Multicentre study, Diagnostic or prognostic study, Retrospective, Dementia, Computer Applications-Detection, diagnosis, MR, CAD, Neuroradiology brain, Artificial Intelligence, Artificial Intelligence and Machine Learning
S. Alam1, A. S. Lundervold2, A. Lundervold2; 1Bergen, Hordaland, Norway/NO, 2Bergen/NO
Methods and materials
We used T1w brain scans from collected by the Alzheimer's Disease Neuroimaging Initiative (ADNI) to construct and evaluate our classifiers (SVMs). Three classification tasks were considered: AD (n=195 subjects) vs. CN (n= 195 subjects); AD (n=195 subjects) vs. MCI (n=195 subjects); and CN (n=215 subjects) vs. MCI (n=215 subjects). The age range was 56-95 yrs. (mean 75.79) for the AD subjects, 55-93 yrs. (mean 75.09) for the MCI subjects, and 55-95 yrs. (mean 76.98) for the CN subjects. The MALF method provided ROIs at five different granularity levels (Fig. 1). Fig. 2 shows the proposed model. The classification performance was assessed regarding accuracy, sensitivity, specificity, and receiver operating characteristics/area under the curve (ROC-AUC). For the computations, we used the LibSVM library in MATLAB R2019a, MRICloud (https://mricloud.org) for running the MALF, and the feature importance for a given classification task was assessed with the rankfeatures function in MATLAB.