Automated and Interpretable Detection of Hippocampal Sclerosis in temporal lobe epilepsy: AID-HS

Abstract Hippocampal Sclerosis (HS) can elude visual detection on MRI scans of patients with temporal lobe epilepsy (TLE), causing delays in surgical treatment and reducing the likelihood of postsurgical seizure-freedom. We developed an open-source software that (1) detects HS from structural MRI scans, (2) generalises across a heterogeneous multicentre cohort of children and adults, and (3) generates individualised predictions for clinical evaluation. We included a cohort of 363 participants (152 patients with HS, 90 disease controls with focal cortical dysplasia, and 121 healthy controls) from four epilepsy centres in the UK, North America, and China. We used the open-source software HippUnfold to extract morphological surface-based features and volumes of the hippocampus from T1w MRI scans. We compared pathological hippocampal morphology in patients with HS to normative growth charts generated from healthy controls, and to the contralateral hippocampi in patients with HS. HS was characterised by decreased volume, thickness and gyrification as well as increased mean and intrinsic curvature. A logistic regression classifier trained on these features detected 90.1% of HS patients, and accurately lateralised 97.4% of the HS cohort. Crucially, in patients with MRI-negative histopathologically confirmed HS, the classifier detected HS in 79.2% (19/24) and accurately lateralised the lesions in 91.7% (22/24). The Automated and Interpretable Detection of Hippocampal Sclerosis classifier (AID-HS) was packaged into an open-source pipeline, which detects and lateralises HS and generates individualised patient reports that characterise hippocampal developmental abnormalities. AID-HS is capable of accurately detecting and lateralising HS in a large, heterogeneous, multi-centre, cohort of paediatric and adult patients with diagnostically challenging HS. Moreover, by offering transparent, robust and interpretable results, AID-HS can support the presurgical evaluation of patients with suspected TLE.