Search Results (120)

Additive manufacturing is one of the most common technologies used in prototyping and manufacturing usable parts. Currently, industrial robots are also increasingly being used to carry out this process. This is due to a robot’s capability to fabricate components with structural configurations that are unattainable using conventional 3D printers. The number of degrees of freedom of the robot, combined with its working range and precision, allows the construction of parts with greater dimensions and better strength in comparison to conventional 3D printing. However, the implementation of a robot into the 3D printing process requires the development of novel solutions to streamline and facilitate the prototyping and manufacturing processes. This work focuses on the need to develop new slicing methods for robotic additive manufacturing. A solution for alternative control code generation without external slicer utilization is presented. The implementation of the proposed method enables a reduction of over 80% in the time required to generate new G-code, significantly outperforming traditional approaches. The paper presents a novel approach to the slicing process in robotic additive manufacturing that is adopted for the fused granular fabrication process using thermoplastic polymers. Full article

Background/Objectives: Dental cavity preparation is a critical procedure in restorative dentistry that involves the removal of decayed tissue while preserving a healthy tooth structure. Excessive stress during tooth preparation leads to enamel cracking, dentin damage, and long term compressive pulp health. This study employed finite element analysis (FEA) to investigate the stress distribution in dental structures during cavity preparation using round diamond burs of varying diameters and depths of cut (DOC). Methods: A three-dimensional human maxillary first molar was generated from computed tomography (CT) scan data using 3D Slicer, Fusion 360, and ANSYS Space Claim 2024 R-2. Finite element analysis (FEA) was conducted using ANSYS Workbench 2024. Round diamond burs with diameters of 1, 2, and 3 mm were modeled. Cutting simulations were performed for DOC of 1 mm and 2 mm. The burs were treated as rigid bodies, whereas the dental structures were modeled as deformable bodies using the Cowper–Symonds model. Results: The simulations revealed that larger bur diameters and deeper cuts led to higher stress magnitudes, particularly in the enamel and dentin. The maximum von Mises stress was reached at 136.98 MPa, and dentin 140.33 MPa. Smaller burs (≤2 mm) and lower depths of cut (≤1 mm) produced lower stress values and were optimal for minimizing dental structural damage. Pulpal stress remained low but showed an increasing trend with increased DOC and bur size. Conclusions: This study provides clinically relevant guidance for reducing mechanical damage during cavity preparation by recommending the use of smaller burs and controlled cutting depths. The originality of this study lies in its integration of CT-based anatomy with dynamic FEA modeling, enabling a realistic simulation of tool–tissue interaction in dentistry. These insights can inform bur selection, cutting protocols, and future experimental validations. Full article

Background/Objectives: The infraorbital canal (IOC) is a critical anatomical structure that passes through the anterior surface of the maxilla and opens at the infraorbital foramen, containing the infraorbital nerve, artery, and vein. Accurate localization of this canal in maxillofacial, dental implant, and orbital surgeries is of great importance to preventing nerve damage, reducing complications, and enabling successful surgical planning. The aim of this study is to perform automatic segmentation of the infraorbital canal in cone-beam computed tomography (CBCT) images using an artificial intelligence (AI)-based model. Methods: A total of 220 CBCT images of the IOC from 110 patients were labeled using the 3D Slicer software (version 4.10.2; MIT, Cambridge, MA, USA). The dataset was split into training, validation, and test sets at a ratio of 8:1:1. The nnU-Net v2 architecture was applied to the training and test datasets to predict and generate appropriate algorithm weight factors. The confusion matrix was used to check the accuracy and performance of the model. As a result of the test, the Dice Coefficient (DC), Intersection over the Union (IoU), F1-score, and 95% Hausdorff distance (95% HD) metrics were calculated. Results: By testing the model, the DC, IoU, F1-score, and 95% HD metric values were found to be 0.7792, 0.6402, 0.787, and 0.7661, respectively. According to the data obtained, the receiver operating characteristic (ROC) curve was drawn, and the AUC value under the curve was determined to be 0.91. Conclusions: Accurate identification and preservation of the IOC during surgical procedures are of critical importance to maintaining a patient’s functional and sensory integrity. The findings of this study demonstrated that the IOC can be detected with high precision and accuracy using an AI-based automatic segmentation method in CBCT images. This approach has significant potential to reduce surgical risks and to enhance the safety of critical anatomical structures. Full article

Background/Objectives. With a rapid palatal expander (RPE) is reported to be effective in increasing the volume of nasal cavities, with a restoration of physiological nasal airflow. The purpose of this retrospective clinical study was to evaluate, using Cone Beam Computed Tomography (CBCT), the volumetric changes and airflow velocity changes in the nasal cavities, retro-palatal and retro-glossal airways, resulting from the use of RPE with dental anchorage (group A), also comparing these data with patients non treated with RPE (group B). Methods. Sixteen subjects (aged 9.34 years) with transverse maxillary deficiency and unilateral posterior crossbite were treated with RPE with dental anchorage. Additionally, 8 patients (aged 11.11 years) with juvenile idiopathic arthritis, who did not undergo any orthodontic treatment, were selected as a control group. Expansion was performed until overcorrection was achieved, and the device was left in place for 6 months as fixed retention, followed by another 6 months of night-time removable retention. From the retrospective evaluation, all patients presented two CBCT scans at baseline (T0) and 1-year follow-up (T1). The 3D-Slicer software was used for each CBCT to measure the nasal (VN), retropalatal (VRP), and retroglossal (VRG) volumes, while an iterative Excel spreadsheet allowed for a pilot approximated modeling and calculation of airway flow-related data. Results. Regarding mean age, a statistically significant difference (p = 0.01 *) was found between groups, suggesting that group B is closer to the pubertal growth peak. Analysis between T0 and T1 revealed: (i) a statistically significant increase for volumes VN, VRP and VRG in group A; (ii) a statistically significant increase for VN in group B; (iii) a statistically significant decrease for all variables related to airflow velocity in both groups. Furthermore, comparison between group A and B, regarding variations between T0 and T1, found a statistically significant difference only for VN. Conclusions. Within the limitations of this pilot evaluation, the treatment with RPE revealed promising outcomes for retro-palatal, retro-glossal and nasal volumes, together with clinical changes in airflow velocities. Full article

Background: Prostate cancer (PCa) represents a matter at the forefront of healthcare, being divided into clinically significant (csPCa) and indolent PCa based on prognostic and treatment options. Although multi-parametric magnetic resonance imaging (mpMRI) has enabled significant advances, it cannot differentiate between the aforementioned categories; therefore, in order to render the initial diagnosis, invasive procedures such as transrectal prostate biopsy are still necessary. In response to these challenges, artificial intelligence (AI)-based algorithms combined with radiomics features offer the possibility of creating a textural pixel pattern-based surrogate, which has the potential of correlating the medical imagery with the pathological report in a one-to-one manner. Objective: The aim of the present study was to develop a machine learning model that can differentiate indolent from csPCa lesions, as well as individually classifying each nodule into corresponding ISUP grades prior to prostate biopsy, using textural features derived from mpMRI T2WI acquisitions. Materials and Methods: The study was conducted in 154 patients and 201 individual prostatic lesions. All cases were scanned using the same 1.5 Tesla mpMRI machine, employing a standard protocol. Each nodule was manually delineated using the 3D Slicer platform (version 5.2.2) and textural parameters were derived using the PyRadiomics database (version 3.1.0). We compared three machine learning classification models (Random Forest, Support Vector Machine, and Logistic Regression) in full, partial and no correlation settings, in order to differentiate between indolent and csPCa, as well as between ISUP 2 and ISUP 3 lesions. Results: The median age was 65 years (IQR: 61–69), the mean PSA value was 10.27 ng/mL, and 76.61% of the segmented lesions had a PI-RADS score of 4 or higher. Overall, the highest performance was registered for the Random Forest model in the partial correlation setting, differentiating between indolent and csPCa and between ISUP 2 versus ISUP 3 lesions, with accuracies of 88.13% and 82.5%, respectively. When the models were trained on combined clinical data and radiomic signatures, these accuracies increased to 91.11% and 91.39%, respectively. Conclusions: We developed a machine learning decision support tool that accurately predicts the ISUP grade prior to prostate biopsy, based on the textural features extracted from T2 MRI acquisitions. Full article

Background/Objectives: Background: COVID-19 pneumonia leads to alveolar collapse and parenchymal infiltration, contributing to lung volume loss and respiratory failure. Objectives: To quantify lung volume loss and recovery in moderate and severe cases, explore mechanisms of respiratory failure, and correlate imaging findings with histopathological changes. Methods: We retrospectively analyzed 43 patients with moderate/severe COVID-19. CT scans from the acute phase and at 3–12 months follow-ups were processed using 3D Slicer. Infiltrated (−650 to −200 HU) and collapsed (−200 to 0 HU) lung regions were quantified and summed to define the affected lung volume. CT severity scores and total affected percentage were compared with lung volume loss. Histopathological analysis of three autopsy cases was used to support imaging findings. Results: Median acute phase lung volume loss was 30.6%. Patients with <25%, 25–50%, and >50% affected lung had median losses of 6.5%, 35.7%, and 39.8%, respectively. Volume loss strongly correlated with affected lung percentage (r = 0.72, p < 0.000001) and moderately with CT severity score (r = 0.52, p < 0.01). Histology confirmed alveolar area reductions over 65% in infiltrated regions. Conclusions: Lung volume loss reflects both imaging severity and histopathological damage, offering insights into the mechanisms of COVID-19 respiratory failure. CT volumetry is a valuable tool for assessing parenchymal injury and monitoring recovery, and 3D Slicer provides an accessible platform for implementing this approach. Full article

Background: A micropapillary pattern (MP) and solid pattern (SP) in lung adenocarcinoma (LUAD), a major subtype of non-small-cell lung cancer (NSCLC), are associated with a poor prognosis and necessitate accurate preoperative identification. This study aimed to develop and validate a predictive model combining clinical and radiomics features for differentiating a high-risk MP/SP in LUAD. Methods: This retrospective study analyzed 180 surgically confirmed NSCLC patients (Stages I–IIIA), randomly divided into training (70%, n = 126) and validation (30%, n = 54) cohorts. Three prediction models were constructed: (1) a clinical model based on independent clinical and CT morphological features (e.g., nodule size, lobulation, spiculation, pleural indentation, and vascular abnormalities), (2) a radiomics model utilizing LASSO-selected features extracted using 3D Slicer, and (3) a comprehensive model integrating both clinical and radiomics data. Results: The clinical model yielded AUCs of 0.7975 (training) and 0.8462 (validation). The radiomics model showed superior performance with AUCs of 0.8896 and 0.8901, respectively. The comprehensive model achieved the highest diagnostic accuracy, with training and validation AUCs of 0.9186 and 0.9396, respectively (DeLong test, p < 0.05). Decision curve analysis demonstrated the enhanced clinical utility of the combined approach. Conclusions: Integrating clinical and radiomics features significantly improves the preoperative identification of aggressive NSCLC patterns. The comprehensive model offers a promising tool for guiding surgical and adjuvant therapy decisions. Full article

Background and Objectives: The foramen lacerum (FL), located at the base of the skull, is generally considered the safest anatomical pathway for accessing the internal carotid artery (ICA) and the vidian canal (VC) during surgical procedures. We aimed to evaluate the morphometric characteristics of FL, VC, and related structures. Materials and Methods: This study utilized cranial computed tomography (CT) images obtained between 2016 and 2018 at Balıkesir University Faculty of Medicine for various clinical indications. A retrospective analysis was performed on cranial CT images from 77 patients, comprising 42 females and 35 males. The length and width of the FL, the length of the VC, and the angles formed between the VC and the pterygosphenoidal fissure and between the VC and the palatovaginal canal were measured. All measurements were performed using the three-dimensional (3D) Slicer software to ensure precision and consistency. Results: Males had significantly longer right and left FL lengths and left FL width than females (p < 0.05). No significant gender-based differences were found in VC length on either side. The angle between the VC and the pterygosphenoidal fissure was significantly larger in males (p < 0.05). Additionally, increased FL length and width were significantly correlated with larger angles between the VC and the pterygosphenoidal fissure in all subjects (p < 0.05). The anatomical variations of the FL Type 1 (normal) were identified as the most prevalent configuration across the study population. Type 2 (canal-shaped) ranked as the second most frequent variant in females, whereas Type 3 (bridged) was the second most commonly observed form in males. Conclusions: Preoperative identification of FL anatomical variations, which differ between individuals and sexes, may enhance the safety of skull base surgeries and minimize postoperative complications. The morphometric data presented in this study provide valuable guidance for clinicians planning interventions involving the FL and surrounding structures, and contribute valuable insights to anatomists regarding regional morphology. Full article

Objective: The primary objective of this study was to develop and validate a predictive nomogram that integrates radiomic features derived from contrast-enhanced computed tomography (CECT) images with clinical variables to predict overall survival (OS) in patients with hepatocellular carcinoma (HCC) after surgical resection. Methods: This retrospective study analyzed the preoperative enhanced CT images and clinical data of 202 patients with HCC who underwent surgical resection at the Affiliated Hospital of North Sichuan Medical College (Institution 1) from June 2017 to June 2021 and at Nanchong Central Hospital (Institution 2) from June 2020 to June 2022. Among these patients, 162 patients from Institution 1 were randomly divided into a training cohort (112 patients) and an internal validation cohort (50 patients) at a 7:3 ratio, whereas 40 patients from Institution 2 were assigned as an independent external validation cohort. Univariate and multivariate Cox proportional hazards regression analyses were performed to identify clinical risk factors associated with OS after HCC resection. Using 3D-Slicer software, tumor lesions were manually delineated slice by slice on preoperative non-contrast-enhanced (NCE) CT, arterial phase (AP), and portal venous phase (PVP) images to generate volumetric regions of interest (VOIs). Radiomic features were subsequently extracted from these VOIs. LASSO Cox regression analysis was employed for dimensionality reduction and feature selection, culminating in the construction of a radiomic signature (Radscore). Cox proportional hazards regression models, including a clinical model, a radiomic model, and a radiomic–clinical model, were subsequently developed for OS prediction. The predictive performance of these models was assessed via the concordance index (C-index) and time–ROC curves. The optimal performance model was further visualized as a nomogram, and its predictive accuracy was evaluated via calibration curves and decision curve analysis (DCA). Finally, the risk factors in the optimal performance model were interpreted via Shapley additive explanations (SHAP). Results: Univariate and multivariate Cox regression analyses revealed that BCLC stage, the albumin–bilirubin index (ALBI), and the NLR–PLR score were independent predictors of OS after HCC resection. Among these three models, the radiomic–clinical model exhibited the highest predictive performance, with C-indices of 0.789, 0.726, and 0.764 in the training, internal and external validation cohorts, respectively. Furthermore, the time–ROC curves for the radiomic–clinical model showed 1-year and 3-year AUCs of 0.837 and 0.845 in the training cohort, 0.801 and 0.880 in the internal validation cohort, and 0.773 and 0.840 in the external validation cohort. Calibration curves and DCA demonstrated the model’s excellent calibration and clinical applicability. Conclusions: The nomogram combining CECT radiomic features and clinical variables provides an accurate prediction of OS after HCC resection. This model is beneficial for clinicians in developing individualized treatment strategies for patients with HCC. Full article

Evaluating bone tunnels is crucial for assessing functional recovery after anterior cruciate ligament reconstruction. Conventional methods are imprecise, time-consuming, and labor-intensive. This study introduces a novel deep learning-based system for accurate bone tunnel segmentation and assessment. The system has two primary stages. Firstly, the ResNet50-Unet network is employed to capture the bone tunnel area in each slice. Subsequently, in the bone texture analysis, the open-source software 3D Slicer is leveraged to execute three-dimensional reconstruction based on the segmented outcomes from the previous stage. The ResNet50-Unet network was trained and validated using a newly developed dataset named tunnel bone segmentation (TB-Seg). The outcomes reveal commendable performance metrics, with mean intersection over union (mIoU), mean average precision (mAP), precision, and recall on the validation set reaching 76%, 85%, 88%, and 85%, respectively. To assess the robustness of our innovative bone texture system, we conducted tests on a cohort of 24 patients, successfully extracting bone volume/total volume, trabecular thickness, trabecular separation, trabecular number, and volumetric information. The system excels with substantial significance in facilitating the subsequent analysis of the intricate interplay between bone tunnel characteristics and the postoperative recovery trajectory after anterior cruciate ligament reconstruction. Furthermore, in our five randomly selected cases, clinicians utilizing our system completed the entire analytical workflow in a mere 357–429 s, representing a substantial improvement compared to the conventional duration exceeding one hour. Full article

Background/Objectives: This study evaluated the trueness and precision of three low-cost 3D printers compared to a professional-grade printer in fabricating orthodontic models. Methods: Two upper dental models, one crowded and one non-crowded, were designed using Blenderfordental and Autolign. The models were printed with Anycubic M3 Premium, Anycubic Photon D2, Phrozen Sonic Mini 8K, and Ackuretta Sol at 45° and 90° using Elegoo orthodontic and Ackuretta Curo resins. A total of 384 models were produced: 256 crowded (128 at 90° and 128 at 45°) and 128 non-crowded (all at 45°). Chitubox Dental Slicer and ALPHA AI slicer were used for slicing. Post-processing involved cleaning with Ackuretta Cleani and curing in Ackuretta Curie. The models were scanned with Smartoptics Vinyl Open Air. Trueness was assessed using RMS deviation analysis in CloudCompare and linear measurements. Results: One-way ANOVA showed significant differences in trueness among the printers at 45° (p < 0.001) and 90° (p < 0.001). The Ackuretta Sol (LCD) exhibited the highest trueness, with the lowest mean RMS values at 45° (0.095 ± 0.008 mm) and 90° (0.115 ± 0.010 mm). The Anycubic M3 Premium (LCD) had the lowest trueness, with RMS values at 45° (0.136 ± 0.015 mm) and 90° (0.149 ± 0.012 mm). The 45° build angle resulted in significantly better trueness than 90° (p < 0.001). In linear measurements, deviations exceeding 0.25 mm were observed only in the R1 distance, except for the Ackuretta SOL, which remained below this threshold. Conclusions: The professional-grade printer demonstrated the best performance overall. Printing at a 45° build angle resulted in improved accuracy. Despite differences among devices, all printers produced results within clinically acceptable limits for orthodontic use. Full article

Background/Objectives: 3D printing has become an invaluable tool in medicine, enabling the creation of precise anatomical models for surgical planning and medical education. This study presents a comprehensive protocol for converting DICOM files into three-dimensional models and their subsequent transformation into GCODE files ready for 3D printing. Methods: We employed the open-source software “3D Slicer” for the initial conversion of the DICOM files, capitalising on its robust capabilities in segmentation and medical image processing. An optimised workflow was developed for the precise and efficient conversion of medical images into STL models, ensuring high fidelity in anatomical structures. The protocol was validated through three case studies, achieving elevated structural fidelity based on deviation analysis between the STL models and the original DICOM data. Furthermore, the segmentation process preserved morphological accuracy within a narrow deviation range, ensuring the reliable replication of anatomical features for medical applications. Our protocol provides an effective and accessible approach to generating 3D anatomical models with enhanced accuracy and reproducibility. In later stages, we utilised the “Ultimaker Cura” software to generate customised GCODE files tailored to the specifications of the 3D printer. Results: Our protocol offers an effective, accessible, and more accurate solution for creating 3D anatomical models from DICOM images. Furthermore, the versatility of this approach allows for its adaptation to various 3D printers and materials, expanding its utility in the medical and scientific community. Conclusions: This study presents a robust and reproducible approach for converting medical data into physical three-dimensional objects, paving the way for a wide range of applications in personalised medicine and advanced clinical practice. The selection of sample datasets from the 3D Slicer repository ensures standardisation and reproducibility, allowing for independent validation of the proposed workflow without ethical or logistical constraints related to patient data access. However, we acknowledge that future work could expand upon this by incorporating real patient datasets and benchmarking the protocol against alternative segmentation methods and software packages to further assess performance across different clinical scenarios. Essentially, this protocol can be particularly characterised by its commitment to open-source software and low-cost solutions, making advanced 3D modelling accessible to a wider audience. By leveraging open-access tools such as “3D Slicer” and “Ultimaker Cura”, we democratise the creation of anatomical models, ensuring that institutions with limited resources can also benefit from this technology, promoting innovation and inclusivity in medical sciences and education. Full article

Epicardial fat density (EFD) is implicated in cardiovascular diseases. This study aimed to assess the regional variability of epicardial fat density (EFD) using coronary computed tomography (CCT) and evaluate the feasibility of ROI-based measurements as an alternative to full segmentation. A retrospective analysis was conducted on 171 patients undergoing coronary CCT. EFD was measured on non-contrast scans acquired globally and in three predefined regions of interest (ROIs) for coronary calcium scoring: the aortic bulb, right posterolateral wall, and cardiac apex. Global EFD was quantified using semi-automated segmentation software (3D Slicer 5.6.2), while regional EFD values were manually determined. Statistical analyses were performed to compare global and regional EFD measurements. Global EFD averaged −83.92 ± 5.19 HU, while regional EFD showed significant variability. The aortic bulb had lower EFD values (−97.54 ± 12.80 HU) compared to the apex (−93.42 ± 18.94 HU) and right posterolateral wall (−94.99 ± 12.16 HU). Paired t-tests confirmed statistically significant differences between global and regional EFD values (p < 0.000). This study highlights significant regional variability in EFD across specific cardiac regions, suggesting that ROI-based assessments may not reliably reflect global EFD characteristics. Full article

The ANB angle, the cephalometric parameter of choice for assessing the anteroposterior relationship between the maxilla and mandible, is subject to several limitations, prompting the investigation of alternative parameters. Objective: This study aimed to investigate the ABwise measurement as an alternative to the ANB angle for evaluating maxillomandibular relationships in orthodontics, particularly addressing the impact of skeletal discrepancies on conventional methods. Methods: A retrospective analysis was performed on a CBCT dataset of patients attending the University of Milan’s Department of Orthodontics and Maxillofacial Surgery, selected based on high-quality imaging, a full-cranium field of view, and a slice thickness between 150 and 300 μm. Eight craniofacial landmarks were annotated using the 3D Slicer software to calculate the ANB values and the new ABwise measurement. Statistical analyses included Spearman’s correlation (ρ), linear regression, and inter-rater agreement (Cohen’s κ score), with data classified into skeletal Classes I, II, and III based on defined thresholds. Results: 354 CBCT were selected and analyzed (mean age: 18.6 years). ABwise showed a strong correlation with the ANB angle (ρ = 0.805) and new normative ranges for ABwise were established: Class I (−1.4 ± 2.3 mm), Class II (>0.9 mm), and Class III (<−3.7 mm). Moderate agreement was observed between the ABwise and ANB classifications (κ = 0.527). ABwise effectively addressed limitations associated with divergence and vertical discrepancies, providing a more reliable assessment of skeletal sagittal relationships. Conclusions: ABwise presents a viable alternative to the ANB angle for three-dimensional cephalometric analysis, offering improved accuracy and alignment with radioprotection principles by reducing the CBCT field of view needed for its measurement. Further research is required in order to validate these findings across diverse populations and clinical scenarios. Full article

Background: Acute subdural hematoma (aSDH) is associated with a high risk of epilepsy, a complication linked to poor outcomes. Craniotomy is a known risk factor, with an epilepsy incidence of approximately 25%. This study evaluated radiomic features from preoperative CT scans to predict epilepsy risk in aSDH patients undergoing craniotomy. Methods: A retrospective analysis of 178 adult aSDH patients treated between 2016 and 2022 identified 64 patients meeting inclusion criteria. Radiomic features (e.g., Feret diameter, elongation, flatness, surface area, and volume) from preoperative CT scans within 24 h of surgery were analyzed alongside clinical factors, including cardiac comorbidities, pupillary response, SOFA score, age, and anticoagulation status. Results: Of the 64 patients, 18 (28%) developed generalized seizures. Univariate analysis showed significant associations with Feret diameter (p = 0.045), elongation (p = 0.005), cardiac comorbidities (p = 0.017), and SOFA score (p = 0.036). ROC analysis showed excellent discriminatory ability for elongation (AUC = 0.82). Multivariate analysis identified elongation as an independent predictor (p = 0.003); elongation ≥ 1.45 increased seizure risk 7.78-fold (OR = 7.778; 95% CI = 1.969–30.723). Conclusions: Radiomic features, particularly elongation, may help predict epilepsy risk in aSDH patients undergoing craniotomy. Prospective validation is needed. Full article