ArticleNPJ digital medicine2023
Explainable deep learning approach for extracting cognitive features from hand-drawn images of intersecting pentagons.
Article in NPJ digital medicine, 2023. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 11 papers.
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The trial behind it
Trials whose registry record cites this paper, or whose number appears in the abstract. A trial that started after this paper was published is citing it as background, not reporting it.
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Who cites it
11 citing papers in PubMed.
- Computer Vision Scoring of Figure Copy and Recall.medRxiv : the preprint server for health sciences · 2026Article
- Blood-Brain Network-Based Polygenic Risk Scores Reveal Biomarker Signatures and the Progression of Alzheimer's Disease.Journal of clinical medicine · 2026Article
- Multi-stream deep learning framework integrating images and feature representations to predict mild cognitive impairment using the rey complex figure test.Scientific reports · 2026Article
- An Exploration of Craniofacial Features in Obstructive Sleep Apnea Using the Explainable Deep Learning Approaches.Nature and science of sleep · 2026Article
- Molecular and neuropathological determinants of neuronal dysfunction in Alzheimer's disease.bioRxiv : the preprint server for biology · 2025Article
- Molecular hallmarks of excitatory and inhibitory neuronal resilience to Alzheimer's disease.Molecular neurodegeneration · 2025Article
- Article
- Developing an accessible dementia assessment tool: Leveraging a residual network, the trail making test, and demographic data.Journal of Alzheimer's disease : JAD · 2025Article
- Loss of the APP regulator RHBDL4 preserves memory in an Alzheimer's disease mouse model.Cell death & disease · 2025Article
- Translating the Post-Mortem Brain Multi-Omics Molecular Taxonomy of Alzheimer's Dementia to Living Humans.bioRxiv : the preprint server for biology · 2025Article
- Digital quantification of the MMSE interlocking pentagon areas: a three-stage algorithm.Scientific reports · 2024Article
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Authors and funding
7 authors.
Funding
Abstract
Hand drawing, which requires multiple neural systems for planning and controlling sequential movements, is a useful cognitive test for older adults. However, the conventional visual assessment of these drawings only captures limited attributes and overlooks subtle details that could help track cognitive states. Here, we utilized a deep-learning model, PentaMind, to examine cognition-related features from hand-drawn images of intersecting pentagons. PentaMind, trained on 13,777 images from 3111 participants in three aging cohorts, explained 23.3% of the variance in the global cognitive scores, 1.92 times more than the conventional rating. This accuracy improvement was due to capturing additional drawing features associated with motor impairments and cerebrovascular pathologies. By systematically modifying the input images, we discovered several important drawing attributes for cognition, including line waviness. Our results demonstrate that deep learning models can extract novel drawing metrics to improve the assessment and monitoring of cognitive decline and dementia in older adults.
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Registered trials
Read under generation 80e0d062 · epoch 390. Bibliography from PubMed, PubMed Central and OpenAlex; grants from NIH RePORTER; trial links from ClinicalTrials.gov; estimates, votes and beliefs from the OpenQuestion graph.