Considering the radiation hazard of X-ray, safer, more convenient and cost-effective ultrasound methods are gradually becoming new diagnostic approaches for scoliosis. For ultrasound images of spine regions, it is challenging to accurately identify spine regions in images due to relatively small target areas and the presence of a lot of interfering information. Therefore, we developed a novel neural network that incorporates prior knowledge to precisely segment spine regions in ultrasound images. We constructed a dataset of ultrasound images of spine regions for semantic segmentation. The dataset contains 3136 images of 30 patients with scoliosis. And we propose a network model (ICPPNet), which fully utilizes inter-class positional prior knowledge by combining an inter-class positional probability heatmap, to achieve accurate segmentation of target areas. ICPPNet achieved an average Dice similarity coefficient of 70.83% and an average 95% Hausdorff distance of 11.28 mm on the dataset, demonstrating its excellent performance. The average error between the Cobb angle measured by our method and the Cobb angle measured by X-ray images is 1.41 degrees, and the coefficient of determination is 0.9879 with a strong correlation. ICPPNet provides a new solution for the medical image segmentation task with positional prior knowledge between target classes. And ICPPNet strongly supports the subsequent reconstruction of spine models using ultrasound images.

Magnetic resonance fingerprinting (MRF), as an emerging versatile and noninvasive imaging technique, provides simultaneous quantification of multiple quantitative MRI parameters, which have been used to detect changes in cartilage composition and structure in osteoarthritis. Deep learning (DL)-based methods for quantification mapping in MRF overcome the memory constraints and offer faster processing compared to the conventional dictionary matching (DM) method. However, limited attention has been given to the fine-tuning of neural networks (NNs) in DL and fair comparison with DM. In this study, we investigate the impact of training parameter choices on NN performance and compare the fine-tuned NN with DM for multiparametric mapping in MRF. Our approach includes optimizing NN hyperparameters, analyzing the singular value decomposition (SVD) components of MRF data, and optimization of the DM method. We conducted experiments on synthetic data, the NIST/ISMRM MRI system phantom with ground truth, and in vivo knee data from 14 healthy volunteers. The results demonstrate the critical importance of selecting appropriate training parameters, as these significantly affect NN performance. The findings also show that NNs improve the accuracy and robustness of T₁, T₂, and T_1ρ mappings compared to DM in synthetic datasets. For in vivo knee data, the NN achieved comparable results for T₁, with slightly lower T₂ and slightly higher T_1ρ measurements compared to DM. In conclusion, the fine-tuned NN can be used to increase accuracy and robustness for multiparametric quantitative mapping from MRF of the knee joint.

Identifying patients at risk of low bone mineral density (BMD) from X-rays presents an attractive approach to increase case finding. This paper showed the diagnostic accuracy, reproducibility, and robustness of a new technology: OsteoSight™. OsteoSight could increase diagnosis and preventive treatment rates for patients with low BMD. This study aimed to evaluate the diagnostic accuracy, reproducibility, and robustness of OsteoSight™, an automated image analysis tool designed to identify low bone mineral density (BMD) from routine hip and pelvic X-rays. Given the global rise in osteoporosis-related fractures and the limitations of current diagnostic paradigms, OsteoSight offers a scalable solution that integrates into existing clinical workflows. Performance of the technology was tested across three key areas: (1) diagnostic accuracy in identifying low BMD as compared to dual-energy X-ray absorptiometry (DXA), the clinical gold standard; (2) reproducibility, through analysis of two images from the same patient; and (3) robustness, by evaluating the tool's performance across different patient demographics and X-ray scanner hardware. The diagnostic accuracy of OsteoSight for identifying patients at risk of low BMD was area under the receiver operating characteristic curve (AUROC) 0.834 [0.789-0.880], with consistent results across subgroups of clinical confounders and X-ray scanner hardware. Specificity 0.852 [0.783-0.930] and sensitivity 0.628 [0.538-0.743] met pre-specified acceptance criteria. The pre-processing pipeline successfully excluded unsuitable cases including incorrect body parts, metalwork, and unacceptable femur positioning. The results demonstrate that OsteoSight is accurate in identifying patients with low BMD. This suggests its utility as an opportunistic assessment tool, especially in settings where DXA accessibility is limited or not recently performed. The tool's reproducibility and robust performance across various clinical confounders further supports its integration into routine orthopedic and medical practices, potentially broadening the reach of osteoporosis assessment and enabling earlier intervention for at-risk patients.

Neuromuscular diseases (NMDs) are rare disorders characterized by progressive muscle fibre loss, leading to replacement by fibrotic and fatty tissue, muscle weakness and disability. Early diagnosis is critical for therapeutic decisions, care planning and genetic counselling. Muscle magnetic resonance imaging (MRI) has emerged as a valuable diagnostic tool by identifying characteristic patterns of muscle involvement. However, the increasing complexity of these patterns complicates their interpretation, limiting their clinical utility. Additionally, multi-study data aggregation introduces heterogeneity challenges. This study presents a novel multi-study harmonization pipeline for muscle MRI and an AI-driven diagnostic tool to assist clinicians in identifying disease-specific muscle involvement patterns. We developed a preprocessing pipeline to standardize MRI fat content across datasets, minimizing source bias. An ensemble of XGBoost models was trained to classify patients based on intramuscular fat replacement, age at MRI and sex. The SHapley Additive exPlanations (SHAP) framework was adapted to analyse model predictions and identify disease-specific muscle involvement patterns. To address class imbalance, training and evaluation were conducted using class-balanced metrics. The model's performance was compared against four expert clinicians using 14 previously unseen MRI scans. Using our harmonization approach, we curated a dataset of 2961 MRI samples from genetically confirmed cases of 20 paediatric and adult NMDs. The model achieved a balanced accuracy of 64.8% ± 3.4%, with a weighted top-3 accuracy of 84.7% ± 1.8% and top-5 accuracy of 90.2% ± 2.4%. It also identified key features relevant for differential diagnosis, aiding clinical decision-making. Compared to four expert clinicians, the model obtained the highest top-3 accuracy (75.0% ± 4.8%). The diagnostic tool has been implemented as a free web platform, providing global access to the medical community. The application of AI in muscle MRI for NMD diagnosis remains underexplored due to data scarcity. This study introduces a framework for dataset harmonization, enabling advanced computational techniques. Our findings demonstrate the potential of AI-based approaches to enhance differential diagnosis by identifying disease-specific muscle involvement patterns. The developed tool surpasses expert performance in diagnostic ranking and is accessible to clinicians worldwide via the Myo-Guide online platform.

Segmentation is a critical process in medical image interpretation. It is also essential for preparing training datasets for machine learning (ML)-based solutions. Despite technological advancements, achieving fully automatic segmentation is still challenging. User interaction is required to initiate the process, either by defining points or regions of interest, or by verifying and refining the output. One of the complex structures that requires semi-automatic segmentation procedures or manually defined training datasets is the lumbar spine. Automating the placement of a point within each lumbar vertebral body could significantly reduce user interaction in these procedures. A new method for automatically locating lumbar vertebral bodies in sagittal magnetic resonance images (MRI) is presented. The method integrates different image processing techniques and relies on the vertebral body morphology. Testing was mainly performed using 50 MRI scans that were previously annotated manually by placing a point at the centre of each lumbar vertebral body. A complementary public dataset was also used to assess robustness. Evaluation metrics included the correct labelling of each structure, the inclusion of each point within the corresponding vertebral body area, and the accuracy of the locations relative to the vertebral body centres using root mean squared error (RMSE) and mean absolute error (MAE). A one-sample Student's t-test was also performed to find the distance beyond which differences are considered significant (α = 0.05). All lumbar vertebral bodies from the primary dataset were correctly labelled, and the average RMSE and MAE between the automatic and manual locations were less than 5 mm. Distances to the vertebral body centres were found to be significantly less than 4.33 mm with a p-value < 0.05, and significantly less than half the average minimum diameter of a lumbar vertebral body with a p-value < 0.00001. Results from the complementary public dataset include high labelling and inclusion rates (85.1% and 94.3%, respectively), and similar accuracy values. The proposed method successfully achieves robust and accurate automatic placement of points within each lumbar vertebral body. The automation of this process enables the transition from semi-automatic to fully automatic methods, thus reducing error-prone and time-consuming user interaction, and facilitating the creation of training datasets for ML-based solutions.

Osteopenia is a bone disorder that causes low bone density and affects millions of people worldwide. Diagnosis of this condition is commonly achieved through clinical assessment of bone mineral density (BMD). State of the art machine learning (ML) techniques, such as convolutional neural networks (CNNs) and transformer models, have gained increasing popularity in medicine. In this work, we employ six deep networks for osteopenia vs. healthy bone classification using X-ray imaging from the pediatric wrist dataset GRAZPEDWRI-DX. We apply two explainable AI techniques to analyze and interpret visual explanations for network decisions. Experimental results show that deep networks are able to effectively learn osteopenic and healthy bone features, achieving high classification accuracy rates. Among the six evaluated networks, DenseNet201 with transfer learning yielded the top classification accuracy at 95.2 %. Furthermore, visual explanations of CNN decisions provide valuable insight into the blackbox inner workings and present interpretable results. Our evaluation of deep network classification results highlights their capability to accurately differentiate between osteopenic and healthy bones in pediatric wrist X-rays. The combination of high classification accuracy and interpretable visual explanations underscores the promise of incorporating machine learning techniques into clinical workflows for the early and accurate diagnosis of osteopenia.

The article discusses advanced three-dimensional evaluation of hallux valgus deformity using weightbearing computed tomography. Conventional two-dimensional radiographs fall short in assessing the complexity of hallux valgus deformities, whereas weightbearing computed tomography provides detailed insights into bone alignment and joint stability in a weightbearing state. Recent studies have highlighted the significance of first ray hypermobility and intrinsic metatarsal rotation in hallux valgus, influencing surgical planning and outcomes. The integration of semiautomatic and artificial intelligence-assisted tools with weightbearing computed tomography is enhancing the precision of deformity assessment, leading to more personalized and effective hallux valgus management.

VSBONE^® BSI (VSBONE), an automated bone scan index (BSI) measurement system was updated from version 2.1 (ver.2) to 3.0 (ver.3). VSBONE ver.3 incorporates deep learning of the skeletal structures of 957 new women, and it can be applied in patients with breast cancer. However, the performance of the updated VSBONE remains unclear. This study aimed to validate the diagnostic accuracy of the VSBONE system in patients with breast cancer. In total, 220 Japanese patients with breast cancer who underwent bone scintigraphy with single-photon emission computed tomography/computed tomography (SPECT/CT) were retrospectively analyzed. The patients were diagnosed with active bone metastases (<i>n</i> = 20) and non-bone metastases (<i>n</i> = 200) according to the physician's radiographic image interpretation. The patients were assessed using the VSBONE ver.2 and VSBONE ver.3, and the BSI findings were compared with the interpretation results by the physicians. The occurrence of segmentation errors, the association of BSI between VSBONE ver.2 and VSBONE ver.3, and the diagnostic accuracy of the systems were evaluated. VSBONE ver.2 and VSBONE ver.3 had segmentation errors in four and two patients. Significant positive linear correlations were confirmed in both versions of the BSI (<i>r</i> = 0.92). The diagnostic accuracy was 54.1% in VSBOBE ver.2, and 80.5% in VSBONE ver.3 <i>(P</i> < 0.001), respectively. The diagnostic accuracy of VSBONE was improved through deep learning of the female skeletal structures. The updated VSBONE ver.3 can be a reliable automated system for measuring BSI in patients with breast cancer.

The management of skeletal-related events (SREs), particularly the prevention of pathological fractures, is crucial for cancer patients. Current clinical assessment of fracture risk is mostly based on medical images, but incorporating sequential images in the assessment remains challenging. This study addressed this issue by leveraging a comprehensive dataset consisting of 260 longitudinal micro-computed tomography (μCT) scans acquired in normal and breast cancer bearing mice. A machine learning (ML) model based on a spatial-temporal neural network was built to forecast bone structures from previous μCT scans, which were found to have an overall similarity coefficient (Dice) of 0.814 with ground truths. Despite the predicted lesion volumes (18.5 ​% ​± ​15.3 ​%) being underestimated by ∼21 ​% than the ground truths' (22.1 ​% ​± ​14.8 ​%), the time course of the lesion growth was better represented in the predicted images than the preceding scans (10.8 ​% ​± ​6.5 ​%). Under virtual biomechanical testing using finite element analysis (FEA), the predicted bone structures recapitulated the loading carrying behaviors of the ground truth structures with a positive correlation (y ​= ​0.863x) and a high coefficient of determination (R² ​= ​0.955). Interestingly, the compliances of the predicted and ground truth structures demonstrated nearly identical linear relationships with the lesion volumes. In summary, we have demonstrated that bone deterioration could be proficiently predicted using machine learning in our preclinical dataset, suggesting the importance of large longitudinal clinical imaging datasets in fracture risk assessment for cancer bone metastasis.

This study employs Convolutional Neural Networks (CNNs) as feature extractors with appended regression layers for the non-invasive prediction of Cobb Angle (CA) from Surface Topography (ST) scans in adolescents with Idiopathic Scoliosis (AIS). The aim is to minimize radiation exposure during critical growth periods by offering a reliable, non-invasive assessment tool. The efficacy of various CNN-based feature extractors-DenseNet121, EfficientNetB0, ResNet18, SqueezeNet, and a modified U-Net-was evaluated on a dataset of 654 ST scans using a regression analysis framework for accurate CA prediction. The dataset comprised 590 training and 64 testing scans. Performance was evaluated using Mean Absolute Error (MAE), Root Mean Square Error (RMSE), and accuracy in classifying scoliosis severity (mild, moderate, severe) based on CA measurements. The EfficientNetB0 feature extractor outperformed other models, demonstrating strong performance on the training set (R=0.96, R=20.93) and achieving an MAE of 6.13^∘ and RMSE of 7.5^∘ on the test set. In terms of scoliosis severity classification, it achieved high precision (84.62%) and specificity (95.65% for mild cases and 82.98% for severe cases), highlighting its clinical applicability in AIS management. The regression-based approach using the EfficientNetB0 as a feature extractor presents a significant advancement for accurately determining CA from ST scans, offering a promising tool for improving scoliosis severity categorization and management in adolescents.