Evaluation of deep learning to predict the diagnosis of LBD using 18F-FDG PET

Summary

The main purpose of this project is to compare deep machine learning methods to human visual interpretation in clinical applications. We want to evaluate whether Artificial Intelligence (AI) models – including shallow and deep learning algorithms – could be trained to predict the final clinical diagnoses in patients who underwent 18F-fluorodeoxyglucose Positron Emission Tomography scans (18F-FDG PET scans) of the brain and, once trained, how these algorithms compare with the current standard clinical reading methods in differentiation of patients with final diagnosis of Lewy body dementia (LBD) or no evidence of dementia.

We hypothesized that the AI model could detect features or patterns that are not evident on standard clinical review of images (both visual and quantitatively with the available commercial programs for brain quantification) and thereby an earlier detection of pathology, improving the final diagnostic classification of individuals.

There are various problems within this domain including intra-observer differences and limited number of nuclear medicine specialists with experience in 18F-FDG PET brain scans. We believe that we can contribute to develop an AI algorithm that is more invariant to different nuclear medicine specialist and help in coming faster to a diagnosis from the images thus improving healthcare for these patients.

More information

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