AI-based differential diagnosis of dementia etiologies on multimodal data

Differential diagnosis of dementia, with its overlapping symptomatology, remains a significant challenge in neurology. Here we present an algorithmic framework employing state-of-the-art techniques such as transformers as well as self-supervised frameworks and harnessing a broad array of data including demographics, person-level and family medical history, medication use, neuropsychological exams, functional evaluations, and multimodal neuroimaging to identify the etiologies contributing to dementia in individuals. The study utilized 9 independent, geographically diverse datasets, including the National Alzheimers Coordinating Center with 45,349 participants, the Alzheimers Disease Neuroimaging Initiative encompassing 1,821 participants, and the Frontotemporal Lobar Degeneration Neuroimaging Initiative comprising 253 participants. Additionally, the Parkinsons Progression Marker Initiative with 198 participants, the Australian Imaging, Biomarker and Lifestyle Flagship Study of Ageing cohort including 661 participants, the Open Access Series of Imaging Studies dataset with 491 participants, and the 4 Repeat Tauopathy Neuroimaging Initiative comprising 80 participants were used. The study also included two in-house datasets: one from the Lewy Body Dementia Center for Excellence at Stanford University with 182 participants, and another from the Framingham Heart Study including 1,651 individuals. Our model traverses the intricate spectrum of dementia by mirroring real-world clinical settings, aligning diagnoses with similar management strategies, and delivering robust predictions, even in the face of incomplete data. On the testing cohort, our model achieved a micro-averaged area under the receiver operating characteristic curve (AUROC) of 0.93, and a micro-averaged area under precision-recall curve (AUPR) of 0.87, in classifying individuals with normal cognition, mild cognitive impairment and dementia. Also, the micro-averaged AUROC was 0.95 and micro-averaged AUPR was 0.68 in differentiating 10 distinct dementia etiologies, defined through a consensus among a team of neurologists. One key strength lies in our model's capability to address mixed dementias, a prevalent challenge in clinical practice, and the incorporation of interpretability techniques further unveiled vital disease-specific patterns. On a randomly selected subset (n=100), our model differentiated true positive and true negative cases across 12 out of 13 categories (p<0.01), as opposed to the neurologists' expertise in identifying 9 out of these 13 categories (p<0.01). Furthermore, the model's correlations with different proteinopathies were substantiated through postmortem analyses. This included a significant association with the global Alzheimers disease neuropathologic change (ADNC) score (p<0.001), and notable correlations with TDP-pathology, the presence of old microinfarcts, arteriosclerosis, and Prion disease (all with p<0.05). Our framework has the potential to be integrated as a screening tool for dementia in various clinical settings and drug trials, with promising implications for person-level management.