William Ewing Palmer

In spine magnetic resonance imaging (MRI), incidental abnormalities are difficult to distinguish from actual pain generators (the cause of symptoms). Spine specialists see patients face to face and take clinical histories to learn symptoms. They use this symptom information to focus on only the most relevant MRI abnormalities, diagnose pain generators, and make treatment decisions. In the absence of symptom information, radiologists seek to identify all MRI abnormalities and list them in dictated reports without ranking their relative importance. Like spine specialists, radiologists can match symptoms with MRI findings to improve diagnostic accuracy and confidence. Symptom-MRI correlation requires radiologists to have access to high-quality clinical information and understand the causal relationships between symptoms and pain generators. This article covers symptom-MRI correlation and pitfalls related to spine MRI interpretation.

Magnetic resonance imaging is the gold standard imaging tool to identify and characterize shoulder disorders involving bone and soft tissues. The spatial and contrast resolutions of magnetic resonance imaging allow for visualization of complex anatomical structures and subtle pathologies. Diagnostic sensitivity and specificity are excellent, but accurate magnetic resonance imaging interpretation does require knowledge of developmental variations that can be confused with symptomatic abnormalities. In this article, we review commonly encountered magnetic resonance imaging findings that are normal and asymptomatic but can mimic treatable shoulder lesions.

Automated grading of Knee Osteoarthritis (KOA) from radiographs is challenged by significant inter-observer variability and the limited robustness of deep learning models, particularly near critical decision boundaries. To address these limitations, this paper proposes a novel framework, Diffusion-based Counterfactual Augmentation (DCA), which enhances model robustness and interpretability by generating targeted counterfactual examples. The method navigates the latent space of a diffusion model using a Stochastic Differential Equation (SDE), governed by balancing a classifier-informed boundary drive with a manifold constraint. The resulting counterfactuals are then used within a self-corrective learning strategy to improve the classifier by focusing on its specific areas of uncertainty. Extensive experiments on the public Osteoarthritis Initiative (OAI) and Multicenter Osteoarthritis Study (MOST) datasets demonstrate that this approach significantly improves classification accuracy across multiple model architectures. Furthermore, the method provides interpretability by visualizing minimal pathological changes and revealing that the learned latent space topology aligns with clinical knowledge of KOA progression. The DCA framework effectively converts model uncertainty into a robust training signal, offering a promising pathway to developing more accurate and trustworthy automated diagnostic systems. Our code is available at https://github.com/ZWang78/DCA.

Radiologists achieved higher levels of diagnostic certainty and agreement with clinical specialists when they interpreted lumbar spine MRI using patient-reported symptom information.

Lumbar spine MRI is associated with a high prevalence of morphological abnormalities in both symptomatic and asymptomatic individuals. A difficult challenge, therefore, is distinguishing the relevant findings causing symptoms from incidental findings. The accurate diagnosis of pain generator is important because misdiagnosis can negatively impact patient management and outcome. Spine physicians use clinical symptoms and signs to interpret lumbar spine MRI and make treatment decisions. Symptom-MRI correlation enables the targeted inspection of images for pain generator. Radiologists can also use clinical information to improve diagnostic confidence and the value of dictated reports. Because high-quality clinical information may be difficult to obtain, radiologists often generate lists of lumbar spine abnormalities that are otherwise difficult to rank as pain generators. Based on the literature review, this article attempts to distinguish the MRI abnormalities that may represent incidental findings from the abnormalities that are more commonly associated with lumbar spine-related symptoms.