Disease detection and classification in temporal lobe epilepsy: step-wise versus simultaneous AI decision models in a multisite neuroimaging study
Erik Kaestner, Jay Sawant, Donatello Arienzo, Kyle A Hasenstab, Ezequiel Gleichgerrcht, Taha Gholipour, Anees Abrol, Reihaneh Hassanzadeh, Sophia I Thomopoulos, Clarissa L Yasuda, Lucas Scárdua Silva, Marina K M Alvim, Patrick Moloney, Andre Altmann, Helena Martins Custodio, Ev-Christin Heide, Nishant Sinha, Alice Ballerini, Julie Absil, Sara Larivière, Kai M Schubert, Carolina Ferreira-Atuesta, Gian Marco Duma, Raphaël Christin, Elisa Barbi, Renzo Guerrini, Theodor Rüber, Tobias Bauer, Benjamin Sinclair, Jacob Bunyamin, Merran R Courtney, Meng Law, Angelo Labate, Pasquale Striano, Lucy Vivash, Terence J O'Brien, Matteo Lenge, Luca Saba, Jonathan K Kleen, Paolo Bonanni, Leigh N Sepeta, Marian Galovic, Emanuele Bartolini, Victoria Ives-Deliperi, Boris C Bernhardt, Pascal Martin, Chantal Depondt, Travis Stoub, Anna Elisabetta Vaudano, Stefano Meletti, Ruben Kuzniecky, Luis Concha, Anto I Bagić, Kathryn A Davis, Richard J Staba, Niels K N Focke, Heath Pardoe, Patricia C Dugan, Orrin Devinsky, Daniel L Drane, Zhiqiang Zhang, Antonio Gambardella, Alexandra Parashos, Fernando Cendes, Paul M Thompson, Sanjay M Sisodiya, Vince D Calhoun, Leonardo Bonilha, Carrie R McDonaldAbstract
Diagnostic MRI evaluation of temporal lobe epilepsy (TLE) depends on the subjective visual interpretation of MRI images. These interpretations could be enhanced by quantitative artificial intelligence (AI) support tools. Humans often make sequential and conditional decisions during their radiological interpretations, such as whether an abnormality is present and if present, characterizing the abnormality. It is not known whether it is superior to train AI to treat every decision separately in a similar step-wise manner or to train a model holistically on all decisions simultaneously. Here, we analyzed three large epilepsy MRI datasets [n=3,676, 2,320 people with epilepsy and 1,356 healthy controls (HC)] to perform two tasks: 1) establish the presence of a TLE pattern on MRI and 2) determine TLE pattern lateralization. We compared Step-wise models that independently classify TLE versus HC and lateralize patients as left TLE (L-TLE) or right TLE (R-TLE), against a Simultaneous model trained to distinguish all three classes in a single step. To do this, 3D volumetric T1-weighted images were input into an EfficientNetV2 model multiple times to ensure reproducibility of results. Class prediction, model classification confidence, and saliency maps were output for interpretability. Step-wise models outperformed the Simultaneous model on both tasks (both ps<.001), with an average ∼2.8% accuracy increase for discriminating HC from TLE and an average 12.7% accuracy increase for distinguishing L-TLE from R-TLE. For both the Step-wise and Simultaneous models, important features discriminating TLE from HC included the known TLE limbic pattern involving the hippocampus, para-hippocampal cortical regions, cingulate cortex, and lateral temporal regions. However, there was less concordance between the Step-wise and Simultaneous models for the L-TLE versus R-TLE task (all Fisher’s Zs>10.5, ps<.001); the Step-wise model focused less on subcortical regions such as the thalamus and hippocampus and focused more on distributed cortical pathology. Across the two Step-wise models, 95.1% of TLE patients had accurate classifications in either HC versus TLE and/or L-TLE versus R-TLE tasks. These results included 69.6% of patients being both correctly labeled as TLE and lateralized, 13.9% being correctly labeled TLE but lateralized incorrectly, and 11.6% being lateralized correctly but not detected as TLE. These findings provide evidence that diagnostic tasks with simpler, Step-wise AI models may enhance diagnostic performance and interpretability in clinical workflows. Future AI clinical support tools can leverage this step-wise approach in the early identification of TLE-related structural patterns, supporting timely diagnosis and treatment decisions.