ArticleBMJ open ophthalmology2025
Deep learning-based automatic differentiation of acute angle closure with or without zonulopathy using ultrasound biomicroscopy: a comparison of diagnostic performance with ophthalmologists.
Article in BMJ open ophthalmology, 2025. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 2 papers.
What it found
Each row is one number read from the abstract, on the scale the paper reported it, with its interval. Left of the dashed line favours the treatment, right favours the comparator. Under each row is the sentence it came from. New to these charts? A ten-minute tutorial.
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.
The trial behind it
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.
Neither the registry nor the abstract names a trial number. If this is a trial report, that itself is worth knowing.
Who cites it
2 citing papers in PubMed.
- Artificial Intelligence for In-Flight Detection of Space-Related Ocular Trauma: Bridging Diagnostic Gaps in Microgravity.Vision (Basel, Switzerland) · 2026Review
- Improvement of the detection ability for zonular weakness before cataract surgery by comparing predicted and measured left-right differences in anterior chamber depth.Japanese journal of ophthalmology · 2026Article
Corrections and comments
PubMed lists nothing against this paper. Absence here is not a guarantee, only a check that was made.
Authors and funding
11 authors.
Funding
No grant is acknowledged in the PubMed record.
Abstract
objectiveThis study aims to develop ultrasound biomicroscopy (UBM)-based artificial intelligence (AI) models for preoperative differentiation of acute angle closure (AAC) with or without zonulopathy and to compare their comprehensive diagnostic performance against ophthalmologists as a cross-sectional study. METHODS AND ANALYSIS: Three AI models were developed to differentiate AAC with or without zonular laxity or lens subluxation using UBM images and ocular parameters. Their diagnostic performances were analysed, with the best-performing model then compared with two diagnostic methods used by ophthalmologists (logistic regression and UBM image analysis). Additionally, a robustness validation dataset, including images from UBM and anterior segment optical coherence tomography (AS-OCT), was used to validate the robustness of the best-performing AI model.
resultsA total of 537 eyes were included in this study. The best-performing AI model was image-based and achieved a macro-area under the curve (AUC) of 0.9046 with a diagnostic processing time of 0.03 s per image in differentiating AAC with or without zonulopathy. The manually calculated multinomial logistic regression model achieved a macro-AUC of 0.9373, requiring 1200.00 s per analysis. UBM image analysis achieved a mean accuracy and processing time of 64.17% and 20.13 s, respectively, per image. Robustness validation of the image-based AI model showed an accuracy of 66.67% and 61.11% for UBM and AS-OCT images.
conclusionsAI models and ophthalmologists effectively differentiated AAC with or without zonulopathy. However, when evaluated in terms of both accuracy and efficiency, the AI model showed superior comprehensive diagnostic performance, demonstrating high clinical applicability for preoperative diagnosis.
Indexed as
Identifiers
What OpenQuestion holds
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.