Exploring the Potential of a Deep Learning Model for Early CT Detection of High-Grade Metastatic Epidural Spinal Cord Compression and Its Impact on Treatment Delays.

Background: Delay in diagnosing metastatic epidural spinal cord compression (MESCC) adversely impacts clinical outcomes. High-grade MESCC is frequently overlooked on routine staging CT scans. We aim to assess the potential of our deep learning model (DLM) in detecting high-grade MESCC and reducing diagnostic delays.

Evaluating the Accuracy of Privacy-Preserving Large Language Models in Calculating the Spinal Instability Neoplastic Score (SINS).

Large language models (LLMs) have emerged as powerful tools in healthcare. In diagnostic radiology, LLMs can assist in the computation of the Spine Instability Neoplastic Score (SINS), which is a critical tool for assessing spinal metastases. However, the accuracy of LLMs in calculating the SINS based on radiological reports remains underexplored.

An Institutional Large Language Model for Musculoskeletal MRI Improves Protocol Adherence and Accuracy.

Background: Privacy-preserving large language models (PP-LLMs) hold potential for assisting clinicians with documentation. We evaluated a PP-LLM to improve the clinical information on radiology request forms for musculoskeletal magnetic resonance imaging (MRI) and to automate protocoling, which ensures that the most appropriate imaging is performed.

Methods: The present retrospective study included musculoskeletal MRI radiology request forms that had been randomly collected from June to December 2023.

Nitinol in Orthopedic Applications: Clinical Insights, Performance Challenges, and Future Directions.

The increasing prevalence of spinal disorders has spurred continuous innovation in implant design and biomaterials. Among emerging options, Nitinol has gained significant interest due to its unique combination of shape memory effect, superelasticity, and mechanical compatibility with bone tissue. These characteristics make it a promising candidate for spinal implants that support minimally invasive surgery and motion preservation.

Enhancing Radiologist Productivity with Artificial Intelligence in Magnetic Resonance Imaging (MRI): A Narrative Review.

Artificial intelligence (AI) shows promise in streamlining MRI workflows by reducing radiologists' workload and improving diagnostic accuracy. Despite MRI's extensive clinical use, systematic evaluation of AI-driven productivity gains in MRI remains limited. This review addresses that gap by synthesizing evidence on how AI can shorten scanning and reading times, optimize worklist triage, and automate segmentation.

The use of orthogonal plain radiographs in determining the position of thoracic pedicle screws: a cadaveric study.

Background: Plain radiography remains the standard for intra- and post-operative assessment of pedicle screw placement in spine surgery. While newer technologies like intraoperative CT and navigation have been introduced, they do not consistently improve thoracic pedicle screw accuracy. The aim of this study was to evaluate the accuracy of anteroposterior (AP) alone versus orthogonal (AP and lateral) radiographs in assessing thoracic pedicle screw position across different observer experience levels and to determine the reliability of traditional fluoroscopy as compared to newer modalities.

Using deep learning to enhance reporting efficiency and accuracy in degenerative cervical spine MRI.

Background Context: Cervical spine MRI is essential for evaluating degenerative cervical spondylosis (DCS) but is time-consuming to report and subject to interobserver variability. The integration of artificial intelligence in medical imaging offers potential solutions to enhance productivity and diagnostic consistency.

Purpose: To assess whether a transformer-based deep learning model (DLM) can improve the efficiency and accuracy of radiologists in reporting DCS MRIs.

Routine 2D MRI identifies the anterior talofibular ligament fascicles and lateral fibulotalocalcaneal ligament complex with moderate to substantial interobserver reliability.

Purpose: The anterior talofibular ligament (ATFL) consists of two fascicles, with the intra-articular superior fascicle and extra-articular inferior fascicle connected to the calcaneofibular ligament (CFL) via the arciform fibres, forming the lateral fibulotalocalcaneal ligament (LFTCL) complex. Accurate identification of which fascicles have been injured is useful for determining the optimal treatment of patients with lateral ligament injuries. There is a lack of imaging studies demonstrating the distinctive anatomy of these important structures on magnetic resonance imaging (MRI).

Patterns of Treatment Delay in Patients with Symptomatic Metastatic Epidural Spinal Cord Compression.

Background: Delays in the diagnosis and treatment of metastatic epidural spinal cord compression (MESCC) can potentially result in serious, deleterious effects on patient outcomes and postoperative morbidity. Delays may occur any at any step in the chain of care, increasing the risk of preventable complications.

Objectives: This study, thus, aims to identify patterns of treatment delays and determine the predictive factors of postoperative ambulatory function in patients with symptomatic MESCC.

Does Intraoperative Cell-Salvaged Autologous Blood Transfusion in Metastatic Spine Tumour Surgery Impact Clinical Outcomes: A Prospective Clinical Study With 4-year Follow-Up.

Study designProspective Clinical Study.ObjectiveAllogeneic blood transfusion (ABT) is the current standard of blood replenishment for metastatic spine tumour surgery (MSTS) despite known complications. Salvaged blood transfusion (SBT) addresses majority of complications related to ABT.

Magnetic resonance imaging safety in Asia-Oceania: call for action.

Magnetic Resonance Imaging (MRI) safety is a critical concern in the Asia-Oceania region, as it is elsewhere in the world, due to the unique and complex MRI environment that demands attention. This call-for-action outlines ten critical steps to enhance MRI safety and promote a culture of responsibility and accountability in the Asia-Oceania region. Key focus areas include strengthening education and expertise, improving quality assurance, fostering collaboration, increasing public awareness, and establishing national safety boards.

MRI spine request form enhancement and auto protocoling using a secure institutional large language model.

Background Context: Secure institutional large language models (LLM) could reduce the burden of noninterpretative tasks for radiologists.

Purpose: Assess the utility of a secure institutional LLM for MRI spine request form enhancement and auto-protocoling.

Study Design/setting: Retrospective study conducted from December 2023 to February 2024, including patients with clinical entries accessible on the electronic medical record (EMR).

Applications of Artificial Intelligence and Machine Learning in Spine MRI.

Diagnostic imaging, particularly MRI, plays a key role in the evaluation of many spine pathologies. Recent progress in artificial intelligence and its subset, machine learning, has led to many applications within spine MRI, which we sought to examine in this review. A literature search of the major databases (PubMed, MEDLINE, Web of Science, ClinicalTrials.

Oncologic Applications of Artificial Intelligence and Deep Learning Methods in CT Spine Imaging-A Systematic Review.

In spinal oncology, integrating deep learning with computed tomography (CT) imaging has shown promise in enhancing diagnostic accuracy, treatment planning, and patient outcomes. This systematic review synthesizes evidence on artificial intelligence (AI) applications in CT imaging for spinal tumors. A PRISMA-guided search identified 33 studies: 12 (36.

Association of head and neck CT-derived sarcopenia with mortality and adverse outcomes: A systematic review.

Background: There is growing interest in the association of CT-assessed sarcopenia with adverse outcomes in non-oncological settings.

Purpose: The aim of this systematic review is to summarize existing literature on the prognostic implications of CT-assessed sarcopenia in non-oncological patients.

Materials And Methods: Three independent authors searched Medline/PubMed, Embase and Cochrane Library up to 30 December 2023 for observational studies that reported the presence of sarcopenia defined on CT head and neck in association with mortality estimates and other adverse outcomes, in non-oncological patients.

Reducing Delays in MRIs Under Sedation and General Anesthesia Using Quality Improvement Tools.

Objective: Develop structured, quality improvement interventions to achieve a 15%-point reduction in MRIs performed under sedation or general anesthesia (GA) delayed more than 15 min within a 6-month period.

Methods: A prospective audit of MRIs under sedation or GA from January 2022 to June 2023 was conducted. A multidisciplinary team performed process mapping and root cause analysis for delays.

Application of Artificial Intelligence Methods on Osteoporosis Classification with Radiographs-A Systematic Review.

Osteoporosis is a complex endocrine disease characterized by a decline in bone mass and microstructural integrity. It constitutes a major global health problem. Recent progress in the field of artificial intelligence (AI) has opened new avenues for the effective diagnosis of osteoporosis via radiographs.

Pedicle Screw Pseudofracture on Computed Tomography Secondary to Metal Artifact Reduction.

Metal artifact reduction (MAR) algorithms are commonly used in computed tomography (CT) scans where metal implants are involved. However, MAR algorithms also have the potential to create new artifacts in reconstructed images. We present a case of a screw pseudofracture due to MAR on CT.

Artificial Intelligence Applications for Osteoporosis Classification Using Computed Tomography.

Osteoporosis, marked by low bone mineral density (BMD) and a high fracture risk, is a major health issue. Recent progress in medical imaging, especially CT scans, offers new ways of diagnosing and assessing osteoporosis. This review examines the use of AI analysis of CT scans to stratify BMD and diagnose osteoporosis.

Common Healthcare Related Instruments Subjected To Magnetic Attraction Study (CHRISTMAS): prospective in situ experimental study.

Objective: To investigate the behaviour of common healthcare related objects in a 3 tesla (T) MRI (magnetic resonance imaging) scanner, examining their ability to self-propel towards the scanner bore and their potential for tissue penetration.

Design: Prospective in situ experimental study.

Setting: Clinical 3 T MRI scanner.

Outcomes and Complications of Surgery for Symptomatic Spinal Metastases; a Comparison Between Patients Aged ≥ 70 and <70.

Study Design: Retrospective cohort study.

Objective: Physicians may be deterred from operating on elderly patients due to fears of poorer outcomes and complications. We aimed to compare the outcomes of surgical treatment of spinal metastases patients aged ≥70-yrs and <70-yrs.