State-of-the-art computer- and robot-assisted surgery systems rely on intraoperative imaging technologies such as computed tomography and fluoroscopy to provide detailed 3D visualizations of patient anatomy. However, these methods expose both patients and clinicians to ionizing radiation. This study introduces a radiation-free approach for 3D spine reconstruction using RGB-D data. Inspired by the "mental map" surgeons form during procedures, we present SurgPointTransformer, a shape completion method that reconstructs unexposed spinal regions from sparse surface observations. The method begins with a vertebra segmentation step that extracts vertebra-level point clouds for subsequent shape completion. SurgPointTransformer then uses an attention mechanism to learn the relationship between visible surface features and the complete spine structure. The approach is evaluated on an <i>ex vivo</i> dataset comprising nine samples, with CT-derived data used as ground truth. SurgPointTransformer significantly outperforms state-of-the-art baselines, achieving a Chamfer distance of 5.39 mm, an F-score of 0.85, an Earth mover's distance of 11.00 and a signal-to-noise ratio of 22.90 dB. These results demonstrate the potential of our method to reconstruct 3D vertebral shapes without exposing patients to ionizing radiation. This work contributes to the advancement of computer-aided and robot-assisted surgery by enhancing system perception and intelligence.

The development of the Artificial Intelligence (AI)-based severity inspection model for ankylosing spondylitis (AS) could support health professionals to rapidly assess the severity of the disease, enhance proficiency, and reduce the demands of human resources. This paper aims to develop an AI-based severity inspection model for AS using patients' X-ray images and modified Stoke Ankylosing Spondylitis Spinal Score (mSASSS). The numerical simulation with AI is developed following the progress of data preprocessing, building and testing the model, and then the model. The training data is preprocessed by inviting three experts to check the X-ray images of 222 patients following the Gold Standard. The model is then developed through two stages, including keypoint detection and mSASSS evaluation. The two-stage AI-based severity inspection model for AS was developed to automatically detect spine points and evaluate mSASSS scores. At last, the data obtained from the developed model was compared with those from experts' assessment to analyse the accuracy of the model. The study was conducted in accordance with the ethical principles outlined in the Declaration of Helsinki. The spine point detection at the first stage achieved 1.57 micrometres in mean error distance with the ground truth, and the second stage of the classification network can reach 0.81 in mean accuracy. The model can correctly identify 97.4% patches belonging to mSASSS score 3, while those belonging to score 0 can still be classified into scores 1 or 2. The automatic severity inspection model for AS developed in this paper is accurate and can support health professionals in rapidly assessing the severity of AS, enhancing assessment proficiency, and reducing the demands of human resources.

Patients with recurrent complaints after total knee arthroplasty may suffer from aseptic implant loosening. Current imaging modalities do not quantify looseness of knee arthroplasty components. A recently developed and validated workflow quantifies the tibial component displacement relative to the bone from CT scans acquired under valgus and varus load. The 3D analysis approach includes segmentation and registration of the tibial component and bone. In the current approach, the semi-automatic segmentation requires user interaction, adding complexity to the analysis. The research question is whether the segmentation step can be fully automated while keeping outcomes indifferent. In this study, different deep-learning (DL) models for fully automatic segmentation are proposed and evaluated. For this, we employ three different datasets for model development (20 cadaveric CT pairs and 10 cadaveric CT scans) and evaluation (72 patient CT pairs). Based on the performance on the development dataset, the final model was selected, and its predictions replaced the semi-automatic segmentation in the current approach. Implant displacement was quantified by the rotation about the screw-axis, maximum total point motion, and mean target registration error. The displacement parameters of the proposed approach showed a statistically significant difference between fixed and loose samples in a cadaver dataset, as well as between asymptomatic and loose samples in a patient dataset, similar to the outcomes of the current approach. The methodological error calculated on a reproducibility dataset showed values that were not statistically significant different between the two approaches. The results of the proposed and current approaches showed excellent reliability for one and three operators on two datasets. The conclusion is that a full automation in knee implant displacement assessment is feasible by utilizing a DL-based segmentation model while maintaining the capability of distinguishing between fixed and loose implants.

Osteoporosis, characterized by reduced bone mineral density (BMD) and compromised bone microstructure, increases fracture risk in aging populations. While dual-energy X-ray absorptiometry (DXA) is the clinical standard for BMD assessment, its limited accessibility hinders diagnosis in resource-limited regions. Opportunistic computed tomography (CT) analysis has emerged as a promising alternative for osteoporosis diagnosis using existing imaging data. Current approaches, however, face three limitations: (1) underutilization of unlabeled vertebral data, (2) systematic bias from device-specific DXA discrepancies, and (3) insufficient integration of clinical knowledge such as spatial BMD distribution patterns. To address these, we propose a unified deep learning framework with three innovations. First, a self-supervised learning method using radiomic representations to leverage unlabeled CT data and preserve bone texture. Second, a Mixture of Experts (MoE) architecture with learned gating mechanisms to enhance cross-device adaptability. Third, a multi-task learning framework integrating osteoporosis diagnosis, BMD regression, and vertebra location prediction. Validated across three clinical sites and an external hospital, our approach demonstrates superior generalizability and accuracy over existing methods for opportunistic osteoporosis screening and diagnosis.

Musculoskeletal ultrasound is a key tool in rheumatology for diagnosing and managing inflammatory arthritis. Traditional ultrasound systems, while effective, can be cumbersome and costly, limiting their use in many clinical settings. Handheld ultrasound (HHUS) devices, which are portable, affordable, and user-friendly, have emerged as a promising alternative. This review explores the role of HHUS in rheumatology, specifically evaluating its impact on diagnostic accuracy, ease of use, and utility in screening for inflammatory arthritis. The review also addresses key challenges, such as image quality, storage and data security, and the potential for integrating artificial intelligence to improve device performance. We compare HHUS devices to cart-based ultrasound machines, discuss their advantages and limitations, and examine the potential for widespread adoption. Our findings suggest that HHUS devices can effectively support musculoskeletal assessments and offer significant benefits in resource-limited settings. However, proper training, standardized protocols, and continued technological advancements are essential for optimizing their use in clinical practice.

Accurate interpretation of knee MRI scans relies on expert clinical judgment, often with high variability and limited scalability. Existing radiomic approaches use a fixed set of radiomic features (the signature), selected at the population level and applied uniformly to all patients. While interpretable, these signatures are often too constrained to represent individual pathological variations. As a result, conventional radiomic-based approaches are found to be limited in performance, compared with recent end-to-end deep learning (DL) alternatives without using interpretable radiomic features. We argue that the individual-agnostic nature in current radiomic selection is not central to its intepretability, but is responsible for the poor generalization in our application. Here, we propose a novel radiomic fingerprint framework, in which a radiomic feature set (the fingerprint) is dynamically constructed for each patient, selected by a DL model. Unlike the existing radiomic signatures, our fingerprints are derived on a per-patient basis by predicting the feature relevance in a large radiomic feature pool, and selecting only those that are predictive of clinical conditions for individual patients. The radiomic-selecting model is trained simultaneously with a low-dimensional (considered relatively explainable) logistic regression for downstream classification. We validate our methods across multiple diagnostic tasks including general knee abnormalities, anterior cruciate ligament (ACL) tears, and meniscus tears, demonstrating comparable or superior diagnostic accuracy relative to state-of-the-art end-to-end DL models. More importantly, we show that the interpretability inherent in our approach facilitates meaningful clinical insights and potential biomarker discovery, with detailed discussion, quantitative and qualitative analysis of real-world clinical cases to evidence these advantages.

Ultrasound shear strain is a potential biomarker of myofascial dysfunction. However, the quality of estimated shear strains can be impacted by differences in ultrasound displacement tracking techniques, potentially altering clinical conclusions surrounding myofascial pain. This work assesses the reliability of four displacement estimation algorithms under a novel clinical hypothesis that the shear strain between muscles on a stroke-affected (paretic) shoulder with myofascial pain is lower than that on the non-paretic side of the same patient. After initial validation with simulations, four approaches were evaluated with in vivo data acquired from ten research participants with myofascial post-stroke shoulder pain: (1) Search is a common window-based method that determines displacements by searching for maximum normalized cross-correlations within windowed data, whereas (2) OVERWIND-Search, (3) SOUL-Search, and (4) $L1$-SOUL-Search fine-tune the Search initial estimates by optimizing cost functions comprising data and regularization terms, utilizing $L1$-norm-based first-order regularization, $L2$-norm-based first- and second-order regularization, and $L1$-norm-based first- and second-order regularization, respectively. SOUL-Search and $L1$-SOUL-Search most accurately and reliably estimate shear strain relative to our clinical hypothesis, when validated with visual inspection of ultrasound cine loops and quantitative T1$\rho$ magnetic resonance imaging. In addition, $L1$-SOUL-Search produced the most reliable displacement tracking performance by generating lateral displacement images with smooth displacement gradients (measured as the mean and variance of displacement derivatives) and sharp edges (which enables distinction of shoulder muscle layers). Among the four investigated methods, $L1$-SOUL-Search emerged as the most suitable option to investigate myofascial pain and dysfunction, despite the drawback of slow runtimes, which can potentially be resolved with a deep learning solution. This work advances musculoskeletal health, ultrasound shear strain imaging, and related applications by establishing the foundation required to develop reliable image-based biomarkers for accurate diagnoses and treatments.

To investigate the clinical value of deep learning reconstruction (DLR) in accelerated magnetic resonance imaging (MRI) of the knee and compare its visual quality and diagnostic performance metrics with conventional fast spin-echo T2-weighted imaging with fat suppression (FSE-T2WI-FS). This prospective study included 116 patients with knee injuries. All patients underwent both conventional FSE-T2WI-FS and DLR-accelerated FSE-T2WI-FS scans on a 1.5‑T MRI scanner. Two radiologists independently evaluated overall image quality, artifacts, and image sharpness using a 5-point Likert scale. The signal-to-noise ratio (SNR) and contrast-to-noise ratio (CNR) of lesion regions were measured. Subjective scores were compared using the Wilcoxon signed-rank test, SNR/CNR differences were analyzed via paired t tests, and inter-reader agreement was assessed using Cohen's kappa. The accelerated sequences with DLR achieved a 36 % reduction in total scan time compared to conventional sequences (p < 0.05), shortening acquisition from 9 min 50 s to 6 min 15 s. Moreover, DLR demonstrated superior artifact suppression and enhanced quantitative image quality, with significantly higher SNR and CNR (p < 0.001). Despite these improvements, diagnostic equivalence was maintained: No significant differences were observed in overall image quality, sharpness (p > 0.05), or lesion detection rates. Inter-reader agreement was good (κ> 0.75), further validating the clinical reliability of the DLR technique. Using DLR-accelerated FSE-T2WI-FS reduces scan time, suppresses artifacts, and improves quantitative image quality while maintaining diagnostic accuracy comparable to conventional sequences. This technology holds promise for optimizing clinical workflows in MRI of the knee.

This study employed a retrospective data analysis approach combined with model development and validation. The present study introduces a 2.5D convolutional neural network (CNN) model leveraging CT imaging to facilitate the early detection of malignant vertebral compression fractures (MVCFs), potentially reducing reliance on invasive biopsies. Vertebral histopathological biopsy is recognized as the gold standard for differentiating between osteoporotic and malignant vertebral compression fractures (VCFs). Nevertheless, its application is restricted due to its invasive nature and high cost, highlighting the necessity for alternative methods to identify MVCFs. The clinical, imaging, and pathological data of patients who underwent vertebral augmentation and biopsy at Institution 1 and Institution 2 were collected and analyzed. Based on the vertebral CT images of these patients, 2D, 2.5D, and 3D CNN models were developed to identify the patients with osteoporotic vertebral compression fractures (OVCF) and MVCF. To verify the clinical application value of the CNN model, two rounds of reader studies were performed. The 2.5D CNN model performed well, and its performance in identifying MVCF patients was significantly superior to that of the 2D and 3D CNN models. In the training dataset, the area under the receiver operating characteristic curve (AUC) of the 2.5D CNN model was 0.996 and an F1 score of 0.915. In the external cohort test, the AUC was 0.815 and an F1 score of 0.714. And clinicians' ability to identify MVCF patients has been enhanced by the 2.5D CNN model. With the assistance of the 2.5D CNN model, the AUC of senior clinicians was 0.882, and the F1 score was 0.774. For junior clinicians, the 2.5D CNN model-assisted AUC was 0.784 and the F1 score was 0.667. The development of our 2.5D CNN model marks a significant step towards non-invasive identification of MVCF patients,. The 2.5D CNN model may be a potential model to assist clinicians in better identifying MVCF patients.

Radiographic knee alignment (KA) measurement is important for predicting joint health and surgical outcomes after total knee replacement. Traditional methods for KA measurements are manual, time-consuming and require long-leg radiographs. This study proposes a deep learning-based method to measure KA in anteroposterior knee radiographs via automatically localized knee anatomical landmarks. Our method builds on hourglass networks and incorporates an attention gate structure to enhance robustness and focus on key anatomical features. To our knowledge, this is the first deep learning-based method to localize over 100 knee anatomical landmarks to fully outline the knee shape while integrating KA measurements on both pre-operative and post-operative images. It provides highly accurate and reliable anatomical varus/valgus KA measurements using the anatomical tibiofemoral angle, achieving mean absolute differences ~1{\deg} when compared to clinical ground truth measurements. Agreement between automated and clinical measurements was excellent pre-operatively (intra-class correlation coefficient (ICC) = 0.97) and good post-operatively (ICC = 0.86). Our findings demonstrate that KA assessment can be automated with high accuracy, creating opportunities for digitally enhanced clinical workflows.