3D Bone Models Research Articles

High variability exists in 3D leg alignment analysis, but underlying principles that might lead to agreement on a universal framework could be identified: A systematic review.

To (1) investigate the hypothesis that there is high variability in the reported methods to derive axes and joint orientations from three-dimensional (3D) bone models to (a) perform 3D knee-related leg alignment analysis and (b) define coordinate systems for the femur, tibia and leg and (2) identify underlying principles that might lead to agreement on a universal 3D leg alignment analysis framework. A systematic review of the literature between January 2006 and June 2024 was performed. Articles explicitly reporting methods to derive axes and joint orientations from CT-based 3D bone models for alignment parameters and/or coordinate systems of the femur, tibia and leg were included. Study characteristics and reported methods were extracted and presented as a qualitative synthesis. A total of 93 studies were included. There was high variability in the reported methods to derive axes and joint orientations from 3D bone models. Nevertheless, the reported methods could be categorized into four groups, and several underlying principles of the four groups could be identified. Furthermore, the definitions of femoral and tibial coordinate systems were most frequently based on the mechanical axis (femoral, 13/19 [68%]; tibial, 13/26 [50%]) and a central medial-lateral axis (femoral, 16/19 [84%]; tibial, 12/26 [46%]); no leg coordinate system was reported. Interestingly, of the included studies that reported on leg alignment parameters (76/93, 82%), only a minority reported expressing these in a complete coordinate system (25/76, 33%). There is high variability in 3D knee-related leg alignment analysis. Therefore, universal 3D reference values for alignment parameters cannot yet be defined, and comparison of alignment parameter values between different studies is impossible. However, several underlying principles to the reported methods were identified, which could serve to reach more agreement on a future universal 3D framework for leg alignment analysis. Level I (1).

Hybrid closing-wedge DTO using PSI was selected for a patient wit severe deformity post-fracture malunion, which enable good alignment correction and patient satisfaction

BackgroundHigh tibial osteotomy (HTO) is an effective treatment option for deformity correction after fracture. However, performing precise corrective osteotomy for cases with a severe varus deformity and a significant posterior slope poses a significant challenge. Three-dimensional (3D) bone model construction and patient-specific instrumentation (PSI) created from preoperative Computed tomography (CT) may be useful tools in achieving successful outcome for such cases. The present technique describes a hybrid closing-wedge distal tuberosity tibial osteotomy (Hybrid CWDTO) using two PSIs.MethodsPreoperative planning was performed in 3D with reference to the contralateral normal lower extremity CT taken preoperatively, which was then mirrored for analysis. A full-scale bone model and two PSIs were constructed based on this plan to allow for complex correction. During surgery, osteotomy was performed using these sterilized PSIs as guides.ResultsRadiographic imaging showed that medial proximal tibial angle (MPTA) improved from 68 to 84 degrees and posterior tibial slope (PTS) improved from 19 to 6 degrees. The standing leg radiograph showed a mechanical varus alignment improvement from 12 to 3 degrees. The 2011 Knee Society Scoring system (2011 KSS) improved from 31 to 95 in objective knee indicators, from 10 to 24 in symptoms, from 14 to 40 in patient satisfaction and from 51 to 95 in activities.ConclusionHybrid CWDTO using PSIs is a useful surgical technique for alignment correction post-malunion while also achieving high patient satisfaction. This can assist surgeons in treating complex deformities that are otherwise difficult to treat.

A whole-body micro-CT scan library that captures the skeletal diversity of Lake Malawi cichlid fishes

Here we describe a dataset of freely available, readily processed, whole-body μCT-scans of 56 species (116 specimens) of Lake Malawi cichlid fishes that captures a considerable majority of the morphological variation present in this remarkable adaptive radiation. We contextualise the scanned specimens within a discussion of their respective ecomorphological groupings and suggest possible macroevolutionary studies that could be conducted with these data. In addition, we describe a methodology to efficiently μCT-scan (on average) 23 specimens per hour, limiting scanning time and alleviating the financial cost whilst maintaining high resolution. We demonstrate the utility of this method by reconstructing 3D models of multiple bones from multiple specimens within the dataset. We hope this dataset will enable further morphological study of this fascinating system and permit wider-scale comparisons with other cichlid adaptive radiations.

Haptic experience to significantly motivate anatomy learning in medical students

BackgroundCurrently, multiple tools exist to teach and learn anatomy, but finding an adequate activity is challenging. However, it can be achieved through haptic experiences, where motivation is the means of a significant learning process. This study aimed to evaluate a haptic experience to determine if a tactile and painting with color marker interactive experience, established a better learning process in comparison to the traditional 2D workshop on printed paper with photographs.MethodsPlaster bone models of the scapulae, humerus and clavicle were elaborated from a computerized scan tomography. Second year undergraduate medical students were invited to participate, where subjects were randomly assigned to the traditional 2D method or the 3D plaster bone model. A third group decided not to join any workshop. Following, all three groups were evaluated on bone landmarks and view, laterality, muscle insertions and functions. 2D and 3D workshop students were asked their opinion in a focus group and answered a survey regarding the overall perception and learning experience. Evaluation grades are presented as mean ± standard deviation, and answers from the survey are presented as percentages.ResultsThe survey demonstrated the students in the 3D model graded the experience as outstanding, and in five out of the six questions, answers were very good or excellent. In contrast, for students participating in the 2D workshop the most common answers were fair or good. The exception was the answer regarding the quiz, where both groups considered it good, despite the average among all groups not being a passing grade.ConclusionsTo learn the anatomy of the shoulder, the conventional methodology was compared with a haptic experience, where plaster bone models were used, enabling students to touch and paint on them. Based on the focus group and survey this study revealed the 3D workshop was an interactive experience where, the sense of touch and painting greatly contributed to their learning process. Even though this activity was useful in terms of learning bone landmarks, view muscle insertions, and establish relations, further activities must be developed to increase their understanding regarding their function, and its relevance in a clinical setting.

Deep Learning-Based Workflow for Bone Segmentation and 3D Modeling in Cone-Beam CT Orthopedic Imaging

In this study, a deep learning-based workflow designed for the segmentation and 3D modeling of bones in cone beam computed tomography (CBCT) orthopedic imaging is presented. This workflow uses a convolutional neural network (CNN), specifically a U-Net architecture, to perform precise bone segmentation even in challenging anatomical regions such as limbs, joints, and extremities, where bone boundaries are less distinct and densities are highly variable. The effectiveness of the proposed workflow was evaluated by comparing the generated 3D models against those obtained through other segmentation methods, including SegNet, binary thresholding, and graph cut algorithms. The accuracy of these models was quantitatively assessed using the Jaccard index, the Dice coefficient, and the Hausdorff distance metrics. The results indicate that the U-Net-based segmentation consistently outperforms other techniques, producing more accurate and reliable 3D bone models. The user interface developed for this workflow facilitates intuitive visualization and manipulation of the 3D models, enhancing the usability and effectiveness of the segmentation process in both clinical and research settings. The findings suggest that the proposed deep learning-based workflow holds significant potential for improving the accuracy of bone segmentation and the quality of 3D models derived from CBCT scans, contributing to better diagnostic and pre-surgical planning outcomes in orthopedic practice.

Modeling and Validation of Rabbit Tibia Bone by Reverse Engineering

In the medical field, 3D modeling and prototyping of hard or soft tissues of living organisms provides great convenience for surgeons before the operation and during the simulation phase of the operation. In this study, 3D regular mesh and prototype models were created from normal CT data of the right tibia bone of a New Zealand albino rabbit and the errors occurring during this creation were compared. According to the results obtained, the lowest errors occurred in the TL dimension, while the largest errors occurred in the ITDD dimension. The measured error amounts vary between 0.8% and 7.2%. Considering these error margins compared to similar studies, it is seen that the modeling is quite normal and a satisfactory way to simulate surgeons has been created. Key Words: Rabbit Tibia, 3D Bone Modeling, Additive Manufacturing

Biomechanical assessment and comparison of fixation methods for bilateral sacroiliac joint luxation in 3D-printed feline pelvic bone models.

Bilateral sacroiliac joint luxation, a condition primarily observed in cats, can significantly impact their quality of life. This study aimed to compare a control with three distinct fixation methods to identify the most robust fixation method capable of withstanding significant tensile stress. Twenty pelvic bone models of cats were made using a 3D printer with polylactic acid plastic. Each model was assembled by cutting the sacroiliac joints and pelvic girdle symphysis with a handsaw, then bonded with cyanoacrylate glue. 3D feline pelvic bone models were categorized into four significant groups, each consisting of five models. The study discovered that the three groups used distinct fixation methods: Two lag screws (DS), K-wires at the ilium wing and sacroiliac joints (TK), and K-wires at the sacroiliac joints (DK). The final group, not fixed, was the control. The results were characterized further through a mechanical compression force test using a universal testing machine. The most robust method at the sacroiliac joints, the DK technique, sustained a maximum force of up to 183.86 N while maintaining the correct bone alignment. The fixation method is more accessible and faster to implement in comparison to the DS method. The DK group exhibited the greatest maximum load capacity among all groups. Sacroiliac joint luxation treatment can effectively be addressed using the K-wires fixation method. However, the DK need space of sacral body same as DS for fixation. Further clinical study should be performed.

Analysis of healthy glenohumeral arthrokinematics using four-dimensional computed tomography throughout internal rotation and forward elevation

BackgroundThe glenohumeral joint is the most mobile joint in the human body and can translate in addition to rotating in its socket. Currently, it is not well established in literature how much the healthy humeral head translates, and how that changes overtime as people age. The objective of this study is to quantify glenohumeral joint proximity and translation in healthy patients and determine if there are any age, position, or direction related differences. MethodsThirty-one participants were recruited for this study and split into two cohorts: young (≤ 37 years old) and old (≥45 years old). Four-dimensional computed tomography scans were taken as these participants completed internal rotation to the back (IR) and forward elevation (FE). 3D bone models of the humerus and scapula were created using 3D Slicer. An inter-bone distance algorithm, and an Iterative Closest Point algorithm were used to determine glenohumeral joint proximity and translation, respectively. ResultsThis study found that older participants displayed significantly closer joint proximity (63% of glenoid surface was within 4mm of humeral head) during the middle of IR, compared to younger participants (52% of glenoid surface within 4mm of humeral head). Additionally, younger participants had significantly more translation in the Superior/Inferior direction (16% of glenoid height) compared to the Anterior/Posterior direction (10% of glenoid width) throughout IR. ConclusionThis study demonstrates the significance of translational movements within the glenohumeral joint throughout IR and FE, which will aid implant manufacturers in designing implants that will allow for more normalized glenohumeral translations.

Geometric accuracy of low-dose CT scans for use in shoulder musculoskeletal research applications

Computed tomography (CT) imaging is frequently employed in a variety of musculoskeletal research applications. Although research studies often use imaging protocols developed for clinical applications, lower dose protocols are likely possible when the goal is to reconstruct 3D bone models. Our purpose was to describe the dose-accuracy trade-off between incrementally lower-dose CT scans and the geometric reconstruction accuracy of the humerus, scapula, and clavicle. Six shoulder specimens were acquired and scanned using 5 helical CT protocols: 1) 120 kVp, 450 mA (full-dose); 2) 120 kVp, 120 mA; 3) 120 kVp, 100 mA; 4) 100 kVp, 100 mA; 5) 80 kVp, 80 mA. Scans were segmented and reconstructed into 3D surface meshes. Geometric error was assessed by comparing the surfaces of the low-dose meshes to the full-dose (gold standard) mesh and was described using mean absolute error, bias, precision, and worst-case error. All low-dose protocols resulted in a >70 % reduction in the effective dose. Lower dose scans resulted in higher geometric errors; however, error magnitudes were generally <0.5 mm. These data suggest that the effective dose associated with CT imaging can be substantially reduced without a significant loss of geometric reconstruction accuracy.

Morphological human bone features and demography controlling damage accumulation and fracture: a finite element study

Prediction of bone fracture risk is clinically challenging. Computational modeling plays a vital role in understanding bone structure and diagnosing bone diseases, leading to novel therapies. The research objectives were to demonstrate the anisotropic structure of the bone at the micro-level taking into consideration the density and subject demography, such as age, gender, body mass index (BMI), height, weight, and their roles in damage accumulation. Out of 438 developed 3D bone models at the micro-level, 46.12% were female. The age distribution ranged from 23 to 95 years. The research unfolds in two phases: micro-morphological features examination and stress distribution investigation. Models were developed using Mimics 22.0 and SolidWorks. The anisotropic material properties were defined before importing into Ansys for simulation. Computational simulations further uncovered variations in maximum von-Misses stress, highlighting that young Black males experienced the highest stress at 127.852 ± 10.035 MPa, while elderly Caucasian females exhibited the least stress at 97.224 ± 14.504 MPa. Furthermore, age-related variations in stress levels for both normal and osteoporotic bone micro models were elucidated, emphasizing the intricate interplay of demographic factors in bone biomechanics. Additionally, a prediction equation for bone density incorporating demographic variables was proposed, offering a personalized modeling approach. In general, this study, which carefully examines the complexities of how bones behave at the micro-level, emphasizes the need for an enhanced approach in orthopedics. We suggest taking individual characteristics into account to make therapeutic interventions more precise and effective.

A Rigorous 2D-3D Registration Method for a High-Speed Bi-Planar Videoradiography Imaging System.

High-speed biplanar videoradiography can derive the dynamic bony translations and rotations required for joint cartilage contact mechanics to provide insights into the mechanical processes and mechanisms of joint degeneration or pathology. A key challenge is the accurate registration of 3D bone models (from MRI or CT scans) with 2D X-ray image pairs. Marker-based or model-based 2D-3D registration can be performed. The former has higher registration accuracy owing to corresponding marker pairs. The latter avoids bead implantation and uses radiograph intensity or features. A rigorous new method based on projection strategy and least-squares estimation that can be used for both methods is proposed and validated by a 3D-printed bone with implanted beads. The results show that it can achieve greater marker-based registration accuracy than the state-of-the-art RSA method. Model-based registration achieved a 3D reconstruction accuracy of 0.79 mm. Systematic offsets between detected edges in the radiographs and their actual position were observed and modeled to improve the reconstruction accuracy to 0.56 mm (tibia) and 0.64 mm (femur). This method is demonstrated on in vivo data, achieving a registration precision of 0.68 mm (tibia) and 0.60 mm (femur). The proposed method allows the determination of accurate 3D kinematic parameters that can be used to calculate joint cartilage contact mechanics.

3-Dimensional Printed Model of the Temporal Bone for Neurosurgical Training.

The development of neurosurgical skills stands out as a paramount objective for neurosurgery residents during their formative years. Mastery of intricate and complex procedures is a time-intensive process marked by a gradually ascending learning curve. Consequently, the study and simulation on surgical models assume significant importance. One of the most intricate neuroanatomical regions includes the petrous and mastoid portions of the temporal bone. These regions host critical, highly functional, and vital neurovascular structures, including the facial nerve, cochlea, semicircular canals, internal carotid artery, and middle ear. This fully open-source 3-dimensional (3D) model of the temporal bone, created for educational purposes, should be easily and economically reproducible using a 3D printer, offering all residents the opportunity to understand the spatial location, three-dimensional anatomical structures, and fundamental intricacies of mastoidectomy. A 3D model of the temporal bone was fabricated using a computed tomography (CT) scan derived from an actual human body. The CT scan of the model was meticulously juxtaposed with the reference sample CT scan. Neurosurgical residents were recruited as participants for this study. Each participant was tasked with executing a mastoidectomy on 2 separate occasions, with a 2-week interval between attempts. Throughout these sessions, various parameters, including the time taken for task completion, the volume of bone removal, and any potential complications, were systematically registered. The mean volume of bone removed increased by 34.5%, and the mean task time and the mean number of complications decreased by 10.3% and 25%, respectively, during the training. Engaging in training with cost-effective anatomical models constitutes a valuable tool for refining technical skills during residency. We posit that this type of model training should be incorporated as part of the trainee's curriculum during the residency program because of the myriad advantages evidenced by the findings of this study.

A 3D bioreactor model to study osteocyte differentiation and mechanobiology under perfusion and compressive mechanical loading

Osteocytes perceive and process mechanical stimuli in the lacuno-canalicular network in bone. As a result, they secrete signaling molecules that mediate bone formation and resorption. To date, few three-dimensional (3D) models exist to study the response of mature osteocytes to biophysical stimuli that mimic fluid shear stress and substrate strain in a mineralized, biomimetic bone-like environment. Here we established a biomimetic 3D bone model by utilizing a state-of-art perfusion bioreactor platform where immortomouse/Dmp1-GFP-derived osteoblastic IDG-SW3 cells were differentiated into mature osteocytes. We evaluated proliferation and differentiation properties of the cells on 3D microporous scaffolds of decellularized bone (dBone), poly(L-lactide-co-trimethylene carbonate) lactide (LTMC), and beta-tricalcium phosphate (β-TCP) under physiological fluid flow conditions over 21 days. Osteocyte viability and proliferation were similar on the scaffolds with equal distribution of IDG-SW3 cells on dBone and LTMC scaffolds. After seven days, the differentiation marker alkaline phosphatase (Alpl), dentin matrix acidic phosphoprotein 1 (Dmp1), and sclerostin (Sost) were significantly upregulated in IDG-SW3 cells (p = 0.05) on LTMC scaffolds under fluid flow conditions at 1.7 ml/min, indicating rapid and efficient maturation into osteocytes. Osteocytes responded by inducing the mechanoresponsive genes FBJ osteosarcoma oncogene (Fos) and prostaglandin-endoperoxide synthase 2 (Ptgs2) under perfusion and dynamic compressive loading at 1 Hz with 5 % strain. Together, we successfully created a 3D biomimetic platform as a robust tool to evaluate osteocyte differentiation and mechanobiology in vitro while recapitulating in vivo mechanical cues such as fluid flow within the lacuno-canalicular network. Statement of significanceThis study highlights the importance of creating a three-dimensional (3D) in vitro model to study osteocyte differentiation and mechanobiology, as cellular functions are limited in two-dimensional (2D) models lacking in vivo tissue organization. By using a perfusion bioreactor platform, physiological conditions of fluid flow and compressive loading were mimicked to which osteocytes are exposed in vivo. Microporous poly(L-lactide-co-trimethylene carbonate) lactide (LTMC) scaffolds in 3D are identified as a valuable tool to create a favorable environment for osteocyte differentiation and to enable mechanical stimulation of osteocytes by perfusion and compressive loading. The LTMC platform imitates the mechanical bone environment of osteocytes, allowing the analysis of the interaction with other cell types in bone under in vivo biophysical stimuli.

Poster 265: Bony Morphology Is a Poor Predictor of Anterior Cruciate Ligament Elongation During High Demand Activities

Objectives: Bony morphology has been proposed as a potential risk factor for anterior cruciate ligament (ACL) injury. There have been several reports on the relationship between bony morphology and knee kinematics, however, no reports have investigated if knee bony morphological parameters that are associated with knee kinematics are also associated with ACL elongation. The purpose of this study was to determine if bony morphological parameters that have been associated with ACL injury risk and knee kinematics are also predictive of ACL elongation during fast running and double-legged drop jump. Our hypothesis was that a steeper lateral posterior tibial slope (LPTS), a deeper medial tibial plateau (MTP) depth, and a larger lateral femoral condyle anteroposterior width (LCAP)/ lateral tibial plateau anteroposterior width (TPAP) (LCAP/ TPAP) would predict more ACL elongation during high demand activities. Methods: Written informed consent was obtained from 19 healthy collegiate athletes with no history of knee injury who were active in sports that require running, jumping, and/or cutting (11 males and 8 females). Both knees were imaged within a biplane radiography imaging system (150 images/sec, 90kV, 160mA, 1ms exposure) for three trials per knee during fast running (5.0m/s on an instrumented treadmill) and double-legged drop jump off a 60cm platform. Tibiofemoral motion was tracked using a previously validated volumetric model-based tracking process that matched CT based subject-specific 3D bone models to the synchronized biplane radiographs to measure knee kinematics with an accuracy of 0.7 mm in translation and 0.9° in rotation. ACL elongation was measured as the distance between the femoral and tibial ACL attachment points, identified on magnetic resonance imaging (MRI) and registered to the CT based subject-specific 3D bones. Bony morphological parameters of LPTS, MTP depth, and LCAP/ TPAP were measured on MRI using Mimics version 24.0 (Materialise, Leuven, Belgium) as previously reported (Figure 1) and input to a multiple linear regression model to predict knee kinematics (the range of tibial internal-external rotation and the range of tibial anteroposterior translation) and ACL elongation at 3 instants during each activity (0%, 30%, and 60% of the stance phase in fast running and 0%, 50%, and 100% of the stance phase in double-legged drop jump). Significance level was set as P &lt; 0.05. Results: Participant’s average age was 20.1±1.3 years and the mean BMI was 24.0±2.8 kg/m2. The mean LPTS was 4.7±1.8°, the mean MTP depth was 1.8±0.5 mm, and the mean LCAP/TPAP was 1.5±0.1. None of the bony morphology features predicted ACL elongation or knee kinematics during fast running (Tables 1, 2). During double-legged drop jump, deeper MTP predicted greater ACL elongation at toe off of the drop jump (β= 0.456, p= 0.006) (Table 1). In contrast, several bone morphology parameters predicted knee kinematics: A steeper LPTS and a deeper MTP depth predicted a greater range of tibiofemoral internal/external rotation (β= 0.382, p= 0.012 and β= 0.331, p= 0.028, respectively) and shallower MTP depth and a larger LCAP/TPAP predicted a greater range of anterior tibial translation (β= -0.352, p= 0.018 and β= 0.441, p= 0.005, respectively) during drop jump (Table 2). Conclusions: As in previous reports, bony morphology predicted knee kinematics, but only during the double-legged drop jump activity. However, contrary to our hypothesis, only at toe-off during the double-legged drop jump, when the knee was maximally extended, a deeper MTP depth was associated with greater ACL elongation. These findings suggest that bone morphology features that are associated with kinematics that in isolation increase ACL elongation ( e.g., anterior tibial translation) may not predict ACL elongation during high demand activities. The lack of association between ACL relative elongation and bony morphology during fast running may be due to the overriding influence of large forces applied to the knee during flexion immediately after foot strike. These results are limited to healthy athletes performing fast running and drop jump activities in a controlled laboratory setting, and to knees with relatively normal bony morphology. In conclusion, previously reported relationships between bony morphology and kinematics or ACL injury risk should not be extrapolated to suggest that those bone morphology features are also associated with ACL elongation during high demand activities.

Effects of 3D Bone Models on Anatomy Education: Student Survey

Effects of 3D Bone Models on Anatomy Education: Student Survey

3D reconstruction of foot metatarsal bones of women using CT images

Bone morphology is a fundamental factor in human anatomy. However, foot and ankle bones have yet to be adequately evaluated in 3-dimensional. It is essential to present the biometric data of anatomical structures. This study formed 3D models of the metatarsal bones of the feet of young women using image processing techniques to examine biometric measurements and determine morphology on these 3D models. This study investigated bone lengths in the metatarsal bones of women feet in Türkiye. A total of ten young female subjects were included as the test group to measure the lengths of their foot metatarsal bones using CT (Computed Tomography) scans, and 20 feet (left/right) were examined. The parameters that were used for the analyses were detector collimation of 64x0.5 mm, section thickness of 0.5 mm, current of 100 mA, tube voltage of 120 kVp, and pixel spacing of 512x512 pixels with a monochrome resolution providing 16-bit gray levels. CT images were processed, and a 3D metatarsal reconstruction was gathered. Then, the biometric measurements were calculated on this 3D model. For the lengths of the volunteers' right/left foot metatarsal bones, statistically significant differences were calculated using a one-sample t-test. For the female metatarsal bones of the left and right feet, statistically significant differences in length were calculated on 3D models. The mean results of the metatarsal length measurements were MT1(metatarsal): 59.52±1.42 mm, MT2: 70.45±1.82 mm, MT3: 66.25±1.82 mm, MT4: 65.12±1.81 mm and MT5: 63.63±1.81 mm. The level of statistical significance was accepted as p &amp;lt;0.05 for the one-sample t-test conducted for each metatarsal bone. The lengths of the right foot metatarsal bones were different from those of the left foot metatarsal bones in the sample. However, this difference was approximately one-tenth of a millimeter. The shortest bone was MT1, and the longest bone was MT2. These measurements are consistent with the anatomical information in the literature. The 3D models from the CT images and the biometric measurements of the metatarsal bones were found to be reliable and accurate.

Computational modeling of revision total hip arthroplasty involving acetabular defects: A systematic review.

Revision total hip arthroplasty (rTHA) involving acetabular defects is a complex procedure associated with lower rates of success than primary THA. Computational modeling has played a key role in surgical planning and prediction of postoperative outcomes following primary THA, but modeling applications in rTHA for acetabular defects remain poorly understood. This study aimed to systematically review the use of computational modeling in acetabular defect classification, implant selection and placement, implant design, and postoperative joint functional performance evaluation following rTHA involving acetabular defects. The databases of Web of Science, Scopus, Medline, Embase, Global Health and Central were searched. Fifty-three relevant articles met the inclusion criteria, and their quality were evaluated using a modified Downs and Black evaluation criteria framework. Manual image segmentation from computed tomography scans, which is time consuming, remains the primary method used to generate 3D models of hip bone; however, statistical shape models, once developed, can be used to estimate pre-defect anatomy rapidly. Finite element modeling, which has been used to estimate bone stresses and strains, and implant micromotion postoperatively, has played a key role in custom and off-the-shelf implant design, mitigation of stress shielding, and prediction of bone remodeling and implant stability. However, model validation is challenging and requires rigorous evaluation and comparison with respect to mid- to long-term clinical outcomes. Development of fast, accurate methods to model acetabular defects, including statistical shape models and artificial neural networks, may ultimately improve uptake of and expand applications in modeling and simulation of rTHA for the research setting and clinic.

Convolutional neural network for assisting accuracy of personalized clavicle bone implant designs

&lt;span lang="EN-US"&gt;The clavicle is a long bone that tends to be frequently fractured in the midshaft region. The plate and screw fixing method is mainly applied to address this issue. This study aims to construct a clavicle bone implant design with a consideration to achieve a high accuracy and high-quality surface between the plate and the clavicle surface. The computational tomography scanning (CT-scan) image series data were processed using a convolutional neural network (CNN) to classify the clavicle image. The CNN outcomes were gathered as three-dimensional (3D) volume data of clavicle bone. This 3D model was then proposed for the plate design. The CNN testing results of 97.4% for the image clavicle bones classification, whereas the prints of the 3D model from clavicle bone and its plate and screw design reveal compatibility between the bone surface and the plate surface. Overall, the CNN application to the series of CT images could ease the classification of clavicle bone images that would precisely construct the 3D model of clavicle bone and its suitable clavicle bone plate design. This study could contribute as a guideline for other bone plate areas that need to fit the patient’s bone geometry.&lt;/span&gt;

Utilizing Artificial Neural Networks for Geometric Bone Model Reconstruction in Mandibular Prognathism Patients

Patient-specific 3D models of the human mandible are finding increasing utility in medical fields such as oral and maxillofacial surgery, orthodontics, dentistry, and forensic sciences. The efficient creation of personalized 3D bone models poses a key challenge in these applications. Existing solutions often rely on 3D statistical models of human bone, offering advantages in rapid bone geometry adaptation and flexibility by capturing a range of anatomical variations, but also a disadvantage in terms of reduced precision in representing specific shapes. Considering this, the proposed parametric model allows for precise manipulation using morphometric parameters acquired from medical images. This paper highlights the significance of employing the parametric model in the creation of a personalized bone model, exemplified through a case study targeting mandibular prognathism reconstruction. A personalized model is described as 3D point cloud determined through the utilization of series of parametric functions, determined by the application of geometrical morphometrics, morphology properties, and artificial neural networks in the input dataset of human mandible samples. With 95.05% of the personalized model’s surface area displaying deviations within −1.00–1.00 mm relative to the input polygonal model, and a maximum deviation of 2.52 mm, this research accentuates the benefits of the parametric approach, particularly in the preoperative planning of mandibular deformity surgeries.

Development of Augmented Reality Vision for Osteosynthesis Using a 3D Camera.

We developed a 3D camera system to track motion in a surgical field. This system has the potential to introduce augmented reality (AR) systems non-invasively, eliminating the need for the invasive AR markers conventionally required. The present study was performed to verify the real-time tracking accuracy of this system, assess the feasibility of integrating this system into the surgical workflow, and establish its potential to enhance the accuracy and efficiency of orthopedic procedures. To evaluate the accuracy of AR technology using a 3D camera, a forearm bone model was created. The forearm model was depicted using a 3D camera, and its accuracy was verified in terms of the positional relationship with a 3D bone model created from previously imaged CT data. Images of the surgical field (capturing the actual forearm) were taken and saved in nine poses by rotating the forearm from pronation to supination. The alignment of the reference points was computed at the three points of CT versus the three points of the 3D camera, yielding a 3D rotation matrix representing the positional relationship. In the original system, a stereo vision-based 3D camera, with a depth image resolution of 1280×720 pixels, 30 frames per second, and a lens field of view of 64 specifications, with a baseline of 3 cm, capable of optimally acquiring real-time 3D data at a distance of 40-60 cm from the subject was used. In the modified system, the following modifications were made to improve tracking performance: (1) color filter processing was changed from HSV to RGB, (2) positional detection accuracy was modified with supporting marker sizes of 8 mm in diameter, and (3) the detection of marker positions was stabilized by calculating the marker position for each frame. Tracking accuracy was examined with the original system and modified system for the following parameters: differences in the rotation matrix, maximum and minimum inter-reference point errors between CT-based and camera-based 3D data, and the average error for the three reference points. In the original system, the average difference in rotation matrices was 5.51±2.68 mm. Average minimum and maximum errors were 1.10±0.61 and 15.53±12.51 mm, respectively. The average error of reference points was 6.26±4.49 mm. In the modified system, the average difference in rotation matrices was 4.22±1.73 mm. Average minimum and maximum errors were 0.79±0.49 and 1.94±0.87 mm, respectively. The average error of reference points was 1.41±0.58 mm. In the original system, once tracking failed, it was difficult to recover tracking accuracy. This resulted in a large maximum error in supination positions. These issues were resolved by the modified system. Significant improvements were achieved in maximum errors and average errors using the modified system (P<0.05). AR technology using a 3D camera was developed. This system allows direct comparisons of 3D data from preoperative CT scans with 3D data acquired from the surgical field using a 3D camera. This method has the advantage of introducing AR into the surgical field without invasive markers.