Chang, Allen J, Roth, Rebecca, Bougioukli, Eleni, Ruber, Theodor, Keller, Simon S 
ORCID: 0000-0001-5247-9795, Drane, Daniel L, Gross, Robert E, Welsh, James, Abrol, Anees, Calhoun, Vince et al (show 6 more authors)
(2023)
MRI-based deep learning can discriminate between temporal lobe epilepsy, Alzheimer's disease, and healthy controls.
COMMUNICATIONS MEDICINE, 3 (1).
33-.
Abstract
<h4>Background</h4>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.<h4>Method</h4>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.<h4>Results</h4>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.<h4>Conclusions</h4>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).
| Item Type: | Article |
|---|---|
| Uncontrolled Keywords: | Alzheimer’s Disease Neuroimaging Initiative |
| Divisions: | Faculty of Health and Life Sciences Faculty of Health and Life Sciences > Institute of Systems, Molecular and Integrative Biology |
| Depositing User: | Symplectic Admin |
| Date Deposited: | 20 Apr 2023 08:22 |
| Last Modified: | 20 Apr 2023 08:22 |
| DOI: | 10.1038/s43856-023-00262-4 |
| Open Access URL: | https://doi.org/10.1038/s43856-023-00262-4 |
| Related URLs: | |
| URI: | https://livrepository.liverpool.ac.uk/id/eprint/3169799 |
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