A deep learning model that simulates a clinician’s diagnostic process can accurately diagnose Alzheimer’s disease from cognitively normal patients, according to a study published July 16 in Neurocomputing.

Fan Zhang, with Henan University in China, and colleagues, combined two deep learning algorithms—one designed to read MRI and another to read PET images—with a clinical neuropsychological diagnosis to determine if a patient had Alzheimer’s disease, mild cognitive impairment (MCI) or was cognitively normal.

Upon testing, the platform achieved a 97% sensitivity and 88% accuracy for identifying patients with Alzheimer’s from those with healthy cognitive abilities.


MCI is often misdiagnosed as the symptoms of normal aging, which results to miss the best opportunity of treatment. Therefore, the accurate diagnosis of MCI is essential for the early diagnosis and treatment of AD.

The experimental results show that the proposed multi-modal auxiliary diagnosis can achieve an excellent diagnostic efficiency.
 Zhang et al


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Health Imaging