Digital language markers distinguish frontal from right anterior temporal lobe atrophy in frontotemporal dementia.

Automated speech analysis effectively distinguished the overall FTD group from controls and differentiated between frontal and rATL atrophy. The neuroimaging findings for individual features highlight the neural basis of language impairments in these FTD variants, and when considered together, underscore the importance of utilizing features' combined power to identify impaired language patterns. Automated speech analysis could enhance early diagnosis and monitoring of FTD, offering a scalable, non-invasive alternative to traditional methods, particularly in resource-limited settings. Further research should aim to integrate automated speech analysis into multi-modal diagnostic frameworks.