Cascaded Multimodal Deep Learning in the Differential Diagnosis, Progression Prediction, and Staging of Alzheimer's and Frontotemporal Dementia

Dementia syndromes are complex sequelae whose multifaceted nature poses significant challenges in the diagnosis, prognosis, and treatment of patients. Despite the availability of large open-source data fueling a wealth of promising research, effective translation of preclinical findings to clinical practice remains difficult. This barrier is largely due to the complexity of unstructured and disparate preclinical and clinical data, which traditional analytical methods struggle to handle. Novel analytical techniques involving Deep Learning (DL), however, are gaining significant traction in this regard. Here, we have investigated the potential of a cascaded multimodal DL-based system (TelDem), assessing the ability to integrate and analyze a large, heterogeneous dataset (n=7159 patients), applied to three clinically relevant use cases. Using a Cascaded Multi-Modal Mixing Transformer (CMT), we assessed TelDems validity and (using a Cross Modal Fusion Norm - CMFN) model explainability in (i) differential diagnosis between healthy individuals, AD, and three sub-types of frontotemporal lobar degeneration (ii) disease staging from healthy cognition to mild cognitive impairment (MCI) and AD, and (iii) predicting progression from MCI to AD. Our findings show that the CMT enhances diagnostic and prognostic accuracy when incorporating multimodal data compared to unimodal modeling and that cerebrospinal fluid (CSF) biomarkers play a key role in accurate model decision making. These results reinforce the power of DL technology in tapping deeper into already existing data, thereby accelerating preclinical dementia research by utilizing clinically relevant information to disentangle complex dementia pathophysiology. ### Competing Interest Statement The authors have declared no competing interest. ### Funding Statement This work was supported by Row Fogo Charitable Trust (grant no. BRO-D.FID3668413, MVH). This study was supported by grants from the German Research Foundation (SCHR 774/5-1 to MLS), and the eHealthSax Initiative of the Sachsische Aufbaubank (Project TelDem). Accordingly, this study was co-financed with tax revenue based on the budget approved by the Saxon state parliament. ### Author Declarations I confirm all relevant ethical guidelines have been followed, and any necessary IRB and/or ethics committee approvals have been obtained. Yes The details of the IRB/oversight body that provided approval or exemption for the research described are given below: All used data are publicly available through the https://ida.loni.usc.edu/ website. I confirm that all necessary patient/participant consent has been obtained and the appropriate institutional forms have been archived, and that any patient/participant/sample identifiers included were not known to anyone (e.g., hospital staff, patients or participants themselves) outside the research group so cannot be used to identify individuals. Yes I understand that all clinical trials and any other prospective interventional studies must be registered with an ICMJE-approved registry, such as ClinicalTrials.gov. I confirm that any such study reported in the manuscript has been registered and the trial registration ID is provided (note: if posting a prospective study registered retrospectively, please provide a statement in the trial ID field explaining why the study was not registered in advance). Yes I have followed all appropriate research reporting guidelines, such as any relevant EQUATOR Network research reporting checklist(s) and other pertinent material, if applicable. Yes All used data are publicly available through the https://ida.loni.usc.edu/ website.