ArticleIndian journal of ophthalmology2020
Application of deep learning and image processing analysis of photographs for amblyopia screening.
Article in Indian journal of ophthalmology, 2020. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 9 papers.
What it found
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The abstract states no effect estimate the extractor could read, or names no intervention and outcome on the map, so this paper lights no cell and moves no belief. It is still indexed, cited and linked below.
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Trials whose registry record cites this paper, or whose number appears in the abstract. A trial that started after this paper was published is citing it as background, not reporting it.
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Who cites it
9 citing papers in PubMed, 14 citations in OpenAlex.
- The Fundus Red Reflex Test Using a Smartphone Showed High Accuracy Compared to an Autorefractor to Detect Refractive Errors in Children.Journal of ophthalmic & vision research · 2026Article
- Article
- Predicting onset of myopic refractive error in children using machine learning on routine pediatric eye examinations only.Scientific reports · 2025Article
- Artificial Intelligence in Optometry: Current and Future Perspectives.Clinical optometry · 2025Review
- Artificial intelligence-aided diagnosis and treatment in the field of optometry.International journal of ophthalmology · 2023Article
- Advances in artificial intelligence models and algorithms in the field of optometry.Frontiers in cell and developmental biology · 2023Review
- Effectiveness of Kanna photoscreener in detecting amblyopia risk factors.Indian journal of ophthalmology · 2021Article
- Commentary: How useful is a deep learning smartphone application for screening for amblyogenic risk factors?Indian journal of ophthalmology · 2020Article
- Integration of smartphone technology and artificial intelligence for advanced ophthalmic care: A systematic review.Advances in ophthalmology practice and researchReview
Corrections and comments
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Authors and funding
4 authors at 1 institution in 2 countries.
Funding
No grant is acknowledged in the PubMed record.
Abstract
Purpose: Photo screeners and autorefractors have been used to screen children for amblyopia risk factors (ARF) but are limited by cost and efficacy. We looked for a deep learning and image processing analysis-based system to screen for ARF. Methods: An android smartphone was used to capture images using a specially coded application that modified the camera setting. An algorithm was developed to process images taken in different light conditions in an automated manner to predict the presence of ARF. Deep learning and image processing models were used to segment images of the face. Light settings and distances were tested to obtain the necessary features. Deep learning was thereafter used to formulate normalized risks using sigmoidal models for each ARF creating a risk dashboard. The model was tested on 54 young adults and results statistically analyzed. Results: A combination of low-light and ambient-light images was needed for screening for exclusive ARF. The algorithm had an F-Score of 73.2% with an accuracy of 79.6%, a sensitivity of 88.2%, and a specificity of 75.6% in detecting the ARF. Conclusion: Deep-learning and image-processing analysis of photographs acquired from a smartphone are useful in screening for ARF in children and young adults for a referral to doctors for further diagnosis and treatment.
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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.