ArticleSichuan da xue xue bao. Yi xue ban = Journal of Sichuan University. Medical science edition2026
[Construction of a Multi-Factor Quantitative Scoring Model for Chromosome Karyotype Analysis].
Article in Sichuan da xue xue bao. Yi xue ban = Journal of Sichuan University. Medical science edition, 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
Objective: To design a multi-factor quantitative evaluation model for chromosomal karyotype image quality to assist manual image selection, thereby improving the efficiency of image selection and the accuracy of karyotype analysis. Methods: Based on existing literature and clinical expert opinion, five indicators-chromosomal crossover degree, length, clarity, dispersion, and curvature-were selected as quantitative measures for constructing the image quality evaluation model, and corresponding algorithms were designed to score each indicator. Using expert manual review results as the gold standard, a dataset comprising chromosomal karyotype images labeled with whether the analytical results were correct was established. Univariate and multivariate analyses were performed to determine whether significant differences existed in each quantitative indicator between karyotypically correct and incorrect images. Indicators demonstrating significant associations were retained to construct a selection quality index and to establish a predictive model for karyotype analysis outcomes. Results: A total of 13 540 chromosomal images from 2708 patients underwent manual analysis, of which 416 images were identified by the expert panel as containing karyotype recognition errors. Measurement algorithms were developed and scores were calculated for each candidate image quality indicator. Univariate and multivariate analyses revealed that the scores for chromosomal curvature, crossover degree, clarity, and length significantly influenced the accuracy of karyotype recognition, whereas no statistically significant difference in dispersion score was observed between the correct and incorrect recognition groups ( Conclusion: The image quality index and the predictive model for karyotype evaluation outcomes can effectively assist manual image selection in chromosomal karyotype analysis.
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