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Related Experiment Video

Updated: Sep 10, 2025

In Vivo Quantification of Hip Arthrokinematics during Dynamic Weight-bearing Activities using Dual Fluoroscopy
07:43

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PredictMed-CDSS: Artificial Intelligence-Based Decision Support System Predicting the Probability to Develop

Carlo M Bertoncelli1,2, Federico Solla2, Michal Latalski3

  • 1Department of Computer Science, Hal Marcus College of Science & Engineering, University of West Florida, Pensacola, FL 32514, USA.

Bioengineering (Basel, Switzerland)
|August 28, 2025
PubMed
Summary

A new Clinical Decision Support System (CDSS) effectively predicts neuromuscular hip dysplasia (NHD) in children with cerebral palsy (CP). Machine learning, particularly neural networks, shows high accuracy in identifying at-risk individuals for early intervention.

Keywords:
Clinical Decision Support System (CDSS)Support Vector Machine (SVM)cerebral palsy (CP)ensemble of machine learning (ML) algorithmslogistic regression (LR)neural network (NN)neuromuscular hip dysplasia (NHD)

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Area of Science:

  • Biomedical Engineering
  • Artificial Intelligence in Healthcare
  • Pediatric Orthopedics

Background:

  • Neuromuscular hip dysplasia (NHD) is a frequent complication in children with cerebral palsy (CP).
  • Predictive models for NHD are currently limited, necessitating advanced diagnostic tools.
  • Early identification of NHD risk is crucial for timely intervention in CP patients.

Purpose of the Study:

  • To develop and evaluate a Clinical Decision Support System (CDSS) for predicting NHD probability in children with CP.
  • To leverage machine learning algorithms for enhanced NHD risk assessment using clinical data.
  • To introduce the PredictMed-CDSS prototype for improved clinical decision-making in CP management.

Main Methods:

  • An ensemble of machine learning algorithms (Neural Network, Support Vector Machine, Logistic Regression) was employed.
  • The CDSS was developed and validated following DECIDE-AI guidelines, using data from 182 CP patients (aged 12-18).
  • Clinical and functional data were prospectively collected and analyzed to identify NHD predictors.

Main Results:

  • Logistic regression identified key predictors: previous orthopedic surgery, poor motor function, truncal tone disorder, scoliosis, limb involvement, and epilepsy.
  • The Neural Network (NN) model achieved the highest predictive performance with 83.7% accuracy and 0.92 AUROC.
  • The PredictMed-CDSS demonstrated robust predictive capabilities for NHD development in the studied cohort.

Conclusions:

  • The developed CDSS, PredictMed-CDSS, shows significant potential in predicting NHD in children with CP.
  • Neural networks offer superior accuracy for NHD risk prediction compared to other tested ML models.
  • This AI-driven tool can aid clinicians in proactive management and early intervention strategies for NHD in CP.