Bertoncelli Carlo M, Solla Federico, Latalski Michal, Bagui Sikha, Bagui Subhash C, Costantini Stefania, Bertoncelli Domenico
Department of Computer Science, Hal Marcus College of Science & Engineering, University of West Florida, Pensacola, FL 32514, USA.
Department of Pediatric Orthopedic Surgery, Pediatric University Hospital Lenval, 06000 Nice, France.
Bioengineering (Basel). 2025 Aug 6;12(8):846. doi: 10.3390/bioengineering12080846.
Neuromuscular hip dysplasia (NHD) is a common deformity in children with cerebral palsy (CP). Although some predictive factors of NHD are known, the prediction of NHD is in its infancy. We present a Clinical Decision Support System (CDSS) designed to calculate the probability of developing NHD in children with CP. The system utilizes an ensemble of three machine learning (ML) algorithms: Neural Network (NN), Support Vector Machine (SVM), and Logistic Regression (LR). The development and evaluation of the CDSS followed the DECIDE-AI guidelines for AI-driven clinical decision support tools. The ensemble was trained on a data series from 182 subjects. Inclusion criteria were age between 12 and 18 years and diagnosis of CP from two specialized units. Clinical and functional data were collected prospectively between 2005 and 2023, and then analyzed in a cross-sectional study. Accuracy and area under the receiver operating characteristic (AUROC) were calculated for each method. Best logistic regression scores highlighted history of previous orthopedic surgery ( = 0.001), poor motor function ( = 0.004), truncal tone disorder ( = 0.008), scoliosis ( = 0.031), number of affected limbs ( = 0.05), and epilepsy ( = 0.05) as predictors of NHD. Both accuracy and AUROC were highest for NN, 83.7% and 0.92, respectively. The novelty of this study lies in the development of an efficient Clinical Decision Support System (CDSS) prototype, specifically designed to predict future outcomes of neuromuscular hip dysplasia (NHD) in patients with cerebral palsy (CP) using clinical data. The proposed system, PredictMed-CDSS, demonstrated strong predictive performance for estimating the probability of NHD development in children with CP, with the highest accuracy achieved using neural networks (NN). PredictMed-CDSS has the potential to assist clinicians in anticipating the need for early interventions and preventive strategies in the management of NHD among CP patients.
神经肌肉性髋关节发育不良（NHD）是脑瘫（CP）患儿常见的畸形。尽管已知一些NHD的预测因素，但对NHD的预测尚处于起步阶段。我们提出了一种临床决策支持系统（CDSS），旨在计算CP患儿发生NHD的概率。该系统利用了三种机器学习（ML）算法的集成：神经网络（NN）、支持向量机（SVM）和逻辑回归（LR）。CDSS的开发和评估遵循了AI驱动的临床决策支持工具的DECIDE-AI指南。该集成在来自182名受试者的数据系列上进行训练。纳入标准为年龄在12至18岁之间且由两个专业单位诊断为CP。临床和功能数据在2005年至2023年期间前瞻性收集，然后在横断面研究中进行分析。计算每种方法的准确性和受试者操作特征曲线下面积（AUROC）。最佳逻辑回归分数突出显示既往骨科手术史（ = 0.001）、运动功能差（ = 0.004）、躯干肌张力障碍（ = 0.008）、脊柱侧弯（ = 0.031）、受累肢体数量（ = 0.05）和癫痫（ = 0.05）作为NHD的预测因素。NN的准确性和AUROC最高，分别为83.7%和0.92。本研究的新颖之处在于开发了一个高效的临床决策支持系统（CDSS）原型，专门设计用于使用临床数据预测脑瘫（CP）患者神经肌肉性髋关节发育不良（NHD）的未来结果。所提出的系统PredictMed-CDSS在估计CP患儿发生NHD的概率方面表现出强大的预测性能，使用神经网络（NN）实现了最高的准确性。PredictMed-CDSS有可能帮助临床医生在CP患者中NHD的管理中预测早期干预和预防策略的需求。
