ArticleFrontiers in medicine2025
Multimodal AI diagnostic system for neuromyelitis optica based on ultrawide-field fundus photography.
Article in Frontiers in medicine, 2025. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 3 papers.
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Who cites it
3 citing papers in PubMed.
- Differentiation of optic disc edema and pseudopapilledema with deep learning on near-infrared reflectance images.BMC ophthalmology · 2025Article
- Review
- Decoding paraneoplastic neuromyelitis optica: a multi-omics investigation of tumor-driven T and B cell dynamics.Frontiers in immunology · 2025Review
Corrections and comments
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Authors and funding
12 authors.
Funding
No grant is acknowledged in the PubMed record.
Abstract
Purpose: While deep learning (DL) has demonstrated significant utility in ocular diseases, no clinically validated algorithm currently exists for diagnosing neuromyelitis optica (NMO). This study aimed to develop a proof-of-concept multimodal artificial intelligence (AI) diagnostic model that synergistically integrates ultrawide field fundus photographs (UWFs) with clinical examination data for predicting the onset and stage of suspected NMO. Methods: The study utilized the UWFs of 330 eyes from 285 NMO patients and 1,288 eyes from 770 non-NMO participants, along with clinical examination reports, to develop an AI model for predicting the onset or stage of suspected NMO. The performance of the AI model was evaluated based on the area under the receiver operating characteristic curve (AUC), sensitivity, and specificity. Results: The multimodal AI diagnostic model achieved an AUC of 0.9923, a maximum Youden index of 0.9389, a sensitivity of 97.0% and a specificity of 96.9% in predicting the prevalence of NMO on test data set. Conclusion: Our study demonstrates the feasibility of DL algorithms in diagnosing and predicting of NMO.
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