A5045014039- Total Knee Arthroplasty Outcomes
- Advanced X-ray and CT Imaging
- Radiomics and Machine Learning in Medical Imaging
- Cultural and Sociopolitical Studies
- Metabolism and Genetic Disorders
- Parvovirus B19 Infection Studies
- Orthopaedic implants and arthroplasty
- Cell Adhesion Molecules Research
- Immunodeficiency and Autoimmune Disorders
- Menopause: Health Impacts and Treatments
- Child and Adolescent Psychosocial and Emotional Development
- Voice and Speech Disorders
- Social Media in Health Education
- Fetal and Pediatric Neurological Disorders
- Attachment and Relationship Dynamics
- Bone health and osteoporosis research
- Osteoarthritis Treatment and Mechanisms
- Shoulder Injury and Treatment
- Agricultural and Rural Development Research
- Health Literacy and Information Accessibility
- Anxiety, Depression, Psychometrics, Treatment, Cognitive Processes
- Systemic Lupus Erythematosus Research
- Turkish Urban and Social Issues
State Hospital
2024-2025
Sivas State Hospital
2025
Erciyes University
2021-2023
ABSTRACT Aims: This study aimed to evaluate the quality, reliability, and readability of online information on Haglund deformity. Methods: The three most popular browsers were selected, two reviewers categorized websites by type. quality each site was assessed based its adherence HONcode evaluated using scoring instruments like DISCERN, JAMA benchmark, GQS. Flesch-Kincaid grade level (FKGL) score utilized websites. Results: Academic webpages exhibited markedly superior ratings in JAMA, GQS,...
Aim: This study aims to evaluate the quality and readability of online health information related snapping hip syndrome (SHS). Methods: A cross-sectional analysis was conducted by searching term "Snapping Hip Syndrome" on Google, Bing, Yahoo. The first 30 results from each search engine were assessed, duplicate or irrelevant websites excluded. remaining 90 unique web pages categorized into academic, physician, commercial, medical professional, non-identified groups. Quality assessed using...
Aims: This study aimed to investigate the use of a convolutional neural network (CNN) deep learning approach accurately identify total knee arthroplasty (TKA) implants from X-ray radiographs.
 Methods: retrospective employed CNN system analyze pre-revision and post-operative X-rays TKA patients. We excluded cases involving unicondylar revision replacements, as well low-quality or unavailable images those with other implants. Ten cruciate-retaining replacement models were assessed...