Automated and Interpretable Detection of Hippocampal Sclerosis in Temporal Lobe Epilepsy: AID-HS
| dc.contributor.author | Ripart, Mathilde | |
| dc.contributor.author | DeKraker, Jordan | |
| dc.contributor.author | Eriksson, Maria H | |
| dc.contributor.author | Piper, Rory J | |
| dc.contributor.author | Gopinath, Siby | |
| dc.contributor.author | Parasuram, Harilal | |
| dc.contributor.author | Mo, Jiajie | |
| dc.contributor.author | Likeman, Marcus | |
| dc.contributor.author | Ciobotaru, Georgian | |
| dc.contributor.author | Sequeiros-Peggs, Philip | |
| dc.contributor.author | Hamandi, Khalid | |
| dc.contributor.author | Xie, Hua | |
| dc.contributor.author | Cohen, Nathan T | |
| dc.contributor.author | Su, Ting-Yu | |
| dc.contributor.author | Kochi, Ryuzaburo | |
| dc.contributor.author | Wang, Irene | |
| dc.contributor.author | Rojas-Costa, Gonzalo M | |
| dc.contributor.author | Gálvez, Marcelo | |
| dc.contributor.author | Parodi, Constanza | |
| dc.contributor.author | Riva, Antonella | |
| dc.contributor.author | D'Arco, Felipe | |
| dc.contributor.author | Mankad, Kshitij | |
| dc.contributor.author | Clark, Chris A | |
| dc.contributor.author | Carbó, Adrián Valls | |
| dc.contributor.author | Toledano, Rafael | |
| dc.contributor.author | Taylor, Peter | |
| dc.contributor.author | Napolitano, Antonio | |
| dc.contributor.author | Rossi-Espagnet, Maria Camilla | |
| dc.contributor.author | Willard, Anna | |
| dc.contributor.author | Sinclair, Benjamin | |
| dc.contributor.author | Pepper, Joshua | |
| dc.contributor.author | Seri, Stefano | |
| dc.contributor.author | Devinsky, Orrin | |
| dc.contributor.author | Pardoe, Heath R | |
| dc.contributor.author | Winston, Gavin P | |
| dc.contributor.author | Duncan, John S | |
| dc.contributor.author | Yasuda, Clarissa L | |
| dc.contributor.author | Scárdua-Silva, Lucas | |
| dc.contributor.author | Walger, Lennart | |
| dc.contributor.author | Rüber, Theodor | |
| dc.contributor.author | Khan, Aali R | |
| dc.contributor.author | Baldeweg, Torsten | |
| dc.contributor.author | Adler, Sophie | |
| dc.contributor.author | Wagstyl, Konrad | |
| dc.contributor.author | MELD HS study group | |
| dc.coverage.spatial | Estados Unidos | |
| dc.date.accessioned | 2026-08-04T19:02:55Z | |
| dc.date.available | 2026-08-04T19:02:55Z | |
| dc.date.issued | 2024-11-14 | |
| dc.description.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. | |
| dc.identifier.citation | Annals of Neurology, Vol. 97, N°. 1 (2024) pp. 62-75 | |
| dc.identifier.doi | https://doi.org/10.1002/ana.27089 | |
| dc.identifier.issn | 0364-5134 | |
| dc.identifier.issn | 1531-8249 | |
| dc.identifier.orcid | https://orcid.org/0000-0002-6228-2678 | |
| dc.identifier.uri | https://hdl.handle.net/20.500.12254/7689 | |
| dc.language.iso | en | |
| dc.publisher | Wiley Periodicals LLC | |
| dc.rights | Atribución-NoComercial-CompartirIgual 3.0 Chile (CC BY-NC-SA 3.0 CL) | |
| dc.rights.uri | http://creativecommons.org/licenses/by-nc-sa/3.0/cl/ | |
| dc.title | Automated and Interpretable Detection of Hippocampal Sclerosis in Temporal Lobe Epilepsy: AID-HS | |
| dc.type | Article |
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