Lifespan tree of brain anatomy: diagnostic values for motor and cognitive neurodegenerative diseases
This work addresses the problem of differential diagnosis for clinicians dealing with neurodegenerative diseases like dementia and parkinsonism, offering a tool for conditions lacking effective biological markers, though it appears incremental as it builds on prior AI methods for brain imaging.
The authors tackled the challenge of differential diagnosis for neurodegenerative diseases with overlapping symptoms by developing a novel machine learning framework called the lifespan tree of brain anatomy, which improved diagnostic efficiency in an external validation cohort of 1754 cases, outperforming existing state-of-the-art techniques.
The differential diagnosis of neurodegenerative diseases, characterized by overlapping symptoms, may be challenging. Brain imaging coupled with artificial intelligence has been previously proposed for diagnostic support, but most of these methods have been trained to discriminate only isolated diseases from controls. Here, we develop a novel machine learning framework, named lifespan tree of brain anatomy, dedicated to the differential diagnosis between multiple diseases simultaneously. It integrates the modeling of volume changes for 124 brain structures during the lifespan with non-linear dimensionality reduction and synthetic sampling techniques to create easily interpretable representations of brain anatomy over the course of disease progression. As clinically relevant proof-of-concept applications, we constructed a cognitive lifespan tree of brain anatomy for the differential diagnosis of six causes of neurodegenerative dementia and a motor lifespan tree of brain anatomy for the differential diagnosis of four causes of parkinsonism using 37594 MRI as a training dataset. This original approach enhanced significantly the efficiency of differential diagnosis in the external validation cohort of 1754 cases, outperforming existing state-of-the art machine learning techniques. Lifespan tree holds promise as a valuable tool for differential diagnostic in relevant clinical conditions, especially for diseases still lacking effective biological markers.