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Updated: Aug 5, 2026
A Machine Learning Approach to Design an Efficient Selective Screening of Mild Cognitive Impairment
Published on: January 11, 2020
Screening efficiency over experience: Rapid target detection in low-power field as a modifiable cognitive biomarker
Naoya Abe1,2,3, Yukari Nishimura1,2,3, Kazuya Yamashita3
1Division of Clinical Cytology, Department of Medical Laboratory Sciences, School of Allied Health Sciences, Kitasato University, Kanagawa, Japan.
Background:
Traditionally, cytology expertise has been equated with professional experience. However, the transition to whole-slide imaging and artificial intelligence (AI) necessitates a shift from exhaustive screening to rapid verification. The goal of this study was to identify cognitive biomarkers associated with diagnostic accuracy and evaluate their modifiability.
Methods:
In phase 1, 100 cytotechnologists with 1-40 years of experience diagnosed 30 digital cytology images using eye-tracking. Gaze metrics across areas of interest were analyzed via nominal logistic regression. In phase 2, 28 students completed a 3-month cytotechnology training program. Pre- and post-training metrics were compared using Wilcoxon signed-rank tests and effect sizes (r).
Results:
Years of experience showed no significant correlation with diagnostic accuracy (r = 0.189, p > .05). Multivariate analysis identified shorter total fixation duration on the "low-power field (LPF) main object" as the sole independent predictor of high accuracy (p = .045), suggesting a "pop-out" detection mechanism. Experience correlated only with attention to sample information. Post-training (phase 2), students' time to first target fixation decreased substantially (r = 0.62-0.86), whereas their attention to normal backgrounds decreased (r = 0.71). This demonstrates the rapid acquisition of expert-like selective attention.
Conclusions:
Efficiency in LPF target detection is a better predictor of diagnostic accuracy than professional experience. This study identifies LPF efficiency as a modifiable cognitive biomarker that can be acquired through standard education. Quantifying these gaze metrics provides an objective means of evaluating skill development and readiness for the AI era.
