Construction of a Multi-Factor Quantitative Scoring Model for Chromosome Karyotype Analysis

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 (P = 0.133). Based on the identified contributing factors, a composite image quality index was constructed. Validation demonstrated that for every 1-point increase in the quality index, the risk of analytical error decreased by 8.4%. A declining trend in error rates was observed with increasing quality index scores; the error rate among images with a quality index below 75 was 3.88%, compared with 1.95% among images scoring 75 or above. A predictive model for the correctness of karyotype analysis outcomes, established based on the quality index, demonstrated favorable predictive performance with an area under the ROC curve (AUC) of 0.72 (95% CI: 0.67-0.77, P < 0.001).

Conclusion The image quality index and the predictive model for karyotype evaluation outcomes can effectively assist manual image selection in chromosomal karyotype analysis.