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| http://dx.doi.org/10.1091/mbc.9.12.3259 | DOI Listing |
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Deep Convolutional Neural Networks on Multiclass Classification of Three-Dimensional Brain Images for Parkinson's Disease Stage Prediction.
J Imaging Inform Med
January 2025
Department of Anesthesiology, E-Da Cancer Hospital, I-Shou University, Kaohsiung, Taiwan.
Parkinson's disease (PD), a degenerative disorder of the central nervous system, is commonly diagnosed using functional medical imaging techniques such as single-photon emission computed tomography (SPECT). In this study, we utilized two SPECT data sets (n = 634 and n = 202) from different hospitals to develop a model capable of accurately predicting PD stages, a multiclass classification task. We used the entire three-dimensional (3D) brain images as input and experimented with various model architectures.
View Article and Find Full Text PDFUnlocking the Power of 3D Convolutional Neural Networks for COVID-19 Detection: A Comprehensive Review.
J Imaging Inform Med
January 2025
College of Science and Engineering, Hamad Bin Khalifa University, Ar-Rayyan, Qatar.
The advent of three-dimensional convolutional neural networks (3D CNNs) has revolutionized the detection and analysis of COVID-19 cases. As imaging technologies have advanced, 3D CNNs have emerged as a powerful tool for segmenting and classifying COVID-19 in medical images. These networks have demonstrated both high accuracy and rapid detection capabilities, making them crucial for effective COVID-19 diagnostics.
View Article and Find Full Text PDFEffect of a Training System Utilizing 3D-Printed Patient-Specific Vascular Models on Endovascular Catheterization Performance.
J Vasc Interv Radiol
January 2025
Department of Diagnostic Imaging, Faculty of Medicine, Hokkaido University, N-15, W-7, Kita-ku, Sapporo, Hokkaido 060-8638, Japan; Global Center for Biomedical Science and Engineering, Faculty of Medicine, Hokkaido University, N-14, W-5, Kita-ku, Sapporo, Hokkaido 060-8648, Japan.
This study aimed to examine the effect of simulation training using a three-dimensional (3D)-printed patient-specific vascular model on the advanced vascular catheterization skills of experienced interventional radiologists (IRs). Two specific anatomic types of 3D-printed patient-specific models from two patients with challenging celiac axis arterial anatomy were constructed. The Global Rating Scale of Endovascular Performance (GRS-EP) was used to evaluate vascular insertion skills.
View Article and Find Full Text PDFHigh-Throughput Chemotherapeutic Drug Screening System for Gastric Cancer (Cure-GA).
Ann Surg Oncol
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Department of Surgery, Seoul National University Bundang Hospital, Seongnam, Republic of Korea.
Background: Three dimensional (3D) cell cultures can be effectively used for drug discovery and development but there are still challenges in their general application to high-throughput screening. In this study, we developed a novel high-throughput chemotherapeutic 3D drug screening system for gastric cancer, named 'Cure-GA', to discover clinically applicable anticancer drugs and predict therapeutic responses.
Methods: Primary cancer cells were isolated from 143 fresh surgical specimens by enzymatic treatment.
Quantitative profiling and mapping of small molecules by laser desorption/ionization mass spectrometry: combinations of carbon-based nano-matrices and sample preparation protocols.
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January 2025
Jiangsu Key Laboratory for Carbon-based Functional Materials and Devices, Institute of Functional Nano and Soft Materials (FUNSOM), Soochow University, Suzhou, Jiangsu Province 215123, China.
The choices of matrices and protocols for sample deposition are critical factors, which impact each other in the matrix-assisted laser desorption/ionization (MALDI) mass spectrometry imaging (MSI). Previous reports on MALDI MS matrices have only compared their performances in terms of their MS signal intensities and provided optical microphotos or MALDI MS images of sample spots but typically lacked quantitative evaluation. Therefore, there is an urgent need to develop a multivariate model to evaluate the performance of different combinations of matrices and sample protocols.
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