cs.CVOct 15, 2026

International Multi-Centre Validation of Unsupervised Domain Adaptation for Precise Discrimination between Normal and Abnormal Retinal Development

Authors: Zhanhan Tu, Nikhil Reddy, Helen J. Kuht, Ha-Jun Yoon, Gail D. E. Maconachie, Ravi Purohit, Prateek Pani, Line Kessel, 

Organizations: The University of Leicester Ulverscroft Eye Unit, School of Psychology and Vision Sciences, University of Leicester, RKCSB, PO Box 65, Leicester, LE2 7LX, UK · International Institute of Information Technology, Gachibowli, Hyderabad - 500 032, India · inAmind Laboratory, Department of Neuroscience, Psychology and Behaviour, University of Leicester, George Davies Centre, Lancaster Road, Leicester, LE1 7RH, UK. · Academic Unit of Ophthalmology and Orthoptics, University of Sheffield, Sheffield, UK · 

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Abstract

Arrested retinal development can cause childhood visual impairment, but grading optical coherence tomography scans is difficult to standardise across devices and centres. We developed and evaluated an unsupervised domain adaptation model to classify normal and abnormal retinal development across different OCT systems. This retrospective multicentre study used OCT scans from five centres in four countries and the UK Biobank. Data covered four OCT devices, including table mounted and handheld systems. A ResNet50 based Domain Adversarial Neural Network was trained using labelled images from a source device and unlabelled images from a target device. We evaluated six source to target pairings and compared model performance with seven clinician graders. A separate Heidelberg Spectralis dataset was reserved for external evaluation. Across validation sets, accuracy ranged from 87.75% to 96.06%, sensitivity from 88.68% to 98.38%, and specificity from 78.00% to 98.02%. The best performing model, adapted from TM OCT1 to HH OCT1, achieved 96.06% accuracy and an area under the receiver operating characteristic curve of 0.994, with a 95% confidence interval from 0.981 to 0.999. Several models also maintained discrimination on the unseen Heidelberg device. Unsupervised domain adaptation enabled OCT classification across devices without labelled target device data. The approach may support more consistent assessment of retinal development and large scale analysis of existing imaging datasets.

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Unsupervised Domain Adaptation

Unsupervised domain adaptation transfers models from labeled source data to unlabeled target domains with different data distributions.

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