The results of the first and second groups of 50 consecutive primary, uncemented porous-coated anatomic arthroplasties were analyzed to evaluate the learning curve associated with the procedure. Femoral fit, acetabular cup angle, femoral fracture rate, minimum two-year clinical hip ratings, and clinical symptoms were compared between the two groups. Significant improvement in achieving better femoral canal filling with the prosthesis and lower acetabular cup angle placements was documented in the second 50 cases. Although a definite learning curve in mastering the technique of uncemented total hip arthroplasty was observed, thigh pain rate and clinical ratings were not improved after two years.
Download full-text PDF |
Source |
---|
Biomed Phys Eng Express
January 2025
Radiation Oncology, Emory University, Emory Midtown Hospital, Atlanta, Georgia, 30322, UNITED STATES.
Although radiotherapy techniques are the primary treatment for head and neck cancer (HNC), they are still associated with substantial toxicity, and side effect. Machine learning (ML) based radiomics models for predicting toxicity mostly rely on features extracted from pre-treatment imaging data. This study aims to compare different models in predicting radiation-induced xerostomia and sticky saliva in both early and late stage of HNC patients using CT and MRI image features along with demographics and dosimetric information.
View Article and Find Full Text PDFPLoS One
January 2025
Department of Computer Science, Faculty of Computing, Federal University of Lafia, Lafia, Nasarawa State, Nigeria.
The emergence of Next Generation Sequencing (NGS) technology has catalyzed a paradigm shift in clinical diagnostics and personalized medicine, enabling unprecedented access to high-throughput microbiome data. However, the inherent high dimensionality, noise, and variability of microbiome data present substantial obstacles to conventional statistical methods and machine learning techniques. Even the promising deep learning (DL) methods are not immune to these challenges.
View Article and Find Full Text PDFMed Phys
January 2025
Deparment of Radiation Oncology, Duke University, Durham, North Carolina, USA.
Background: Stereotactic radiosurgery (SRS) is widely used for managing brain metastases (BMs), but an adverse effect, radionecrosis, complicates post-SRS management. Differentiating radionecrosis from tumor recurrence non-invasively remains a major clinical challenge, as conventional imaging techniques often necessitate surgical biopsy for accurate diagnosis. Machine learning and deep learning models have shown potential in distinguishing radionecrosis from tumor recurrence.
View Article and Find Full Text PDFNeurooncol Adv
January 2025
Institute for Artificial Intelligence in Medicine, University Hospital Essen, Germany.
Background: This study aimed to develop an automated algorithm to noninvasively distinguish gliomas from other intracranial pathologies, preventing misdiagnosis and ensuring accurate analysis before further glioma assessment.
Methods: A cohort of 1280 patients with a variety of intracranial pathologies was included. It comprised 218 gliomas (mean age 54.
Enter search terms and have AI summaries delivered each week - change queries or unsubscribe any time!