학술논문

More Than Meets the Eye: Human Versus Computer in the Neuroimaging of Temporal Lobe Epilepsy.
Document Type
Article
Source
Epilepsy Currents. Sep/Oct2023, Vol. 23 Issue 5, p289-290. 2p.
Subject
*TEMPORAL lobe epilepsy
*CONVOLUTIONAL neural networks
*EPILEPSY
*ALZHEIMER'S disease
*HIPPOCAMPAL sclerosis
*MAGNETIC resonance imaging
Language
ISSN
1535-7597
Abstract
MRI-Based Deep Learning Can Discriminate Between Temporal Lobe Epilepsy, Alzheimer's Disease, and Healthy Controls Chang AJ, Roth R, Bougioukli E, Ruber T, Keller SS, Drane DL, Gross RE, Welsh J, Abrol A, Calhoun V, Karakis I, Kaestner E, Weber B, McDonald C, Gleichgerrcht E, Bonilha L; Alzheimer's Disease Neuroimaging Initiative. Commun Med (Lond). 2023;3(1):33. doi:10.1038/s43856-023-00262-4. Background: Radiological identification of temporal lobe epilepsy (TLE) is crucial for diagnosis and treatment planning. TLE neuroimaging abnormalities are pervasive at the group level, but they can be subtle and difficult to identify by visual inspection of individual scans, prompting applications of artificial intelligence (AI) assisted technologies. Method: We assessed the ability of a convolutional neural network (CNN) algorithm to classify TLE vs. patients with AD vs. healthy controls using T1-weighted magnetic resonance imaging (MRI) scans. We used feature visualization techniques to identify regions the CNN employed to differentiate disease types. Results: We show the following classification results: healthy control accuracy = 81.54% (SD = 1.77%), precision = 0.81 (SD = 0.02), recall = 0.85 (SD = 0.03), and F1-score = 0.83 (SD = 0.02); TLE accuracy = 90.45% (SD = 1.59%), precision = 0.86 (SD = 0.03), recall = 0.86 (SD = 0.04), and F1-score = 0.85 (SD = 0.04); and AD accuracy = 88.52% (SD = 1.27%), precision = 0.64 (SD = 0.05), recall = 0.53 (SD = 0.07), and F1 score = 0.58 (0.05). The high accuracy in identification of TLE was remarkable, considering that only 47% of the cohort had deemed to be lesional based on MRI alone. Model predictions were also considerably better than random permutation classifications (p < 0.01) and were independent of age effects. Conclusions: AI (CNN deep learning) can classify and distinguish TLE, underscoring its potential utility for future computer-aided radiological assessments of epilepsy, especially for patients who do not exhibit easily identifiable TLE associated MRI features (e.g., hippocampal sclerosis). [ABSTRACT FROM AUTHOR]