The clinical significance of medullary abnormalities in the appendicular skeleton detected by computed tomography (CT) in patients with multiple myeloma (MM) remains incompletely elucidated. This study aims to validate novel low-dose CT-based methods for quantifying myeloma bone marrow (BM) volume in the appendicular skeleton of MM patients undergoing [¹⁸F]FDG PET/CT. Seventy-two newly diagnosed, transplantation eligible MM patients enrolled in the randomised phase 3 GMMG-HD7 trial underwent whole-body [¹⁸F]FDG PET/CT prior to treatment and after induction therapy with either isatuximab plus lenalidomide, bortezomib, and dexamethasone or lenalidomide, bortezomib, and dexamethasone alone. Two CT-based methods using the Medical Imaging Toolkit (MITK 2.4.0.0, Heidelberg, Germany) were used to quantify BM infiltration in the appendicular skeleton: (1) Manual approach, based on calculation of the highest mean CT value (CTv) within bony canals. (2) Semi-automated approach, based on summation of CT values across the appendicular skeleton to compute cumulative CT values (cCTv). PET/CT data were analyzed visually and via standardized uptake value (SUV) metrics, applying the Italian Myeloma criteria for PET Use (IMPeTUs). Additionally, an AI-based method was used to automatically derive whole-body metabolic tumor volume (MTV) and total lesion glycolysis (TLG) from PET scans. Post-induction, all patients were evaluated for minimal residual disease (MRD) using BM multiparametric flow cytometry. Correlation analyses were performed between imaging data and clinical, histopathological, and cytogenetic parameters, as well as treatment response. Statistical significance was defined as p < 0.05. At baseline, the median CTv (manual) was 26.1 Hounsfield units (HU) and the median cCTv (semi-automated) was 5.5 HU. Both CT-based methods showed weak but significant correlations with disease burden indicators: CTv correlated with BM plasma cell infiltration (r = 0.29; p = 0.02) and β2-microglobulin levels (r = 0.28; p = 0.02), while cCTv correlated with BM plasma cell infiltration (r = 0.25; p = 0.04). Appendicular CT values further demonstrated significant associations with PET-derived parameters. Notably, SUVmax values from the BM of long bones were strongly correlated with both CTv (r = 0.61; p < 0.001) and moderately with cCTv (r = 0.45; p < 0.001). Patients classified as having increased [¹⁸F]FDG uptake in the BM (Deauville Score ≥ 4), according to the IMPeTUs criteria, exhibited significantly higher CTv and cCTv values compared to those with Deauville Score <4 (p = 0.002 for both). AI-based analysis of PET data revealed additional weak-to-moderate significant associations, with MTV correlating with CTv (r = 0.32; p = 0.008) and cCTv (r = 0.45; p < 0.001), and TLG showing correlations with CTv (r = 0.36; p = 0.002) and cCTv (r = 0.46; p < 0.001). Following induction therapy, CT values decreased significantly from baseline (median CTv = -13.8 HU, median cCTv = 5.2 HU; p < 0.001 for both), and CTv significantly correlated with SUVmax values from the BM of long bones (r = 0.59; p < 0.001). In parallel, the incidence of follow-up pathological PET/CT scans, SUV values, Deauville Scores, and AI-derived MTV and TLG values showed a significant reduction after therapy (all p < 0.001). No significant differences in CTv, cCTv, or PET-derived metrics were observed between MRD-positive and MRD-negative patients. Novel CT-based quantification approaches for assessing BM involvement in the appendicular skeleton correlate with key clinical and PET parameters in MM. As low-dose, standardized techniques, they show promise for inclusion in MM imaging protocols, potentially enhancing assessment of disease extent and treatment response.

Artificial intelligence (AI) represents a transformative opportunity for pain medicine, offering potential solutions to longstanding challenges in pain assessment and management. This review synthesizes the current state of AI applications with a strategic framework for implementation, highlighting established adaptation pathways from adjacent medical fields. In acute pain, AI systems have achieved regulatory approval for ultrasound guidance in regional anesthesia and shown promise in automated pain scoring through facial expression analysis. For chronic pain management, machine learning algorithms have improved diagnostic accuracy for musculoskeletal conditions and enhanced treatment selection through predictive modeling. Successful integration requires interdisciplinary collaboration and physician coleadership throughout the development process, with specific adaptations needed for pain-specific challenges. This roadmap outlines a comprehensive methodological framework for AI in pain medicine, emphasizing four key phases: problem definition, algorithm development, validation, and implementation. Critical areas for future development include perioperative pain trajectory prediction, real-time procedural guidance, and personalized treatment optimization. Success ultimately depends on maintaining strong partnerships between clinicians, developers, and researchers while addressing ethical, regulatory, and educational considerations.

Femoroacetabular impingement (FAI) with cam-type morphology is a common hip disorder that can result in groin pain and eventually osteoarthritis. The pre-operative assessment is based on parameters obtained from x-ray or computed tomography (CT) scans, namely alpha angle (AA) and femoral head-neck offset (FHNO). The goal of our study was to develop a computer-aided detection (CAD) system to automatically select the hip region and measure diagnostic parameters from CT scans to overcome the limitations of the tedious and time-consuming process of subjectively selecting CT image slices to obtain parameters. 271 cases of ordinary abdominopelvic CT examination were collected retrospectively from two hospitals between 2018 and 2022, each equipped with a distinct CT scanner. First, a convolution neural network (CNN) was designed to select hip region slices among abdominopelvic CT scan image series. This CNN was trained using 80 CT scans divided into 50%, 20%, and 30% for training, validation and testing groups, respectively. Second, the most appropriate oblique slice passing through the femoral head-neck complex was selected, and AA and FHNO landmarks were calculated using image-processing algorithms. The best oblique slices were selected/measured manually for each hip as ground truth and its related parameters. CT hip-region selection using CNN yielded 99.34% accuracy. Pearson correlation coefficient between manual and automatic parameters measurement were 0.964 and 0.856 for AA and FHNO, respectively. The results of this study are promising for future development of a CAD software application for screening CT scans that may aid physicians to assess FAI. Question Femoroacetabular impingement is a common, underdiagnosed hip disorder requiring time-consuming image-based measurements. Can AI improve the efficiency and consistency of its radiologic assessment? Findings Automated slice selection and landmark detection using a hybrid AI method improved measurement efficiency and accuracy, with minimal bias confirmed through Bland-Altman analysis. Clinical relevance An AI-based method enables faster, more consistent evaluation of cam-type femoroacetabular impingement in routine CT images, supporting earlier identification and reducing dependency on operator experience in clinical workflows.

Knee Osteoarthritis (K-OA) is characterized as a progressive joint condition with global prevalence, exhibiting deterioration over time and impacting a significant portion of the population. It happens because joints wear out slowly. The main reason for osteoarthritis is the wearing away of the cushion in the joints, which makes the bones rub together. This causes feelings of stiffness, unease, and difficulty moving. Persons with osteoarthritis find it hard to do simple things like walking, standing, or going up stairs. Besides that, it can also make people feel sad or worried because of the ongoing pain and trouble it causes. Knee osteoarthritis exerts a sustained impact on both the economy and society. Typically, radiologists assess knee health through MRI or X-ray images, assigning KL-grades. MRI excels in visualizing soft tissues like cartilage, menisci, and ligaments, directly revealing cartilage degeneration and joint inflammation crucial for osteoarthritis (OA) diagnosis. In contrast, X-rays primarily show bone, only inferring cartilage loss through joint space narrowing-a late indicator of OA. This makes MRI superior for detecting early changes and subtle lesions often missed by X-rays. However, manual diagnosis of Knee osteoarthritis is laborious and time-consuming. In response, deep learning methodologies such as vision transformer (ViT) has been implemented to enhance efficiency and streamline workflows in clinical settings. This research leverages ViT for Knee Osteoarthritis KL grading, achieving an accuracy of 88%. It illustrates that employing a simple transfer learning technique with this model yields superior performance compared to more intricate architectures.

Patient satisfaction after one year of distal radius fracture fixation is influenced by various aspects such as the surgical approach, the patient's physical functioning, and psychological factors. Hence, a multimodal machine learning prediction model combining traditional rating scales and postoperative X-ray images of patients was developed to predict patient satisfaction one year after surgery for personalized clinical treatment. In this study, we reviewed 385 patients who underwent internal fixation with a palmar plate or external fixation bracket fixation in 2018-2020. After one year of postoperative follow-up, 169 patients completed the patient wrist evaluation (PRWE), EuroQol5D (EQ-5D), and forgotten joint score-12 (FJS-12) questionnaires and were subjected to X-ray capture. The region of interest (ROI) of postoperative X-rays was outlined using 3D Slicer, and the training and test sets were divided based on the satisfaction of the patients. Python was used to extract 848 image features, and random forest embedding was used to reduce feature dimensionality. Also, a machine learning model combining the patient's functional rating scale with the downscaled X-ray-related image features was built, followed by hyperparameter debugging using the grid search method during the modeling process. The stability of the Radiomics and Integrated models was first verified using the five-fold cross-validation method, and then receiver operating characteristic curves, calibration curves, and decision curve analysis were used to evaluate the performance of the model on the training and test sets. The feature dimensionality reduction yielded 16 imaging features. The accuracy of the two models was 0.831, 0.784 and 0.966, 0.804 on the training and test sets, respectively. The area under the curve (AUC) values for the Radiomics and Integrated model were 0.937, 0.673 and 0.997, 0.823 for the training and test sets, respectively. The calibration curves and decision curve analysis (DCA) of the Integrated model for the training and test sets had a more accurate prediction probability and clinical significance than the Radiomics model. A multimodal machine learning predictive model combining imaging and patient functional rating scales demonstrated optimal predictive performance for one-year postoperative satisfaction in patients with radial fractures, providing a basis for personalized postoperative patient management.

An anterior cruciate ligament (ACL) tear is a prevalent knee injury among athletes, and aged people with osteoporosis are at increased risk for it. For early detection and treatment, precise and rapid identification of ACL tears is significant. A fully automated system that can identify ACL tear is necessary to aid healthcare providers in determining the nature of injuries detected on Magnetic Resonance Imaging (MRI) scans. Two Convolutional Neural Networks (CNN), the pretrained model and the CustomNet model are trained and tested using 581 MRI scans of the knee. Feature extraction is done with the pre-trained ResNet-18 model, and the ISOMAP algorithm is used in the CustomNet model. Linear and nonlinear dimensionality reduction techniques are employed to extract the needed features from the image. For the ResNet-18 model, the accuracy rate ranges between 86% and 92% for various data partitions. After performing PCA, the improved classification rate ranges between 92% and 96.2%. The CustomNet model's accuracy rate ranges from 40 to 70%, 70-90%, 60-70%, and 50-70% for different hyperparameter ensembles. Five-fold cross validation is implemented in CustomNet and it achieved an overall accuracy of 85.6%. These two models demonstrate superior efficiency and accuracy in classifying normal and ACL torn Knee MR images.

In high-velocity sports, hamstring strain injuries are common causes of missed play and have high rates of reinjury. Evaluating the severity and location of a hamstring strain injury, currently graded by a clinician using a semiqualitative muscle injury classification score (e.g. as one method, British Athletics Muscle Injury Classification - BAMIC) to describe edema presence and location, aids in guiding athlete recovery. In this study, automated artificial intelligence (AI) models were developed and deployed to automatically segment edema, hamstring muscle and tendon structures using T2-weighted and T1-weighted magnetic resonance images (MRI), respectively. MR scans were collected from collegiate football athletes at time-of-hamstring injury and return to sport. Volume, length, and cross-sectional (CSA) measurements were performed on all structures and subregions (i.e. free tendon and aponeurosis). The edema and hamstring muscle/tendon AI models compared favorably with ground-truth segmentations. AI volumetric output correlated with ground truth for edema (R = 0.97), hamstring muscles (R ≥ 0.99), and hamstring tendon (R ≥ 0.42) structures. Edema volume and percentage of muscle impacted by edema significantly increased with clinical BAMIC grade (p < 0.05). Taken together, these results demonstrate a promising new approach for AI-based quantification of edema which reflects differing levels of injury severity and supports clinical validity. Main Body.

Osteoporosis is an under-screened musculoskeletal disorder that results in diminished quality of life and significant burden to the healthcare system. We aimed to evaluate the ability of Rho, an artificial intelligence (AI) tool, to prospectively identify patients at-risk for low bone mineral density (BMD) from standard x-rays, its adoption rate by radiologists, and acceptance by primary care providers (PCPs). Patients ≥50 years were recruited when undergoing an x-ray of a Rho-eligible body part for any clinical indication. Questionnaires were completed at baseline and 6-month follow-up, and PCPs of "Rho-Positive" patients (those likely to have low BMD) were asked for feedback. Positive predictive value (PPV) was calculated in patients who returned within 6 months for a DXA. Of 1145 patients consented, 987 had x-rays screened by Rho, and 655 were flagged as Rho-Positive. Radiologists included this finding in 524 (80%) of reports. Of all Rho-Positive patients, 125 had a DXA within 6 months; Rho had a 74% PPV for DXA T-Score <-1. From 51 PCP responses, 78% found Rho beneficial. Of 389 patients with follow-up questionnaire data, a greater proportion of Rho-Positive versus -negative patients had discussed bone health with their PCP since study start (36% vs 18%, <i>P</i> < .001), or were newly diagnosed with osteoporosis (11% vs 5%; <i>P</i> = .03). By identifying patients at-risk of low BMD, with acceptability of reporting by radiologists and generally positive feedback from PCPs, Rho has the potential to improve low screening rates for osteoporosis by leveraging existing x-ray data.

To develop and evaluate the clinical feasibility of a dynamic computed tomography (CT) protocol for assessing patellofemoral (PF) and tibiofemoral (TF) kinematics before and after total knee arthroplasty (TKA), and to quantify postoperative kinematic changes in a pilot study. In this prospective single-centre study, patients with primary osteoarthritis scheduled for cemented TKA underwent dynamic CT scans preoperatively and at 1-year follow-up during active flexion-extension-flexion. Preoperatively, the femur, tibia and patella were segmented using a neural network. Postoperatively, computer-aided design (CAD) implant models were aligned to CT data to determine relative implant-bone orientation. Due to metal artefacts, preoperative patella meshes were manually aligned to postoperative scans by four raters, and averaged for analysis. Anatomical coordinate systems were applied to quantify patellar flexion, tilt, proximal tip rotation, mediolateral translation and femoral condyle anterior-posterior translation. Descriptive statistics were reported, and interoperator agreement for patellar registration was assessed using intraclass correlation coefficients (ICCs). Ten patients (mean age, 65 ± 8 years; 6 men) were analysed across a shared flexion range of 14°-55°. Postoperatively, the patella showed increased flexion (median difference: 0.9°-3.9°), medial proximal tip rotation (median difference: 1.5°-6.0°), lateral tilt (median difference: 2.7°-5.5°), and lateral shift (median difference: -1.5 to -2.8 mm). The medial and lateral femoral condyles translated 2-4 mm anterior-posteriorly during knee flexion. Interoperator agreement for patellar registration ranged from good to excellent across all parameters (ICC = 0.85-1.00). This pilot study demonstrates that dynamic CT enables in vivo assessment of PF and TF kinematics before and after TKA. The protocol quantified postoperative kinematic changes and demonstrated potential as research tool. Further automation is needed to investigate relationships between these kinematic patterns and patient outcomes in larger-scale studies. Level III.

In the clinical treatment of bone defects that exceed the critical size threshold, traditional methods using metal fixation devices, autografts, and allografts exhibit significant limitations. Meanwhile, bone scaffolds with minimal risks of secondary injury, low immune rejection are emerging as a promising alternative. The effective design of porosity, pore size, and trabecular thickness in bone scaffolds is critical; however, current strategies often struggle to optimally balance these parameters. Here, we propose a bionic bone scaffold design method that mimics multiple properties of natural cancellous bone using a diffusion model. First, we develop a classifier-free conditional diffusion model and train it on a Micro-CT (μCT) image dataset of porcine vertebral cancellous bone. The training model can produce personalized 2-dimensional images of natural-like bone with tunable microstructures. Subsequently, we stack images layer by layer to form 3-dimensional scaffolds, mimicking the CT/μCT image reconstruction process. Finally, computational fluid dynamics analysis is conducted to validate the scaffold models' fluid properties, while bioresin bone scaffold samples are 3D-printed for mechanical testing and biocompatibility assessment. The three key morphological parameters of the generated images-porosity (50-70%), pore size (468-936 μm), and trabecular thickness (156-312 μm)-can be precisely and independently controlled. Fluid simulation and mechanical testing confirm scaffolds' robust performance in permeability (10⁻⁹ to 10⁻⁸ m²), average fluid shear stress (0.1-0.3 Pa), Young's modulus (14-fold adjustable range), compressive strength (9-fold adjustable range), and viscoelastic properties. The scaffolds also exhibit good biocompatibility, meeting the basic requirements for clinical implantation. These promising results highlight the potential of our method for the personalized design of scaffolds to effectively repair large bone defects.