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Updated: May 8, 2026

A Machine Learning Approach to Design an Efficient Selective Screening of Mild Cognitive Impairment
Published on: January 11, 2020
Can we improve clinical prediction of at-risk older drivers?
Alex R Bowers1, R Julius Anastasio, Sarah S Sheldon
1Schepens Eye Research Institute, Massachusetts Eye and Ear, Boston, MA, USA; Department of Ophthalmology, Harvard Medical School, Boston, MA, USA.
Objectives:
To conduct a pilot study to evaluate the predictive value of the Montreal Cognitive Assessment test (MoCA) and a brief test of multiple object tracking (MOT) relative to other tests of cognition and attention in identifying at-risk older drivers, and to determine which combination of tests provided the best overall prediction.
Methods:
Forty-seven currently licensed drivers (58-95 years), primarily from a clinical driving evaluation program, participated. Their performance was measured on: (1) a screening test battery, comprising MoCA, MOT, Mini-Mental State Examination (MMSE), Trail-Making Test, visual acuity, contrast sensitivity, and Useful Field of View (UFOV) and (2) a standardized road test.
Results:
Eighteen participants were rated at-risk on the road test. UFOV subtest 2 was the best single predictor with an area under the curve (AUC) of .84. Neither MoCA nor MOT was a better predictor of the at-risk outcome than either MMSE or UFOV, respectively. The best four-test combination (MMSE, UFOV subtest 2, visual acuity and contrast sensitivity) was able to identify at-risk drivers with 95% specificity and 80% sensitivity (.91 AUC).
Conclusions:
Although the best four-test combination was much better than a single test in identifying at-risk drivers, there is still much work to do in this field to establish test batteries that have both high sensitivity and specificity.
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