Osteoid osteoma (OO) is a benign osteoblastic tumor of bone that usually affects children and young adults. They are usually located on metaphysis or diaphysis of long bones. Their clinical, anamnesis and radiological findings are typical. Intra-articular OO however has different properties due to its placement within joints. Sclerosis around the lesion is either minimal or non-existent, but synovitis can be seen in the joint. For this reason, they are usually diagnosed later. In this case series, we diagnosed three cases (2 ankles and 1 hip joint) that were diagnosed with osteochondral lesions previously and had in chronic pain which did not respond to several treatments in different centers with intra-articular OO and treated them with radiofrequency ablation using computerized tomography. Knowing the radiological properties of intra-articular OO and being aware of this condition during differential diagnosis of joint pain cases will be useful to diagnose this rare pathology.

This research underscores the importance of enhancing the early detection of diabetic retinopathy and glaucoma, two prominent culprits behind vision loss. Typically, retinal diseases lurk without symptoms until they inflict severe vision impairment, underscoring the critical need for early identification. The research is centered on the potential of leveraging fundus images, which offer invaluable insights by analyzing various attributes of retinal blood vessels, such as their length, width, tortuosity, and branching patterns. The conventional practice of manually segmenting retinal vessels by medical professionals is both intricate and time-consuming, demanding specialized expertise. This approach, reliant on pathologists, grapples with limitations related to scalability and accessibility. To surmount these challenges, the research introduces an automated solution employing computer vision. It conducts an evaluation of diverse retinal vessel segmentation and classification methods, including machine learning, filtering-based, and model-based techniques. Robust performance assessments, involving metrics like the true positive rate, true negative rate, and accuracy, facilitate a comprehensive comparison of these methodologies. The ultimate goal of this research is to create more efficient and accessible diagnostic tools, consequently enhancing the early detection of eye diseases through automated retinal vessel segmentation and classification. This endeavor combines the capabilities of computer vision and deep learning to pioneer new benchmarks in the realm of biomedical imaging, thereby addressing the pressing issues surrounding eye disease diagnosis.

Diagnosis-related groups (DRGs) are gaining prominence in healthcare systems worldwide to standardize potential payments to hospitals. This study, conducted across public hospitals, investigates the impact of DRG implementation on human resource allocation and management practices. The research findings reveal significant changes in job roles and skill requirements based on a mixed-methods approach involving 70 healthcare professionals across various roles. 50% of respondents reported changes in daily responsibilities, and 42% noted the creation of new roles in their organizations. Significant challenges include inadequate training (46%), and coding complexity (38%). Factor analysis revealed a complex relationship between DRG familiarity, job satisfaction, and staff morale. The study also found a moderate negative correlation between the impact on morale and years of service in the current hospital, suggesting that longer-tenured staff may require additional support in adapting to DRG systems. This study addresses a knowledge gap in the human resource aspects of DRG implementation. It provides healthcare administrators and policymakers with evidence to inform strategies for effective DRG adoption and workforce management in public hospitals.