Radiomics and Machine Learning for Skeletal Muscle Injury Recovery Prediction

Abstract

Radiomics as a novel quantitative approach to medical imaging is an emerging area in the field of radiology. Artificial intelligence offers promising tools for exploiting and analyzing radiomics. The objective of the present study is to propose a methodology for the design, development, and evaluation of machine learning (ML) models for the prediction of the recovery progress of skeletal muscle injury over time in rats using radiomics. Radiomics were extracted from contrast enhanced computed tomography (CT) data and ML algorithms were trained and compared for their predictive value based on different CT imaging parameters. Ten different ML regression algorithms were tested and the optimal combination of radiomics for each algorithm and CT imaging parameter settings combination was studied. The best ensemble learning model, trained on the 70 kVp, 100 mA imaging parameter dataset, achieved a mean absolute error score of 1.22. The results suggest that radiomics extracted from CT images can be used as input in ML regression algorithms to predict the volume of a skeletal muscle injury in rats. Moreover, the results show that CT imaging settings impact the predictive performance of the ML regression models, indicating that lower values of tube current and peak kilovoltage contribute to more accurate predictions.