ArticleScientific reports2023
AI-based diagnosis of nuclear cataract from slit-lamp videos.
Article in Scientific reports, 2023. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 17 papers.
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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
17 citing papers in PubMed.
- Diagnostic accuracy of ophthalmology residents in emergency department consultations: trends and potential implications for training.BMC medical education · 2026Article
- AI for Anterior Segment Disease Using Transfer Learning: Adapting Slit-Lamp-Trained Model for Analysis of Smartphone Corneal Images.Translational vision science & technology · 2026Article
- Advancement of cataract classification with artificial intelligence using anterior segment optical coherence tomography images with self-supervised vision transformer.Japanese journal of ophthalmology · 2026Article
- Smartphone-Based Portable Slit Lamp in Anterior Segment Diseases: A Narrative Review of Clinical Assessment and Integration with Artificial Intelligence.Ophthalmology and therapy · 2026Review
- Validation of an Artificial Intelligence-Based Anterior Chamber Depth Estimation Using a Smartphone-Compatible Slit Lamp Device.Ophthalmology science · 2026Article
- Telemedicine-based diagnosis and management of ocular chemical injury in a remote setting: A case report.Journal of general and family medicine · 2025Article
- Enhancing Ophthalmic Diagnosis and Treatment with Artificial Intelligence.Medicina (Kaunas, Lithuania) · 2025Review
- Artificial intelligence in cataract grading system: a LOCS III-based hybrid model achieving high-precision classification.Frontiers in cell and developmental biology · 2025Article
- Evaluation of the effectiveness of a portable device for diagnosing cataract.Arquivos brasileiros de oftalmologia · 2025Article
- Sustainability in Cataract and Refractive Surgery: Current Challenges and Future Perspectives.Journal of ophthalmology · 2025Review
- Deep learning-driven approach for cataract management: towards precise identification and predictive analytics.Frontiers in cell and developmental biology · 2025Review
- The Use of Artificial Intelligence for Estimating Anterior Chamber Depth from Slit-Lamp Images Developed Using Anterior-Segment Optical Coherence Tomography.Bioengineering (Basel, Switzerland) · 2024Article
- A Case of Traumatic Hyphema Diagnoses by Telemedicine Between a Remote Island and the Mainland of Tokyo.Cureus · 2024Article
- Development of the AI Pipeline for Corneal Opacity Detection.Bioengineering (Basel, Switzerland) · 2024Article
- Applications of Artificial Intelligence in Cataract Surgery: A Review.Clinical ophthalmology (Auckland, N.Z.) · 2024Review
- Artificial intelligence in the anterior segment of eye diseases.International journal of ophthalmology · 2024Review
- Review
Corrections and comments
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Authors and funding
11 authors.
Funding
Abstract
In ophthalmology, the availability of many fundus photographs and optical coherence tomography images has spurred consideration of using artificial intelligence (AI) for diagnosing retinal and optic nerve disorders. However, AI application for diagnosing anterior segment eye conditions remains unfeasible due to limited standardized images and analysis models. We addressed this limitation by augmenting the quantity of standardized optical images using a video-recordable slit-lamp device. We then investigated whether our proposed machine learning (ML) AI algorithm could accurately diagnose cataracts from videos recorded with this device. We collected 206,574 cataract frames from 1812 cataract eye videos. Ophthalmologists graded the nuclear cataracts (NUCs) using the cataract grading scale of the World Health Organization. These gradings were used to train and validate an ML algorithm. A validation dataset was used to compare the NUC diagnosis and grading of AI and ophthalmologists. The results of individual cataract gradings were: NUC 0: area under the curve (AUC) = 0.967; NUC 1: AUC = 0.928; NUC 2: AUC = 0.923; and NUC 3: AUC = 0.949. Our ML-based cataract diagnostic model achieved performance comparable to a conventional device, presenting a promising and accurate auto diagnostic AI tool.
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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.