ArticleQuantitative imaging in medicine and surgery2026
Automated method for quantitative analysis of iris fluorescein angiography based on machine learning.
Article in Quantitative imaging in medicine and surgery, 2026. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Not yet cited in PubMed.
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
0 citing papers in PubMed.
No citing paper in PubMed yet.
Corrections and comments
PubMed lists nothing against this paper. Absence here is not a guarantee, only a check that was made.
Authors and funding
6 authors.
Funding
No grant is acknowledged in the PubMed record.
Abstract
Background: Diabetic retinopathy is a leading cause of vision impairment, often progressing to neovascular glaucoma. Early detection of neovascularisation of the iris (NVI) is crucial for timely intervention. Traditional diagnostic methods, such as slit-lamp examination, have limitations in identifying early-stage NVI. This study presents a deep learning-based automated approach for analysing iris fluorescein angiography (IFA) images to detect and quantify peripupillary leakage, a key indicator of NVI. Methods: A dataset of 2,449 IFA images was used to train a YOLOv8n-based segmentation model for precise pupil localisation. A leakage circularity detection algorithm was developed to quantify peripupillary fluorescein leakage. The algorithm's performance was evaluated using an independent test set of 131 clinically standardized IFA images. Performance metrics included mean absolute error (MAE), mean absolute percentage error (MAPE), and intersection over union (IoU). Results were compared with manual annotations from two clinical experts. Results: The proposed method demonstrated a significant reduction in MAE (20.81 degrees) and MAPE (21.64%) compared to Clinical Staff 1 (MAE: 34.23 degrees, MAPE: 58.38%) and Clinical Staff 2 (MAE: 43.17 degrees, MAPE: 75.71%). The algorithm achieved an IoU of 39.3%, slightly lower than Clinical Staff 1 (44.5%) and Clinical Staff 2 (41.7%), indicating high segmentation accuracy but minor spatial misalignment. The inter-clinician agreement yielded an IoU of 54.8%, highlighting subjectivity in human assessments. Conclusions: The deep learning-based approach provides superior consistency and accuracy in quantifying peripupillary fluorescein leakage compared to manual expert annotations. While human experts demonstrated slightly higher spatial precision, the algorithm significantly reduces variability and subjectivity in leakage quantification. This automated method has the potential to enhance early detection of NVI, improve clinical workflow efficiency, and assist ophthalmologists in diagnosing DR. Further optimization will focus on refining spatial segmentation accuracy.
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.