HIGHLIGHTS
SUMMARY
There have been efforts to develop such a tool using machine_learning (ML) methodologies, with structural MRI, which are high-performing in distinguishing "healthy controls (HC) vs. AD" (80-100% accuracy), "MCI vs. AD" (50-85% accuracy), "HC vs. MCI" (60-90% accuracy), and "HC vs. MCI vs. AD" (59-77% accuracy). The fact that both the classifier built to distinguish protocols ("ADNI MPRAGE vs. ADNI IR-SPGR") and the classifier built to distinguish datasets ("OASIS vs. ADNI") did not "choose" to use GT-based metrics even when these were given as input, this indicates . . .
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