Background: The development of refractive corneal surgery involves numerous attempts to isolate the effect of individual factors on surgical outcome. Computer simulation of refractive keratotomy allows the surgeon to alter variables of the technique and to isolate the effect of specific factors independent of other factors, something that cannot easily be done in any of the currently available experimental models.
Methods: We used the finite element numerical method to construct a mathematical model of the eye. The model analyzed stress-strain relationships in the normal corneoscleral shell and after astigmatic surgery. The model made the following assumptions: an axisymmetric eye, an idealized aspheric anterior corneal surface, transversal isotropy of the cornea, nonlinear strain tensor for large displacements, and near incompressibility of the corneoscleral shell. The eye was assumed to be fixed at the level of the optic nerve. The model described the acute elastic response of the eye to corneal surgery.
Results: We analyzed the effect of paired transverse arcuate corneal incisions for the correction of astigmatism. We evaluated the following incision variables and their effect on change in curvature of the incised and unincised meridians: length (longer, more steepening of unincised meridian), distance from the center of the cornea (farther, less flattening of incised meridian), depth (deeper, more effect), and the initial amount of astigmatism (small effect).
Conclusions: Our finite element computer model gives reasonably accurate information about the relative effects of different surgical variables, and demonstrates the feasibility of using nonlinear, anisotropic assumptions in the construction of such a computer model. Comparison of these computer-generated results to clinically achieved results may help refine the computer model.
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Radiology
January 2025
Stanford University School of Medicine, Department of Radiation Oncology, Stanford, CA, US.
Background Detection and segmentation of lung tumors on CT scans are critical for monitoring cancer progression, evaluating treatment responses, and planning radiation therapy; however, manual delineation is labor-intensive and subject to physician variability. Purpose To develop and evaluate an ensemble deep learning model for automating identification and segmentation of lung tumors on CT scans. Materials and Methods A retrospective study was conducted between July 2019 and November 2024 using a large dataset of CT simulation scans and clinical lung tumor segmentations from radiotherapy plans.
View Article and Find Full Text PDFElectromagn Biol Med
January 2025
Department of Computer Applications, Kalasalingam Academy of Research and Education - Deemed to be University, Krishnankoil, India.
Brain tumors can cause difficulties in normal brain function and are capable of developing in various regions of the brain. Malignant tumours can develop quickly, pass through neighboring tissues, and extend to further brain regions or the central nervous system. In contrast, healthy tumors typically develop slowly and do not invade surrounding tissues.
View Article and Find Full Text PDFJ Acoust Soc Am
January 2025
Department of Electronics Engineering, Pusan National University, Busan, South Korea.
The amount of information contained in speech signals is a fundamental concern of speech-based technologies and is particularly relevant in speech perception. Measuring the mutual information of actual speech signals is non-trivial, and quantitative measurements have not been extensively conducted to date. Recent advancements in machine learning have made it possible to directly measure mutual information using data.
View Article and Find Full Text PDFAnalyst
January 2025
Department of Chemistry, University of Victoria, Victoria, British Columbia, V8W 3V6, Canada.
Infrared absorption spectroscopy and surface-enhanced Raman spectroscopy were integrated into three data fusion strategies-hybrid (concatenated spectra), mid-level (extracted features from both datasets) and high-level (fusion of predictions from both models)-to enhance the predictive accuracy for xylazine detection in illicit opioid samples. Three chemometric approaches-random forest, support vector machine, and -nearest neighbor algorithms-were employed and optimized using a 5-fold cross-validation grid search for all fusion strategies. Validation results identified the random forest classifier as the optimal model for all fusion strategies, achieving high sensitivity (88% for hybrid, 92% for mid-level, and 96% for high-level) and specificity (88% for hybrid, mid-level, and high-level).
View Article and Find Full Text PDFAnal Chem
January 2025
Department of Engineering and Chemical Sciences, Karlstad University, SE-651 88 Karlstad, Sweden.
This work introduces the Adsorption Energy Distribution (AED) calculation using competitive adsorption isotherm data, enabling investigation of the simultaneous AED of two components for the first time. The AED provides crucial insights by visualizing competitive adsorption processes, offering an alternative adsorption isotherm model without prior assuming adsorption heterogeneity, and assisting in model selection for more accurate retention mechanistic modeling. The competitive AED enhances our understanding of multicomponent interactions on stationary phases, which is crucial for understanding how analytes compete on the stationary phase surface and for selecting adsorption models for numerical optimization of preparative chromatography.
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