Ripart, MathildeDeKraker, JordanEriksson, Maria HPiper, Rory JGopinath, SibyParasuram, HarilalMo, JiajieLikeman, MarcusCiobotaru, GeorgianSequeiros-Peggs, PhilipHamandi, KhalidXie, HuaCohen, Nathan TSu, Ting-YuKochi, RyuzaburoWang, IreneRojas-Costa, Gonzalo MGálvez, MarceloParodi, ConstanzaRiva, AntonellaD'Arco, FelipeMankad, KshitijClark, Chris ACarbó, Adrián VallsToledano, RafaelTaylor, PeterNapolitano, AntonioRossi-Espagnet, Maria CamillaWillard, AnnaSinclair, BenjaminPepper, JoshuaSeri, StefanoDevinsky, OrrinPardoe, Heath RWinston, Gavin PDuncan, John SYasuda, Clarissa LScárdua-Silva, LucasWalger, LennartRüber, TheodorKhan, Aali RBaldeweg, TorstenAdler, SophieWagstyl, KonradMELD HS study group2026-08-042026-08-042024-11-14Annals of Neurology, Vol. 97, N°. 1 (2024) pp. 62-750364-51341531-8249https://hdl.handle.net/20.500.12254/7689Objective: Hippocampal sclerosis (HS), the most common pathology associated with temporal lobe epilepsy (TLE), is not always visible on magnetic resonance imaging (MRI), causing surgical delays and reduced postsurgical seizure-freedom. We developed an open-source software to characterize and localize HS to aid the presurgical evaluation of children and adults with suspected TLE. Methods: We included a multicenter cohort of 365 participants (154 HS; 90 disease controls; 121 healthy controls). HippUnfold was used to extract morphological surface-based features and volumes of the hippocampus from T1-weighted MRI scans. We characterized pathological hippocampi in patients by comparing them to normative growth charts and analyzing within-subject feature asymmetries. Feature asymmetry scores were used to train a logistic regression classifier to detect and lateralize HS. The classifier was validated on an independent multicenter cohort of 275 patients with HS and 161 healthy and disease controls. Results: HS was characterized by decreased volume, thickness, and gyrification alongside increased mean and intrinsic curvature. The classifier detected 90.1% of unilateral HS patients and lateralized lesions in 97.4%. In patients with MRI-negative histopathologically-confirmed HS, the classifier detected 79.2% (19/24) and lateralized 91.7% (22/24). The model achieved similar performances on the independent cohort, demonstrating its ability to generalize to new data. Individual patient reports contextualize a patient's hippocampal features in relation to normative growth trajectories, visualise feature asymmetries, and report classifier predictions. Interpretation: Automated and Interpretable Detection of Hippocampal Sclerosis (AID-HS) is an open-source pipeline for detecting and lateralizing HS and outputting clinically-relevant reports.enAtribución-NoComercial-CompartirIgual 3.0 Chile (CC BY-NC-SA 3.0 CL)Articlehttps://orcid.org/0000-0002-6228-2678https://doi.org/10.1002/ana.27089