Dalal A. Bubshait

Developmental dysplasia of the hip (DDH) is a disorder characterized by abnormal hip development that frequently manifests in infancy and early childhood. Preventing DDH from occurring relies on a timely and accurate diagnosis, which requires careful assessment by medical specialists during early X-ray scans. However, this process can be challenging for medical personnel to achieve without proper training. To address this challenge, we propose a computational framework to detect DDH in pelvic X-ray imaging of infants that utilizes a pipelined deep learning-based technique consisting of two stages: instance segmentation and keypoint detection models to measure acetabular index angle and assess DDH affliction in the presented case. The main aim of this process is to provide an objective and unified approach to DDH diagnosis. The model achieved an average pixel error of 2.862 ± 2.392 and an error range of 2.402 ± 1.963° for the acetabular angle measurement relative to the ground truth annotation. Ultimately, the deep-learning model will be integrated into the fully developed mobile application to make it easily accessible for medical specialists to test and evaluate. This will reduce the burden on medical specialists while providing an accurate and explainable DDH diagnosis for infants, thereby increasing their chances of successful treatment and recovery.

Background: Musculoskeletal symptoms account for the majority of work-related illnesses resulting in a significant economic burden on society. Computer users are subject to unique repetitive strains that predispose them to musculoskeletal symptoms. In the digitalized field of radiology, radiologists spend long hours interpreting medical images on computers. This study aimed to determine the prevalence of musculoskeletal symptoms among radiologists in Saudi Arabia and their contributing factors. Methods: An online survey was sent to radiologists in all hospitals (academic, public and private) in the major cities of the Eastern Province of Saudi Arabia covering demographic characteristics, workload (e.g. the time spent at a computer workstation), and workstation environments including the number of monitors as well as the adjustability of the height of the workstation and the viewing distance. This survey of 263 radiologists was conducted in April 2019. It included an evaluation of musculoskeletal symptoms using the Nordic Musculoskeletal Questionnaire. The study outcome was the presence of disabling musculoskeletal symptoms in any body region, which restricted the performance of normal activities in the last 12 months. Results were analyzed descriptively using a Chi-square test and logistic regression analysis to estimate the odd ratio of experiencing disabling musculoskeletal symptoms in the last 12 months. Results: The survey was completed by 198 participants (111 men and 87 women) with a response rate of 75.3%. Most participants (71.2%) were aged below 40 years. A multivariate logistic regression analysis revealed being a female radiologist (OR = 2.7; 95% CI: 1.2–6.5), aged 30–39 years (OR = 4.1; 95% CI: 1.1–15.3), and predominantly reviewing computed tomography (CT) images (OR = 4.1; 95% CI: 1.4–12.3) or ultrasound scans (OR = 5.9; 95% CI: 1.4–25.3) were associated with higher prevalence of disabling musculoskeletal symptoms, compared to those aged below 30 years and those who reviewed various imaging modalities, respectively. Conclusions: Musculoskeletal symptoms are common among radiologists with lower back and neck pain being the most frequent complaints. Being a female radiologist, aged 30–39 years, and reviewing CT or ultrasound scans were associated with higher rates of disabling musculoskeletal symptoms.