Prediction of directional solidification in freeze casting of biomaterial scaffolds using physics-informed neural networks.

Freeze casting, a manufacturing technique widely applied in biomedical fields for fabricating biomaterial scaffolds, poses challenges for predicting directional solidification due to its highly nonlinear behavior and complex interplay of process parameters. Conventional numerical methods, such as computational fluid dynamics (CFD), require adequate and accurate boundary condition knowledge, limiting their utility in real-world transient solidification applications due to technical limitations. In this study, we address this challenge by developing a physics-informed neural networks (PINNs) model to predict directional solidification in freeze-casting processes. The PINNs model integrates physical constraints with neural network predictions, requiring significantly fewer predetermined boundary conditions compared to CFD. Through a comparison with CFD simulations, the PINNs model demonstrates comparable accuracy in predicting temperature distribution and solidification patterns. This promising model achieves such a performance with only 5000 data points in space and time, equivalent to 250,000 timesteps, showcasing its ability to predict solidification dynamics with high accuracy. The study's major contributions lie in providing insights into solidification patterns during freeze-casting scaffold fabrication, facilitating the design of biomaterial scaffolds with finely tuned microstructures essential for various tissue engineering applications. Furthermore, the reduced computational demands of the PINNs model offer potential cost and time savings in scaffold fabrication, promising advancements in biomedical engineering research and development.

Predicting one-year left ventricular mass index regression following transcatheter aortic valve replacement in patients with severe aortic stenosis: A new era is coming.

Aortic stenosis (AS) is the most common valvular heart disease in the western world, particularly worrisome with an ever-aging population wherein postoperative outcome for aortic valve replacement is strongly related to the timing of surgery in the natural course of disease. Yet, guidelines for therapy planning overlook insightful, quantified measures from medical imaging to educate clinical decisions. Herein, we leverage statistical shape analysis (SSA) techniques combined with customized machine learning methods to extract latent information from segmented left ventricle (LV) shapes. This enabled us to predict left ventricular mass index (LVMI) regression a year after transcatheter aortic valve replacement (TAVR). LVMI regression is an expected phenomena in patients undergone aortic valve replacement reported to be tightly correlated with survival one and five year after the intervention. In brief, LV geometries were extracted from medical images of a cohort of AS patients using deep learning tools, and then analyzed to create a set of statistical shape models (SSMs). Then, the supervised shape features were extracted to feed a support vector regression (SVR) model to predict the LVMI regression. The average accuracy of the predictions was validated against clinical measurements calculating root mean square error and R 2 score which yielded the satisfactory values of 0.28 and 0.67, respectively, on test data. Our work reveals the promising capability of advanced mathematical and bioinformatics approaches such as SSA and machine learning to improve medical output prediction and treatment planning.

CardioVision: A fully automated deep learning package for medical image segmentation and reconstruction generating digital twins for patients with aortic stenosis.

Aortic stenosis (AS) is the most prevalent heart valve disease in western countries that poses a significant public health challenge due to the lack of a medical treatment to prevent valve calcification. Given the aging population demographic, the prevalence of AS is projected to rise, resulting in a progressively significant healthcare and economic burden. While surgical aortic valve replacement (SAVR) has been the gold standard approach, the less invasive transcatheter aortic valve replacement (TAVR) is poised to become the dominant method for high- and medium-risk interventions. Computational simulations using patient-specific models, have opened new research avenues for optimizing emerging devices and predicting clinical outcomes. The traditional techniques of generating digital replicas of patients' aortic root, native valve, and calcification are time-consuming and labor-intensive processes requiring specialized tools and expertise in anatomy. Alternatively, deep learning models, such as the U-Net architecture, have emerged as reliable and fully automated methods for medical image segmentation. Two-dimensional U-Nets have been shown to produce comparable or more accurate results than trained clinicians' manual segmentation while significantly reducing computational costs. In this study, we have developed a fully automatic AI tool capable of reconstructing the digital twin geometry and analyzing the calcification distribution on the aortic valve. The developed automatic segmentation package enables the modeling of patient-specific anatomies, which can then be used to simulate virtual interventional procedures, optimize emerging prosthetic devices, and predict clinical outcomes.

Segmentation and Pore Structure Estimation in SEM Images of Tissue Engineering Scaffolds Using Genetic Algorithm.

A python computer package is developed to segment and analyze scanning electron microscope (SEM) images of scaffolds for bone tissue engineering. The method requires only a portion of an SEM image to be labeled and used for training. The algorithm is then able to detect the pore characteristics for other SEM images acquired at different ambient conditions from different scaffolds with the same material as the labeled image. The quality of SEM images is first enhanced using histogram equalization. Then, a global thresholding method is used to perform the image analysis. The thresholding values for the SEM images are obtained using genetic algorithm (GA). The image analysis results include pore distributions of pore size, pore elongation and pore orientation. The results agree satisfactorily with the experimental data for the chitosan-alginate porous scaffolds considered. Applications of the method developed for image segmentation is not limited to scaffold pore structure analysis. The method can also be used for any SEM image containing multiple objects such as different types of cells and subcellular components.

Effect of Mold Geometry on Pore Size in Freeze-Cast Chitosan-Alginate Scaffolds for Tissue Engineering.

Freeze-casting is a popular method to produce biomaterial scaffolds with highly porous structures. The pore structure of freeze-cast biomaterial scaffolds is influenced by processing parameters but has mostly been controlled experimentally. A mathematical model integrating Computational Fluid Dynamics with Population Balance Model was developed to predict average pore size (APS) of 3D porous chitosan-alginate scaffolds and to assess the influence of the geometrical parameters of mold on scaffold pore structure. The model predicted the crystallization pattern and APS for scaffolds cast in different diameter molds and filled to different heights. The predictions demonstrated that the temperature gradient and solidification pattern affect ice crystal nucleation and growth, subsequently influencing APS homogeneity. The predicted APS compared favorably with APS measurements from a corresponding experimental dataset, validating the model. Sensitivity analysis was performed to assess the response of the APS to the three geometrical parameters of the mold: well radius; solution fill height; and spacing between wells. The pore size was most sensitive to the distance between the wells and least sensitive to solution height. This validated model demonstrates a method for optimizing the APS of freeze-cast biomaterial scaffolds that could be applied to other compositions or applications.

Effective Teaching Methods in Higher Education: Requirements and Barriers.

INTRODUCTION: Teaching is one of the main components in educational planning which is a key factor in conducting educational plans. Despite the importance of good teaching, the outcomes are far from ideal. The present qualitative study aimed to investigate effective teaching in higher education in Iran based on the experiences of best professors in the country and the best local professors of Isfahan University of Technology. METHODS: This qualitative content analysis study was conducted through purposeful sampling. Semi-structured interviews were conducted with ten faculty members (3 of them from the best professors in the country and 7 from the best local professors). Content analysis was performed by MAXQDA software. The codes, categories and themes were explored through an inductive process that began from semantic units or direct quotations to general themes. RESULTS: According to the results of this study, the best teaching approach is the mixed method (student-centered together with teacher-centered) plus educational planning and previous readiness. But whenever the teachers can teach using this method confront with some barriers and requirements; some of these requirements are prerequisite in professors' behavior and some of these are prerequisite in professors' outlook. Also, there are some major barriers, some of which are associated with the professors' operation and others are related to laws and regulations. Implications of these findings for teachers' preparation in education are discussed. CONCLUSION: In the present study, it was illustrated that a good teaching method helps the students to question their preconceptions, and motivates them to learn, by putting them in a situation in which they come to see themselves as the authors of answers, as the agents of responsibility for change. But training through this method has some barriers and requirements. To have an effective teaching; the faculty members of the universities should be awarded of these barriers and requirements as a way to improve teaching quality. The nationally and locally recognized professors are good leaders in providing ideas, insight, and the best strategies to educators who are passionate for effective teaching in the higher education. Finally, it is supposed that there is an important role for nationally and locally recognized professors in higher education to become more involved in the regulation of teaching rules.