Asthma identification through multimodal data fusion: towards reliable clinical decision support
Farooq, Umar Trinh, Matt Ly, Angelica Ameer, Asif Razzak, Imran Singh, Sonit
Farooq, Umar
Trinh, Matt
Ly, Angelica
Ameer, Asif
Razzak, Imran
Singh, Sonit
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Computational Biology
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Abstract
Asthma is a chronic respiratory disease with systemic and ocular manifestations. We present a multimodal deep-learning framework to predict asthma by fusing colour fundus photographs (CFP) with optical coherence tomography (OCT), and introduce a novel retinal dataset pairing fundus images with OCT measurements. The pipeline combined fundus CNN features with a feed-forward projection of OCT-derived optic nerve head (ONH) measurements for joint learning. Across five CNN backbones, fusion outperformed unimodal baselines; the best achieved AUC of 0.98, accuracy of 0.92, and F1 of 0.91. Findings supported retinal imaging as a noninvasive biomarker for systemic diseases (e.g., Parkinson, Alzheimer and cardiometabolic), constituting the first multimodal ocular-fusion approach to asthma diagnosis.
Citation
U. Farooq, M. Trinh, A. Ly, A. Ameer, I. Razzak, S. Singh, "Asthma identification through multimodal data fusion: towards reliable clinical decision support," 2026, pp. 141140b-141140b-8-141140b-141140b-8.
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Proceedings of SPIE - The International Society for Optical Engineering
Conference
Eighteenth International Conference on Machine Vision (ICMV 2025)
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40 Engineering, 4006 Communications Engineering, 4009 Electronics, Sensors and Digital Hardware, 51 Physical Sciences, 5102 Atomic, Molecular and Optical Physics
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Eighteenth International Conference on Machine Vision (ICMV 2025)
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SPIE