ArticleJournal of current ophthalmology
Performance of Deep Transfer Learning for Detecting Abnormal Fundus Images.
Article in Journal of current ophthalmology. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 1 paper, 1 of them a synthesis that pooled it.
What it found
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Who cites it
1 citing paper in PubMed, 1 synthesis or guideline pooled it, 6 citations in OpenAlex.
- In-depth analysis of research hotspots and emerging trends in AI for retinal diseases over the past decade.Frontiers in medicine · 2024Pooled it
Corrections and comments
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Authors and funding
7 authors at 3 institutions in 1 country.
Funding
No grant is acknowledged in the PubMed record.
Abstract
purposeTo develop and validate a deep transfer learning (DTL) algorithm for detecting abnormalities in fundus images from non-mydriatic fundus photography examinations.
methodsA total of 1295 fundus images were collected to develop and validate a DTL algorithm for detecting abnormal fundus images. After removing 366 poor images, the DTL model was developed using 929 (370 normal and 559 abnormal) fundus images. Data preprocessing was performed to normalize the images. The inception-ResNet-v2 architecture was applied to achieve transfer learning. We tested our model using a subset of the publicly available Messidor dataset (using 366 images) and evaluated the testing performance of the DTL model for detecting abnormal fundus images.
resultsIn the internal validation dataset (
conclusionDTL showed high sensitivity and specificity for detecting abnormal fundus-related diseases. Further research is necessary to improve this method and evaluate the applicability of DTL in community health-care centers.
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Registered trials
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.