Disease Markers / 2022 / Article / Tab 4 / Review Article
Research Progress of Artificial Intelligence Image Analysis in Systemic Disease-Related Ophthalmopathy Table 4 Summary of NMO diagnosis model based on deep learning method.
Study Task Sample size AI model Output Huang et al. [37 ] Detection and identification 116 images of magnetic resonance A multi-parameter multivariate random forest model In training, the accuracy of the MM-RF model was 0.849, and the AUC value was 0.826; for testing, the accuracy of the MM-RF model was 0.871, and the AUC value was 0.902. Hagiwara et al. [38 ] Detection and identification 53 patients’ examination results SqueezeNet The AUC value of the model is 0.859, and the accuracies of NMO and MS are 81.1% and 83.3%, respectively. Kim et al. [39 ] Detection and identification 338 patients’ images of magnetic resonance ResNeXt The AUC value of the model was 0.82, and the accuracy was 71.1%. Khoury et al. [40 ] Identification 202 serum samples A random forest classification machine learning algorithm The sensitivity and specificity were 1.00 and 1.00.