Development and multicenter validation of an AI driven model for quantitative meibomian gland evaluation
Abstract This multicenter retrospective study developed and validated an AI driven model for automated segmentation and quantitative evaluation of meibomian glands using infrared meibography images acquired by the Keratograph 5M device. A total of 1350 infrared meibography images were collected and...
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| Format: | Article |
| Language: | English |
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Nature Portfolio
2025-07-01
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| Series: | npj Digital Medicine |
| Online Access: | https://doi.org/10.1038/s41746-025-01753-5 |
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| _version_ | 1849389285901336576 |
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| author | Li Li Kunhong Xiao Taichen Lai Kunfeng Lai Jiawen Lin Zongyuan Ge Lingyi Liang Hao Huang Xiaoshan Zhang Li Liu Yujie Wang Xianwen Shang Mingguang He Ying Xue Zhuoting Zhu |
| author_facet | Li Li Kunhong Xiao Taichen Lai Kunfeng Lai Jiawen Lin Zongyuan Ge Lingyi Liang Hao Huang Xiaoshan Zhang Li Liu Yujie Wang Xianwen Shang Mingguang He Ying Xue Zhuoting Zhu |
| author_sort | Li Li |
| collection | DOAJ |
| description | Abstract This multicenter retrospective study developed and validated an AI driven model for automated segmentation and quantitative evaluation of meibomian glands using infrared meibography images acquired by the Keratograph 5M device. A total of 1350 infrared meibography images were collected and annotated for model training and validation. The model demonstrated high segmentation performance, with an Intersection over Union of 81.67% (95% Confidence Interval [CI]: 81.03–82.31) and accuracy of 97.49% (95% CI: 97.38–97.62), outperforming conventional algorithms. The agreement was observed between AI-based and manual gland grading (Kappa = 0.93) and gland counting (Spearman r = 0.9334). Repeatability analysis confirmed the model’s stability, and external validation across four independent centers yielded consistent results with AUCs exceeding 0.99. This AI tool offers a standardized, efficient, and objective method for meibography image analysis, which may improve diagnostic precision and assist in the clinical management of meibomian gland dysfunction across diverse populations. |
| format | Article |
| id | doaj-art-c52e20654e064943b8e1fc79bbd94a40 |
| institution | Kabale University |
| issn | 2398-6352 |
| language | English |
| publishDate | 2025-07-01 |
| publisher | Nature Portfolio |
| record_format | Article |
| series | npj Digital Medicine |
| spelling | doaj-art-c52e20654e064943b8e1fc79bbd94a402025-08-20T03:42:00ZengNature Portfolionpj Digital Medicine2398-63522025-07-01811910.1038/s41746-025-01753-5Development and multicenter validation of an AI driven model for quantitative meibomian gland evaluationLi Li0Kunhong Xiao1Taichen Lai2Kunfeng Lai3Jiawen Lin4Zongyuan Ge5Lingyi Liang6Hao Huang7Xiaoshan Zhang8Li Liu9Yujie Wang10Xianwen Shang11Mingguang He12Ying Xue13Zhuoting Zhu14Centre for Eye Research Australia, Royal Victorian Eye and Ear HospitalDepartment of Ophthalmology and Optometry, Fujian Medical UniversityDepartment of Ophthalmology, Shengli Clinical Medical College of Fujian Medical University, Fujian Provincial Hospital, Fuzhou University Affiliated Provincial HospitalCollege of Computer and Data Science, Fuzhou UniversityCollege of Computer and Data Science, Fuzhou UniversityMonash e-Research Centre, Monash UniversityState Key Laboratory of Ophthalmology, Zhongshan Ophthalmic Center, Sun Yat-sen University, Guangdong Provincial Key Laboratory of Ophthalmology and Visual ScienceDepartment of Ophthalmology, Zhuzhou Hospital Affiliated to Xiangya School of Medicine, Central South UniversityDongguan Huaxia Eye HospitalHuaxia Eye Hospital Group Putian Eye HospitalCentre for Eye Research Australia, Royal Victorian Eye and Ear HospitalSchool of Optometry, The Hong Kong Polytechnic UniversitySchool of Optometry, The Hong Kong Polytechnic UniversityDepartment of Ophthalmology, Shengli Clinical Medical College of Fujian Medical University, Fujian Provincial Hospital, Fuzhou University Affiliated Provincial HospitalCentre for Eye Research Australia, Royal Victorian Eye and Ear HospitalAbstract This multicenter retrospective study developed and validated an AI driven model for automated segmentation and quantitative evaluation of meibomian glands using infrared meibography images acquired by the Keratograph 5M device. A total of 1350 infrared meibography images were collected and annotated for model training and validation. The model demonstrated high segmentation performance, with an Intersection over Union of 81.67% (95% Confidence Interval [CI]: 81.03–82.31) and accuracy of 97.49% (95% CI: 97.38–97.62), outperforming conventional algorithms. The agreement was observed between AI-based and manual gland grading (Kappa = 0.93) and gland counting (Spearman r = 0.9334). Repeatability analysis confirmed the model’s stability, and external validation across four independent centers yielded consistent results with AUCs exceeding 0.99. This AI tool offers a standardized, efficient, and objective method for meibography image analysis, which may improve diagnostic precision and assist in the clinical management of meibomian gland dysfunction across diverse populations.https://doi.org/10.1038/s41746-025-01753-5 |
| spellingShingle | Li Li Kunhong Xiao Taichen Lai Kunfeng Lai Jiawen Lin Zongyuan Ge Lingyi Liang Hao Huang Xiaoshan Zhang Li Liu Yujie Wang Xianwen Shang Mingguang He Ying Xue Zhuoting Zhu Development and multicenter validation of an AI driven model for quantitative meibomian gland evaluation npj Digital Medicine |
| title | Development and multicenter validation of an AI driven model for quantitative meibomian gland evaluation |
| title_full | Development and multicenter validation of an AI driven model for quantitative meibomian gland evaluation |
| title_fullStr | Development and multicenter validation of an AI driven model for quantitative meibomian gland evaluation |
| title_full_unstemmed | Development and multicenter validation of an AI driven model for quantitative meibomian gland evaluation |
| title_short | Development and multicenter validation of an AI driven model for quantitative meibomian gland evaluation |
| title_sort | development and multicenter validation of an ai driven model for quantitative meibomian gland evaluation |
| url | https://doi.org/10.1038/s41746-025-01753-5 |
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