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Background <p>Diabetic retinopathy (DR) is a leading cause of vision loss, yet conventional retinal screening remains costly and resource-intensive. This study developed and validated machine-learning (ML) models using routine laboratory data to provide a cost-effective, accessible alternative for DR risk stratification and triage.</p> Methods <p>We analyzed data from 750 patients (363 T2DM; 387 DR) and externally validated the findings with 451 additional cases. Fifty hematological and biochemical parameters were screened. Six algorithms were trained via five-fold cross-validation, with XGBoost emerging as the top performer. Model interpretability and feature selection were conducted using SHapley Additive exPlanations (SHAP) and ablation analysis.</p> Results <p>The XGBoost model achieved high discriminative performance (AUC = 0.87). Feature ablation identified a streamlined set of four key predictors—total cholesterol (TC), blood urea nitrogen (BUN), fibrinogen (FIB), and glucose (GLU)—maintaining an AUC of 0.87. External validation confirmed robustness (AUC = 0.86) with balanced sensitivity (0.73) and specificity (0.80). Decision curve analysis indicated significant clinical utility, while SHAP provided individualized prediction transparency.</p> Conclusions <p>Routine laboratory parameters effectively power ML models for DR prediction. The resulting web-based XGBoost tool offers an interpretable, accessible solution for adjunct risk scoring and early triage, particularly beneficial for prioritizing high-risk patients in community and primary-care settings where specialized retinal imaging is unavailable.</p>

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Machine learning-based prediction of diabetic retinopathy using clinlabomics: a multi-center study

  • 期刊论文

  • 出版模式：

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  • 发表日期：

    2026年5月11日

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  • Lu He,
  • Mengyu Zhang,
  • Xuanxuan Wang,
  • Tian Wang,
  • Peng Wang

摘要

Background

Diabetic retinopathy (DR) is a leading cause of vision loss, yet conventional retinal screening remains costly and resource-intensive. This study developed and validated machine-learning (ML) models using routine laboratory data to provide a cost-effective, accessible alternative for DR risk stratification and triage.

Methods

We analyzed data from 750 patients (363 T2DM; 387 DR) and externally validated the findings with 451 additional cases. Fifty hematological and biochemical parameters were screened. Six algorithms were trained via five-fold cross-validation, with XGBoost emerging as the top performer. Model interpretability and feature selection were conducted using SHapley Additive exPlanations (SHAP) and ablation analysis.

Results

The XGBoost model achieved high discriminative performance (AUC = 0.87). Feature ablation identified a streamlined set of four key predictors—total cholesterol (TC), blood urea nitrogen (BUN), fibrinogen (FIB), and glucose (GLU)—maintaining an AUC of 0.87. External validation confirmed robustness (AUC = 0.86) with balanced sensitivity (0.73) and specificity (0.80). Decision curve analysis indicated significant clinical utility, while SHAP provided individualized prediction transparency.

Conclusions

Routine laboratory parameters effectively power ML models for DR prediction. The resulting web-based XGBoost tool offers an interpretable, accessible solution for adjunct risk scoring and early triage, particularly beneficial for prioritizing high-risk patients in community and primary-care settings where specialized retinal imaging is unavailable.