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Ligato I, De Magistris G, Dilaghi E, Cozza G, Ciardiello A, Panzuto F, Giagu S, Annibale B, Napoli C, Esposito G. Convolutional Neural Network Model for Intestinal Metaplasia Recognition in Gastric Corpus Using Endoscopic Image Patches. Diagnostics (Basel) 2024; 14:1376. [PMID: 39001267 PMCID: PMC11241412 DOI: 10.3390/diagnostics14131376] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 05/27/2024] [Revised: 06/23/2024] [Accepted: 06/26/2024] [Indexed: 07/16/2024] Open
Abstract
Gastric cancer (GC) is a significant healthcare concern, and the identification of high-risk patients is crucial. Indeed, gastric precancerous conditions present significant diagnostic challenges, particularly early intestinal metaplasia (IM) detection. This study developed a deep learning system to assist in IM detection using image patches from gastric corpus examined using virtual chromoendoscopy in a Western country. Utilizing a retrospective dataset of endoscopic images from Sant'Andrea University Hospital of Rome, collected between January 2020 and December 2023, the system extracted 200 × 200 pixel patches, classifying them with a voting scheme. The specificity and sensitivity on the patch test set were 76% and 72%, respectively. The optimization of a learnable voting scheme on a validation set achieved a specificity of 70% and sensitivity of 100% for entire images. Despite data limitations and the absence of pre-trained models, the system shows promising results for preliminary screening in gastric precancerous condition diagnostics, providing an explainable and robust Artificial Intelligence approach.
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Affiliation(s)
- Irene Ligato
- Department of Medical-Surgical Sciences and Translational Medicine, Sant’Andrea Hospital, Sapienza University of Rome, 00185 Roma, Italy; (I.L.); (E.D.); (G.C.); (F.P.); (B.A.)
| | - Giorgio De Magistris
- Department of Computer, Control, and Management Engineering, Sapienza University of Rome, Via Ariosto 25, 00185 Rome, Italy; (G.D.M.); (C.N.)
| | - Emanuele Dilaghi
- Department of Medical-Surgical Sciences and Translational Medicine, Sant’Andrea Hospital, Sapienza University of Rome, 00185 Roma, Italy; (I.L.); (E.D.); (G.C.); (F.P.); (B.A.)
| | - Giulio Cozza
- Department of Medical-Surgical Sciences and Translational Medicine, Sant’Andrea Hospital, Sapienza University of Rome, 00185 Roma, Italy; (I.L.); (E.D.); (G.C.); (F.P.); (B.A.)
| | - Andrea Ciardiello
- Department of Physics, Sapienza University of Rome, P.le A. Moro 5, 00185 Rome, Italy; (A.C.); (S.G.)
| | - Francesco Panzuto
- Department of Medical-Surgical Sciences and Translational Medicine, Sant’Andrea Hospital, Sapienza University of Rome, 00185 Roma, Italy; (I.L.); (E.D.); (G.C.); (F.P.); (B.A.)
| | - Stefano Giagu
- Department of Physics, Sapienza University of Rome, P.le A. Moro 5, 00185 Rome, Italy; (A.C.); (S.G.)
| | - Bruno Annibale
- Department of Medical-Surgical Sciences and Translational Medicine, Sant’Andrea Hospital, Sapienza University of Rome, 00185 Roma, Italy; (I.L.); (E.D.); (G.C.); (F.P.); (B.A.)
| | - Christian Napoli
- Department of Computer, Control, and Management Engineering, Sapienza University of Rome, Via Ariosto 25, 00185 Rome, Italy; (G.D.M.); (C.N.)
| | - Gianluca Esposito
- Department of Medical-Surgical Sciences and Translational Medicine, Sant’Andrea Hospital, Sapienza University of Rome, 00185 Roma, Italy; (I.L.); (E.D.); (G.C.); (F.P.); (B.A.)
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Chen H, Liu SY, Huang SH, Liu M, Chen GX. Applications of artificial intelligence in gastroscopy: a narrative review. J Int Med Res 2024; 52:3000605231223454. [PMID: 38235690 PMCID: PMC10798083 DOI: 10.1177/03000605231223454] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 09/20/2023] [Accepted: 12/11/2023] [Indexed: 01/19/2024] Open
Abstract
Gastroscopy, a critical tool for the diagnosis of upper gastrointestinal diseases, has recently incorporated artificial intelligence (AI) technology to alleviate the challenges involved in endoscopic diagnosis of some lesions, thereby enhancing diagnostic accuracy. This narrative review covers the current status of research concerning various applications of AI technology to gastroscopy, then discusses future research directions. By providing this review, we hope to promote the integration of gastroscopy and AI technology, with long-term clinical applications that can assist patients.
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Affiliation(s)
- Hu Chen
- The First Clinical Medical School, Xuzhou Medical University, Xuzhou, Jiangsu, China
| | - Shi-yu Liu
- Department of Gastroenterology, Xuzhou Municipal Hospital Affiliated to Xuzhou Medical University, Xuzhou, Jiangsu, China
| | - Si-hui Huang
- Department of Gastroenterology, Xuzhou Municipal Hospital Affiliated to Xuzhou Medical University, Xuzhou, Jiangsu, China
| | - Min Liu
- School of Chemical Engineering & Technology, China University of Mining and Technology, Xuzhou, Jiangsu, China
| | - Guang-xia Chen
- Department of Gastroenterology, Xuzhou Municipal Hospital Affiliated to Xuzhou Medical University, Xuzhou, Jiangsu, China
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Ge Z, Fang Y, Chang J, Yu Z, Qiao Y, Zhang J, Yang X, Duan Z. Using deep learning to assess the function of gastroesophageal flap valve according to the Hill classification system. Ann Med 2023; 55:2279239. [PMID: 37949083 PMCID: PMC10653650 DOI: 10.1080/07853890.2023.2279239] [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: 06/27/2023] [Accepted: 10/26/2023] [Indexed: 11/12/2023] Open
Abstract
BACKGROUND The endoscopic Hill classification of the gastroesophageal flap valve (GEFV) is of great importance for understanding the functional status of the esophagogastric junction (EGJ). Deep learning (DL) methods have been extensively employed in the area of digestive endoscopy. To improve the efficiency and accuracy of the endoscopist's Hill classification and assist in incorporating it into routine endoscopy reports and GERD assessment examinations, this study first employed DL to establish a four-category model based on the Hill classification. MATERIALS AND METHODS A dataset consisting of 3256 GEFV endoscopic images has been constructed for training and evaluation. Furthermore, a new attention mechanism module has been provided to improve the performance of the DL model. Combined with the attention mechanism module, numerous experiments were conducted on the GEFV endoscopic image dataset, and 12 mainstream DL models were tested and evaluated. The classification accuracy of the DL model and endoscopists with different experience levels was compared. RESULTS 12 mainstream backbone networks were trained and tested, and four outstanding feature extraction backbone networks (ResNet-50, VGG-16, VGG-19, and Xception) were selected for further DL model development. The ResNet-50 showed the best Hill classification performance; its area under the curve (AUC) reached 0.989, and the classification accuracy (93.39%) was significantly higher than that of junior (74.83%) and senior (78.00%) endoscopists. CONCLUSIONS The DL model combined with the attention mechanism module in this paper demonstrated outstanding classification performance based on the Hill grading and has great potential for improving the accuracy of the Hill classification by endoscopists.
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Affiliation(s)
- Zhenyang Ge
- Department of Gastroenterology, The First Affiliated Hospital of Dalian Medical University, Dalian, Liaoning, China
- Department of Digestive Endoscopy, Dalian Municipal Central Hospital, Dalian, Liaoning, China
| | - Youjiang Fang
- Department of Computer Science, Dalian University of Technology, Dalian, Liaoning, China
| | - Jiuyang Chang
- Department of Cardiology, The First Affiliated Hospital of Dalian Medical University, Dalian, Liaoning, China
| | - Zequn Yu
- Department of Respiratory and Critical Care Medicine, The First Affiliated Hospital of Dalian Medical University, Dalian, Liaoning, China
| | - Yu Qiao
- Department of Computer Science, Dalian University of Technology, Dalian, Liaoning, China
| | - Jing Zhang
- Department of Digestive Endoscopy, Dalian Municipal Central Hospital, Dalian, Liaoning, China
| | - Xin Yang
- Department of Computer Science, Dalian University of Technology, Dalian, Liaoning, China
| | - Zhijun Duan
- Department of Gastroenterology, The First Affiliated Hospital of Dalian Medical University, Dalian, Liaoning, China
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Nakajo K, Ninomiya Y, Kondo H, Takeshita N, Uchida E, Aoyama N, Inaba A, Ikematsu H, Shinozaki T, Matsuura K, Hayashi R, Akimoto T, Yano T. Anatomical classification of pharyngeal and laryngeal endoscopic images using artificial intelligence. Head Neck 2023; 45:1549-1557. [PMID: 37045798 DOI: 10.1002/hed.27370] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 10/13/2022] [Revised: 02/22/2023] [Accepted: 04/02/2023] [Indexed: 04/14/2023] Open
Abstract
BACKGROUND The entire pharynx should be observed endoscopically to avoid missing pharyngeal lesions. An artificial intelligence (AI) model recognizing anatomical locations can help identify blind spots. We developed and evaluated an AI model classifying pharyngeal and laryngeal endoscopic locations. METHODS The AI model was trained using 5382 endoscopic images, categorized into 15 anatomical locations, and evaluated using an independent dataset of 1110 images. The main outcomes were model accuracy, precision, recall, and F1-score. Moreover, we investigated focused regions in the input images contributing to the model predictions using gradient-weighted class activation mapping (Grad-CAM) and Guided Grad-CAM. RESULTS Our AI model correctly classified pharyngeal and laryngeal images into 15 anatomical locations, with an accuracy of 93.3%. The weighted averages of precision, recall, and F1-score were 0.934, 0.933, and 0.933, respectively. CONCLUSION Our AI model has an excellent performance determining pharyngeal and laryngeal anatomical locations, helping endoscopists notify of blind spots.
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Affiliation(s)
- Keiichiro Nakajo
- Department of Gastroenterology and Endoscopy, National Cancer Center Hospital East, Kashiwa, Japan
- Cancer Medicine, Cooperative Graduate School, The Jikei University Graduate School of Medicine, Tokyo, Japan
- Medical Device Innovation Center, National Cancer Center Hospital East, Kashiwa, Japan
| | - Youichi Ninomiya
- Medical Device Innovation Center, National Cancer Center Hospital East, Kashiwa, Japan
| | - Hibiki Kondo
- Medical Device Innovation Center, National Cancer Center Hospital East, Kashiwa, Japan
| | - Nobuyoshi Takeshita
- Medical Device Innovation Center, National Cancer Center Hospital East, Kashiwa, Japan
| | - Erika Uchida
- Department of Gastroenterology and Endoscopy, National Cancer Center Hospital East, Kashiwa, Japan
| | - Naoki Aoyama
- Department of Gastroenterology and Endoscopy, National Cancer Center Hospital East, Kashiwa, Japan
| | - Atsushi Inaba
- Department of Gastroenterology and Endoscopy, National Cancer Center Hospital East, Kashiwa, Japan
| | - Hiroaki Ikematsu
- Department of Gastroenterology and Endoscopy, National Cancer Center Hospital East, Kashiwa, Japan
- Medical Device Innovation Center, National Cancer Center Hospital East, Kashiwa, Japan
| | - Takeshi Shinozaki
- Department of Head and Neck Surgery, National Cancer Center Hospital East, Kashiwa, Japan
| | - Kazuto Matsuura
- Department of Head and Neck Surgery, National Cancer Center Hospital East, Kashiwa, Japan
| | - Ryuichi Hayashi
- Department of Head and Neck Surgery, National Cancer Center Hospital East, Kashiwa, Japan
| | - Tetsuo Akimoto
- Cancer Medicine, Cooperative Graduate School, The Jikei University Graduate School of Medicine, Tokyo, Japan
- Department of Radiation Oncology and Particle Therapy, National Cancer Center Hospital East, Kashiwa, Japan
| | - Tomonori Yano
- Department of Gastroenterology and Endoscopy, National Cancer Center Hospital East, Kashiwa, Japan
- Medical Device Innovation Center, National Cancer Center Hospital East, Kashiwa, Japan
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