ArticleChildren (Basel, Switzerland)2025
Neural Network-Based Prediction of Post-Operative Visual Outcomes Following Secondary Pediatric Intraocular Lens Implantation.
Article in Children (Basel, Switzerland), 2025. 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
3 authors.
Funding
No grant is acknowledged in the PubMed record.
Abstract
objectivesTo develop a proof-of-concept machine learning (ML) neural network model to predict post-operative visual outcomes in children with congenital cataracts undergoing intraocular lens (IOL) implantation, thereby guiding the optimal timing for IOL insertion. Determining the ideal timing and predicting outcomes for IOL implantation in children remains clinically complex due to variability in eye development and measurement accuracy.
methodsRetrospective analysis using a publicly available dataset from 110 children diagnosed with congenital cataracts, who underwent IOL implantation at the Eye and ENT Hospital of Fudan University. A neural network model with a hidden layer of 10 nodes was developed in MATLAB 2024a using the scaled conjugate gradient algorithm. Input variables included demographic and clinical features; the target was achieving visual acuity greater than 20/40. Performance metrics were evaluated using cross-entropy loss, sensitivity, specificity, and accuracy.
resultsTraining completed after 14 epochs with the test set reaching the highest performance metrics: 88.2% accuracy, 88.9% sensitivity, and 87.5% specificity. ROC curve analysis showed AUC values of 0.942 (training), 0.920 (validation), 0.885 (test), and 0.917 (overall).
conclusionsThe neural network effectively predicted post-operative visual outcomes, offering potential clinical utility in guiding IOL implantation decisions. Despite limitations in dataset diversity, this study lays the foundation for future development of personalized strategies in pediatric cataract care.
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.