Evidence map›Paper›PMID 41186742›Full record

ArticleInternational ophthalmology2025

Deep learning-based non-invasive differential diagnosis of eyelid basal cell and sebaceous gland carcinomas using photographic images.

Jiayi Zhang, Jie Chen, Rui Zhang, Yingxiu Luo, Wei Shi, Ziyue Huang, Yechen Zhu, Chunyue Ma, Jinjiang Cui, Shiqiong Xu and 2 more

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Article in International ophthalmology, 2025. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 1 paper.

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0cells of the map it votes in
1citing papers in PubMed
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1 · What the graph read from it

What it found

Each row is one number read from the abstract, on the scale the paper reported it, with its interval. Left of the dashed line favours the treatment, right favours the comparator. Under each row is the sentence it came from. New to these charts? A ten-minute tutorial.

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2 · The registry

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3 · Its place in the literature

Who cites it

1 citing paper in PubMed.

  1. Review
4 · The record

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5 · Who and what money

Authors and funding

12 authors.

Jiayi Zhang *School of Biomedical Engineering (Suzhou), Division of Life Sciences and Medicine, University of Science and Technology of China, Hefei, China.
Jie Chen *Department of Ophthalmology, Shanghai Ninth People's Hospital, Shanghai Key Laboratory of Orbital Diseases and Ocular Oncology, Shanghai Jiao Tong University School of Medicine, Shanghai, China.
Rui ZhangSchool of Biomedical Engineering (Suzhou), Division of Life Sciences and Medicine, University of Science and Technology of China, Hefei, China.
Yingxiu LuoDepartment of Ophthalmology, Shanghai Ninth People's Hospital, Shanghai Key Laboratory of Orbital Diseases and Ocular Oncology, Shanghai Jiao Tong University School of Medicine, Shanghai, China.
Wei ShiSchool of Biomedical Engineering (Suzhou), Division of Life Sciences and Medicine, University of Science and Technology of China, Hefei, China.
Ziyue HuangDepartment of Ophthalmology, Shanghai Ninth People's Hospital, Shanghai Key Laboratory of Orbital Diseases and Ocular Oncology, Shanghai Jiao Tong University School of Medicine, Shanghai, China.
Yechen ZhuSchool of Biomedical Engineering (Suzhou), Division of Life Sciences and Medicine, University of Science and Technology of China, Hefei, China.
Chunyue MaDepartment of Oral and Maxillofacial-Head and Neck Oncology, Shanghai Key Laboratory of Stomatology, National Center for Stomatology, Shanghai Ninth People's Hospital, Shanghai Jiao Tong University School of Medicine, National Clinical Research Center for Oral Diseases, Shanghai, China.
Jinjiang CuiSuzhou Institute of Biomedical Engineering and Technology, Chinese Academy of Sciences, Suzhou, China.
Shiqiong XuDepartment of Ophthalmology, Shanghai Ninth People's Hospital, Shanghai Key Laboratory of Orbital Diseases and Ocular Oncology, Shanghai Jiao Tong University School of Medicine, Shanghai, China. 115033@sh9hospital.org.cn.
Xin GaoSuzhou Institute of Biomedical Engineering and Technology, Chinese Academy of Sciences, Suzhou, China. xingaosam@163.com.
Renbing JiaDepartment of Ophthalmology, Shanghai Ninth People's Hospital, Shanghai Key Laboratory of Orbital Diseases and Ocular Oncology, Shanghai Jiao Tong University School of Medicine, Shanghai, China. renbingjia@sjtu.edu.cn.

Funding

Hainan Provincial Natural Science Foundation of China 824MS153Key Research and Development Program of Jiangsu BE2023714National Science Foundation of China 82372052National Science Foundation of China 82402373Program of Innovative Research Team of High‑Level Local Universities in Shanghai SHSMU‑ZDCX20210902Project of Jiangsu Province High-End Medical Devices Technology Innovation Center BM2022012Science and Technology Commission of Shanghai 22Y31900700Science Foundation of Shandong ZR2022QF071Science Foundation of Shandong ZR2022QF099Scientific and Technological Innovation Carrier Program-New Construction of Municipal Key Laboratory SZS2022008Shanghai Municipal Health Commission 2022YQ01Taishan Industrial Experts Program tscx202312131
6 · The paper itself

Abstract

purposePathological examination, the current gold standard for differentiating eyelid basal cell carcinoma (BCC) and sebaceous gland carcinoma (SGC), is invasive, time-consuming, and often inaccessible in primary care hospitals. Therefore, a non-invasive, early differential diagnostic method for eyelid BCC and SGC is needed to reduce diagnostic delays and improve accuracy, which holds significant clinical value, especially for Asian populations.

methodsUsing 199 eyelid BCC and 171 eyelid SGC photographic images from Shanghai Ninth People's Hospital, Shanghai Jiao Tong University School of Medicine (2016-2022), we developed a ResNet50-based deep learning model. The model's performance was evaluated using classification accuracy and the F1 score and was compared with that of four ophthalmologists (three junior and one senior). To assess its clinical utility, we further evaluated the diagnostic accuracy of ophthalmologists with and without the model's assistance.

resultsThe developed model achieved a differential diagnosis accuracy of 0.892 (95% CI 0.821-0.937), with a sensitivity of 0.863 (95% CI 0.743-0.932) and a specificity of 0.917 (95% CI 0.819-0.964), outperforming all four ophthalmologists (junior: 0.703 (95% CI 0.612-0.780), 0.757 (95% CI 0.669-0.827), 0.811 (95% CI 0.729-0.873); senior: 0.874 (95% CI 0.799-0.923)). With the model's assistance, diagnostic accuracy improved by 14.1%, 16.6%, and 2.2% for junior ophthalmologists and by 1.0% for the senior ophthalmologist.

conclusionThe developed model accurately differentiates eyelid BCC and SGC and effectively improves diagnostic performance, particularly for junior ophthalmologists. It may facilitate timely and appropriate treatment planning in clinical settings for eyelid BCC and SGC patients.

Indexed as

Adenocarcinoma, SebaceousBasal Cell CarcinomaDeep LearningEyelid NeoplasmsEyelidsPhotographySebaceous Gland NeoplasmsAdultAgedAged, 80 and overDiagnosis, DifferentialFemaleHumansMaleMiddle AgedRetrospective StudiesBasal cell carcinomaDeep learningEyelid carcinomasNon-invasive differential diagnosisSebaceous gland carcinoma

Identifiers

PMID41186742

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Read under generation 80e0d062 · epoch 390. Bibliography from PubMed, PubMed Central and OpenAlex; grants from NIH RePORTER; trial links from ClinicalTrials.gov; estimates, votes and beliefs from the OpenQuestion graph.