Three-Dimensional Printed Anatomic Models Derived From Magnetic Resonance Imaging Data: Current State and Image Acquisition Recommendations for Appropriate Clinical Scenarios

Artificial neural network-based prediction of multiple sclerosis using blood-based metabolomics data

Multiple sclerosis (MS) remains a challenging neurological condition for diagnosis and management and is often detected in late stages, delaying treatment. Artificial intelligence (AI) is emerging as a promising approach to extracting MS information when applied to different patient datasets. Given the critical role of metabolites in MS profiling, metabolomics data may be an ideal platform for the application of AI to predict disease. In the present study, a machine-learning (ML) approach was used for a detailed analysis of metabolite profiles and related pathways in patients with MS and healthy controls (HC). This approach identified unique alterations in biochemical metabolites and their correlation with disease severity parameters. To enhance the efficiency of using metabolic profiles to determine disease severity or the presence of MS, we trained an AI model on a large volume of blood-based metabolomics datasets. We constructed this model using an artificial neural network (ANN) architecture with perceptrons. Data were divided into training, validation, and testing sets to determine model accuracy. After training, accuracy reached 87 %, sensitivity was 82.5 %, specificity was 89 %, and precision was 77.3 %. Thus, the developed model seems highly robust, generalizable with a wide scope and can handle large amounts of data, which could potentially assist neurologists. However, a large multicenter cohort study is necessary for further validation of large-scale datasets to allow the integration of AI in clinical settings for accurate diagnosis and improved MS management.

Trends of "Artificial Intelligence, Machine Learning, Virtual Reality, and Radiomics in Urolithiasis" over the Last 30 Years (1994-2023) as Published in the Literature (PubMed): A Comprehensive Review

Purpose: To analyze the bibliometric publication trend on the application of "Artificial Intelligence (AI) and its subsets (Machine Learning-ML, Virtual reality-VR, Radiomics) in Urolithiasis" over 3 decades. We looked at the publication trends associated with AI and stone disease, including both clinical and surgical applications, and training in endourology. Methods: Through a MeshTerms research on PubMed, we performed a comprehensive review from 1994-2023 for all published articles on "AI, ML, VR, and Radiomics." Articles were then divided into three categories as follows: A-Clinical (Nonsurgical), B-Clinical (Surgical), and C-Training articles, and articles were then assigned to following three periods: Period-1 (1994-2003), Period-2 (2004-2013), and Period-3 (2014-2023). Results: A total of 343 articles were noted (Groups A-129, B-163, and C-51), and trends increased from Period-1 to Period-2 at 123% (p = 0.009) and to period-3 at 453% (p = 0.003). This increase from Period-2 to Period-3 for groups A, B, and C was 476% (p = 0.019), 616% (0.001), and 185% (p < 0.001), respectively. Group A articles included rise in articles on "stone characteristics" (+2100%; p = 0.011), "renal function" (p = 0.002), "stone diagnosis" (+192%), "prediction of stone passage" (+400%), and "quality of life" (+1000%). Group B articles included rise in articles on "URS" (+2650%, p = 0.008), "PCNL"(+600%, p = 0.001), and "SWL" (+650%, p = 0.018). Articles on "Targeting" (+453%, p < 0.001), "Outcomes" (+850%, p = 0.013), and "Technological Innovation" (p = 0.0311) had rising trends. Group C articles included rise in articles on "PCNL" (+300%, p = 0.039) and "URS" (+188%, p = 0.003). Conclusion: Publications on AI and its subset areas for urolithiasis have seen an exponential increase over the last decade, with an increase in surgical and nonsurgical clinical areas, as well as in training. Future AI related growth in the field of endourology and urolithiasis is likely to improve training, patient centered decision-making, and clinical outcomes.

Pilot Evaluation of Silicone Surrogates for Oral Mucosa Simulation in Craniofacial Surgical Training

Surgical simulators are crucial in early craniofacial and plastic surgical training, necessitating synthetic materials that accurately replicate tissue properties. Recent critiques of our lab's currently deployed silicone surrogate have highlighted numerous areas for improvement. To further refine our models, our group's objective is to find a composition of materials that is closest in fidelity to native oral mucosa during surgical rehearsal by expert craniofacial surgeons. Fifteen platinum silicone-based surrogate samples were constructed with variable hardness and slacker percentages. These samples underwent evaluation of tactile sensation, hardness, needle puncture, cut resistance, suture retention, defect repair, and tensile elasticity. Expert craniofacial surgeon evaluators provided focused qualitative feedback on selected top-performing samples for further assessment and statistical comparisons. An evaluation revealed surrogate characteristics that were satisfactory and exhibited good performance. Sample 977 exhibited the highest performance, and comparison with the original surrogate (sample 810) demonstrated significant improvements in critical areas, emphasizing the efficacy of the refined composition. The study identified a silicone composition that directly addresses the feedback received by our team's original silicone surrogate. The study underscores the delicate balance between biofidelity and practicality in surgical simulation. The need for ongoing refinement in surrogate materials is evident to optimize training experiences for early surgical learners.

A Systematic Analysis of Additive Manufacturing Techniques in the Bioengineering of In Vitro Cardiovascular Models

Additive Manufacturing is noted for ease of product customization and short production run cost-effectiveness. As our global population approaches 8 billion, additive manufacturing has a future in maintaining and improving average human life expectancy for the same reasons that it has advantaged general manufacturing. In recent years, additive manufacturing has been applied to tissue engineering, regenerative medicine, and drug delivery. Additive Manufacturing combined with tissue engineering and biocompatibility studies offers future opportunities for various complex cardiovascular implants and surgeries. This paper is a comprehensive overview of current technological advancements in additive manufacturing with potential for cardiovascular application. The current limitations and prospects of the technology for cardiovascular applications are explored and evaluated.

MRI-based training model for left atrial appendage closure

PURPOSE

Percutaneous closure of the left atrial appendage (LAA) reduces the risk of embolic stroke in patients with atrial fibrillation. Thereby, the optimal transseptal puncture (TSP) site differs due to the highly variable anatomical shape of the LAA, which is rarely considered in existing training models. Based on non-contrast-enhanced magnetic resonance imaging (MRI) volumes, we propose a training model for LAA closure with interchangeable and patient-specific LAA enabling LAA-specific identification of the TSP site best suited.

METHODS

Based on patient-specific MRI data, silicone models of the LAAs were produced using a 3D-printed cast model. In addition, an MRI-derived 3D-printed base model was set up, including the right and left atrium with predefined passages in the septum, mimicking multiple TSP sites. The various silicone models and a tube mimicking venous access were connected to the base model. Empirical use of the model allowed the demonstration of its usability.

RESULTS

Patient-specific silicone models of the LAA could be generated from all LAA patient MRI datasets. The influence of various combinations regarding TSP sites and LAA shapes could be demonstrated as well as the technical functionality of the occluder system. Via the attached tube mimicking the venous access, the correct handling of the deployment catheter even in case of not optimal puncture site could be practiced.

CONCLUSION

The proposed contrast-agent and radiation-free MRI-based training model for percutaneous LAA closure enables the pre-interventional assessment of the influence of the TSP site on the access of patient-specific LAA shapes. A straightforward replication of this work is measured by using clinically available imaging protocols and a widespread 3D printer technique to build the model.

Patient-Specific 3D-Printed Models in Pediatric Congenital Heart Disease

Three-dimensional (3D) printing technology has become increasingly used in the medical field, with reports demonstrating its superior advantages in both educational and clinical value when compared with standard image visualizations or current diagnostic approaches. Patient-specific or personalized 3D printed models serve as a valuable tool in cardiovascular disease because of the difficulty associated with comprehending cardiovascular anatomy and pathology on 2D flat screens. Additionally, the added value of using 3D-printed models is especially apparent in congenital heart disease (CHD), due to its wide spectrum of anomalies and its complexity. This review provides an overview of 3D-printed models in pediatric CHD, with a focus on educational value for medical students or graduates, clinical applications such as pre-operative planning and simulation of congenital heart surgical procedures, and communication between physicians and patients/parents of patients and between colleagues in the diagnosis and treatment of CHD. Limitations and perspectives on future research directions for the application of 3D printing technology into pediatric cardiology practice are highlighted.

Quality assurance in 3D-printing: A dimensional accuracy study of patient-specific 3D-printed vascular anatomical models

3D printing enables the rapid manufacture of patient-specific anatomical models that substantially improve patient consultation and offer unprecedented opportunities for surgical planning and training. However, the multistep preparation process may inadvertently lead to inaccurate anatomical representations which may impact clinical decision making detrimentally. Here, we investigated the dimensional accuracy of patient-specific vascular anatomical models manufactured via digital anatomical segmentation and Fused-Deposition Modelling (FDM), Stereolithography (SLA), Selective Laser Sintering (SLS), and PolyJet 3D printing, respectively. All printing modalities reliably produced hand-held patient-specific models of high quality. Quantitative assessment revealed an overall dimensional error of 0.20 ± 3.23%, 0.53 ± 3.16%, -0.11 ± 2.81% and -0.72 ± 2.72% for FDM, SLA, PolyJet and SLS printed models, respectively, compared to unmodified Computed Tomography Angiograms (CTAs) data. Comparison of digital 3D models to CTA data revealed an average relative dimensional error of -0.83 ± 2.13% resulting from digital anatomical segmentation and processing. Therefore, dimensional error resulting from the print modality alone were 0.76 ± 2.88%, + 0.90 ± 2.26%, + 1.62 ± 2.20% and +0.88 ± 1.97%, for FDM, SLA, PolyJet and SLS printed models, respectively. Impact on absolute measurements of feature size were minimal and assessment of relative error showed a propensity for models to be marginally underestimated. This study revealed a high level of dimensional accuracy of 3D-printed patient-specific vascular anatomical models, suggesting they meet the requirements to be used as medical devices for clinical applications.

3D Extrusion Printing of Biphasic Anthropomorphic Brain Phantoms Mimicking MR Relaxation Times Based on Alginate-Agarose-Carrageenan Blends

The availability of adapted phantoms mimicking different body parts is fundamental to establishing the stability and reliability of magnetic resonance imaging (MRI) methods. The primary purpose of such phantoms is the mimicking of physiologically relevant, contrast-creating relaxation times T1 and T2. For the head, frequently examined by MRI, an anthropomorphic design of brain phantoms would imply the discrimination of gray matter and white matter (WM) within defined, spatially distributed compartments. Multichannel extrusion printing allows the layer-by-layer fabrication of multiple pastelike materials in a spatially defined manner with a predefined shape. In this study, the advantages of this method are used to fabricate biphasic brain phantoms mimicking MR relaxation times and anthropomorphic geometry. The printable ink was based on purely naturally derived polymers: alginate as a calcium-cross-linkable gelling agent, agarose, ι-carrageenan, and GdCl3 in different concentrations (0-280 μmol kg-1) as the paramagnetic component. The suggested inks (e.g., 3Alg-1Agar-6Car) fulfilled the requirements of viscoelastic behavior and printability of large constructs (>150 mL). The microstructure and distribution of GdCl3 were assessed by scanning electron microscopy (SEM) with energy-dispersive X-ray spectroscopy (EDX). In closely monitored steps of technological development and characterization, from monophasic and biphasic samples as printable inks and cross-linked gels, we describe the construction of large-scale phantom models whose relaxation times were characterized and checked for stability over time.

Establishing 3D Printing at the Point of Care: Basic Principles and Tools for Success

As the medical applications of three-dimensional (3D) printing increase, so does the number of health care organizations in which adoption or expansion of 3D printing facilities is under consideration. With recent advancements in 3D printing technology, medical practitioners have embraced this powerful tool to help them to deliver high-quality patient care, with a focus on sustainability. The use of 3D printing in the hospital or clinic at the point of care (POC) has profound potential, but its adoption is not without unanticipated challenges and considerations. The authors provide the basic principles and considerations for building the infrastructure to support 3D printing inside the hospital. This process includes building a business case; determining the requirements for facilities, space, and staff; designing a digital workflow; and considering how electronic health records may have a role in the future. The authors also discuss the supported applications and benefits of medical 3D printing and briefly highlight quality and regulatory considerations. The information presented is meant to be a practical guide to assist radiology departments in exploring the possibilities of POC 3D printing and expanding it from a niche application to a fixture of clinical care. An invited commentary by Ballard is available online. ^©RSNA, 2022.

A workflow to generate patient-specific three-dimensional augmented reality models from medical imaging data and example applications in urologic oncology

Augmented reality (AR) and virtual reality (VR) are burgeoning technologies that have the potential to greatly enhance patient care. Visualizing patient-specific three-dimensional (3D) imaging data in these enhanced virtual environments may improve surgeons' understanding of anatomy and surgical pathology, thereby allowing for improved surgical planning, superior intra-operative guidance, and ultimately improved patient care. It is important that radiologists are familiar with these technologies, especially since the number of institutions utilizing VR and AR is increasing. This article gives an overview of AR and VR and describes the workflow required to create anatomical 3D models for use in AR using the Microsoft HoloLens device. Case examples in urologic oncology (prostate cancer and renal cancer) are provided which depict how AR has been used to guide surgery at our institution.