Development and validation of an interpretable deep learning framework for Alzheimer’s disease classification

Abstract

Alzheimer’s disease is the primary cause of dementia worldwide, with an increasing morbidity burden that may outstrip diagnosis and management capacity as the population ages. Current methods integrate patient history, neuropsychological testing and MRI to identify likely cases, yet effective practices remain variably applied and lacking in sensitivity and specificity. Here we report an interpretable deep learning strategy that delineates unique Alzheimer’s disease signatures from multimodal inputs of MRI, age, gender, and Mini-Mental State Examination score. Our framework linked a fully convolutional network, which constructs high resolution maps of disease probability from local brain structure to a multilayer perceptron and generates precise, intuitive visualization of individual Alzheimer’s disease risk en route to accurate diagnosis. The model was trained using clinically diagnosed Alzheimer’s disease and cognitively normal subjects from the Alzheimer’s Disease Neuroimaging Initiative (ADNI) dataset (n = 417) and validated on three independent cohorts: the Australian Imaging, Biomarker and Lifestyle Flagship Study of Ageing (AIBL) (n = 382), the Framingham Heart Study (n = 102), and the National Alzheimer’s Coordinating Center (NACC) (n = 582). Performance of the model that used the multimodal inputs was consistent across datasets, with mean area under curve values of 0.996, 0.974, 0.876 and 0.954 for the ADNI study, AIBL, Framingham Heart Study and NACC datasets, respectively. Moreover, our approach exceeded the diagnostic performance of a multi-institutional team of practicing neurologists (n = 11), and high-risk cerebral regions predicted by the model closely tracked post-mortem histopathological findings. This framework provides a clinically adaptable strategy for using routinely available imaging techniques such as MRI to generate nuanced neuroimaging signatures for Alzheimer’s disease diagnosis, as well as a generalizable approach for linking deep learning to pathophysiological processes in human disease.

Enhancing magnetic resonance imaging-driven Alzheimer’s disease classification performance using generative adversarial learning

“…With the strategy of randomly sampling patches over the whole volume, a degree of data augmentation was achieved because the model was trained with more variance of the inputs sampled from various locations. Similar FCN frameworks have been used recently to generate high performance AD classification models [ 31 ].…”

A Large-scale Comparison of Cortical and Subcortical Structural Segmentation Methods in Alzheimer’s Disease: a Statistical Approach

“…This rate can be helpful to have a more informed understanding whether an individual with MCI will later progress to AD or not 105,143,144 . The output of automated segmentation methods can also be used in training of intelligent classification methods such as those using artificial neural networks and support vector machines, which has shown promising results [145][146][147][148][149][150][151][152][153][154] .…”

Machine Learning and Deep Learning Approaches for Brain Disease Diagnosis: Principles and Recent Advances

“…Utilizing sagittal MRI, a DL approach for the automatic identification of AD was studied in [98] and a satisfactory performance was obtained as compared to the state-of-art method. A DL strategy to improve the diagnosis of AD from multi-modal inputs was proposed in [99]. The model was trained using AD and NC subjects from ADNI and validated on three different databases such as AIBL, FHS, and NACC.…”