Skeletal Survey Imaging in Clinical Practice: Challenges, Innovations, and Future Directions
Mohd.Arfat
*
Medical Radiology & Imaging Technology, Faculty of Medicine, Paramedical College, Aligarh Muslim University (AMU) Aligarh, India.
Hiba Shakeel
Medical Radiology & Imaging Technology, Faculty of Medicine, Paramedical College, Aligarh Muslim University (AMU) Aligarh, India.
Iba Chaudhry
Medical Radiology & Imaging Technology, Faculty of Medicine, Paramedical College, Aligarh Muslim University (AMU) Aligarh, India.
*Author to whom correspondence should be addressed.
Abstract
Skeletal survey (SS) imaging continues to form the basis of the diagnosis of suspected non-accidental injury (NAI), particularly in case of child abuse, and complex skeletal diseases. This article recounts the present condition of SS practice, indicating that standard practice and strict Quality Assurance (QA) are key factors to guarantee high diagnostic yield. We talk of game changers like Dual-Energy CT (DECT) in order to have a good tissue characterization and the up-to-date Photon-Counting Detector CT (PCD-CT). Also, Artificial Intelligence (AI) is fast gaining ground in the field; deep learning models are as accurate as possible in identifying fractures, and Large Language Models (LLMs) simplify the process of radiology reporting. Finally, the implementation of these technological advancements and serious ethical, medico-legal, and radiation safety considerations ought to co-exist in terms of patient outcomes improvement, and creating new trends of musculoskeletal radiology.
Keywords: Skeletal survey, Non-Accidental Injury (NAI), Photon-Counting Detector CT (PCD-CT), Artificial Intelligence (AI), Quality Assurance (QA), Follow-up Skeletal Survey (FUSS)