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AI model accurately classifies retinal development across OCT devices

By PulseAugur Editorial · [1 sources] ·

Researchers have developed and validated an unsupervised domain adaptation model to accurately classify normal versus abnormal retinal development from optical coherence tomography (OCT) scans. This model, based on a ResNet50 architecture and Domain Adversarial Neural Networks, can generalize across different OCT devices and clinical centers without requiring labeled data from the target devices. The system demonstrated high accuracy, sensitivity, and specificity in distinguishing between normal and abnormal retinal development, showing potential for more consistent assessment and large-scale analysis of retinal imaging data. AI

IMPACT This AI model could standardize the assessment of retinal development, enabling more consistent clinical evaluations and facilitating large-scale analysis of existing imaging datasets.

RANK_REASON The cluster contains an academic paper detailing a new model and its validation. [lever_c_demoted from research: ic=1 ai=1.0]

Read on arXiv cs.LG →

AI-generated summary · Google Gemini · from 1 sources. How we write summaries →

AI model accurately classifies retinal development across OCT devices

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17 / 100
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The cluster contains an academic paper detailing a new model and its validation. [lever_c_demoted from research: ic=1 ai=1.0]
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paper, other
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COVERAGE [1]

  1. arXiv cs.LG TIER_1 English(EN) · Zhanhan Tu, Nikhil Reddy, Helen J. Kuht, Ha-Jun Yoon, Gail D. E. Maconachie, Ravi Purohit, Prateek Pani, Line Kessel, Jinu Han, Jing Jin, Shui-Hua Wang, Karthik Bharadwaj, Viral Sheth, Michael Hisaund, Rebecca J. McLean, George Garratt, Chetan Kantibhai … ·

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

    arXiv:2610.21719v1 Announce Type: cross 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 adaptatio…

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