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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\u2010freedom. We developed an open\u2010source software to characterize and localize HS to aid the presurgical evaluation of children and adults with suspected TLE.<\/jats:p>\n                  <\/jats:sec>\n                  <jats:sec>\n                    <jats:title>Methods<\/jats:title>\n                    <jats:p>We included a multicenter cohort of 365 participants (154 HS; 90 disease controls; 121 healthy controls). HippUnfold was used to extract morphological surface\u2010based features and volumes of the hippocampus from T1\u2010weighted MRI scans. We characterized pathological hippocampi in patients by comparing them to normative growth charts and analyzing within\u2010subject 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.<\/jats:p>\n                  <\/jats:sec>\n                  <jats:sec>\n                    <jats:title>Results<\/jats:title>\n                    <jats:p>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\u2010negative histopathologically\u2010confirmed 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.<\/jats:p>\n                  <\/jats:sec>\n                  <jats:sec>\n                    <jats:title>Interpretation<\/jats:title>\n                    <jats:p>Automated and Interpretable Detection of Hippocampal Sclerosis (AID\u2010HS) is an open\u2010source pipeline for detecting and lateralizing HS and outputting clinically\u2010relevant reports. ANN NEUROL 2025;97:62\u201375<\/jats:p>\n                  <\/jats:sec>","DOI":"10.1002\/ana.27089","type":"journal-article","created":{"date-parts":[[2024,11,15]],"date-time":"2024-11-15T01:14:56Z","timestamp":1731633296000},"page":"62-75","update-policy":"https:\/\/doi.org\/10.1002\/crossmark_policy","source":"Crossref","is-referenced-by-count":18,"title":["Automated and Interpretable Detection of Hippocampal Sclerosis in Temporal Lobe Epilepsy:\n                    <scp>AID<\/scp>\n                    \u2010\n                    <scp>HS<\/scp>"],"prefix":"10.1002","volume":"97","author":[{"ORCID":"https:\/\/orcid.org\/0000-0002-1761-5859","authenticated-orcid":false,"given":"Mathilde","family":"Ripart","sequence":"first","affiliation":[{"name":"UCL Great Ormond Street Institute of Child Health  London UK"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Jordan","family":"DeKraker","sequence":"additional","affiliation":[{"name":"McGill University  Montreal 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