Artificial intelligence-assisted tear meniscus height measurement: a multicenter study
This algorithm exhibits strong generalization capabilities, accurately segments key areas, and automatically provides quantitative analysis of the TMH. The measurements obtained using this AI algorithm exhibit high consistency with the GT, surpassing the reliability of special inspection doctors. This provides significant support in the diagnosis of dry eye disease (DED).
Authors
- Kesheng Wang (ORCID: https://orcid.org/0000-0001-7118-3877)
- Qi Dai (ORCID: https://orcid.org/0000-0002-9950-6161)
- Shoujun Huang (ORCID: https://orcid.org/0000-0002-4385-6023)
- Weihua Yang (ORCID: https://orcid.org/0000-0002-7629-0193)
- Dexing Kong (ORCID: https://orcid.org/0000-0001-9339-8086)
- Chunlei He (ORCID: https://orcid.org/0009-0001-6562-6915)
- Jianfeng Zhang (ORCID: https://orcid.org/0000-0001-8883-3450)
- Bowen Zhang (ORCID: https://orcid.org/0000-0002-3581-9476)
- Ying Wang (ORCID: https://orcid.org/0000-0001-8745-0140)
- Kunhui Xu (ORCID: https://orcid.org/0009-0001-5881-9858)
- Fenfen Li (ORCID: https://orcid.org/0000-0002-9776-7165)
- Chun Xiao (ORCID: https://orcid.org/0000-0001-7732-0449)
- Xiaoyu Chen (ORCID: https://orcid.org/0000-0002-9980-5524)
Institutions
- Zhejiang Normal University (CN)
- Jinan University (CN)
- Wenzhou Medical University (CN)
- Affiliated Eye Hospital of Wenzhou Medical College (CN)
Publication Details
- Journal
- Quantitative Imaging in Medicine and Surgery
- Published
- 2025-04-21
- DOI
- https://doi.org/10.21037/qims-24-1948
- Citations
- 5
- Primary Topic
- Shoulder Injury and Treatment
- Type
- article
- Field-Weighted Citation Impact
- 10.03