Radiomics and machine learning–based methods offer exciting opportunities for improving
diagnostic performance and efficiency in musculoskeletal radiology for various tasks …

Background Osteoporosis is a significant health problem in the skeletal system, associated
with bone tissue changes and its strength. Machine Learning (ML), on the other hand, has …

Differentiating benign renal oncocytic tumors and malignant renal cell carcinoma (RCC) on
imaging and histopathology is a critical problem that presents an everyday clinical …

Simple Summary This study focuses on the integration of 99mTc Sestamibi SPECT/CT and
radiomics analysis to characterize benign renal oncocytic neoplasia. Our research includes …

Objective This study aimed to evaluate a new deep-learning model for diagnosing avascular
necrosis of the femoral head (AVNFH) by analyzing pelvic anteroposterior digital …

Detecting active inflammatory sacroiliitis at an early stage is vital for prescribing medications
that can modulate disease progression and significantly delay or prevent debilitating forms …

Objectives To develop a deep learning methodology that distinguishes early from late
stages of avascular necrosis of the hip (AVN) to determine treatment decisions. Methods …

Transient osteoporosis of the hip is indubitably a comparatively infrequent entity affecting
both men and women worldwide. Its occurrence in the course of pregnancy, specifically in …

Purpose To develop a predictive model combining clinical, radiomic, and deep learning
features based on X-ray and MRI to identify risk factors for early femoral head deformity in …

Objectives To evaluate the performance of extreme gradient boosting (XGBoost) combined
with multiparameters from dual-energy computed tomography (mpDECT) to differentiate …