A Deep Learning Model for Early Prediction of the Diagnosis of Alzheimer Disease from 18F-FDG PET scan of the Brain
Over 5 million Americans are affected by Alzheimer's disease and have cost over 200 billion dollars in direct and indirect costs . Early and accurate diagnosis of Alzheimer's disease is important because it opens the possibility of therapeutic intervention to slow or halt the disease progression. Unfortunately, Alzheimer disease (AD) remains a diagnosis based on clinical grounds and most diagnosis gets established at a late stage when too many neurons have been lost. Much research effort has been made on biochemical and imaging tests to improve our early diagnostic capability but most have met with mild to moderate accuracy. 18F-FDG PET scans of the brain utilize radioactive glucose to image the energy uptake pattern in various parts of the brain, which has recently been implicated to change in subtle ways in Alzheimer's disease. In this study, we develop and validate a deep learning algorithm that predicts the final diagnosis of Alzheimer disease (AD), mild cognitive impairment, or neither, approximately 6 years before the final diagnosis from these PET scans.
Prospective 18F-FDG PET brain images from the Alzheimer's Disease Neuroimaging Initiative (ADNI) (2109 imaging studies from 2005 to 2017, 1002 patients) and retrospective independent test set (40 imaging studies from 2006 to 2016, 40 patients) were collected. Final clinical diagnosis at follow-up was recorded. Convolutional neural network of Inception architecture was trained on 90% of ADNI data set and tested on the remaining 10%, as well as the independent test set, with performance compared to radiologic readers. Model was analyzed with sensitivity, specificity, receiver operating characteristic (ROC), saliency map, and t-distributed stochastic neighbor embedding. The algorithm achieved area under the ROC curve of 0.98 (95% confidence interval: 0.94, 1.00) when evaluated on predicting the final clinical diagnosis of AD in the independent test set (82% specificity at 100% sensitivity), an average of 75.8 months prior to the final diagnosis, which in ROC space outperformed reader performance (57% [four of seven] sensitivity, 91% [30 of 33] specificity; P < .05). Saliency map demonstrated attention to known areas of interest but with focus on the entire brain.
By using fluorine 18 fluorodeoxyglucose PET of the brain, a deep learning algorithm developed for early prediction of Alzheimer disease achieved 82% specificity at 100% sensitivity, an average of 75.8 months prior to the final diagnosis.
Jae Ho Sohn, MD, MS is a radiology resident at UCSF Medical Center. As a physician with engineering background, his research focuses on the intersection of big data and radiology. He and his team has been working on a number of computer vision and natural language processing algorithms to boost diagnostic accuracy, increase clinical workflow efficiency, and develop radiological domain specific machine learning approaches. He leads a multidisciplinary research team, Big Data in Radiology (BDRAD), to bring together physicians and engineers around the country to develop algorithms that can have practical and measurable impact on patient care. For further information, visit https://profiles.ucsf.edu/jae.sohn