Objective: To explore the application effect of the “dual-line integration teaching mode” in the experimental teaching of systematic anatomy. Methods: A total of 120 students majoring in clinical medicine (Grade 2022) from Hunan University of Medicine were selected and randomly divided into a control group (traditional offline teaching mode) and an experimental group (dual-line integration teaching mode), with 60 students in each group. The control group adopted the traditional teaching mode, where teachers gave explanations and students observed specimens; the experimental group implemented online-offline blended teaching based on the 3D human anatomy specimen teaching system, specimen demonstration videos, and a case-based learning case bank. The teaching effect was evaluated through questionnaire surveys and final comprehensive scores (including theoretical scores, experimental skill assessment scores, and daily performance scores). Results: Compared with the control group, the students in the experimental group showed significant improvements in knowledge mastery ability, hands-on operation ability, teamwork ability, and communication and expression ability (P < 0.05); the final theoretical scores (44.4 ± 3.4 points vs. 40.8 ± 5.3 points) and experimental skill assessment scores (17.4 ± 2.3 points vs. 14.2 ± 3.9 points) of the experimental group were significantly higher than those of the control group, with statistically significant differences (P < 0.05). Conclusion: The “dual-line integration teaching mode” can effectively improve the quality of experimental teaching in systematic anatomy, promote the comprehensive development of students’ learning abilities, and provide new ideas for medical experimental teaching.

Triggers and maintenance of idiopathic atrial fibrillation: A multiscale computational simulation study.

Evaluating a problem-based learning model integrated with 3D anatomy software and software-assisted annotation in undergraduate spinal surgery education: a randomized controlled trial.

Background/Objectives: Diagnosing Rotator Cuff Tears (RCTs) via Magnetic Resonance Imaging (MRI) is clinically challenging due to complex 3D anatomy and significant interobserver variability. Traditional slice-centric Convolutional Neural Networks (CNNs) often fail to capture the necessary volumetric context for accurate grading. This study aims to develop and validate the Patient-Aware Vision Transformer (Pa-ViT), an explainable deep-learning framework designed for the automated, patient-level classification of RCTs (Normal, Partial-Thickness, and Full-Thickness). Methods: A large-scale retrospective dataset comprising 2447 T2-weighted coronal shoulder MRI examinations was utilized. The proposed Pa-ViT framework employs a Vision Transformer (ViT-Base) backbone within a Weakly-Supervised Multiple Instance Learning (MIL) paradigm to aggregate slice-level semantic features into a unified patient diagnosis. The model was trained using a weighted cross-entropy loss to address class imbalance and was benchmarked against widely used CNN architectures and traditional machine-learning classifiers. Results: The Pa-ViT model achieved a high overall accuracy of 91% and a macro-averaged F1-score of 0.91, significantly outperforming the standard VGG-16 baseline (87%). Notably, the model demonstrated superior discriminative power for the challenging Partial-Thickness Tear class (ROC AUC: 0.903). Furthermore, Attention Rollout visualizations confirmed the model’s reliance on genuine anatomical features, such as the supraspinatus footprint, rather than artifacts. Conclusions: By effectively modeling long-range dependencies, the Pa-ViT framework provides a robust alternative to traditional CNNs. It offers a clinically viable, explainable decision support tool that enhances diagnostic sensitivity, particularly for subtle partial-thickness tears.

Spatial omics links molecular measurements to their positions in tissue, revealing cellular organization and interactions. Yet most computational tools highlight common cell types and overlook rare populations that can drive disease. Here we show GARDEN, a computational framework that identifies and characterizes these pathogenic cells or regions in spatial omics by embedding graph-based dynamic attention into a spatially-aware graph fusion contrastive model. GARDEN works consistently across tissues, species and resolution scales, and aligns consecutive sections to reconstruct 3D anatomy. In an Alzheimer's disease model, GARDEN localizes C1qa/C1qb-marked microglia in amyloid-β regions and reveals key immune pathways. In nasopharyngeal carcinoma it identifies tiny tertiary lymphoid structures, and in breast cancer it uncovers inflammatory M1-like macrophages near ductal carcinoma in situ and links them to pro-metastatic signaling. An interpretation module pinpoints key immune signatures, and GARDEN extends to spatial chromatin accessibility, providing insight into epigenetic regulation and informing diagnostics and therapeutic targeting.

Advancing White Matter Knowledge in Neurosurgical Training: Validation and Educational Impact of a Novel 3-Dimensional-Printed Simulator.

The distal radioulnar ligaments (DRULs) serve as primary stabilizers to the distal radioulnar joint (DRUJ). Existing cadaveric studies report heterogeneous morphometric data of the three-dimensional (3D) anatomy of the triangular fibrocartilage complex (TFCC) and the ulnar footprints of the DRULs due to methodological variations and small sample sizes, limiting the translation of precise anatomical knowledge to clinical practice. This study quantitatively evaluated the 3D anatomy of the TFCC and the insertions of both superficial and deep DRULs components using three different methods with subsequent interactive validation: (1) direct measurement, (2) 3D scan, and (3) artificial intelligence (AI) enhanced magnetic resonance imaging. Eleven adult cadaveric upper limbs were included. All specimens underwent 3.0-Tesla MRI scans, which were then processed by AI algorithms for super-resolution enhancement and semi-automatic segmentation. The areas of deep and superficial limbs of DRUL ulnar footprint were measured in the super-resolution MRI images using the Slicer software. The specimens were then dissected and anatomical measurements of dorsal-volar maximal length and radial-ulnar maximum length of deep ulnar DRUL footprint were performed on the specimens' photographs. Anatomical measurements of ulna, radius, triangular fibrocartilage, and ulnar insertions footprint of both superficial and deep DRULs were conducted subsequently using a 3D scanner. Primary outcome measures included the area and morphological classification (irregular quadrilateral, ribbon, semilunar) of the deep and superficial ulnar DRUL footprints. Statistical analysis encompassed intraclass correlation coefficients (ICC) for agreement assessment and multiple linear regression to explore associations. The mean area of the deep foveal fibers of DRUL was 43.39 ± 13.49 mm2 and the superficial footprint was 20.11 ± 10.49 mm2 as measured with the 3D scanner. The morphologic features of the deep footprint shapes varied, with the most common shape being a ribbon (7/11, 64%). The intraclass correlation coefficients (ICCs) for the measurement of dorsal-volar maximal length and radial-ulnar maximum length of the DRUL between direct measurement and the 3D scan were excellent (ICC = 0.97 and 0.98, respectively). The ICCs between the AI-enhanced analysis and the 3D scan for measuring the ulnar deep and superficial DRUL insertion areas were excellent (ICC = 0.95 and 0.96, respectively). Multiple linear regression explained 72.4% of the variance in deep DRUL footprint area (R2 = 0.724, p = 0.147), with the superficial footprint area showing the strongest association (β = 0.639, p = 0.196). Compared to direct measurement and 3D scan, the AI algorithms developed and validated for wrist MRI image enhancement demonstrated high accuracy and reliability in anatomical measurements of DRULs.

Background: Anatomy education is a keystone of medical training, crucial for developing clinical skills across various specialties such as surgery and radiology. Traditionally, anatomy has been taught using physical models, offering tactile engagement and spatial understanding. However, the advent of digital innovations, particularly virtual anatomy tables, has introduced high-resolution, interactive learning experiences, prompting a need to evaluate their educational value in comparison to conventional tools. Objective: To compare the effectiveness, engagement, and preferences of undergraduate medical students regarding traditional anatomy models and 3D digital anatomy tables at Gomal Medical College. Material and Methods: A descriptive cross-sectional survey was conducted among 175 first- and second-year MBBS students at Gomal Medical College from October 2024 to March 2025. A census sampling approach was applied. Data were collected through a self-developed, validated questionnaire. The Anatomage Digital Anatomy Table ,The Asclepius Anatomy Table, developed by Sunglasses Inc. (Taiwan Main Orthopedic Biotechnology Co., Ltd., Taiwan),was used alongside traditional anatomical models. Descriptive and inferential statistics applied using SPSS v30. Results: Among respondents, 92 (52.6%) were male and 83 (47.4%) female, with a mean age of 19.8±1.2 years. While 95 (54.3%) preferred traditional models and 108 (61.7%) favored digital tables, 150 (85.7%) supported integrating both. Digital tables were perceived as more engaging (74.3%) and helpful for 3D understanding (68.6%), whereas traditional models enhanced tactile and spatial learning (65.7%) and long-term retention (57.1%). Conclusion: Both digital and traditional anatomy tools have distinct strengths. Integrating digital tables for visualization with physical models for tactile learning may optimize anatomy education in modern medical curricula.

Spine surgery is a high-risk intervention demanding precise execution, often supported by image-based navigation systems. Recently, supervised learning approaches have gained attention for reconstructing 3D spinal anatomy from sparse fluoroscopic data, significantly reducing reliance on radiation-intensive 3D imaging systems. However, these methods typically require large amounts of annotated training data and may struggle to generalize across varying patient anatomies or imaging conditions. Instance-learning approaches like Gaussian splatting could offer an alternative by avoiding extensive annotation requirements. While Gaussian splatting has shown promise for novel view synthesis, its application to sparse, arbitrarily posed real intraoperative X-rays has remained largely unexplored. This work addresses this limitation by extending the R^{2}-Gaussian splatting framework to reconstruct anatomically consistent 3D volumes under these challenging conditions. We introduce an anatomy-guided radiographic standardization step using style transfer, improving visual consistency across views, and enhancing reconstruction quality. Notably, our framework requires no pretraining, making it inherently adaptable to new patients and anatomies. We evaluated our approach using an ex-vivo dataset. Expert surgical evaluation confirmed the clinical utility of the 3D reconstructions for navigation, especially when using 20–30 views, and highlighted the standardization’s benefit for anatomical clarity. Benchmarking via quantitative 2D metrics (PSNR/SSIM) confirmed performance trade-offs compared to idealized settings, but also validated the improvement gained from standardization over raw inputs. This work demonstrates the feasibility of instance-based volumetric reconstruction from arbitrary sparse-view X-rays, advancing intraoperative 3D imaging for surgical navigation. Code and data to reproduce our results is made available at https://github.com/MrMonk3y/IXGS.

User perception and anatomical understanding from use of 3D anatomy technology by exercise science students: A pilot study

Lymph nodes (LNs) function as pharmacological sanctuary sites in HIV and metastatic cancer due to anatomical barriers that limit drug penetration. Accurate 3D reconstructions of lymph node architecture are essential for computational modeling of drug transport, yet existing methods lack compartment-specific resolution, accessibility, or throughput. Here, we present a scalable, high-fidelity pipeline for the 3D anatomical reconstruction of murine LNs, integrating optimized vibratome sectioning, multiplexed immunofluorescence staining, confocal microscopy, and custom automated segmentation algorithms. Our method precisely reconstructs key LN compartments, including lobules and high endothelial venules, with high spatial accuracy, achieving Sørensen-Dice indices >0.93 for lobules and contour-matching scores up to 77% for vasculature, as validated by quantitative comparison to manual segmentation. Compared to existing methodologies, this pipeline markedly reduces reagent usage (∼88%), labor time (∼97%), and technical complexity, offering a broadly accessible and efficient approach to highfidelity 3D anatomical reconstruction of LN architecture. These digital twins can support computational simulations of drug distribution, immune cell trafficking, and spatial pharmacokinetics, providing critical insights into LN-resident disease mechanisms and informing therapeutic design.

Radiography is routinely used to evaluate the pediatric and young adult pelvis and hips. Unfortunately, the 2D nature of this imaging modality is insufficient in accurately depicting and evaluating complex 3D anatomical structures. In contrast, computed tomography (CT) provides exquisite details of 3D anatomy, typically at the expense of a higher radiation dose. Recent studies have suggested that ultra-low-dose CT (ULDCT) with tin filtration may overcome this diagnostic imaging dilemma by offering high-quality CT images with reduced radiation exposure. To compare patient-specific radiation exposure of diagnostic-quality hip ULDCTs and pelvic radiographs and thereby validate an optimized clinical protocol for hip ULDCT imaging in pediatric and young adult patients. We retrospectively searched the image archive at our large tertiary children's hospital for hip CTs and anteroposterior (AP) pelvic radiographs performed within 6months of each other (Dec2023-May2024). The inclusion criteria were (1) hip CTs performed in accordance with our established ULDCT imaging protocol and (2) AP pelvic radiographs acquired in accordance with the American College of Radiology (ACR) guidelines. To calculate the effective doses of the pelvic radiographs and hip CTs, we used the National Cancer Institute dosimetry system for Radiography and Fluoroscopy (NCIRF) and Computed Tomography (NCICT), respectively. A paired two-tailed t-test was used to compare the effective doses of the hip CTs and AP pelvic radiographs. The study cohort included 29 patients (9 males, 20 females), stratified into the pediatric group (<18years, n=17), young adult group (18-30years, n=12), and entire cohort, with mean ages of 10.7 (SD, 6.0), 22.3 (SD, 3.7), and 15.5 (SD, 6.9) years, respectively. The average effective doses from ULDCT were 0.33mSv (pediatric), 0.23mSv (young adult), and 0.29mSv (entire cohort), not significantly different from AP pelvic radiograph doses of 0.26, 0.29, and 0.27mSv, respectively. In contrast, cumulative radiographic doses were significantly higher at 0.73mSv, 0.76mSv, and 0.74mSv. ULDCT is a clinically feasible approach for pediatric and young adult hip imaging, offering diagnostic-quality CT images withsubstantially reduced radiation exposure (at a radiation dose level comparable to that of a single AP pelvic radiograph).

Targeting the glabellar frown lines with OnabotulinumtoxinA: In-silico evidence supporting concentrated, low-volume injection protocols.

This paper presents the outcomes of the project for applications of 3D printing technology in healthcare. The anatomical 3D modelling was carried out, and the 3D digital anatomical models were developed from the CT scan medical images, through medical images acquisition, segmentation, preparation, 3D printing and post-processing processes. Furthermore, the 3D digital models were converted into 3D physical models through Fused Deposition Modeling (FDM) and Stereolithography (SLA) 3D printing technologies. The segmentation and preparation processes were performed by employing 3D Slicer V5.8.0 and Meshmixer Autodesk V3.5software, respectively. The 3D digital models were prepared for printing using GrabCAD print V1.88 and Preform V3.36.0 software for FDM and SLA 3D printing technologies, respectively. During the models’ printing preparations, the printing parameters’ settings were performed, and the G-Codes were generated, which then sent to the printers. The printed models are to be used for training and research at University of Namibia. In addition to manual segmentation, AI-based segmentation which is an automated segmentation was also performed, and the models generated from the two segmentation methods were compared.

Surgery plays a key role in treating neuroblastoma. To assist surgical planning, anatomical 3D models derived from the segmentation of anatomical structures on MRI scans are often used. Automation using deep learning can make segmentations less time-consuming and more reliable. We organized the Surgical Planning in PedIatric Neuroblastoma (SPPIN) challenge, to stimulate developments and benchmarking of automatic segmentation of neuroblastoma on MRI. SPPIN is the first segmentation challenge in extracranial pediatric oncology. Nine teams provided a valid submission. Evaluation was based on the Dice similarity coefficient (Dice score), the 95th percentile of the Hausdorff distance (HD95), and the volumetric similarity (VS). A combination of these scores determined the ranking of the teams. The spread in the median evaluation scores per team was large (Dice: 0.21–0.82; HD95: 63.31–7.69; VS: 0.31–0.91). The top-performing team achieved a median Dice score of 0.82 (with an HD95 of 7.69 mm and a VS of 0.91) using a large, pre-trained model. However, in the pre-operative segmentations, significantly lower evaluation scores were observed. Our results indicate that pre-training might be useful in small, pediatric datasets. Although the general results of the winning team were high, they were insufficient to use for surgical planning in small, pre-operative tumors.

Patient-specific knee radiofrequency ablation using contrast spread analysis and 3D anatomy knowledge: Teaching Images.

CPAK oversimplifies the complex 3D anatomy of the bony knee: Role of 3D CT for improved analysis - a schematic overview.

Due to the unique nature of the basal structures of the cerebrum, only a limited portion is exposed during surgery, leading to potential risk of damage to surrounding structures. The white matter fiber tracts in the basal cerebrum may be more critical than the cortex in determining the extent of resection. A thorough understanding of the 3D anatomy of these fiber tracts is essential for planning safe and precise surgical approaches and provides an anatomical foundation for studying brain function. This study aimed to examine the topographical anatomy of the fiber tracts and subcortical gray matter in the basal cerebrum, as well as their anatomical relationships with the cerebral cortex, ventricles, and associated nuclei. Using fiber dissection techniques and magnification ranging from ×6 to ×40, the authors studied 10 formalin-fixed human brains. The study focused on the fiber tracts and subcortical nuclei in the basal cerebrum, including the hippocampus, amygdala, and nucleus accumbens, and their relationships were documented through 3D photography. The topographical relationships between the commissural, projection, and association fibers and the significant nuclei in the basal cerebrum were identified. Notable landmarks related to the fiber tracts include the cortical gyri and sulci, major basal nuclei, and lateral ventricles. The fiber tracts also exhibited consistent interrelationships. The 3D microsurgical anatomy of the basal cerebrum provides valuable insights for planning precise and safe surgical approaches and offers anatomical evidence for further studies on brain function.

BackgroundTraditional cadaveric dissection is considered the gold standard in anatomical education; however, its accessibility is limited by ethical, logistical, and financial constraints. Recent advancements in three-dimensional (3D) scanning technologies provide an alternative approach that enhances anatomical visualization while preserving the fidelity of real human specimens.AimThis study aimed to create digitized 3D models of dissected human cadaveric specimens using a handheld structured-light scanner, thus providing a sustainable and accessible resource for educational and clinical applications.MethodsEight human cadaveric specimens were dissected and scanned using the Artec 3D Spider handheld scanner. The obtained scans were processed in Artec Studio 17 Professional and further processed in Blender software. Finalized 3D models were exported in.MP4 format and paired with two-dimensional (2D) images for enhanced anatomical understanding.ResultsA total of 12 anatomical 3D models were successfully created, capturing detailed anatomical landmarks with a resolution of 0.1 mm and an accuracy of 0.05 mm. The models encompassed key anatomical regions or organs, including the brain, skull, face, neck, thorax, heart, abdomen, pelvis, and lower limb. The combination of 3D models alongside 2D images allowed for interactive and immersive learning, as well as improving spatial comprehension of complex anatomical structures.ConclusionThe use of high-fidelity 3D scanning technology provides a promising alternative to traditional dissection by offering an accessible, sustainable, and detailed representation of spatial relationships in the human body. This approach enhances medical education and clinical practice, bridging the gap between theoretical knowledge and practical application.

Plastination is widely used to preserve adult and juvenile cadavers, but its effectiveness in fetal specimens requires further validation. This study aimed to plastinate human fetuses from an archival collection that had been stored in 10% formalin. It evaluated the microbiological safety of the specimens after handling and storage, and assessed their educational impact on first-year students in the Obstetrics and Childcare program. The plastination protocol involved cold acetone dehydration, vacuum-based silicone impregnation, and anatomical positioning to ensure structural fidelity. Microbiological analysis using MALDI-TOF confirmed the absence of fungal and bacterial contamination, supporting the biosafety of plastinated fetuses during repeated handling. The specimens were used in hands-on sessions with first-year obstetrics students, who completed a satisfaction survey reporting high levels of engagement, improved understanding of fetal development, and increased confidence in identifying key anatomical structures. Additionally, 3D reconstruction of one plastinated fetus was performed to illustrate the potential of digital technologies for future anatomical education. The study also addresses ethical considerations related to the use of archival fetal collections, emphasizing the importance of responsible preservation practices and the potential of plastination and 3D reconstruction to reduce dependence on original specimens while upholding educational and ethical standards.