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Development of a Clinically Applicable Deep Learning System Based on Sparse Training Data to Accurately Detect Acute Intracranial Hemorrhage from Non-enhanced Head Computed Tomography.
Neurol Med Chir (Tokyo)
January 2025
College of Biological Science and Technology, National Yang Ming Chiao Tung University.
Non-enhanced head computed tomography is widely used for patients presenting with head trauma or stroke, given acute intracranial hemorrhage significantly influences clinical decision-making. This study aimed to develop a deep learning algorithm, referred to as DeepCT, to detect acute intracranial hemorrhage on non-enhanced head computed tomography images and evaluate its clinical applicability. We retrospectively collected 1,815 computed tomography image sets from a single center for model training.
View Article and Find Full Text PDFAccuracy of full arch scans performed with nine different scanning patterns- an in vitro study.
Clin Oral Investig
January 2025
Department of Prosthetic Dentistry, LMU University Hospital, LMU Munich, Goethestrasse 70, 80336, Munich, Germany.
Objective: Evaluation of the accuracy of direct digitization of maxillary scans depending on the scanning strategy.
Materials And Methods: A maxillary model with a metal bar as a reference structure fixed between the second molars was digitized using the CEREC Primescan AC scanner (N = 225 scans). Nine scanning strategies were selected (n = 25 scans per strategy), differing in scan area segmentation (F = full jaw, H = half jaw, S = sextant) and scan movement pattern (L = linear, Z = zig-zag, C = combined).
Developing a highly efficient 4-hydroxyphenylacetate-3-hydroxylase for salvianic acid A synthesis by computer-aided molecular modification.
Int J Biol Macromol
January 2025
Department of Chemical and Biological Engineering, Zhejiang University, Hangzhou 310027, China; School of Biological and Chemical Engineering, NingboTech University, Ningbo 315100, China; Jinhua Advanced Research Institute, Jinhua 321019, China. Electronic address:
Salvianic acid A (SAA) is a catechol compound known for its diverse physiochemical functions and has significant applications in the food and pharmaceutical industries. 4-Hydroxyphenylacetate-3-hydroxylase (4HPA3H) is a critical enzyme for SAA biosynthesis, and improving its activity towards p-hydroxyphenyllactate acid (4HPLA) is essential for highly efficient SAA production in stable biosynthetic pathways. To address this, the distal site and loops of the substrate pocket were modified to improve 4HPA3H catalytic activity towards 4HPLA using computer-aided molecular modification methods.
View Article and Find Full Text PDFA quantum-optimized approach for breast cancer detection using SqueezeNet-SVM.
Sci Rep
January 2025
College of Information Science and Technology, Hainan Normal University, Haikou, 571158, China.
Breast cancer is one of the most aggressive types of cancer, and its early diagnosis is crucial for reducing mortality rates and ensuring timely treatment. Computer-aided diagnosis systems provide automated mammography image processing, interpretation, and grading. However, since the currently existing methods suffer from such issues as overfitting, lack of adaptability, and dependence on massive annotated datasets, the present work introduces a hybrid approach to enhance breast cancer classification accuracy.
View Article and Find Full Text PDFSeverity grading of hypertensive retinopathy using hybrid deep learning architecture.
Comput Methods Programs Biomed
January 2025
Regional Institute of Ophthalmology, Indira Gandhi Institute of Medical Sciences, Patna, 800025, Bihar, India.
Background And Objectives: Hypertensive Retinopathy (HR) is a retinal manifestation resulting from persistently elevated blood pressure. Severity grading of HR is essential for patient risk stratification, effective management, progression monitoring, timely intervention, and minimizing the risk of vision impairment. Computer-aided diagnosis and artificial intelligence (AI) systems play vital roles in the diagnosis and grading of HR.
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