详情页 - 四川省肿瘤医院知识库

当前位置：首页 > 详情页

A Domain-Specific Pretrained Model for Detecting Malignant and Premalignant Ocular Surface Tumors: A Multicenter Model Development and Evaluation Study

文献详情

资源类型：

Pubmed体系：

收录情况： ◇ 统计源期刊 ◇ 卓越：梯队期刊

作者：

机构： [1]Ningbo Key Laboratory of Medical Research on Blinding Eye Diseases, Ningbo Eye Institute, Ningbo Eye Hospital, Wenzhou Medical University, Ningbo 315040, China. [2]National Clinical Research Center for Ocular Diseases, Eye Hospital, Wenzhou Medical University, Wenzhou 325027, China. [3]Department of Pathology, The Affiliated People’s Hospital of Ningbo University, Ningbo 315100, China. [4]Department of Ophthalmology, Cangnan Hospital, Wenzhou Medical University, Wenzhou 325000, China. [5]Daqing Eye Hospital, Daqing 163711, China. [6]Department of Ophthalmology, West China Second University Hospital, Sichuan University, Chengdu 610041, China. [7]Department of Ophthalmology, The First Affiliated Hospital of Harbin Medical University, Harbin 150001, China. [8]Department of Ophthalmology, The Affiliated Hospital of Guizhou Medical University, Guiyang 550004, China. [9]Ankang Central Hospital, Xi’an Medical University, Xi’an 725000, China. [10]School of Mathematics and Statistics, Xidian University, Xi’an 710071, China. [11]Department of Ophthalmology, Shanghai General Hospital, Shanghai Jiao Tong University, National Clinical Research Center for Eye Disease, Shanghai 20080, China. [12]School of Electronic Engineering, Xi’an University of Posts and Telecommunications, Xi’an 710121, China.

出处：

DOI：

摘要：

Malignant and premalignant ocular surface tumors (OSTs) can be sight-threatening or even life-threatening if not diagnosed and treated promptly. Artificial intelligence holds great promise for the early detection of these diseases. However, training traditional convolutional neural networks (CNNs) for this task presents challenges due to the lack of large, well-annotated datasets containing OST images labeled according to histopathological results. Here, we introduce the ocular surface pretrained model (OSPM), a domain-specific pretrained model designed to address the scarcity of labeled data. OSPM is constructed utilizing self-supervised learning on approximately 0.76 million unlabeled ocular surface images from 10 clinical centers across China and can be readily adapted to the OST classification task. We then develop and evaluate an OSPM-enhanced classification model (OECM) using 1,455 OST images labeled with histopathological diagnoses to differentiate between malignant, premalignant, and benign OSTs. OECM achieves excellent performance with AUROCs ranging from 0.891 to 0.993 on internal, external, and prospective test datasets, significantly outperforming the traditional CNN models. OECM demonstrated performance comparable to that of senior ophthalmologists and increased the diagnostic accuracy of junior ophthalmologists. Greater label efficiency was observed in OECM compared to CNN models. Our proposed model has high potential to enhance the early detection and treatment of malignant and premalignant OSTs, thereby reducing cancer-related mortality and optimizing functional outcomes.Copyright © 2025 Zhongwen Li et al.

基金：

语种：

PubmedID：

第一作者：

第一作者机构： [1]Ningbo Key Laboratory of Medical Research on Blinding Eye Diseases, Ningbo Eye Institute, Ningbo Eye Hospital, Wenzhou Medical University, Ningbo 315040, China. [2]National Clinical Research Center for Ocular Diseases, Eye Hospital, Wenzhou Medical University, Wenzhou 325027, China.

共同第一作者：

通讯作者：

通讯机构： [1]Ningbo Key Laboratory of Medical Research on Blinding Eye Diseases, Ningbo Eye Institute, Ningbo Eye Hospital, Wenzhou Medical University, Ningbo 315040, China. [2]National Clinical Research Center for Ocular Diseases, Eye Hospital, Wenzhou Medical University, Wenzhou 325027, China.

推荐引用方式(GB/T 7714)：

APA：

MLA：