Functional safe zone for THA considering the patient-specific pelvic tilts: An ultrasound-based approach.

The usual Lewinnek orientation for cup positioning in total hip arthroplasty is not suitable for all patients as it does not consider the patient mobility. We propose an ultrasound-based approach to compute a Functional Safe Zone (FSZ) considering daily positions. Our goal was to validate it, and to evaluate how the input parameters impact the FSZ size and barycentre. The accuracy of the FSZ was first assessed by comparing the FSZ computed by the proposed approach and the true FSZ determined by 3D modelling. Then, the input parameters' impact on the FSZ was studied using a principal component analysis. The FSZ was estimated with errors below 0.5° for mean anteversion, mean inclination, and at edges. The pelvic tilts and the neck orientation were found correlated to the FSZ mean orientation, and the target ROM and the prosthesis dimensions to the FSZ size. Integrated into the clinical workflow, this non-ionising approach can be used to easily determine an optimal patient-specific cup orientation minimising the risks of dislocation.

Artificial intelligence: Deep learning in oncological radiomics and challenges of interpretability and data harmonization.

Over the last decade there has been an extensive evolution in the Artificial Intelligence (AI) field. Modern radiation oncology is based on the exploitation of advanced computational methods aiming to personalization and high diagnostic and therapeutic precision. The quantity of the available imaging data and the increased developments of Machine Learning (ML), particularly Deep Learning (DL), triggered the research on uncovering "hidden" biomarkers and quantitative features from anatomical and functional medical images. Deep Neural Networks (DNN) have achieved outstanding performance and broad implementation in image processing tasks. Lately, DNNs have been considered for radiomics and their potentials for explainable AI (XAI) may help classification and prediction in clinical practice. However, most of them are using limited datasets and lack generalized applicability. In this study we review the basics of radiomics feature extraction, DNNs in image analysis, and major interpretability methods that help enable explainable AI. Furthermore, we discuss the crucial requirement of multicenter recruitment of large datasets, increasing the biomarkers variability, so as to establish the potential clinical value of radiomics and the development of robust explainable AI models.