Clinical variables, deep learning and radiomics features help predict the prognosis of anti-NMDA receptor encephalitis in Southwest China

Abstract

The establishment and validation of accurate prognostic models in anti-NMDA receptor (NMDAR) encephalitis is lacking. This study aims to conduct an artifificial intelligence (AI) scheme to predict the prognosis of patients with anti-NMDAR encephalitis using clinical and machine learning features. We first bulid the clinical, deep learning and radiomics models, respectively. Then, we fuse the three schemes to build a fusion model and use an independent external dataset for further validation. The new fusion model significantly outperforms all other models. It demonstrates that applying AI method is an effective way to improve the performance of prognosis prediction in anti-NMDAR encephalitis.