BACKGROUND: Autoimmune encephalitis (AE) is a noninfectious emergency with severe clinical attacks. It is difficult for the earlier diagnosis of acute AE due to the lack of antibody detection resources. PURPOSE: To construct a deep learning (DL) algorithm using multi-sequence magnetic resonance imaging (MRI) for the identification of acute AE. STUDY TYPE: Retrospective. POPULATION: One hundred and sixty AE patients (90 women; median age 36), 177 herpes simplex virus encephalitis (HSVE) (89 women; median age 39), and 184 healthy controls (HC) (95 women; median age 39) were included. Fifty-two patients from another site were enrolled for external validation. FIELD STRENGTH/SEQUENCE: 3.0 T; fast spin-echo (T1 WI, T2 WI, fluid attenuated inversion recovery imaging) and spin-echo echo-planar diffusion weighted imaging. ASSESSMENT: Five DL models based on individual or combined four MRI sequences to classify the datasets as AE, HSVE, or HC. Reader experiment was further carried out by radiologists. STATISTICAL TESTS: The discriminative performance of different models was assessed using the area under the receiver operating characteristic curve (AUC). The optimal threshold cut-off was identified when sensitivity and specificity were maximized (sensitivity + specificity - 1) in the validation set. Classification performance using confusion matrices was reported to evaluate the diagnostic value of the models and the radiologists' assessments before being assessed by the paired t-test (P < 0.05 was considered significant). RESULTS: In the internal test set, the fusion model achieved the significantly greatest diagnostic performance than single-sequence DL models with AUCs of 0.828, 0.884, and 0.899 for AE, HSVE, and HC, respectively. The model demonstrated a consistently high performance in the external validation set with AUCs of 0.831 (AE), 0.882 (HSVE), and 0.892 (HC). The fusion model also demonstrated significantly higher performance than all radiologists in identifying AE (accuracy between the fuse model vs. average radiologist: 83% vs. 72%). DATA CONCLUSION: The proposed DL algorithm derived from multi-sequence MRI provided desirable identification and classification of acute AE. LEVEL OF EVIDENCE: 3 TECHNICAL EFFICACY: Stage 2.
BACKGROUND: Autoimmune encephalitis (AE) is a noninfectious emergency with severe clinical attacks. It is difficult for the earlier diagnosis of acute AE due to the lack of antibody detection resources. PURPOSE: To construct a deep learning (DL) algorithm using multi-sequence magnetic resonance imaging (MRI) for the identification of acute AE. STUDY TYPE: Retrospective. POPULATION: One hundred and sixty AE patients (90 women; median age 36), 177 herpes simplex virus encephalitis (HSVE) (89 women; median age 39), and 184 healthy controls (HC) (95 women; median age 39) were included. Fifty-two patients from another site were enrolled for external validation. FIELD STRENGTH/SEQUENCE: 3.0 T; fast spin-echo (T1 WI, T2 WI, fluid attenuated inversion recovery imaging) and spin-echo echo-planar diffusion weighted imaging. ASSESSMENT: Five DL models based on individual or combined four MRI sequences to classify the datasets as AE, HSVE, or HC. Reader experiment was further carried out by radiologists. STATISTICAL TESTS: The discriminative performance of different models was assessed using the area under the receiver operating characteristic curve (AUC). The optimal threshold cut-off was identified when sensitivity and specificity were maximized (sensitivity + specificity - 1) in the validation set. Classification performance using confusion matrices was reported to evaluate the diagnostic value of the models and the radiologists' assessments before being assessed by the paired t-test (P < 0.05 was considered significant). RESULTS: In the internal test set, the fusion model achieved the significantly greatest diagnostic performance than single-sequence DL models with AUCs of 0.828, 0.884, and 0.899 for AE, HSVE, and HC, respectively. The model demonstrated a consistently high performance in the external validation set with AUCs of 0.831 (AE), 0.882 (HSVE), and 0.892 (HC). The fusion model also demonstrated significantly higher performance than all radiologists in identifying AE (accuracy between the fuse model vs. average radiologist: 83% vs. 72%). DATA CONCLUSION: The proposed DL algorithm derived from multi-sequence MRI provided desirable identification and classification of acute AE. LEVEL OF EVIDENCE: 3 TECHNICAL EFFICACY: Stage 2.