How AI May Transform Musculoskeletal Imaging.

While musculoskeletal imaging volumes are increasing, there is a relative shortage of subspecialized musculoskeletal radiologists to interpret the studies. Will artificial intelligence (AI) be the solution? For AI to be the solution, the wide implementation of AI-supported data acquisition methods in clinical practice requires establishing trusted and reliable results. This implementation will demand close collaboration between core AI researchers and clinical radiologists. Upon successful clinical implementation, a wide variety of AI-based tools can improve the musculoskeletal radiologist's workflow by triaging imaging examinations, helping with image interpretation, and decreasing the reporting time. Additional AI applications may also be helpful for business, education, and research purposes if successfully integrated into the daily practice of musculoskeletal radiology. The question is not whether AI will replace radiologists, but rather how musculoskeletal radiologists can take advantage of AI to enhance their expert capabilities.

Imaging of Sacroiliac Joints.

The central role of imaging in diagnosing disorders affecting the sacroiliac joints (SIJs) necessitates a comprehensive understanding of the advantages, limitations, and potential pitfalls of the imaging techniques that can be used. In this article, the anatomy and biomechanics of SIJs are exposed, outlining their unique features, particularly the division into anteroinferior cartilaginous and postero-superior ligamentous parts. Overall, the goal of this article is to offer a comprehensive understanding of imaging techniques, anatomic complexity, and diagnostic considerations relevant to SIJs disorders, facilitating accurate diagnosis and patient management in clinical practice and research.

Integrating Artificial Intelligence and PET Imaging for Drug Discovery: A Paradigm Shift in Immunotherapy.

The integration of artificial intelligence (AI) and positron emission tomography (PET) imaging has the potential to become a powerful tool in drug discovery. This review aims to provide an overview of the current state of research and highlight the potential for this alliance to advance pharmaceutical innovation by accelerating the development and deployment of novel therapeutics. We previously performed a scoping review of three databases (Embase, MEDLINE, and CENTRAL), identifying 87 studies published between 2018 and 2022 relevant to medical imaging (e.g., CT, PET, MRI), immunotherapy, artificial intelligence, and radiomics. Herein, we reexamine the previously identified studies, performing a subgroup analysis on articles specifically utilizing AI and PET imaging for drug discovery purposes in immunotherapy-treated oncology patients. Of the 87 original studies identified, 15 met our updated search criteria. In these studies, radiomics features were primarily extracted from PET/CT images in combination (n = 9, 60.0%) rather than PET imaging alone (n = 6, 40.0%), and patient cohorts were mostly recruited retrospectively and from single institutions (n = 10, 66.7%). AI models were used primarily for prognostication (n = 6, 40.0%) or for assisting in tumor phenotyping (n = 4, 26.7%). About half of the studies stress-tested their models using validation sets (n = 4, 26.7%) or both validation sets and test sets (n = 4, 26.7%), while the remaining six studies (40.0%) either performed no validation at all or used less stringent methods such as cross-validation on the training set. Overall, the integration of AI and PET imaging represents a paradigm shift in drug discovery, offering new avenues for more efficient development of therapeutics. By leveraging AI algorithms and PET imaging analysis, researchers could gain deeper insights into disease mechanisms, identify new drug targets, or optimize treatment regimens. However, further research is needed to validate these findings and address challenges such as data standardization and algorithm robustness.

Neurogenic heterotopic ossification of the hip: Magnetic resonance imaging versus computed tomography for pre-surgical assessment.

PURPOSE: Neurogenic heterotopic ossification (NHO) of the hip is a frequent complication of spinal cord injuries, often requiring surgical management. Pre-surgical imaging assessment is essential, usually with computed tomography (CT)-scan. We aimed to compare magnetic resonance imaging (MRI) and CT for pre-surgical imaging assessment of the NHO, particularly for their relationships with vessels and nerves. METHOD: This prospective study included consecutive patients who underwent surgery for NHO from July 2019 to April 2022. All patients had CT angiography and MRI including Zero Echo Time and TRICKS sequences. Radiologists used standardized reports for CT and MRI to evaluate NHO and their features, bone mineralization, and relation to the arteries, veins and nerves. Agreement between pre-surgical CT and MRI was evaluated. RESULTS: Twenty-four patients (mean age: 53.5 ± 12.2 years) were included, among which 7 had bilateral NHO (31 hips). NHO were anterior in 15/31 hips (48 %), multifragmented in 25/31 hips (81 %). Mild and significant demineralization was most frequent. Gutter and tunnel were reported in 11.1 % of the arteries. Nerves were more often identified in MRI than in CT-scan. Agreement coefficients between CT and MRI were excellent for NHO location (0.95) and implantation (0.92), good for fragmentation (0.70), contact with joint capsule (0.66), bone mineralization (0.74), and relation to arteries (0.85), veins (0.76), sciatic nerve (0.7) and moderate for femoral nerve (0.47). CONCLUSION: MRI exhibited a good agreement with CT for pre-surgical assessment of NHO of the hip, especially to evaluate their relationships with the arteries, veins and sciatic nerve. Femoral nerves were more often identified in MRI than in CT-scan.