ArticleCancer cytopathology2026
Screening efficiency over experience: Rapid target detection in low-power field as a modifiable cognitive biomarker for diagnostic accuracy in digital cytology.
Article in Cancer cytopathology, 2026. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Not yet cited in PubMed.
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Abstract
backgroundTraditionally, 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.
methodsIn 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).
resultsYears 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.
conclusionsEfficiency 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.
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