Digital health technologies in frontotemporal dementia: a scoping review
Bibliographic record
Abstract
Frontotemporal dementia (FTD) is a neurodegenerative disorder characterized by progressive dysfunction in executive function, social cognition, and language, resulting from atrophy in the frontal and temporal lobes of the brain (Rohrer et al., 2011). With no cure currently available and increasing global pressure on healthcare systems due to rising costs and workforce shortages, technological innovation is seen as essential for improving FTD diagnosis and monitoring. Since FTD is a clinical diagnosis with a strong reliance on neuropsychological assessment (NPA) (Poos et al., 2020), digitalizing diagnostic tools is particularly relevant for assessing cognitive decline over time. In addition, research has shown that traditional pen-and-paper tests have limited sensitivity in detecting early cognitive decline in FTD (e.g. Poos et al., 2020; Jiskoot et al., 2021; de Boer et al., 2024; Poos et al., 2024; de Boer et al., 2025), highlighting the need for more sensitive cognitive assessment tools. Numerous technologies have been developed and tested to meet this need, yet a clear overview of these tools is lacking. This scoping review aims to provide a comprehensive overview of the current literature on digital health technologies used for examining cognitive functioning in frontotemporal dementia (FTD). Specifically, we will describe the state-of-the-art of digital health technologies for cognitive assessment in FTD, including results, feasibility, strengths, and limitations, and identify avenues for future research. This scoping review follows the PRISMA-ScR guidelines. A comprehensive literature search will be performed in multiple electronic databases. Titles and abstracts will be screened independently by three reviewers using predefined inclusion and exclusion criteria. Two reviewers will perform full-text screening of potentially relevant studies. A standardized data extraction form will be developed to collect relevant information from included studies. The extracted data will be synthesized, categorizing studies by type (i.e., eye tracking, active remote assessments, passive remote assessments). Results will be discussed, and implications for future research and clinical practice will be provided. With this scoping review, we aim to inform and guide future research projects toward the development, validation, and implementation of sensitive digital cognitive health technologies for FTD. Literature: De Boer, L., van den Berg, E., Poos, J. M., Klop, W., Giannini, L. A., De Houwer, J. F., ... & Jiskoot, L. C. (2024). Impairments in knowledge of social norms in presymptomatic, prodromal, and symptomatic frontotemporal dementia. Alzheimer's & Dementia: Diagnosis, Assessment & Disease Monitoring, 16(3), e12630. De Boer, L., Poos, J. M., Van Den Berg, E., De Houwer, J. F., Swartenbroekx, T., Dopper, E. G., ... & Jiskoot, L. C. (2025). Montreal Cognitive Assessment vs the Mini-Mental State Examination as a Screening Tool for Patients With Genetic Frontotemporal Dementia. Neurology, 104(5), e213401. Jiskoot, L. C., Poos, J. M., Vollebergh, M. E., Franzen, S., van Hemmen, J., Papma, J. M., ... & van den Berg, E. (2021). Emotion recognition of morphed facial expressions in presymptomatic and symptomatic frontotemporal dementia, and Alzheimer’s dementia. Journal of neurology, 268, 102-113. Poos, J. M., Jiskoot, L. C., Leijdesdorff, S. M. J., Seelaar, H., Panman, J. L., Van Der Ende, E. L., ... & van den Berg, E. (2020). Cognitive profiles discriminate between genetic variants of behavioral frontotemporal dementia. Journal of Neurology, 267, 1603-1612. Poos, J. M., van den Berg, E., de Boer, L., Meertens-Gunput, S., Dopper, E. G., Seelaar, H., & Jiskoot, L. C. (2024). Neuropsychological Profiles in Genetic Frontotemporal Dementia: A Meta-Analysis and Systematic Review. Aging and Disease, 16(3), 1378. Rohrer, J. D., & Warren, J. D. (2011). Phenotypic signatures of genetic frontotemporal dementia. Current opinion in neurology, 24(6), 542-549.
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How this classification was reachedexpand
Full frame machine prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.006 | 0.028 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.003 | 0.004 |
| Bibliometrics | 0.017 | 0.015 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.003 | 0.003 |
| Open science | 0.002 | 0.002 |
| Research integrity | 0.003 | 0.002 |
| Insufficient payload (model declined to judge) | 0.006 | 0.001 |
Machine scores (provisional)
The two teacher heads of the student model, read on this work. A score orders the frame for review; it never asserts a category, and the validation status ships verbatim with every row.
Baseline scores from an immature model (maturity gate not passed, 7 training rounds). Scores rank; they never assert a category.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.
How this classification was reached, model by model and score by score, is at the end of the page under "How this classification was reached".