Multidimensional quantitative characterization of periocular morphology: distinguishing esotropia from epicanthus by deep learning network
Our study established a standard deep learning system for characterizing the periocular morphology of epicanthus and esotropia eyes with great accuracy. This objective method could be generalized to other periocular morphological assessments for clinical care.
Authors
- Lixia Lou (ORCID: https://orcid.org/0000-0001-9411-7776)
- Xingru Huang (ORCID: https://orcid.org/0000-0003-3971-8434)
- Huimin Li (ORCID: https://orcid.org/0000-0001-5568-9026)
- Juan Ye (ORCID: https://orcid.org/0000-0002-2838-6836)
- Jing Cao (ORCID: https://orcid.org/0000-0001-6762-5679)
- Ziying Zhou
- Shengqiang Shi
Institutions
- Hangzhou Dianzi University (CN)
Publication Details
- Journal
- Quantitative Imaging in Medicine and Surgery
- Published
- 2024-08-12
- DOI
- https://doi.org/10.21037/qims-24-155
- Citations
- 2
- Primary Topic
- Retinal Imaging and Analysis
- Type
- article
- Field-Weighted Citation Impact
- 0.81
Funders
- National Natural Science Foundation of China
- National Key Research and Development Program of China