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Methods A convolutional neural network (CNN) was trained on 5892 WLI images from Olympus and Fujifilm across 1283 patients, validated on 4529 images from 1224 patients, and evaluated against endoscopists with analyses of cancerous imaging features and assistant diagnostic utility. Results Internal and external validation showed high sensitivity, specificity, accuracy, PPV and NPV, with performance comparable to expert endoscopists and superior to mid-level and junior endoscopists; AI assistance improved manual accuracy, specificity and PPV. Conclusions The system automatically recognizes SESCC with strong generalizability and enhances diagnostic decisions in assisted workflows.",{"@graph":69,"@context":122},[70,84,105],{"@type":71,"itemListElement":72},"BreadcrumbList",[73,77,79,82],{"item":74,"name":75,"@type":76,"position":8},"https://docshare.wps.com","Home","ListItem",{"item":78,"name":9,"@type":76,"position":14},"https://docshare.wps.com/document/",{"item":80,"name":35,"@type":76,"position":81},"https://docshare.wps.com/document/healthcare/",3,{"item":83,"name":65,"@type":76,"position":19},"https://docshare.wps.com/document/artificial-intelligence-assisted-detection-of-superficial-esophageal-squamous-cell-carcinoma-in-white-light-endoscopic-images-by-using-a-generalized-system/384727/",{"url":83,"name":65,"@type":85,"image":86,"author":91,"headline":65,"publisher":94,"fileFormat":97,"inLanguage":63,"description":67,"dateModified":98,"datePublished":99,"encodingFormat":97,"isAccessibleForFree":100,"interactionStatistic":101},"DigitalDocument",{"url":87,"@type":88,"width":89,"height":90},"https://docshare.wps.com/thumbnails/artificial-intelligence-assisted-detection-of-superficial-esophageal-squamous-cell-carcinoma-in-white-light-endoscopic-images-by-using-a-generalized-system/384727.png","ImageObject",300,407,{"name":92,"@type":93},"Rowan","Person",{"url":74,"name":95,"@type":96},"DocShare","Organization","application/pdf","2026-10-25","2026-09-24",true,{"@type":102,"interactionType":103,"userInteractionCount":24},"InteractionCounter",{"@type":104},"ViewAction",{"@type":106,"mainEntity":107},"FAQPage",[108,114,118],{"name":109,"@type":110,"acceptedAnswer":111},"Why was this study designed to address platform dependence in SESCC AI detection?","Question",{"text":112,"@type":113},"AI-assisted WLI systems had limited universal properties because models were often trained on images from only one endoscopy platform. This study aimed to improve compatibility across different platforms by using data from Olympus and Fujifilm systems.","Answer",{"name":115,"@type":110,"acceptedAnswer":116},"How was the AI system trained and validated in the study?",{"text":117,"@type":113},"The CNN used 5892 WLI images from 1283 patients for training and 4529 images from 1224 patients for validation. Performance was assessed using per-image and patient-based analyses and compared with endoscopists.",{"name":119,"@type":110,"acceptedAnswer":120},"Did the AI model help improve clinicians’ manual diagnosis?",{"text":121,"@type":113},"Yes. Manual diagnostic performance was significantly improved with AI assistance, especially for accuracy, specificity, and positive predictive value, indicating practical value as a diagnostic assistant.","https://schema.org",{"og:url":83,"og:type":124,"og:title":65,"og:site_name":95,"og:description":67},"article",{"robots":126,"canonical":83},"index,follow",{"doc_id":128,"site_id":62},384727,1790363885,{"code":4,"msg":5,"data":131},{"doc_id":128,"user_id":132,"nickname":92,"user_avatar":133,"doc_module":4,"category_id":34,"category_name":35,"doc_title":65,"doc_description":67,"doc_content":134,"file_id":135,"file_url":136,"file_type":137,"file_size":138,"view_count":24,"is_deleted":4,"is_public":8,"is_downloadable":8,"audit_status":8,"page_count":139,"language":140,"language_code":63,"site_id":62,"html_lang":63,"table_of_contents":141,"faqs":142,"seo_title":143,"seo_description":67,"update_tm":144,"read_time":41},1099514067415,"https://ap-avatar.wpscdn.com/avatar/100002539d78ffe74a7?x-image-process=image/resize,m_fixed,w_180,h_180&k=1779092875211072502","Brief Communication  \nArtificial intelligence assisted detection of superficial esophageal squamous cell carcinoma in white‑light endoscopic images by using a generalized system  \nYadong Feng1,3 · Yan Liang1 · Peng Li2,5 · Qigang Long2,5 · Jie Song1 · Mengjie Li1 · Xiaofen Wang1 · Cui‑e Cheng3 · Kai Zhao4 · Jifeng Ma6 · Lingxiao Zhao2,5  \nReceived: 10 March 2023 / Accepted: 15 May 2023  \n© The Author(s) 2023 OPEN  \nAbstract  \nBackground The use of artificial intelligence (AI) assisted white light imaging (WLI) detection systems for superficial esophageal squamous cell carcinoma (SESCC) is limited by training with images from one specific endoscopy platform. Methods In this study, we developed an AI system with a convolutional neural network (CNN) model using WLI images from Olympus and Fujifilm endoscopy platforms. The training dataset consisted of 5892 WLI images from 1283 patients, and the validation dataset included 4529 images from 1224 patients. We assessed the diagnostic performance of the AI system and compared it with that of endoscopists. We analyzed the system’s ability to identify cancerous imaging characteristics and investigated the efficacy of the AI system as an assistant in diagnosis.  \nResults In the internal validation set, the AI system’s per-image analysis had a sensitivity, specificity, accuracy, positive predictive value (PPV), and negative predictive value (NPV) of 96.64%, 95.35%, 91.75%, 90.91%, and 98.33%, respectively. In patient-based analysis, these values were 90.17%, 94.34%, 88.38%, 89.50%, and 94.72%, respectively. The diagnostic results in the external validation set were also favorable. The CNN model’s diagnostic performance in recognizing cancerous imaging characteristics was comparable to that of expert endoscopists and significantly higher than that of mid-level and junior endoscopists. This model was competent in localizing SESCC lesions. Manual diagnostic performances were significantly improved with the assistance by AI system, especially in terms of accuracy (75.12% vs. 84.95%, p = 0.008), specificity (63.29% vs. 76.59%, p = 0.017) and PPV (64.95% vs. 75.23%, p = 0.006) .  \nConclusions The results of this study demonstrate that the developed AI system is highly effective in automatically recognizing SESCC, displaying impressive diagnostic performance, and exhibiting strong generalizability. Furthermore, when used as an assistant in the diagnosis process, the system improved manual diagnostic performance.  \nYadong Feng, Yan Liang and Peng Li contributed equally to this work  \nSupplementary Information The online version contains supplementary material available at [https://doi.org/10.1007/s12672-023-](https://doi.org/10.1007/s12672-023-)[ ](https://doi.org/10.1007/s12672-023-)[00694-3](00694-3.)[.](00694-3.)  \n* Yadong Feng, [drfengyd@126.com](drfengyd@126.com); * Lingxiao Zhao, [zhaolx@sibet.ac.cn |](zhaolx@sibet.ac.cn |1Department of Gastroenterology)[1](zhaolx@sibet.ac.cn |1Department of Gastroenterology)[Department of Gastroenterology](zhaolx@sibet.ac.cn |1Department of Gastroenterology), Zhongda Hospital Southeast University, 87 Dingjiaqiao Street, Nanjing 210009, China. 2Suzhou Institute of Biomedical Engineering and Technology, Chinese Academy of Sciences, 88 Keling Road, Suzhou 215163, China. 3Department of Gastroenterology, the Affiliated Changshu Hospital of Nantong University, Changshu No. 2 People’s Hospital, 18 Taishan Road, Suzhou 215500, China. 4Department of Gastroenterology, Changzhou Jintan First People’s Hospital Affiliated to Jiangsu University, 500 Jintan Avenue, Jintan 210036, China. 5School of Biomedical Engineering (Suzhou), Division of Life Sciences and Medicine, University of Science and Technology of China, 96 Jinzhai Road,  \nHefei 230026, China. 6Department of Gastroenterology, General Global Maanshan 17th Metallurgy Hospital, 828 West Hunan Road, Maanshan 243011, China.  \nDiscover Oncology  \n(2023) 14:73  \n| [https://doi.org/10.1007/s12672-023-00694-3](https:/","cbCairAeCtwV6mRt","https://ap.wps.com/l/cbCairAeCtwV6mRt","pdf",1432789,12,"English","# Abstract\n## Background\n## Methods\n## Results\n## Conclusions\n# Introduction\n# Materials and methods","[{\"question\":\"Why was this study designed to address platform dependence in SESCC AI detection?\",\"answer\":\"AI-assisted WLI systems had limited universal properties because models were often trained on images from only one endoscopy platform. This study aimed to improve compatibility across different platforms by using data from Olympus and Fujifilm systems.\"},{\"question\":\"How was the AI system trained and validated in the study?\",\"answer\":\"The CNN used 5892 WLI images from 1283 patients for training and 4529 images from 1224 patients for validation. Performance was assessed using per-image and patient-based analyses and compared with endoscopists.\"},{\"question\":\"Did the AI model help improve clinicians’ manual diagnosis?\",\"answer\":\"Yes. Manual diagnostic performance was significantly improved with AI assistance, especially for accuracy, specificity, and positive predictive value, indicating practical value as a diagnostic assistant.\"}]","Artificial intelligence assisted detection of superficial esophageal squamous cell carcinoma in white-light endoscopic images by using a generalized system | PDF",1790264716]