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Related Experiment Video

Updated: May 13, 2026

Quantification of Diabetes-induced Adherent Leukocytes in Retinal Vasculature
05:54

Quantification of Diabetes-induced Adherent Leukocytes in Retinal Vasculature

Published on: January 24, 2025

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

Lu He1, Mengyu Zhang2, Xuanxuan Wang3

  • 1Ruibao Huibao Pediatric Clinic, Shanghai, 200030, China.

BMC Medical Informatics and Decision Making
|May 12, 2026
PubMed
Summary

Related Concept Videos

Diabetic Retinopathy01:27

Diabetic Retinopathy

DefinitionDiabetic retinopathy is a microvascular complication of diabetes affecting the retinal blood vessels.Risk FactorsDiabetic retinopathy is present in almost all individuals with type 1 diabetes and more than 60% of those with type 2 diabetes after two decades of disease.The risk increases with poor glycemic control, hypertension, dyslipidemia, smoking, pregnancy, and puberty.Although cataracts and glaucoma are also more frequent in people with diabetes, retinopathy remains the leading...

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Machine learning models using routine lab data can predict diabetic retinopathy (DR) risk. This cost-effective approach aids early triage, especially where specialized screening is unavailable.

Area of Science:

  • Ophthalmology
  • Medical Informatics
  • Biochemistry

Background:

  • Diabetic retinopathy (DR) is a primary cause of vision loss.
  • Current screening methods are expensive and require significant resources.
  • Machine learning (ML) offers a potential for cost-effective DR risk assessment.

Purpose of the Study:

  • To develop and validate ML models for DR risk stratification.
  • To utilize routine laboratory data for predicting DR.
  • To create an accessible tool for early DR triage.

Main Methods:

  • Analysis of 750 patients' data (363 Type 2 Diabetes Mellitus, 387 DR).
  • Screening of 50 hematological and biochemical parameters.
  • Training six algorithms, with XGBoost selected as the top performer.
Keywords:
Diabetic retinopathyMachine learningRisk stratificationSHAP

Related Experiment Videos

Last Updated: May 13, 2026

Quantification of Diabetes-induced Adherent Leukocytes in Retinal Vasculature
05:54

Quantification of Diabetes-induced Adherent Leukocytes in Retinal Vasculature

Published on: January 24, 2025

  • Utilizing SHapley Additive exPlanations (SHAP) for interpretability.

    • Main Results:

    • XGBoost model achieved an Area Under the Curve (AUC) of 0.87.
    • A reduced set of four predictors (total cholesterol, blood urea nitrogen, fibrinogen, glucose) maintained AUC of 0.87.
    • External validation confirmed model robustness (AUC=0.86) with good sensitivity (0.73) and specificity (0.80).

    Conclusions:

    • Routine laboratory parameters are effective predictors for DR using ML.
    • An interpretable, web-based XGBoost tool can aid in risk scoring and triage.
    • This solution is particularly valuable for primary care settings lacking specialized imaging.