, Sichuan 611756, China; Manufacturing Industry Chains Collaboration and Information Support Technology Key Laboratory of Sichuan Province, China; Engineering Research Center of Sustainable Urban Intelligent Transportation, Ministry of Education, China; National Engineering Laboratory of Integrated Transportation Big Data Application Technology, China
| | - Xun Gong
- School of Computing and Artificial Intelligence, Southwest Jiaotong University, Chengdu, Sichuan 611756, China; Manufacturing Industry Chains Collaboration and Information Support Technology Key Laboratory of Sichuan Province, China; Engineering Research Center of Sustainable Urban Intelligent Transportation, Ministry of Education, China; National Engineering Laboratory of Integrated Transportation Big Data Application Technology, China.
| | - Cenyang Zheng
- School of Computing and Artificial Intelligence, Southwest Jiaotong University, Chengdu, Sichuan 611756, China; Manufacturing Industry Chains Collaboration and Information Support Technology Key Laboratory of Sichuan Province, China; Engineering Research Center of Sustainable Urban Intelligent Transportation, Ministry of Education, China; National Engineering Laboratory of Integrated Transportation Big Data Application Technology, China
| | - Jiao Li
- Department of Gastroenterology, The Third People's Hospital of Chendu, Affiliated Hospital of Southwest Jiaotong University, Chengdu 610031, China
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12
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Lu Y, Wu J, Hu M, Zhong Q, Er L, Shi H, Cheng W, Chen K, Liu Y, Qiu B, Xu Q, Lai G, Wang Y, Luo Y, Mu J, Zhang W, Zhi M, Sun J. Artificial Intelligence in the Prediction of Gastrointestinal Stromal Tumors on Endoscopic Ultrasonography Images: Development, Validation and Comparison with Endosonographers. Gut Liver 2023; 17:874-883. [PMID: 36700302 PMCID: PMC10651383 DOI: 10.5009/gnl220347] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.5] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 08/08/2022] [Revised: 09/19/2022] [Accepted: 10/07/2022] [Indexed: 01/27/2023] Open
Abstract
Background/Aims The accuracy of endosonographers in diagnosing gastric subepithelial lesions (SELs) using endoscopic ultrasonography (EUS) is influenced by experience and subjectivity. Artificial intelligence (AI) has achieved remarkable development in this field. This study aimed to develop an AI-based EUS diagnostic model for the diagnosis of SELs, and evaluated its efficacy with external validation. Methods We developed the EUS-AI model with ResNeSt50 using EUS images from two hospitals to predict the histopathology of the gastric SELs originating from muscularis propria. The diagnostic performance of the model was also validated using EUS images obtained from four other hospitals. Results A total of 2,057 images from 367 patients (375 SELs) were chosen to build the models, and 914 images from 106 patients (108 SELs) were chosen for external validation. The sensitivity, specificity, positive predictive value, negative predictive value, and accuracy of the model for differentiating gastrointestinal stromal tumors (GISTs) and non-GISTs in the external validation sets by images were 82.01%, 68.22%, 86.77%, 59.86%, and 78.12%, respectively. The sensitivity, specificity, positive predictive value, negative predictive value, and accuracy in the external validation set by tumors were 83.75%, 71.43%, 89.33%, 60.61%, and 80.56%, respectively. The EUS-AI model showed better performance (especially specificity) than some endosonographers. The model helped improve the sensitivity, specificity, and accuracy of certain endosonographers. Conclusions We developed an EUS-AI model to classify gastric SELs originating from muscularis propria into GISTs and non-GISTs with good accuracy. The model may help improve the diagnostic performance of endosonographers. Further work is required to develop a multi-modal EUS-AI system.
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Affiliation(s)
- Yi Lu
- Department of Gastrointestinal Endoscopy, The Sixth Affiliated Hospital, Sun Yat-sen University, Guangzhou, China
- Guangdong Provincial Key Laboratory of Colorectal and Pelvic Floor Diseases, The Sixth Affiliated Hospital, Sun Yat-sen University, Guangzhou, China
| | - Jiachuan Wu
- Digestive Endoscopy Center, Guangdong Second Provincial General Hospital, Sun Yat-sen University, Guangzhou, China
| | - Minhui Hu
- Department of Gastrointestinal Endoscopy, The Sixth Affiliated Hospital, Sun Yat-sen University, Guangzhou, China
- Guangdong Provincial Key Laboratory of Colorectal and Pelvic Floor Diseases, The Sixth Affiliated Hospital, Sun Yat-sen University, Guangzhou, China
| | - Qinghua Zhong
- Department of Endoscopic Surgery, The Sixth Affiliated Hospital, Sun Yat-sen University, Guangzhou, China
| | - Limian Er
- Department of Endoscopy, The Fourth Hospital of Hebei Medical University, Shijiazhuang, China
| | - Huihui Shi
- Department of Endoscopy, The Fourth Hospital of Hebei Medical University, Shijiazhuang, China
| | - Weihui Cheng
- Department of Gastroenterology, Yangjiang Hospital of Traditional Chinese Medicine, Yangjiang, China
| | - Ke Chen
- Department of Endoscopy, Fudan University Shanghai Cancer Center, Shanghai, China
| | - Yuan Liu
- Department of Endoscopy, Fudan University Shanghai Cancer Center, Shanghai, China
| | - Bingfeng Qiu
- Department of Gastroenterology, Zhoushan Hospital of Zhejiang Province, Zhoushan, China
| | - Qiancheng Xu
- Department of Gastroenterology, Zhoushan Hospital of Zhejiang Province, Zhoushan, China
| | - Guangshun Lai
- Department of Gastroenterology, Lianjiang People’s Hospital, Lianjiang, China
| | - Yufeng Wang
- Tianjin Economic-Technological Development Area (TEDA) Yujin Digestive Health Industry Research Institute, Tianjin, China
| | - Yuxuan Luo
- Tianjin Economic-Technological Development Area (TEDA) Yujin Digestive Health Industry Research Institute, Tianjin, China
| | - Jinbao Mu
- Tianjin Economic-Technological Development Area (TEDA) Yujin Digestive Health Industry Research Institute, Tianjin, China
| | - Wenjie Zhang
- Tianjin Center for Medical Devices Evaluation and Inspection, Tianjin, China
| | - Min Zhi
- Guangdong Provincial Key Laboratory of Colorectal and Pelvic Floor Diseases, The Sixth Affiliated Hospital, Sun Yat-sen University, Guangzhou, China
- Department of Gastroenterology, The Sixth Affiliated Hospital, Sun Yat-sen University, Guangzhou, China
| | - Jiachen Sun
- Department of Gastrointestinal Endoscopy, The Sixth Affiliated Hospital, Sun Yat-sen University, Guangzhou, China
- Guangdong Provincial Key Laboratory of Colorectal and Pelvic Floor Diseases, The Sixth Affiliated Hospital, Sun Yat-sen University, Guangzhou, China
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13
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Arif AA, Jiang SX, Byrne MF. Artificial intelligence in endoscopy: Overview, applications, and future directions. Saudi J Gastroenterol 2023; 29:269-277. [PMID: 37787347 PMCID: PMC10644999 DOI: 10.4103/sjg.sjg_286_23] [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: 08/08/2023] [Accepted: 08/16/2023] [Indexed: 09/15/2023] Open
Abstract
Since the emergence of artificial intelligence (AI) in medicine, endoscopy applications in gastroenterology have been at the forefront of innovations. The ever-increasing number of studies necessitates the need to organize and classify applications in a useful way. Separating AI capabilities by computer aided detection (CADe), diagnosis (CADx), and quality assessment (CADq) allows for a systematic evaluation of each application. CADe studies have shown promise in accurate detection of esophageal, gastric and colonic neoplasia as well as identifying sources of bleeding and Crohn's disease in the small bowel. While more advanced CADx applications employ optical biopsies to give further information to characterize neoplasia and grade inflammatory disease, diverse CADq applications ensure quality and increase the efficiency of procedures. Future applications show promise in advanced therapeutic modalities and integrated systems that provide multimodal capabilities. AI is set to revolutionize clinical decision making and performance of endoscopy.
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Affiliation(s)
- Arif A. Arif
- Department of Medicine, University of British Columbia, Vancouver, BC, Canada
| | - Shirley X. Jiang
- Department of Medicine, University of British Columbia, Vancouver, BC, Canada
| | - Michael F. Byrne
- Division of Gastroenterology, Department of Medicine, University of British Columbia, Vancouver, BC, Canada
- Satisfai Health, Vancouver, BC, Canada
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14
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Gomes RSA, de Oliveira GHP, de Moura DTH, Kotinda APST, Matsubayashi CO, Hirsch BS, Veras MDO, Ribeiro Jordão Sasso JG, Trasolini RP, Bernardo WM, de Moura EGH. Endoscopic ultrasound artificial intelligence-assisted for prediction of gastrointestinal stromal tumors diagnosis: A systematic review and meta-analysis. World J Gastrointest Endosc 2023; 15:528-539. [PMID: 37663113 PMCID: PMC10473903 DOI: 10.4253/wjge.v15.i8.528] [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: 03/16/2023] [Revised: 06/15/2023] [Accepted: 07/24/2023] [Indexed: 08/10/2023] Open
Abstract
BACKGROUND Subepithelial lesions (SELs) are gastrointestinal tumors with heterogeneous malignant potential. Endoscopic ultrasonography (EUS) is the leading method for evaluation, but without histopathological analysis, precise differentiation of SEL risk is limited. Artificial intelligence (AI) is a promising aid for the diagnosis of gastrointestinal lesions in the absence of histopathology. AIM To determine the diagnostic accuracy of AI-assisted EUS in diagnosing SELs, especially lesions originating from the muscularis propria layer. METHODS Electronic databases including PubMed, EMBASE, and Cochrane Library were searched. Patients of any sex and > 18 years, with SELs assessed by EUS AI-assisted, with previous histopathological diagnosis, and presented sufficient data values which were extracted to construct a 2 × 2 table. The reference standard was histopathology. The primary outcome was the accuracy of AI for gastrointestinal stromal tumor (GIST). Secondary outcomes were AI-assisted EUS diagnosis for GIST vs gastrointestinal leiomyoma (GIL), the diagnostic performance of experienced endoscopists for GIST, and GIST vs GIL. Pooled sensitivity, specificity, positive, and negative predictive values were calculated. The corresponding summary receiver operating characteristic curve and post-test probability were also analyzed. RESULTS Eight retrospective studies with a total of 2355 patients and 44154 images were included in this meta-analysis. The AI-assisted EUS for GIST diagnosis showed a sensitivity of 92% [95% confidence interval (CI): 0.89-0.95; P < 0.01), specificity of 80% (95%CI: 0.75-0.85; P < 0.01), and area under the curve (AUC) of 0.949. For diagnosis of GIST vs GIL by AI-assisted EUS, specificity was 90% (95%CI: 0.88-0.95; P = 0.02) and AUC of 0.966. The experienced endoscopists' values were sensitivity of 72% (95%CI: 0.67-0.76; P < 0.01), specificity of 70% (95%CI: 0.64-0.76; P < 0.01), and AUC of 0.777 for GIST. Evaluating GIST vs GIL, the experts achieved a sensitivity of 73% (95%CI: 0.65-0.80; P < 0.01) and an AUC of 0.819. CONCLUSION AI-assisted EUS has high diagnostic accuracy for fourth-layer SELs, especially for GIST, demonstrating superiority compared to experienced endoscopists' and improving their diagnostic performance in the absence of invasive procedures.
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Affiliation(s)
- Rômulo Sérgio Araújo Gomes
- Department of Gastroenterology, Hospital das Clínicas da Faculdade de Medicina da Universidade de São Paulo, São Paulo 05403-010, Brazil
| | | | - Diogo Turiani Hourneaux de Moura
- Department of Gastroenterology, Hospital das Clínicas da Faculdade de Medicina da Universidade de São Paulo, São Paulo 05403-010, Brazil
| | - Ana Paula Samy Tanaka Kotinda
- Department of Gastroenterology, Hospital das Clínicas da Faculdade de Medicina da Universidade de São Paulo, São Paulo 05403-010, Brazil
| | - Carolina Ogawa Matsubayashi
- Department of Gastroenterology, Hospital das Clínicas da Faculdade de Medicina da Universidade de São Paulo, São Paulo 05403-010, Brazil
| | - Bruno Salomão Hirsch
- Department of Gastroenterology, Hospital das Clínicas da Faculdade de Medicina da Universidade de São Paulo, São Paulo 05403-010, Brazil
| | - Matheus de Oliveira Veras
- Department of Gastroenterology, Hospital das Clínicas da Faculdade de Medicina da Universidade de São Paulo, São Paulo 05403-010, Brazil
| | | | - Roberto Paolo Trasolini
- Division of Hepatology and Endoscopy, Department of Gastroenterology, Harvard Medical School, Brigham and Women’s Hospital, Boston, MA 02115, United States
| | - Wanderley Marques Bernardo
- Department of Gastroenterology, Hospital das Clínicas da Faculdade de Medicina da Universidade de São Paulo, São Paulo 05403-010, Brazil
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15
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Lu Y, Chen L, Wu J, Er L, Shi H, Cheng W, Chen K, Liu Y, Qiu B, Xu Q, Feng Y, Tang N, Wan F, Sun J, Zhi M. Artificial intelligence in endoscopic ultrasonography: risk stratification of gastric gastrointestinal stromal tumors. Therap Adv Gastroenterol 2023; 16:17562848231177156. [PMID: 37274299 PMCID: PMC10233610 DOI: 10.1177/17562848231177156] [Citation(s) in RCA: 3] [Impact Index Per Article: 1.5] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 01/28/2023] [Accepted: 05/04/2023] [Indexed: 06/06/2023] Open
Abstract
Background Previous studies have identified useful endoscopic ultrasonography (EUS) features to predict the malignant potential of gastrointestinal stromal tumors (GISTs). However, the results of the studies were not consistent. Artificial intelligence (AI) has shown promising results in medicine. Objectives We aimed to build a risk stratification EUS-AI model to predict the malignancy potential of GISTs. Design This was a retrospective study with external validation. Methods We developed two models using EUS images from two hospitals to predict the GIST risk category. Model 1 was the four-category risk EUS-AI model, and Model 2 was the two-category risk EUS-AI model. The diagnostic performance of the models was validated with external cohorts. Results A total of 1320 images (880 were very low-risk, 269 were low-risk, 68 were intermediate-risk, and 103 were high-risk) were finally chosen for building the models and test sets, and a total of 656 images (211 were very low-risk, 266 were low-risk, 88 were intermediate-risk, and 91 were high-risk) were chosen for external validation. The overall accuracy, sensitivity, specificity, positive predictive value (PPV), and negative predictive value (NPV) for the four-category risk EUS-AI model in the external validation sets by tumor were 74.50%, 55.00%, 79.05%, 53.49%, and 81.63%, respectively. The accuracy, sensitivity, specificity, PPV, and NPV for the two-category risk EUS-AI model for the prediction of very low-risk GISTs in the external validation sets by tumor were 86.25%, 94.44%, 79.55%, 79.07%, and 94.59%, respectively. Conclusion We developed a EUS-AI model for the risk stratification of GISTs with promising results, which may complement current clinical practice in the management of GISTs. Registration The study has been registered in the Chinese Clinical Trial Registry (No. ChiCTR2100051191).
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Affiliation(s)
- Yi Lu
- Department of Gastrointestinal Endoscopy,
Guangdong Provincial Key Laboratory of Colorectal and Pelvic Floor Diseases,
The Sixth Affiliated Hospital, Sun Yat-sen University, Guangzhou, People’s
Republic of China
| | - Lu Chen
- Department of Internal Medicine, Advent Health
Palm Coast, Palm Coast, FL, USA
| | - Jiachuan Wu
- Digestive Endoscopy Center, Guangdong Second
Provincial General Hospital, Guangzhou, People’s Republic of China
| | - Limian Er
- Department of Endoscopy, The Fourth Hospital of
Hebei Medical University, Shijiazhuang, People’s Republic of China
| | - Huihui Shi
- Department of Endoscopy, The Fourth Hospital of
Hebei Medical University, Shijiazhuang, People’s Republic of China
| | - Weihui Cheng
- Department of Gastroenterology, Yangjiang
Hospital of Traditional Chinese Medicine, Yangjiang, People’s Republic of
China
| | - Ke Chen
- Department of Endoscopy, Fudan University
Shanghai Cancer Center, Shanghai, People’s Republic of China
| | - Yuan Liu
- Department of Endoscopy, Fudan University
Shanghai Cancer Center, Shanghai, People’s Republic of China
| | - Bingfeng Qiu
- Department of Gastroenterology, Zhoushan
Hospital of Zhejiang Province, Zhoushan, People’s Republic of China
| | - Qiancheng Xu
- Department of Gastroenterology, Zhoushan
Hospital of Zhejiang Province, Zhoushan, People’s Republic of China
| | - Yue Feng
- Tianjin Economic-Technological Development
Area (TEDA) Yujin Digestive Health Industry Research Institute, Tianjin,
People’s Republic of China
| | - Nan Tang
- Tianjin Center for Medical Devices Evaluation
and Inspection, Tianjin, People’s Republic of China
| | - Fuchuan Wan
- Tianjin Economic-Technological Development
Area (TEDA) Yujin Artificial Intelligence Medical Technology Co, Ltd,
Tianjin, People’s Republic of China
| | - Jiachen Sun
- Department of Gastrointestinal Endoscopy,
Guangdong Provincial Key Laboratory of Colorectal and Pelvic Floor Diseases,
The Sixth Affiliated Hospital, Sun Yat-sen University, 26 Yuancun Erheng
Road, Guangzhou 510655, People’s Republic of China
| | - Min Zhi
- Department of Gastroenterology, Guangdong
Provincial Key Laboratory of Colorectal and Pelvic Floor Diseases, The Sixth
Affiliated Hospital, Sun Yat-sen University, 26 Yuancun Erheng Road,
Guangzhou 510655, People’s Republic of China
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16
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Khalaf K, Terrin M, Jovani M, Rizkala T, Spadaccini M, Pawlak KM, Colombo M, Andreozzi M, Fugazza A, Facciorusso A, Grizzi F, Hassan C, Repici A, Carrara S. A Comprehensive Guide to Artificial Intelligence in Endoscopic Ultrasound. J Clin Med 2023; 12:3757. [PMID: 37297953 PMCID: PMC10253269 DOI: 10.3390/jcm12113757] [Citation(s) in RCA: 6] [Impact Index Per Article: 3.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 04/29/2023] [Revised: 05/28/2023] [Accepted: 05/29/2023] [Indexed: 06/12/2023] Open
Abstract
BACKGROUND Endoscopic Ultrasound (EUS) is widely used for the diagnosis of bilio-pancreatic and gastrointestinal (GI) tract diseases, for the evaluation of subepithelial lesions, and for sampling of lymph nodes and solid masses located next to the GI tract. The role of Artificial Intelligence in healthcare in growing. This review aimed to provide an overview of the current state of AI in EUS from imaging to pathological diagnosis and training. METHODS AI algorithms can assist in lesion detection and characterization in EUS by analyzing EUS images and identifying suspicious areas that may require further clinical evaluation or biopsy sampling. Deep learning techniques, such as convolutional neural networks (CNNs), have shown great potential for tumor identification and subepithelial lesion (SEL) evaluation by extracting important features from EUS images and using them to classify or segment the images. RESULTS AI models with new features can increase the accuracy of diagnoses, provide faster diagnoses, identify subtle differences in disease presentation that may be missed by human eyes, and provide more information and insights into disease pathology. CONCLUSIONS The integration of AI in EUS images and biopsies has the potential to improve the diagnostic accuracy, leading to better patient outcomes and to a reduction in repeated procedures in case of non-diagnostic biopsies.
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Affiliation(s)
- Kareem Khalaf
- Division of Gastroenterology, St. Michael’s Hospital, University of Toronto, Toronto, ON M5S 1A1, Canada; (K.K.); (K.M.P.)
| | - Maria Terrin
- Division of Gastroenterology and Digestive Endoscopy, Humanitas Research Hospital IRCCS, Rozzano, 20089 Milan, Italy; (M.T.); (M.S.); (M.C.); (M.A.); (A.F.); (C.H.); (A.R.)
| | - Manol Jovani
- Division of Gastroenterology, Maimonides Medical Center, SUNY Downstate University, Brooklyn, NY 11219, USA;
| | - Tommy Rizkala
- Department of Biomedical Sciences, Humanitas University, Pieve Emanuele, 20089 Milan, Italy;
| | - Marco Spadaccini
- Division of Gastroenterology and Digestive Endoscopy, Humanitas Research Hospital IRCCS, Rozzano, 20089 Milan, Italy; (M.T.); (M.S.); (M.C.); (M.A.); (A.F.); (C.H.); (A.R.)
| | - Katarzyna M. Pawlak
- Division of Gastroenterology, St. Michael’s Hospital, University of Toronto, Toronto, ON M5S 1A1, Canada; (K.K.); (K.M.P.)
| | - Matteo Colombo
- Division of Gastroenterology and Digestive Endoscopy, Humanitas Research Hospital IRCCS, Rozzano, 20089 Milan, Italy; (M.T.); (M.S.); (M.C.); (M.A.); (A.F.); (C.H.); (A.R.)
| | - Marta Andreozzi
- Division of Gastroenterology and Digestive Endoscopy, Humanitas Research Hospital IRCCS, Rozzano, 20089 Milan, Italy; (M.T.); (M.S.); (M.C.); (M.A.); (A.F.); (C.H.); (A.R.)
| | - Alessandro Fugazza
- Division of Gastroenterology and Digestive Endoscopy, Humanitas Research Hospital IRCCS, Rozzano, 20089 Milan, Italy; (M.T.); (M.S.); (M.C.); (M.A.); (A.F.); (C.H.); (A.R.)
| | - Antonio Facciorusso
- Section of Gastroenterology, Department of Medical and Surgical Sciences, University of Foggia, 71122 Foggia, Italy;
| | - Fabio Grizzi
- Department of Immunology and Inflammation, Humanitas Research Hospital IRCCS, Rozzano, 20089 Milan, Italy;
| | - Cesare Hassan
- Division of Gastroenterology and Digestive Endoscopy, Humanitas Research Hospital IRCCS, Rozzano, 20089 Milan, Italy; (M.T.); (M.S.); (M.C.); (M.A.); (A.F.); (C.H.); (A.R.)
- Department of Biomedical Sciences, Humanitas University, Pieve Emanuele, 20089 Milan, Italy;
| | - Alessandro Repici
- Division of Gastroenterology and Digestive Endoscopy, Humanitas Research Hospital IRCCS, Rozzano, 20089 Milan, Italy; (M.T.); (M.S.); (M.C.); (M.A.); (A.F.); (C.H.); (A.R.)
- Department of Biomedical Sciences, Humanitas University, Pieve Emanuele, 20089 Milan, Italy;
| | - Silvia Carrara
- Division of Gastroenterology and Digestive Endoscopy, Humanitas Research Hospital IRCCS, Rozzano, 20089 Milan, Italy; (M.T.); (M.S.); (M.C.); (M.A.); (A.F.); (C.H.); (A.R.)
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17
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Zhang B, Zhu F, Li P, Zhu J. Artificial intelligence-assisted endoscopic ultrasound in the diagnosis of gastrointestinal stromal tumors: a meta-analysis. Surg Endosc 2023; 37:1649-1657. [PMID: 36100781 DOI: 10.1007/s00464-022-09597-w] [Citation(s) in RCA: 15] [Impact Index Per Article: 7.5] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 05/19/2022] [Accepted: 08/25/2022] [Indexed: 10/14/2022]
Abstract
BACKGROUND AND AIMS Endoscopic ultrasonography (EUS) is useful for the diagnosis of gastrointestinal stromal tumors (GISTs), but is limited by subjective interpretation. Studies on artificial intelligence (AI)-assisted diagnosis are under development. Here, we used a meta-analysis to evaluate the diagnostic performance of AI in the diagnosis of GISTs using EUS images. METHODS PubMed, Ovid Medline, Embase, Web of science, and the Cochrane Library databases were searched for studies based on the EUS using AI for the diagnosis of GISTs, and a meta-analysis was performed to examine the accuracy. RESULTS Overall, 7 studies were included in our meta-analysis. A total of 2431 patients containing more than 36,186 images were used as the overall dataset, of which 480 patients were used for the final testing. The pooled sensitivity, specificity, positive, and negative likelihood ratio (LR) of AI-assisted EUS for differentiating GISTs from other submucosal tumors (SMTs) were 0.92 (95% confidence interval [CI] 0.89-0.95), 0.82 (95% CI 0.75-0.87), 4.55 (95% CI 2.64-7.84), and 0.12 (95% CI 0.07-0.20), respectively. The summary diagnostic odds ratio (DOR) and the area under the curve were 64.70 (95% CI 23.83-175.69) and 0.950 (Q* = 0.891). CONCLUSIONS AI-assisted EUS showed high accuracy for the automatic endoscopic diagnosis of GISTs, which could be used as a valuable complementary method for the differentiation of SMTs in the future.
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Affiliation(s)
- Binglan Zhang
- Department of Gastroenterology, The First Affiliated Hospital of Chongqing Medical University, Chongqing, 400016, China
| | - Fuping Zhu
- Department of General Surgery, The Ninth People's Hospital of Chongqing, Chongqing, 400700, China
| | - Pan Li
- Department of Gastroenterology, The First Affiliated Hospital of Chongqing Medical University, Chongqing, 400016, China
| | - Jing Zhu
- Department of Oncology, The First Affiliated Hospital of Chongqing Medical University, Chongqing, 400016, China.
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18
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Pallio S, Crinò SF, Maida M, Sinagra E, Tripodi VF, Facciorusso A, Ofosu A, Conti Bellocchi MC, Shahini E, Melita G. Endoscopic Ultrasound Advanced Techniques for Diagnosis of Gastrointestinal Stromal Tumours. Cancers (Basel) 2023; 15:1285. [PMID: 36831627 PMCID: PMC9954263 DOI: 10.3390/cancers15041285] [Citation(s) in RCA: 3] [Impact Index Per Article: 1.5] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 01/09/2023] [Revised: 02/08/2023] [Accepted: 02/15/2023] [Indexed: 02/22/2023] Open
Abstract
Gastrointestinal Stromal Tumors (GISTs) are subepithelial lesions (SELs) that commonly develop in the gastrointestinal tract. GISTs, unlike other SELs, can exhibit malignant behavior, so differential diagnosis is critical to the decision-making process. Endoscopic ultrasound (EUS) is considered the most accurate imaging method for diagnosing and differentiating SELs in the gastrointestinal tract by assessing the lesions precisely and evaluating their malignant risk. Due to their overlapping imaging characteristics, endosonographers may have difficulty distinguishing GISTs from other SELs using conventional EUS alone, and the collection of tissue samples from these lesions may be technically challenging. Even though it appears to be less effective in the case of smaller lesions, histology is now the gold standard for achieving a final diagnosis and avoiding unnecessary and invasive treatment for benign SELs. The use of enhanced EUS modalities and elastography has improved the diagnostic ability of EUS. Furthermore, recent advancements in artificial intelligence systems that use EUS images have allowed them to distinguish GISTs from other SELs, thereby improving their diagnostic accuracy.
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Affiliation(s)
- Socrate Pallio
- Department of Clinical and Experimental Medicine, University of Messina, 98100 Messina, Italy
| | | | - Marcello Maida
- Gastroenterology and Endoscopy Unit, S. Elia-Raimondi Hospital, 93100 Caltanissetta, Italy
| | - Emanuele Sinagra
- Gastroenterology and Endoscopy Unit, Fondazione Istituto San Raffaele Giglio, 90015 Cefalù, Italy
| | | | - Antonio Facciorusso
- Gastroenterology Unit, Department of Medical and Surgical Sciences, University of Foggia, 71100 Foggia, Italy
| | - Andrew Ofosu
- Division of Digestive Diseases, University of Cincinnati, Cincinnati, OH 45201, USA
| | | | - Endrit Shahini
- Gastroenterology Unit, National Institute of Gastroenterology—IRCCS “Saverio de Bellis” Castellana Grotte, 70013 Castellana Grotte, Italy
| | - Giuseppinella Melita
- Human Pathology of Adult and Child Department, University of Messina, 98100 Messina, Italy
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19
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Query 2: Query over queries for improving gastrointestinal stromal tumour detection in an endoscopic ultrasound. Comput Biol Med 2023; 152:106424. [PMID: 36543005 DOI: 10.1016/j.compbiomed.2022.106424] [Citation(s) in RCA: 2] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 09/13/2022] [Revised: 11/19/2022] [Accepted: 12/13/2022] [Indexed: 12/23/2022]
Abstract
Gastrointestinal stromal tumour (GIST) lesions are mesenchymal neoplasms commonly found in the upper gastrointestinal tract, but non-invasive GIST detection during an endoscopy remains challenging because their ultrasonic images resemble several benign lesions. Techniques for automatic GIST detection and other lesions from endoscopic ultrasound (EUS) images offer great potential to advance the precision and automation of traditional endoscopy and treatment procedures. However, GIST recognition faces several intrinsic challenges, including the input restriction of a single image modality and the mismatch between tasks and models. To address these challenges, we propose a novel Query2 (Query over Queries) framework to identify GISTs from ultrasound images. The proposed Query2 framework applies an anatomical location embedding layer to break the single image modality. A cross-attention module is then applied to query the queries generated from the basic detection head. Moreover, a single-object restricted detection head is applied to infer the lesion categories. Meanwhile, to drive this network, we present GIST514-DB, a GIST dataset that will be made publicly available, which includes the ultrasound images, bounding boxes, categories and anatomical locations from 514 cases. Extensive experiments on the GIST514-DB demonstrate that the proposed Query2 outperforms most of the state-of-the-art methods.
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20
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Yang L, Du D, Zheng T, Liu L, Wang Z, Du J, Yi H, Cui Y, Liu D, Fang Y. Deep learning and radiomics to predict the mitotic index of gastrointestinal stromal tumors based on multiparametric MRI. Front Oncol 2022; 12:948557. [PMID: 36505814 PMCID: PMC9727176 DOI: 10.3389/fonc.2022.948557] [Citation(s) in RCA: 3] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 05/20/2022] [Accepted: 11/02/2022] [Indexed: 11/24/2022] Open
Abstract
Introduction Preoperative evaluation of the mitotic index (MI) of gastrointestinal stromal tumors (GISTs) represents the basis of individualized treatment of patients. However, the accuracy of conventional preoperative imaging methods is limited. The aim of this study was to develop a predictive model based on multiparametric MRI for preoperative MI prediction. Methods A total of 112 patients who were pathologically diagnosed with GIST were enrolled in this study. The dataset was subdivided into the development (n = 81) and test (n = 31) sets based on the time of diagnosis. With the use of T2-weighted imaging (T2WI) and apparent diffusion coefficient (ADC) map, a convolutional neural network (CNN)-based classifier was developed for MI prediction, which used a hybrid approach based on 2D tumor images and radiomics features from 3D tumor shape. The trained model was tested on an internal test set. Then, the hybrid model was comprehensively tested and compared with the conventional ResNet, shape radiomics classifier, and age plus diameter classifier. Results The hybrid model showed good MI prediction ability at the image level; the area under the receiver operating characteristic curve (AUROC), area under the precision-recall curve (AUPRC), and accuracy in the test set were 0.947 (95% confidence interval [CI]: 0.927-0.968), 0.964 (95% CI: 0.930-0.978), and 90.8 (95% CI: 88.0-93.0), respectively. With the average probabilities from multiple samples per patient, good performance was also achieved at the patient level, with AUROC, AUPRC, and accuracy of 0.930 (95% CI: 0.828-1.000), 0.941 (95% CI: 0.792-1.000), and 93.6% (95% CI: 79.3-98.2) in the test set, respectively. Discussion The deep learning-based hybrid model demonstrated the potential to be a good tool for the operative and non-invasive prediction of MI in GIST patients.
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Affiliation(s)
- Linsha Yang
- Medical Imaging Center, The First Hospital of Qinhuangdao, Qinhuangdao, China
| | - Dan Du
- Medical Imaging Center, The First Hospital of Qinhuangdao, Qinhuangdao, China
| | - Tao Zheng
- Medical Imaging Center, The First Hospital of Qinhuangdao, Qinhuangdao, China
| | - Lanxiang Liu
- Medical Imaging Center, The First Hospital of Qinhuangdao, Qinhuangdao, China
| | - Zhanqiu Wang
- Medical Imaging Center, The First Hospital of Qinhuangdao, Qinhuangdao, China
| | - Juan Du
- Medical Imaging Center, The First Hospital of Qinhuangdao, Qinhuangdao, China
| | - Huiling Yi
- Medical Imaging Center, The First Hospital of Qinhuangdao, Qinhuangdao, China
| | - Yujie Cui
- Medical Imaging Center, The First Hospital of Qinhuangdao, Qinhuangdao, China
| | - Defeng Liu
- Medical Imaging Center, The First Hospital of Qinhuangdao, Qinhuangdao, China,*Correspondence: Defeng Liu, ; Yuan Fang,
| | - Yuan Fang
- Medical Imaging Center, Chongqing Yubei District People’s Hospital, Chongqing, China,*Correspondence: Defeng Liu, ; Yuan Fang,
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21
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Minoda Y, Ihara E, Fujimori N, Nagatomo S, Esaki M, Hata Y, Bai X, Tanaka Y, Ogino H, Chinen T, Hu Q, Oki E, Yamamoto H, Ogawa Y. Efficacy of ultrasound endoscopy with artificial intelligence for the differential diagnosis of non-gastric gastrointestinal stromal tumors. Sci Rep 2022; 12:16640. [PMID: 36198726 PMCID: PMC9534932 DOI: 10.1038/s41598-022-20863-8] [Citation(s) in RCA: 4] [Impact Index Per Article: 1.3] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 02/26/2022] [Accepted: 09/20/2022] [Indexed: 12/15/2022] Open
Abstract
Gastrointestinal stromal tumors (GISTs) are common subepithelial lesions (SELs) and require treatment considering their malignant potential. We recently developed an endoscopic ultrasound-based artificial intelligence (EUS-AI) system to differentiate GISTs from non-GISTs in gastric SELs, which were used to train the system. We assessed whether the EUS-AI system designed for diagnosing gastric GISTs could be applied to non-gastric GISTs. Between January 2015 and January 2021, 52 patients with non-gastric SELs (esophagus, n = 15; duodenum, n = 26; colon, n = 11) were enrolled. The ability of EUS-AI to differentiate GISTs from non-GISTs in non-gastric SELs was examined. The accuracy, sensitivity, and specificity of EUS-AI for discriminating GISTs from non-GISTs in non-gastric SELs were 94.4%, 100%, and 86.1%, respectively, with an area under the curve of 0.98 based on the cutoff value set using the Youden index. In the subanalysis, the accuracy, sensitivity, and specificity of EUS-AI were highest in the esophagus (100%, 100%, 100%; duodenum, 96.2%, 100%, 0%; colon, 90.9%, 100%, 0%); the cutoff values were determined using the Youden index or the value determined using stomach cases. The diagnostic accuracy of EUS-AI increased as lesion size increased, regardless of lesion location. EUS-AI based on gastric SELs had good diagnostic ability for non-gastric GISTs.
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Affiliation(s)
- Yosuke Minoda
- Department of Medicine and Bioregulatory Science, Graduate School of Medical Sciences, Kyushu University, 3-1-1 Maidashi, Higashi-Ku, Fukuoka, 812-8582, Japan.,Department of Endoscopic Diagnostics and Therapeutics, Kyushu University Hospital, 3-1-1 Maidashi, Higashi-Ku, Fukuoka, 812-8582, Japan
| | - Eikichi Ihara
- Department of Medicine and Bioregulatory Science, Graduate School of Medical Sciences, Kyushu University, 3-1-1 Maidashi, Higashi-Ku, Fukuoka, 812-8582, Japan. .,Department of Gastroenterology and Metabolism, Graduate School of Medical Sciences, Kyushu University, 3-1-1 Maidashi, Higashi-Ku, Fukuoka, 812-8582, Japan.
| | - Nao Fujimori
- Department of Medicine and Bioregulatory Science, Graduate School of Medical Sciences, Kyushu University, 3-1-1 Maidashi, Higashi-Ku, Fukuoka, 812-8582, Japan
| | - Shuzaburo Nagatomo
- Department of Medicine and Bioregulatory Science, Graduate School of Medical Sciences, Kyushu University, 3-1-1 Maidashi, Higashi-Ku, Fukuoka, 812-8582, Japan
| | - Mitsuru Esaki
- Department of Medicine and Bioregulatory Science, Graduate School of Medical Sciences, Kyushu University, 3-1-1 Maidashi, Higashi-Ku, Fukuoka, 812-8582, Japan
| | - Yoshitaka Hata
- Department of Medicine and Bioregulatory Science, Graduate School of Medical Sciences, Kyushu University, 3-1-1 Maidashi, Higashi-Ku, Fukuoka, 812-8582, Japan
| | - Xiaopeng Bai
- Department of Medicine and Bioregulatory Science, Graduate School of Medical Sciences, Kyushu University, 3-1-1 Maidashi, Higashi-Ku, Fukuoka, 812-8582, Japan
| | - Yoshimasa Tanaka
- Department of Medicine and Bioregulatory Science, Graduate School of Medical Sciences, Kyushu University, 3-1-1 Maidashi, Higashi-Ku, Fukuoka, 812-8582, Japan
| | - Haruei Ogino
- Department of Medicine and Bioregulatory Science, Graduate School of Medical Sciences, Kyushu University, 3-1-1 Maidashi, Higashi-Ku, Fukuoka, 812-8582, Japan
| | - Takatoshi Chinen
- Department of Medicine and Bioregulatory Science, Graduate School of Medical Sciences, Kyushu University, 3-1-1 Maidashi, Higashi-Ku, Fukuoka, 812-8582, Japan
| | - Qingjiang Hu
- Department of Surgery and Science, Graduate School of Medical Sciences, Kyushu University, 3-1-1 Maidashi, Higashi-Ku, Fukuoka, 812-8582, Japan
| | - Eiji Oki
- Department of Surgery and Science, Graduate School of Medical Sciences, Kyushu University, 3-1-1 Maidashi, Higashi-Ku, Fukuoka, 812-8582, Japan
| | - Hidetaka Yamamoto
- Department of Pathological Sciences, Graduate School of Medical Sciences, Kyushu University, 3-1-1 Maidashi, Higashi-Ku, Fukuoka, 812-8582, Japan
| | - Yoshihiro Ogawa
- Department of Medicine and Bioregulatory Science, Graduate School of Medical Sciences, Kyushu University, 3-1-1 Maidashi, Higashi-Ku, Fukuoka, 812-8582, Japan
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22
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Ye XH, Zhao LL, Wang L. Diagnostic accuracy of endoscopic ultrasound with artificial intelligence for gastrointestinal stromal tumors: A meta-analysis. J Dig Dis 2022; 23:253-261. [PMID: 35793389 DOI: 10.1111/1751-2980.13110] [Citation(s) in RCA: 4] [Impact Index Per Article: 1.3] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 03/09/2022] [Revised: 05/21/2022] [Accepted: 07/01/2022] [Indexed: 12/11/2022]
Abstract
OBJECTIVES Gastrointestinal stromal tumors (GISTs) are thought to have a malignant potential. However, utilizing endoscopic ultrasound (EUS) to differentiate GISTs from other types of subepithelial lesions (SELs) remains challenging. Artificial intelligence (AI)-based diagnostic systems for EUS have been reported to have a promising performance, although the results of the previous studies remain controversial. In this meta-analysis, we aimed to assess the diagnostic accuracy of AI-based EUS in distinguishing GISTs from other SELs. METHODS A literature search was conducted on MEDLINE and EMBASE databases to identify relevant articles. The sensitivity, specificity, and area under the summary receiver operating characteristic curve (AUROC) of eligible studies were analyzed. RESULTS Seven studies were eligible for the final analysis. The combined sensitivity and specificity of AI-based EUS were 0.93 (95% confidence interval [CI] 0.88-0.96) and 0.78 (95% CI 0.67-0.87), respectively. The overall diagnostic odds ratio of AI-based EUS for GISTs was 36.74 (95% CI 17.69-76.30) with an AUROC of 0.94. CONCLUSIONS AI-based EUS showed high diagnostic ability and might help better differentiate GISTs from other SELs. More prospective studies on the diagnosis of GISTs using AI-based EUS are warranted in clinical setting.
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Affiliation(s)
- Xiao Hua Ye
- Department of Gastroenterology, Affiliated Jinhua Hospital, Zhejiang University School of Medicine, Jinhua, Zhejiang Province, China
| | - Lin Lin Zhao
- Department of Gastroenterology, Digestive Endoscopy Center, Changhai Hospital, Naval Medical University, Shanghai, China
| | - Lei Wang
- Department of Gastroenterology, Digestive Endoscopy Center, Changhai Hospital, Naval Medical University, Shanghai, China
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Zhuang H, Bao A, Tan Y, Wang H, Xie Q, Qiu M, Xiong W, Liao F. Application and prospect of artificial intelligence in digestive endoscopy. Expert Rev Gastroenterol Hepatol 2022; 16:21-31. [PMID: 34937459 DOI: 10.1080/17474124.2022.2020646] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.3] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Indexed: 12/24/2022]
Abstract
INTRODUCTION With the progress of science and technology, artificial intelligence represented by deep learning has gradually begun to be applied in the medical field. Artificial intelligence has been applied to benign gastrointestinal lesions, tumors, early cancer, inflammatory bowel disease, gallbladder, pancreas, and other diseases. This review summarizes the latest research results on artificial intelligence in digestive endoscopy and discusses the prospect of artificial intelligence in digestive system diseases. AREAS COVERED We retrieved relevant documents on artificial intelligence in digestive tract diseases from PubMed and Medline. This review elaborates on the knowledge of computer-aided diagnosis in digestive endoscopy. EXPERT OPINION Artificial intelligence significantly improves diagnostic accuracy, reduces physicians' workload, and provides a shred of evidence for clinical diagnosis and treatment. Shortly, artificial intelligence will have high application value in the field of medicine.
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Affiliation(s)
- Huangming Zhuang
- Gastroenterology Department, Renmin Hospital of Wuhan University, Wuhan, Hubei, China
| | - Anyu Bao
- Clinical Laboratory, Renmin Hospital of Wuhan University, Wuhan, Hubei, China
| | - Yulin Tan
- Gastroenterology Department, Renmin Hospital of Wuhan University, Wuhan, Hubei, China
| | - Hanyu Wang
- Gastroenterology Department, Renmin Hospital of Wuhan University, Wuhan, Hubei, China
| | - Qingfang Xie
- Gastroenterology Department, Renmin Hospital of Wuhan University, Wuhan, Hubei, China
| | - Meiqi Qiu
- Gastroenterology Department, Renmin Hospital of Wuhan University, Wuhan, Hubei, China
| | - Wanli Xiong
- Gastroenterology Department, Renmin Hospital of Wuhan University, Wuhan, Hubei, China
| | - Fei Liao
- Gastroenterology Department, Renmin Hospital of Wuhan University, Wuhan, Hubei, China
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24
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The emerging role of artificial intelligence in gastrointestinal endoscopy: A review. GASTROENTEROLOGIA Y HEPATOLOGIA 2021; 45:492-497. [PMID: 34793895 DOI: 10.1016/j.gastrohep.2021.11.004] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Track Full Text] [Subscribe] [Scholar Register] [Received: 06/21/2021] [Revised: 10/15/2021] [Accepted: 11/07/2021] [Indexed: 11/19/2022]
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