Performance of automated machine learning in detecting fundus diseases based on ophthalmologic B-scan ultrasound images
AutoML models, developed by clinicians, effectively detected multiple fundus lesions with performance on par with that of deep-learning models crafted by AI specialists. This underscores AutoML's potential to revolutionise ophthalmologic diagnostics, facilitating broader accessibility and application of sophisticated diagnostic technologies.
Authors
- Qiaoling Wei (ORCID: https://orcid.org/0000-0003-2842-4262)
- Rui Jiang (ORCID: https://orcid.org/0000-0002-0390-2515)
- Qian Chen (ORCID: https://orcid.org/0000-0002-9135-4976)
- Chen Zhao (ORCID: https://orcid.org/0000-0003-1373-7637)
Institutions
- Eye & ENT Hospital of Fudan University (CN)
Publication Details
- Journal
- BMJ Open Ophthalmology
- Published
- 2024-12-01
- DOI
- https://doi.org/10.1136/bmjophth-2024-001873
- Citations
- 4
- Primary Topic
- Retinal Imaging and Analysis
- Type
- article
- Field-Weighted Citation Impact
- 1.62
Funders
- National Natural Science Foundation of China