Current Issue : January-March Volume : 2025 Issue Number : 1 Articles : 5 Articles
(1) Background: 3D printable materials with accurately defined iodine content enable the development and production of radiological phantoms that simulate human tissues, including lesions after contrast administration in medical imaging with X-rays. These phantoms provide accurate, stable and reproducible models with defined iodine concentrations, and 3D printing allows maximum flexibility and minimal development and production time, allowing the simulation of anatomically correct anthropomorphic replication of lesions and the production of calibration and QA standards in a typical medical research facility. (2) Methods: Standard printing resins were doped with an iodine contrast agent and printed using a consumer 3D printer, both (resins and printer) available from major online marketplaces, to produce printed specimens with iodine contents ranging from 0 to 3.0% by weight, equivalent to 0 to 3.85% elemental iodine per volume, covering the typical levels found in patients. The printed samples were scanned in a micro-CT scanner to measure the properties of the materials in the range of the iodine concentrations used. (3) Results: Both mass density and attenuation show a linear dependence on iodine concentration (R2 = 1.00), allowing highly accurate, stable, and predictable results. (4) Conclusions: Standard 3D printing resins can be doped with liquids, avoiding the problem of sedimentation, resulting in perfectly homogeneous prints with accurate dopant content. Iodine contrast agents are perfectly suited to dope resins with appropriate iodine concentrations to radiologically mimic tissues after iodine uptake. In combination with computer-aided design, this can be used to produce printed objects with precisely defined iodine concentrations in the range of up to a few percent of elemental iodine, with high precision and anthropomorphic shapes. Applications include radiographic phantoms for detectability studies and calibration standards in projective X-ray imaging modalities, such as contrast-enhanced dual energy mammography (abbreviated CEDEM, CEDM, TICEM, or CESM depending on the equipment manufacturer), and 3-dimensional modalities like CT, including spectral and dual energy CT (DECT), and breast tomosynthesis....
Background: Osteoporosis, a systemic skeletal disorder, is expected to affect 60% of women over 50. While dual-energy X-ray absorptiometry (DXA) scans are the current gold standard for diagnosis, they are typically used only after fractures occur, highlighting the need for early detection tools. Initial studies have shown panoramic radiographs (PRs) to be a potential medium, but these have methodological flaws. This study aims to address these shortcomings by developing a robust AI application for accurate osteoporosis identification in PRs. Methods: A total of 348 PRs were used for development, 58 PRs for validation, and 51 PRs for hold-out testing. Initially, the YOLOv8 object detection model was employed to predict the regions of interest. Subsequently, the predicted regions of interest were extracted from the PRs and processed by the EfficientNet classification model. Results: The model for osteoporosis detection on a PR achieved an overall sensitivity of 0.83 and an F1-score of 0.53. The area under the curve (AUC) was 0.76. The lowest detection sensitivity was for the cropped angulus region (0.66), while the highest sensitivity was for the cropped mental foramen region (0.80). Conclusion: This research presents a proof-of-concept algorithm showing the potential of deep learning to identify osteoporosis in dental radiographs. Furthermore, our thorough evaluation of existing algorithms revealed that many optimistic outcomes lack credibility when subjected to rigorous methodological scrutiny....
Background: In biomedical imaging research, experimental biologists generate vast amounts of data that require advanced computational analysis. Breakthroughs in experimental techniques, such as multiplex immunofluorescence tissue imaging, enable detailed proteomic analysis, but most biomedical researchers lack the programming and Artificial Intelligence (AI) expertise to leverage these innovations effectively. Methods: CINCO de Bio (CdB) is a web-based, collaborative low-code/no-code modelling and execution platform designed to address this challenge. It is designed along Model-Driven Development (MDD) and Service-Orientated Architecture (SOA) to enable modularity and scalability, and it is underpinned by formal methods to ensure correctness. The pre-processing of immunofluorescence images illustrates the ease of use and ease of modelling with CdB in comparison with the current, mostly manual, approaches. Results: CdB simplifies the deployment of data processing services that may use heterogeneous technologies. User-designed models support both a collaborative and user-centred design for biologists. Domain-Specific Languages for the Application domain (A-DSLs) are supported through data and process ontologies/taxonomies. They allow biologists to effectively model workflows in the terminology of their field. Conclusions: Comparative analysis of similar platforms in the literature illustrates the superiority of CdB along a number of comparison dimensions. We are expanding the platform’s capabilities and applying it to other domains of biomedical research....
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-Netbased 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....
Objective Differentiating intramedullary spinal cord tumor (IMSCT) from spinal cord tumefactive demyelinating lesion (scTDL) remains challenging with standard diagnostic approaches. This study aims to develop and evaluate the effectiveness of a magnetic resonance imaging (MRI)-based radiomics model for distinguishing scTDL from IMSCT before treatment initiation. Methods A total of 75 patients were analyzed in this retrospective study, comprising 55 with IMSCT and 20 with scTDL. Radiomics features were extracted from T1- and T2-weighted imaging (T1&T2WI) scans upon admission. Ten classification algorithms were employed: logistic regression (LR); naive bayes (NaiveBayes); support vector machine (SVM); k nearest neighbors (KNN); random forest (RF); extra trees (ExtraTrees); eXtreme gradient boosting (XGBoost); light gradient boosting machine (LightGBM); gradient boosting (GradientBoosting); and multi-Layer perceptron (MLP). The performance of the optimal model was then compared to radiologists’ assessments. Results This study developed 30 predictive models using ten classifiers across two imaging sequences. The MLP model with two sequences (T1&T2WI) emerged as the most effective one, showing superior accuracy in MRI analysis with an area under the curve (AUC) of 0.991 in training and 0.962 in testing. Moreover, statistical analyses highlighted the radiomics model significantly outperformed radiologists’ assessments (p < 0.05) in distinguishing between IMSCT and scTDL. Conclusion We present an MRI-based radiomics model with high diagnostic accuracy in differentiating IMSCT from scTDL. The model’s performance was comparable to junior radiologists, highlighting its potential as an effective diagnostic aid in clinical practice....
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