Multimodal AI diagnostic system for neuromyelitis optica based on ultrawide-field fundus photography

PurposeWhile 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.MethodsThe 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.ResultsThe 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.ConclusionOur study demonstrates the feasibility of DL algorithms in diagnosing and predicting of NMO.