Motor-cognitive functions required for driving in post-stroke individuals identified via machine-learning analysis.
Genta Tabuchi, Akira Furui, Seiji Hama and 6 others
PMID 37853392WHAT IT FOUND
A machine-learning model accurately predicted driving aptitude in post-stroke individuals using a subset of cognitive tests.
Key predictors included time orientation, sustained attention, and visual neglect. This suggests a shorter, targeted screening battery could replace the current week-long assessment.
Key findings
01The proposed neural network model achieved an area under the curve (AUC) of 0.946 for discriminating driving aptitude, significantly outperforming Lasso regression (AUC 0.753).
02Five specific indices were identified as most important for predicting driving aptitude: CPTSRT average reaction time, MMSE Orientation-time, CPTSRT coefficient of validation, Affected side Left, and the Copying test.
03Using these selected indices could reduce the duration of physical and cognitive function testing from approximately five hours spread over a week to a single day.
STILL TO COME
How it was doneWhat they foundWhat it means for PTsWhat it means for OTsWhat it means for SLPs
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What it does not show
The study used a small sample size of 55 participants, which limits the generalizability of the machine-learning model. The same dataset was used for both selecting indices and evaluating the model via cross-validation, meaning the results were not tested on completely unseen data. The false positive rate ranged from 4.9% to 29.3% across methods, which is a significant concern for clinical safety when determining driving fitness. The study did not include brain imaging data, so the relationship between lesion location and driving aptitude was inferred from 'affected side' rather than direct neural correlates. The participants were from a specific hospital in Japan, and cultural or regional differences in driving laws and test standards may limit applicability elsewhere.
Declared interests
The research was supported by non-U.S. government funding from the Japan Science and Technology Agency and the Japan Society for the Promotion of Science. No commercial conflicts of interest were declared.
The easy way to misread this
Do not assume this model is ready for clinical use to clear patients for driving. The high accuracy was achieved on the same data used to build the model, and the false positive rate (predicting someone can drive when they cannot) was up to 29.3% in some comparisons. A patient should never be cleared for driving based solely on these screening tests without a formal on-road evaluation.