To investigate the efficacy of CT measurement parameters combined with AI-assisted 3D planning for personalized femoral prosthesis selection in total hip arthroplasty (THA). A retrospective analysis was conducted on clinical data from 247 patients with unilateral hip or knee joint disorders treated at Renmin Hospital of Hubei University of Medicine between April 2021 and February 2024. All patients underwent preoperative full-pelvis and bilateral full-length femoral CT scans. The raw CT data were imported into Mimics 19.0 software to reconstruct a three-dimensional (3D) model of the healthy femur. Using 3-matic Research 11.0 software, the femoral head rotation center was located, and parameters including femoral head diameter (FHD), femoral neck length (FNL), femoral neck-shaft angle (FNSA), femoral offset (FO), femoral neck anteversion angle (FNAA), tip-apex distance (TAD), and tip-apex angle (TAA) were measured. AI-assisted THA 3D planning system AIJOINT V1.0.0.0 software was used for preoperative planning and design, enabling personalized selection of femoral prostheses with varying neck-shaft angles and surgical simulation. Groups were compared by gender, age, and parameters. ROC curves evaluated prediction efficacy. Females exhibited smaller FHD, FNL, FO, TAD, TAA but larger FNSA/FNAA vs males (P<0.05). Patients >65 years had higher FO, TAD, TAA (P<0.05). TAD-TAA correlation was strong (r=0.954), while FNSA negatively correlated with TAD/TAA (r=-0.773/-0.701). ROC analysis demonstrated high predictive accuracy: TAD (AUC=0.891, sensitivity=91.7%, specificity=87.6%) and TAA (AUC=0.882, sensitivity=100%, specificity=88.8%). CT parameters (TAA, TAD, FNSA, FO) are interrelated and effective predictors for femoral prosthesis selection. Integration with AI-assisted planning optimizes personalized THA, reducing biomechanical mismatch risks.

Cervical spine disorders are becoming increasingly common, particularly among sedentary populations. The accurate segmentation of cervical vertebrae is critical for diagnostic and research applications. Traditional segmentation methods are limited in terms of precision and applicability across imaging modalities. The aim of this study is to develop and evaluate a fully automatic segmentation method and a user-friendly tool for detecting cervical vertebral body using a combined neural network model based on the YOLOv11 and U-Net3 + models. A dataset of X-ray and magnetic resonance imaging (MRI) images was collected, enhanced, and annotated to include 2136 X-ray images and 2184 MRI images. The proposed YOLO-UNet ensemble model was trained and compared with four other groups of image extraction models, including YOLOv11, DeepLabV3+, U-Net3 + for direct image segmentation, and the YOLO-DeepLab network. The evaluation metrics included the Dice coefficient, Hausdorff distance, intersection over union, positive predictive value, and sensitivity. The YOLO-UNet model combined the advantages of the YOLO and U-Net models and demonstrated excellent vertebral body segmentation capabilities on both X-ray and MRI datasets, which were closer to the ground truth images. Compared with other models, it achieved greater accuracy and a more accurate depiction of the vertebral body shape, demonstrated better versatility, and exhibited superior performance across all evaluation indicators. The YOLO-UNet network model provided a robust and versatile solution for cervical vertebral body segmentation, demonstrating excellent accuracy and adaptability across imaging modalities on both X-ray and MRI datasets. The accompanying user-friendly tool enhanced usability, making it accessible to both clinical and research users. In this study, the challenge of large-scale medical annotation tasks was addressed, thereby reducing project costs and supporting advancements in medical information technology and clinical research.

Artificial intelligence (AI) methods have established themselves in cardiovascular magnetic resonance (CMR) as automated quantification tools for ventricular volumes, function, and myocardial tissue characterization. Quality assurance approaches focus on measuring and controlling AI-expert differences but there is a need for tools that better communicate reliability and agreement. This study introduces the Verity plot, a novel statistical visualization that communicates the reliability of quantitative parameters (QP) with clear agreement criteria and descriptive statistics. Tolerance ranges for the acceptability of the bias and variance of AI-expert differences were derived from intra- and interreader evaluations. AI-expert agreement was defined by bias confidence and variance tolerance intervals being within bias and variance tolerance ranges. A reliability plot was designed to communicate this statistical test for agreement. Verity plots merge reliability plots with density and a scatter plot to illustrate AI-expert differences. Their utility was compared against Correlation, Box and Bland-Altman plots. Bias and variance tolerance ranges were established for volume, function, and myocardial tissue characterization QPs. Verity plots provided insights into statstistcal properties, outlier detection, and parametric test assumptions, outperforming Correlation, Box and Bland-Altman plots. Additionally, they offered a framework for determining the acceptability of AI-expert bias and variance. Verity plots offer markers for bias, variance, trends and outliers, in addition to deciding AI quantification acceptability. The plots were successfully applied to various AI methods in CMR and decisively communicated AI-expert agreement.

To enhance glioma segmentation, a 3D-MRI intelligent glioma segmentation method based on deep learning is introduced. This method offers significant guidance for medical diagnosis, grading, and treatment strategy selection. Glioma case data were sourced from the BraTS2023 public dataset. Firstly, we preprocess the dataset, including 3D clipping, resampling, artifact elimination and normalization. Secondly, in order to enhance the perception ability of the network to different scale features, we introduce the space pyramid pool module. Then, by making the model focus on glioma details and suppressing irrelevant background information, we propose a multi-scale fusion attention mechanism; And finally, to address class imbalance and enhance learning of misclassified voxels, a combination of Dice and Focal loss functions was employed, creating a loss function, this method not only maintains the accuracy of segmentation, It also improves the recognition of challenge samples, thus improving the accuracy and generalization of the model in glioma segmentation. Experimental findings reveal that the enhanced 3D U-Net network model stabilizes training loss at 0.1 after 150 training iterations. The refined model demonstrates superior performance with the highest DSC, Recall, and Precision values of 0.7512, 0.7064, and 0.77451, respectively. In Whole Tumor (WT) segmentation, the Dice Similarity Coefficient (DSC), Recall, and Precision scores are 0.9168, 0.9426, and 0.9375, respectively. For Core Tumor (TC) segmentation, these scores are 0.8954, 0.9014, and 0.9369, respectively. In Enhanced Tumor (ET) segmentation, the method achieves DSC, Recall, and Precision values of 0.8674, 0.9045, and 0.9011, respectively. The DSC, Recall, and Precision indices in the WT, TC, and ET segments using this method are the highest recorded, significantly enhancing glioma segmentation. This improvement bolsters the accuracy and reliability of diagnoses, ultimately providing a scientific foundation for clinical diagnosis and treatment.