{"doi":"10.1002/ana.27089","title":"Automated and Interpretable Detection of Hippocampal Sclerosis in Temporal Lobe Epilepsy: <scp>AID</scp> ‐ <scp>HS</scp>","abstract":"OBJECTIVE: 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. ANN NEUROL 2024.","journal":"Annals of Neurology","year":2024,"id":429050,"datarank":0.0,"base_score":0.0,"endowment":0.0,"self_citation_contribution":0.0,"citation_network_contribution":0.0,"self_endowment_contribution":0.0,"citer_contribution":0.0,"corpus_percentile":null,"corpus_rank":null,"citation_count":11,"citer_count":0,"citers_with_citation_signal":0,"citers_with_endowment":0,"datacite_reuse_total":0,"is_dataset":false,"is_dataset_confidence":0.8877,"is_data_producer":false,"deposit_databanks":null,"is_oa":true,"file_count":0,"downloads":0,"has_version_chain":false,"published_date":"2024-01-01","fair_score":null,"fair_percentile":null,"algorithm_id":"datarank_citation_only_1hop_v6","ranking_scope":"data_only","authors":[{"id":847580,"name":"Jordan DeKraker","orcid":"0000-0002-4093-0582","position":1,"is_corresponding":false},{"id":1231147,"name":"Maria H. Eriksson","orcid":"0000-0001-6869-5804","position":2,"is_corresponding":false},{"id":842012,"name":"Rory J. 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Cohen","orcid":"0000-0002-1410-3487","position":12,"is_corresponding":false},{"id":880075,"name":"Ting‐Yu Su","orcid":"0000-0001-5054-9163","position":13,"is_corresponding":false},{"id":1231149,"name":"Ryuzaburo Kochi","orcid":"0009-0008-4462-1710","position":14,"is_corresponding":false},{"id":52288,"name":"Irène Wang","orcid":"0000-0002-3829-5217","position":15,"is_corresponding":false},{"id":1231150,"name":"Gonzalo Rojas","orcid":"0000-0002-6228-2678","position":16,"is_corresponding":false},{"id":1231151,"name":"Marcelo Gálvez","orcid":"0000-0002-8638-5268","position":17,"is_corresponding":false},{"id":787991,"name":"Costanza Parodi","orcid":"0000-0002-4069-7792","position":18,"is_corresponding":false},{"id":865565,"name":"Antonella Riva","orcid":"0000-0001-9152-5571","position":19,"is_corresponding":false},{"id":809059,"name":"Felice D’Arco","orcid":"0000-0001-7396-591X","position":20,"is_corresponding":false},{"id":615361,"name":"Kshitij Mankad","orcid":"0000-0001-5979-9337","position":21,"is_corresponding":false},{"id":553934,"name":"Chris A. 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