Artificial Intelligence, Machine Learning & Radiomics

Artificial intelligence, machine learning and radiomics are advanced analytical approaches used to identify patterns in clinical data, biomarkers and medical imaging, estimate risk and develop research-driven predictive models. Reliable model development requires the integration of appropriate data preprocessing, feature selection, modeling strategies, performance assessment, calibration, validation and explainability methods.

msCRO provides advanced data analytics solutions for clinical research, academic studies, real-world data projects and medical imaging research. Our services include data preprocessing, feature engineering, machine learning model development, clinical predictive modeling, risk scoring, classification and regression algorithms, radiomic feature extraction, model optimization, internal and external validation, decision curve analysis and explainable artificial intelligence.

Our expertise covers logistic regression and regularized models, decision trees, random forests, gradient boosting, XGBoost, LightGBM, CatBoost, support vector machines, artificial neural networks, clustering methods, dimensionality reduction, feature selection, ensemble modeling, class imbalance methods, hyperparameter optimization, calibration analysis, bootstrap validation and external validation.

Radiomics workflows include DICOM preprocessing, post-segmentation feature extraction, image standardization, feature reproducibility assessment, high-dimensional data reduction, integration of clinical and radiomic variables, radiomic signature development, nomogram construction and model performance validation.

Model performance is evaluated using ROC curves, AUC, sensitivity, specificity, positive and negative predictive values, F1 score, accuracy, precision-recall analysis, Brier score, calibration curves, Hosmer–Lemeshow assessment, decision curve analysis and clinical utility measures. Model explainability is supported through methods such as SHAP, feature importance analysis, partial dependence plots and individual prediction explanations.

Depending on project requirements, analyses are conducted using R, Python, TensorFlow, PyTorch, scikit-learn, XGBoost, LightGBM, CatBoost, PyRadiomics, OpenCV, SimpleITK and other advanced imaging and analytical platforms.

Deliverables include data preprocessing reports, model development files, performance and validation reports, ROC and calibration plots, decision curves, feature importance analyses, SHAP visualizations, radiomic feature tables, model comparison results, nomograms, scientific tables and figures and technical analytical documentation.