Comparison of Three 3D Segmentation Software Tools for Hip Surgical Planning

Long-term posttransplant-related bone volumetric changes in eight face transplant recipients - A single-center retrospective case series

BACKGROUND

The long-term stability of allograft or native bone in facial vascularized composite allograft (fVCA) recipients is unclear. This study quantified long-term bone volume changes in facial transplants.

METHODS

Computed tomography scans of eight fVCA recipients (2011-2023) were analyzed with Materialise Mimics. Native bone (soft tissue-only VCAs, n=4) and allotransplanted bone (n=4) were compared. Median bone volumes were assessed for significance using the WilcoxonRanked-Sum Test.

RESULTS

Mean follow-up was 10 years (range 5-13). A significant median decrease in both mandibular (-6520 mm3; p=0.0078) and maxillary (-3548 mm3; p=0.0078) bone volumes was seen in all patients, irrespective of bone origin. Median bone volume loss was -9.92% in the bony allograft cohort and -22.60% in the soft tissue-only cohort, respectively. The histopathological analysis of the limited samples (n=2) showed physiological bone even after ten years.

CONCLUSION

Patients with allotransplanted bone showed less pronounced volume loss compared to those with native bone receiving soft tissue-only allografts. This finding suggests that allotransplanted vascularized bone in fVCAs may not be a primary target of chronic rejection processes that compromise bone volume stability and functionality. Bone volume changes are likely influenced by multiple factors, such as tooth loss, nutrition, chronic immunosuppression (e.g., steroids), mechanical stress/load, varying bone remodeling rates, and other medical comorbidities. Further research is needed to clarify the factors affecting bone volume and remodeling after fVCA.

Retrospective MRI analysis of 418 adult shoulder joints to assess the physiological morphology of the glenoid in a low-grade osteoarthritic population

BACKGROUND

Due to the difference in size between the humeral head and the glenoid, the shoulder joint is prone to instability. Therefore, the reconstruction of the physiological joint morphology is of great importance in shoulder joint preservation and replacement surgery. The aim of this study was to describe physiological reference parameters for the morphology of the glenoid for the first time.

MATERIAL AND METHODS

MRI images of the shoulder joints of 418 patients (mean age: 50.6 years [± 16.3]) were retrospectively analysed in a low-grade osteoarthritic population. The glenoid distance in coronal (GDc) and axial view (GDa), glenoid inclination (GI) and version (GV) as well as scapula neck length (SNL) were measured. Parameters were studied in association for age, gender, side and degeneration grade.

RESULTS

Mean GDc was 33.4 mm (± 3.6), mean GDa 26.8 mm (± 3.2), mean GI 10.5° (± 6.4), mean GV -0.4 mm (± 5.4) and mean SNL was 33.4 mm (± 4.7). GDa was significant higher in right shoulders (p < 0.001). GDc and GDa showed significant higher mean values in older patients (p < 0.001) and in shoulders with more severe degenerative changes (p < 0.05). While GDc, GDa and SNL were significant larger in male patients (p < 0.001), GI had a higher mean value in female shoulders (p = 0.021).

CONCLUSION

Age, gender and shoulder joint degeneration influence changes in the morphological parameters of the glenoid. These findings have to be considered in shoulder diagnostics and surgery.

CLINICAL TRIAL NUMBER

Not applicable.

Context-Aware Dual-Task Deep Network for Concurrent Bone Segmentation and Clinical Assessment to Enhance Shoulder Arthroplasty Preoperative planning

Goal: Effective preoperative planning for shoulder joint replacement requires accurate glenohumeral joint (GH) digital surfaces and reliable clinical staging. Methods: xCEL-UNet was designed as a dual-task deep network for humerus and scapula bone reconstruction in CT scans, and assessment of three GH joint clinical conditions, namely osteophyte size (OS), joint space reduction (JS), and humeroscapular alignment (HSA). Results: Trained on a dataset of 571 patients, the model optimized segmentation and classification through transfer learning. It achieved median root mean squared errors of 0.31 and 0.24 mm, and Hausdorff distances of 2.35 and 3.28 mm for the humerus and scapula, respectively. Classification accuracy was 91 for OS, 93 for JS, and 85% for HSA. GradCAM-based activation maps validated the network's interpretability. Conclusions: this framework delivers accurate 3D bone surface reconstructions and dependable clinical assessments of the GH joint, offering robust support for therapeutic decision-making in shoulder arthroplasty.

A Low-Cost Workflow to Generate Virtual and Physical Three-Dimensional Models of Cardiac Structures

PURPOSE

Three-dimensional modeling and printing (3DMP) of anatomical structures from cross-sectional imaging data can enhance the understanding of spatial relationships in complex congenital heart defects. Partially due to the substantial financial, material and personnel resources required, 3DMP is not yet universally used. Here, we describe a workflow that addresses and eliminates these drawbacks.

DESCRIPTION

The workflow utilizes the open-source software "3D Slicer" (The Slicer Community) and "Blender" (Blender Foundation) for segmentation and post-editing of datasets. This approach enables the generation of virtual or physical 3D models. The physical models are printed using a standard fused deposition modeling printer.

EVALUATION

The financial challenges that likely constrain the wider use of 3DMP are largely addressed by this approach. However, the workflow still requires a considerable amount of time to manually segment the imaging data.

CONCLUSIONS

Three-dimensional modeling and printing might improve planning and safety of congenital cardiac surgical treatment. Furthermore, it is a useful tool for education of parents and medical professionals. This workflow increases its suitability for routine use also in regions with low economic resources.

The untapped potential of 3D virtualization using high resolution scanner-based and photogrammetry technologies for bone bank digital modeling

Three-dimensional (3D) scanning technologies could transform medical practices by creating virtual tissue banks. In bone transplantation, new approaches are needed to provide surgeons with accurate tissue measurements while minimizing contamination risks and avoiding repeated freeze-thaw cycles of banked tissues. This study evaluates three prominent non-contact 3D scanning methods-structured light scanning (SLG), laser scanning (LAS), and photogrammetry (PHG)-to support tissue banking operations. We conducted a thorough examination of each technology and the precision of the 3D scanned bones using relevant anatomical specimens under sterile conditions. Cranial caps were scanned as separate inner and outer surfaces, automatically aligned, and merged with post-processing. A colorimetric analysis based on CIEDE2000 was performed, and the results were compared with questionnaires distributed among neurosurgeons. The findings indicate that certain 3D scanning methods were more appropriate for specific bones. Among the technologies, SLG emerged as optimal for tissue banking, offering a superior balance of accuracy, minimal distortion, cost-efficiency, and ease of use. All methods slightly underestimated the volume of the specimens in their virtual models. According to the colorimetric analysis and the questionnaires given to the neurosurgeons, our low-cost PHG system performed better than others in capturing cranial caps, although it exhibited the least dimensional accuracy. In conclusion, this study provides valuable insights for surgeons and tissue bank personnel in selecting the most efficient 3D non-contact scanning technology and optimizing protocols for modernized tissue banking. Future work will advance towards smart healthcare solutions, explore the development of virtual tissue banks.

Design of the distribution of iron oxide (Fe3O4) nano-particle drug in realistic cholangiocarcinoma model and the simulation of temperature increase during magnetic induction hyperthermia

The prognosis for Cholangiocarcinoma (CCA) is unfavorable, necessitating the development of new therapeutic approach such as magnetic hyperthermia therapy (MHT) which is induced by magnetic nano-particle (MNPs) drug to bridge the treatment gap. Given the deep location of CCA within the abdominal cavity and proximity to vital organs, accurately predict the individualized treatment effects and safety brought by the distribution of MNPs in tumor will be crucial for the advancement of MHT in CCA. The Mimics software was used in this study to conduct three-dimensional reconstruction of abdominal computed tomography (CT) and magnetic reso-nance imaging images from clinical patients, resulting in the generation of a realistic digital geometric model representing the human biliary tract and its adjacent structures. Subsequently, The COMSOL Multiphysics software was utilized for modeling CCA and calculating the heat transfer law resulting from the multi-regional distribution of MNPs in CCA. The temperature within the central region of irregular CCA measured approximately 46°C, and most areas within the tumor displayed temperatures surpassing 41°C. The temperature of the inner edge of CCA is only 39 ∼ 41℃, however, it can be ameliorated by adjusting the local drug concentration through simulation system. For CCA with diverse morphologies and anatomical locations, the multi-regional distribution patterns of intratumoral MNPs and a slight overlap of drug distribution areas synergistically enhance intratumoral temperature while ensuring treatment safety. The present study highlights the practicality and imperative of incorporating personalized intratumoral MNPs distribution strategy into clinical practice for MHT, which can be achieved through the development of an integrated simulation system which incorporates medical image data and numerical calculations.

A Rapid Head Organ Localization System Based on Clinically Realistic Images: A 3D Two Step Progressive Registration Method with CVH Anatomical Knowledge Mapping

Rapid localization of ROI (Region of Interest) for tomographic medical images (TMIs) is an important foundation for efficient image reading, computer-aided education, and well-informed rights of patients. However, due to the multimodality of clinical TMIs, the complexity of anatomy, and the deformation of organs caused by diseases, it is difficult to have a universal and low-cost method for ROI organ localization. This article focuses on actual concerns of TMIs from medical students, engineers, interdisciplinary researchers, and patients, exploring a universal registration method between the clinical CT/MRI dataset and CVH (Chinese Visible Human) to locate the organ ROI in a low-cost and lightweight way. The proposed method is called Two-step Progressive Registration (TSPR), where the first registration adopts "eye-nose triangle" features to determine the spatial orientation, and the second registration adopts the circular contour to determine the spatial scale, ultimately achieving CVH anatomical knowledge automated mapping. Through experimentation with representative clinical TMIs, the registration results are capable of labeling the ROI in the images well and can adapt to the deformation problem of ROI, as well as local extremum problems that are prone to occur in inter-subject registration. Unlike the ideal requirements for TMIs' data quality in laboratory research, TSPR has good adaptability to incomplete and non-thin-layer quality in real clinical data in a low-cost and lightweight way. This helps medical students, engineers, and interdisciplinary researchers independently browse images, receive computer-aided education, and provide patients with better access to well-informed services, highlighting the potential of digital public health and medical education.

Narrative review of patient-specific 3D visualization and reality technologies in skull base neurosurgery: enhancements in surgical training, planning, and navigation

Recent advances in medical imaging, computer vision, 3-dimensional (3D) modeling, and artificial intelligence (AI) integrated technologies paved the way for generating patient-specific, realistic 3D visualization of pathological anatomy in neurosurgical conditions. Immersive surgical simulations through augmented reality (AR), virtual reality (VR), mixed reality (MxR), extended reality (XR), and 3D printing applications further increased their utilization in current surgical practice and training. This narrative review investigates state-of-the-art studies, the limitations of these technologies, and future directions for them in the field of skull base surgery. We begin with a methodology summary to create accurate 3D models customized for each patient by combining several imaging modalities. Then, we explore how these models are employed in surgical planning simulations and real-time navigation systems in surgical procedures involving the anterior, middle, and posterior cranial skull bases, including endoscopic and open microsurgical operations. We also evaluate their influence on surgical decision-making, performance, and education. Accumulating evidence demonstrates that these technologies can enhance the visibility of the neuroanatomical structures situated at the cranial base and assist surgeons in preoperative planning and intraoperative navigation, thus showing great potential to improve surgical results and reduce complications. Maximum effectiveness can be achieved in approach selection, patient positioning, craniotomy placement, anti-target avoidance, and comprehension of spatial interrelationships of neurovascular structures. Finally, we present the obstacles and possible future paths for the broader implementation of these groundbreaking methods in neurosurgery, highlighting the importance of ongoing technological advancements and interdisciplinary collaboration to improve the accuracy and usefulness of 3D visualization and reality technologies in skull base surgeries.

Relationship Between Temporomandibular Joint Effusion, Pain, and Jaw Function Limitation: A 2D and 3D Comparative Study

Purpose

This study aimed to investigate the relationship between temporomandibular joint (TMJ) effusion and TMJ pain, as well as jaw function limitation in patients via two-dimensional (2D) and three-dimensional (3D) magnetic resonance imaging (MRI) evaluation.

Patients and Methods

121 patients diagnosed with temporomandibular disorder (TMD) were included. TMJ effusion was assessed qualitatively using MRI and quantified with 3D Slicer software, then graded accordingly. In addition, a visual analogue scale (VAS) was employed for pain reporting and an 8-item Jaw Functional Limitations Scale (JFLS-8) was utilized to evaluate jaw function limitation. Statistical analyses were performed appropriately for group comparisons and association determination. A probability of p<0.05 was considered statistically significant.

Results

2D qualitative and 3D quantitative strategies were in high agreement for TMJ effusion grades (κ = 0.766). No significant associations were found between joint effusion and TMJ pain, nor with disc displacement and JLFS-8 scores. Moreover, the binary logistic regression analysis showed significant association between sex and the presence of TMJ effusion, exhibiting an Odds Ratio of 5.168 for females (p = 0.008).

Conclusion

2D qualitative evaluation was as effective as 3D quantitative assessment for TMJ effusion diagnosis. No significant associations were found between TMJ effusion and TMJ pain, disc displacement or jaw function limitation. However, it was suggested that female patients suffering from TMD may be at a risk for TMJ effusion. Further prospective research is needed for validation.

Automated 2D and 3D finite element overclosure adjustment and mesh morphing using generalized regression neural networks

Computer representations of three-dimensional (3D) geometries are crucial for simulating systems and processes in engineering and science. In medicine, and more specifically, biomechanics and orthopaedics, obtaining and using 3D geometries is critical to many workflows. However, while many tools exist to obtain 3D geometries of organic structures, little has been done to make them usable for their intended medical purposes. Furthermore, many of the proposed tools are proprietary, limiting their use. This work introduces two novel algorithms based on Generalized Regression Neural Networks (GRNN) and 4 processes to perform mesh morphing and overclosure adjustment. These algorithms were implemented, and test cases were used to validate them against existing algorithms to demonstrate improved performance. The resulting algorithms demonstrate improvements to existing techniques based on Radial Basis Function (RBF) networks by converting to GRNN-based implementations. Implementations in MATLAB of these algorithms and the source code are publicly available at the following locations: https://github.com/thor-andreassen/femors; https://simtk.org/projects/femors-rbf; https://www.mathworks.com/matlabcentral/fileexchange/120353-finite-element-morphing-overclosure-reduction-and-slicing.

Exploring Fracture Patterns: Assessing Representation Methods for Bone Fracture Simulation

Fracture pattern acquisition and representation in human bones play a crucial role in medical simulation, diagnostics, and treatment planning. This article presents a comprehensive review of methodologies employed in acquiring and representing bone fracture patterns. Several techniques, including segmentation algorithms, curvature analysis, and deep learning-based approaches, are reviewed to determine their effectiveness in accurately identifying fracture zones. Additionally, diverse methods for representing fracture patterns are evaluated. The challenges inherent in detecting accurate fracture zones from medical images, the complexities arising from multifragmentary fractures, and the need to automate fracture reduction processes are elucidated. A detailed analysis of the suitability of each representation method for specific medical applications, such as simulation systems, surgical interventions, and educational purposes, is provided. The study explores insights from a broad spectrum of research articles, encompassing diverse methodologies and perspectives. This review elucidates potential directions for future research and contributes to advancements in comprehending the acquisition and representation of fracture patterns in human bone.

Proximal femoral defect classifications in revision total hip arthroplasty from X-rays imaging to advanced 3D imaging: a narrative review

Background and Objective

Femoral bone defect in hip arthroplasty revision surgery represents a complex problem, and the treatment is a challenge for orthopedic surgeons called to assess the residual bone stock in an altered anatomy and obtain stability for the new implant. Classification systems available are mostly based on X-rays two-dimensional images and lack of accuracy and reproducibility and comprehensive therapeutic algorithms. However, there is no record of any classification based on computed tomography (CT)-scan images or three-dimensional (3D) modeling modern techniques. We aimed to review the current literature around femoral defect classifications (FDCs) analyzing their different rationale basis, reliability and accuracy, and their benefit in clinical practice. Moreover, we highlighted the role of CT scan-based 3D modeling techniques in the setting of femoral bone defects and revision hip arthroplasty.

Methods

A narrative review was conducted. The articles were selected from the PubMed and Scopus medical database updated to March 2023. All Level-I to IV studies in the English language were considered for inclusion. The research was performed using relevant search term items: "femoral defects", "classification", "radiographic", "revision hip arthroplasty", "CT scan" and "3D" and we included only articles that evaluated the accuracy or reliability (or both) of the different femoral bone defects classification system.

Key Content and Findings

Our search yielded 408 results, of which 17 were deemed highly relevant. We found seven X-ray-based classification systems which have been attempted to quantify the degree of bone loss with low to good reproducibility. The most used classification system for femoral bone defects were the AAOS and Paprosky classification, which also offers a clinical therapeutic algorithm. In 2021, the FDC interestingly showed a new simple classification system with sub-optimal reproducibility and a practical therapeutic algorithm. Despite the numerous classification system of femoral defects, none of them comprehends the use of CT scan and 3D imaging technologies.

Conclusions

Traditional X-rays-based classification system are still widely used event if their intra-observer and inter-observer reliability is sub-optimal. 3D modeling techniques represent an important diagnostic tool that could improve the understanding of bone defects and residual bone supportive structures, allowing to elaborate new, more precise, classification systems.

Carpal kinematics and morphological correlates of wrist ulnar deviation mobility in nonhuman anthropoid primates

OBJECTIVES

Primates employ wrist ulnar deviation during a variety of locomotor and manipulative behaviors. Extant hominoids share a derived condition in which the ulnar styloid process has limited articulation or is completely separated from the proximal carpals, which is often hypothesized to increase ulnar deviation range of motion. Acute angulation of the hamate's triquetral facet is also hypothesized to facilitate ulnar deviation mobility and mechanics. In this study, we test these longstanding ideas.

METHODS

Three-dimensional (3D) carpal kinematics were examined using a cadaveric sample of Pan troglodytes, Pongo sp., and five monkey species. Ulnar styloid projection and orientation of the hamate's triquetral facet were quantified using 3D models.

RESULTS

Although carpal rotation patterns in Pan and Pongo were uniquely similar in some respects, P. troglodytes exhibited overall kinematic similarity with large terrestrial cercopithecoids (Papio and Mandrillus). Pongo, Macaca, and Ateles had high wrist ulnar deviation ranges of motion, but Pongo did this via a unique mechanism. In Pongo, the triquetrum functions as a distal carpal rather than part of the proximal row. Ulnar styloid projection and wrist ulnar deviation range of motion were not correlated but ulnar deviation range of motion and the triquetrohamate facet orientation were correlated.

CONCLUSIONS

Increased ulnar deviation mobility is not the function of ulnar styloid withdrawal in hominoids. Instead, this feature probably reduces stress on the ulnar side wrist or is a byproduct of adaptations that increase supination. Orientation of the hamate's triquetral facet offers some potential to reconstruct ulnar deviation mobility in extinct primates.

Methodology for Preoperative Planning of Bone Deformities Using Three-dimensional Modeling Software

Rapid prototyping technology, known as three-dimensional (3D) printing, and its use in the medical field are advancing. Studies on severe bone deformity treatment with 3D printing showed benefits in postoperative outcomes thanks to this technology. Even so, preoperative planning guidance for surgeons is lacking. This technical note describes a practical step-by-step guide to help surgeons use this technology to optimize the therapeutic plan with free license software and an intuitive interface. This study aims to organize the 3D modeling process using a preoperative computed tomography (CT) scan. This technology allows a deeper understanding of the case and its particularities, such as the direction, planes, and dimensions of the deformity. Planning considering these topics may reduce the surgical time and result in better functional outcomes by understanding the deformity and how to correct it. Associating planning via software with 3D printing can further enhance this therapeutic method.

Combined Edge Loss UNet for Optimized Segmentation in Total Knee Arthroplasty Preoperative Planning

Bone segmentation and 3D reconstruction are crucial for total knee arthroplasty (TKA) surgical planning with Personalized Surgical Instruments (PSIs). Traditional semi-automatic approaches are time-consuming and operator-dependent, although they provide reliable outcomes. Moreover, the recent expansion of artificial intelligence (AI) tools towards various medical domains is transforming modern healthcare. Accordingly, this study introduces an automated AI-based pipeline to replace the current operator-based tibia and femur 3D reconstruction procedure enhancing TKA preoperative planning. Leveraging an 822 CT image dataset, a novel patch-based method and an improved segmentation label generation algorithm were coupled to a Combined Edge Loss UNet (CEL-UNet), a novel CNN architecture featuring an additional decoding branch to boost the bone boundary segmentation. Root Mean Squared Errors and Hausdorff distances compared the predicted surfaces to the reference bones showing median and interquartile values of 0.26 (0.19-0.36) mm and 0.24 (0.18-0.32) mm, and of 1.06 (0.73-2.15) mm and 1.43 (0.82-2.86) mm for the tibia and femur, respectively, outperforming previous results of our group, state-of-the-art, and UNet models. A feasibility analysis for a PSI-based surgical plan revealed sub-millimetric distance errors and sub-angular alignment uncertainties in the PSI contact areas and the two cutting planes. Finally, operational environment testing underscored the pipeline's efficiency. More than half of the processed cases complied with the PSI prototyping requirements, reducing the overall time from 35 min to 13.1 s, while the remaining ones underwent a manual refinement step to achieve such PSI requirements, performing the procedure four to eleven times faster than the manufacturer standards. To conclude, this research advocates the need for real-world applicability and optimization of AI solutions in orthopedic surgical practice.

Three-Dimensional Segmentation Assisted with Clustering Analysis for Surface and Volume Measurements of Equine Incisor in Multidetector Computed Tomography Data Sets

Dental diagnostic imaging has progressed towards the use of advanced technologies such as 3D image processing. Since multidetector computed tomography (CT) is widely available in equine clinics, CT-based anatomical 3D models, segmentations, and measurements have become clinically applicable. This study aimed to use a 3D segmentation of CT images and volumetric measurements to investigate differences in the surface area and volume of equine incisors. The 3D Slicer was used to segment single incisors of 50 horses' heads and to extract volumetric features. Axial vertical symmetry, but not horizontal, of the incisors was evidenced. The surface area and volume differed significantly between temporary and permanent incisors, allowing for easy eruption-related clustering of the CT-based 3D images with an accuracy of >0.75. The volumetric features differed partially between center, intermediate, and corner incisors, allowing for moderate location-related clustering with an accuracy of >0.69. The volumetric features of mandibular incisors' equine odontoclastic tooth resorption and hypercementosis (EOTRH) degrees were more than those for maxillary incisors; thus, the accuracy of EOTRH degree-related clustering was >0.72 for the mandibula and >0.33 for the maxilla. The CT-based 3D images of equine incisors can be successfully segmented using the routinely achieved multidetector CT data sets and the proposed data-processing approaches.

Virtual Reality for Preoperative Planning in Complex Surgical Oncology: A Single-Center Experience

INTRODUCTION

Virtual reality models (VRM) are three-dimensional (3D) simulations of two-dimensional (2D) images, creating a more accurate mental representation of patient-specific anatomy.

METHODS

Patients were retrospectively identified who underwent complex oncologic resections whose operations differed from preoperative plans between April 2018 and April 2019. Virtual reality modeling was performed based on preoperative 2D images to assess feasibility of use of this technology to create models. Preoperative plans made based upon 2D imaging versus VRM were compared to the final operations performed. Once the use of VRM to create preoperative plans was deemed feasible, individuals undergoing complex oncologic resections whose operative plans were difficult to define preoperatively were enrolled prospectively from July 2019 to December 2021. Preoperative plans made based upon 2D imaging and VRM by both the operating surgeon and a consulting surgeon were compared to the operation performed. Confidence in each operative plan was also measured.

RESULTS

Twenty patients were identified, seven retrospective and 13 prospective, with tumors of the liver, pancreas, retroperitoneum, stomach, and soft tissue. Retrospectively, VRM were unable to be created in one patient due to a poor quality 2D image; the remainder (86%) were successfully able to be created and examined. Virtual reality modeling more clearly defined the extent of resection in 50% of successful cases. Prospectively, all VRM were successfully performed. The concordance of the operative plan with VRM was higher than with 2D imaging (92% versus 54% for the operating surgeon and 69% versus 23% for the consulting surgeon). Confidence in the operative plan after VRM compared to 2D imaging also increased for both surgeons (by 15% and 8% for the operating and consulting surgeons, respectively).

CONCLUSIONS

Virtual reality modeling is feasible and may improve preoperative planning compared to 2D imaging. Further investigation is warranted.

Assessing the impact of 3D image segmentation workshops on anatomical education and image interpretation: A prospective pilot study

Three-dimensional (3D) segmentation, a process involving digitally marking anatomical structures on cross-sectional images such as computed tomography (CT), and 3D printing (3DP) are being increasingly utilized in medical education. Exposure to this technology within medical schools and hospitals remains limited in the United Kingdom. M3dicube UK, a national medical student, and junior doctor-led 3DP interest group piloted a 3D image segmentation workshop to gauge the impact of incorporating 3D segmentation technology on anatomical education. The workshop, piloted with medical students and doctors within the United Kingdom between September 2020 and 2021, introduced participants to 3D segmentation and offered practical experience segmenting anatomical models. Thirty-three participants were recruited, with 33 pre-workshop and 24 post-workshop surveys completed. Two-tailed t-tests were used to compare mean scores. From pre- to post-workshop, increases were noted in participants' confidence in interpreting CT scans (2.36 to 3.13, p = 0.010) and interacting with 3D printing technology (2.15 to 3.33, p = 0.00053), perceived utility of creating 3D models to aid image interpretation (4.18 to 4.45, p = 0.0027), improved anatomical understanding (4.2 to 4.7, p = 0.0018), and utility in medical education (4.45 to 4.79, p = 0.077). This pilot study provides early evidence of the utility of exposing medical students and healthcare professionals in the United Kingdom to 3D segmentation as part of their anatomical education, with additional benefit in imaging interpretation ability.

Effect of Sterilization on the Dimensional and Mechanical Behavior of Polylactic Acid Pieces Produced by Fused Deposition Modeling

To successfully implement additive manufacturing (AM) techniques for custom medical device (MD) production with low-cost resources, it is imperative to understand the effect of common and affordable sterilization processes, such as formaldehyde or steam sterilization, on pieces manufactured by AM. In this way, the performance of low-risk MDs, such as biomodels and surgical guides, could be assessed for complying with safety, precision, and MD delivery requirements. In this context, the aim of the present work was to evaluate the effect of formaldehyde and steam sterilization on the dimensional and mechanical stability of standard polylactic acid (PLA) test pieces produced by fused deposition modeling (FDM). To achieve this, PLA samples were sterilized according to the sterilization protocol of a public hospital in the city of Bucaramanga, Colombia. Significant changes regarding mechanical and dimensional properties were found as a function of manufacturing parameters. This research attempts to contribute to the development of affordable approaches for the fabrication of functional and customized medical devices through AM technologies, an issue of particular interest for low- and middle-income countries.

Overall Accuracy of Radiological Digital Planning for Total Hip Arthroplasty in a Specialized Orthopaedics Hospital

Preoperative radiological planning is a key factor in the prediction of implant size and positioning that influences surgical time, the risk of complications, and functional outcomes. We have tested the accuracy of the digital templating performed in our hospital for a sample of 215 patients that underwent total hip arthroplasty. We assessed the accuracy of correctly predicting implant size for the femoral and acetabular components, as well as the stem neck length. We found that our method of templating proved accurate (within one size) in 95.8% of cases for the stem and 94.9% for the cup when using the anteroposterior view only, while the lateral view was accurate in 95.8% of cases for the stem and 97.2% for the cup. Exact prediction of the stem size was obtained in 77.7% of cases using the anteroposterior view and 67.0% of cases on the lateral view, and 73.0% and 74.4% of cases for the cup on the AP and LL views, respectively. Stem neck size was predicted exactly in 75.35% of cases and within one size in 93.49% of cases. We concluded that our method of digital templating using dedicated software is highly effective in accurately predicting implant size.

A virtual biopsy study of microsatellite instability in gastric cancer based on deep learning radiomics

OBJECTIVES

This study aims to develop and validate a virtual biopsy model to predict microsatellite instability (MSI) status in preoperative gastric cancer (GC) patients based on clinical information and the radiomics of deep learning algorithms.

METHODS

A total of 223 GC patients with MSI status detected by postoperative immunohistochemical staining (IHC) were retrospectively recruited and randomly assigned to the training (n = 167) and testing (n = 56) sets in a 3:1 ratio. In the training set, 982 high-throughput radiomic features were extracted from preoperative abdominal dynamic contrast-enhanced CT (CECT) and screened. According to the deep learning multilayer perceptron (MLP), 15 optimal features were optimized to establish the radiomic feature score (Rad-score), and LASSO regression was used to screen out clinically independent predictors. Based on logistic regression, the Rad-score and clinically independent predictors were integrated to build the clinical radiomics model and visualized as a nomogram and independently verified in the testing set. The performance and clinical applicability of hybrid model in identifying MSI status were evaluated by the area under the receiver operating characteristic (AUC) curve, calibration curve, and decision curve (DCA).

RESULTS

The AUCs of the clinical image model in training set and testing set were 0.883 [95% CI: 0.822-0.945] and 0.802 [95% CI: 0.666-0.937], respectively. This hybrid model showed good consistency in the calibration curve and clinical applicability in the DCA curve, respectively.

CONCLUSIONS

Using preoperative imaging and clinical information, we developed a deep-learning-based radiomics model for the non-invasive evaluation of MSI in GC patients. This model maybe can potentially support clinical treatment decision making for GC patients.

Development of a Statistical Shape Model and Assessment of Anatomical Shape Variations in the Hemipelvis

Knowledge about anatomical shape variations in the pelvis is mandatory for selection, fitting, positioning, and fixation in pelvic surgery. The current knowledge on pelvic shape variation mostly relies on point-to-point measurements on 2D X-ray images and computed tomography (CT) slices. Three-dimensional region-specific assessments of pelvic morphology are scarce. Our aim was to develop a statistical shape model of the hemipelvis to assess anatomical shape variations in the hemipelvis. CT scans of 200 patients (100 male and 100 female) were used to obtain segmentations. An iterative closest point algorithm was performed to register these 3D segmentations, so a principal component analysis (PCA) could be performed, and a statistical shape model (SSM) of the hemipelvis was developed. The first 15 principal components (PCs) described 90% of the total shape variation, and the reconstruction ability of this SSM resulted in a root mean square error of 1.58 (95% CI: 1.53-1.63) mm. In summary, an SSM of the hemipelvis was developed, which describes the shape variations in a Caucasian population and is able to reconstruct an aberrant hemipelvis. Principal component analyses demonstrated that, in a general population, anatomical shape variations were mostly related to differences in the size of the pelvis (e.g., PC1 describes 68% of the total shape variation, which is attributed to size). Differences between the male and female pelvis were most pronounced in the iliac wing and pubic rami regions. These regions are often subject to injuries. Future clinical applications of our newly developed SSM may be relevant for SSM-based semi-automatic virtual reconstruction of a fractured hemipelvis as part of preoperative planning. Lastly, for companies, using our SSM might be interesting in order to assess which sizes of pelvic implants should be produced to provide proper-fitting implants for most of the population.

Physiological Offset Parameters of the Adult Shoulder Joint-A MRI Study of 800 Patients

Background: Humeral offset (HO) and glenoidal offset (GO) are important morphological parameters in diagnostics and therapy for shoulder pathologies. However, physiological reference values have not yet been sufficiently determined. The aim of the present study was to establish physiological reference values for shoulder offset parameters (SOPs). Methods: MRI images of the shoulder joints of 800 patients (mean age: 50.13 years [±16.01]) were analysed retrospectively. HO, GO, lateral glenoidal humeral offset (LGHO), humeral shaft axis offset (HAO) and cortical offset (CO) were measured. SOPs were examined for associations with age, gender, side and osteoarthritis. Results: The mean HO was 26.19 (±2.70), the mean GO was 61.79 (±5.67), the mean LGHO was 54.49 (±4.69), the mean HAO was 28.17 (±2.82) and the mean CO was 16.70 (±3.08). For all SOPs, significantly higher values were measured in male shoulders. There was a significantly (p < 0.001) higher mean value for HO, GO and LGHO in right shoulders. There was a significant correlation between age and LGHO, and HAO and CO, but not between age and HO or GO. Shoulders with osteoarthritis and non-osteoarthritis did not differ in the mean value of HO, GO, LGHO and HAO, except for CO (p = 0.049). Conclusion: Reference values for SOPs in the adult shoulder joint were determined for the first time. Significant gender-specific differences were found for all measured SOPs. In addition, it was seen that for some SOPs, the joint side and the patient’s age has to be taken into account in shoulder diagnostics and surgery.

Smartphone-Based Facial Scanning as a Viable Tool for Facially Driven Orthodontics?

The current paradigm shift in orthodontic treatment planning is based on facially driven diagnostics. This requires an affordable, convenient, and non-invasive solution for face scanning. Therefore, utilization of smartphones' TrueDepth sensors is very tempting. TrueDepth refers to front-facing cameras with a dot projector in Apple devices that provide real-time depth data in addition to visual information. There are several applications that tout themselves as accurate solutions for 3D scanning of the face in dentistry. Their clinical accuracy has been uncertain. This study focuses on evaluating the accuracy of the Bellus3D Dental Pro app, which uses Apple's TrueDepth sensor. The app reconstructs a virtual, high-resolution version of the face, which is available for download as a 3D object. In this paper, sixty TrueDepth scans of the face were compared to sixty corresponding facial surfaces segmented from CBCT. Difference maps were created for each pair and evaluated in specific facial regions. The results confirmed statistically significant differences in some facial regions with amplitudes greater than 3 mm, suggesting that current technology has limited applicability for clinical use. The clinical utilization of facial scanning for orthodontic evaluation, which does not require accuracy in the lip region below 3 mm, can be considered.