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Phantom evaluation of feasibility and applicability of artificial intelligence based pulmonary nodule detection in chest radiographs.
Medicine (Baltimore)
November 2024
Department of Radiology, Kantonsspital Baden, affiliated Hospital for Research and Teaching of the Faculty of Medicine of the University of Zurich, Baden, Switzerland.
The aim of our study was to evaluate the specific performance of an artificial intelligence (AI) algorithm for lung nodule detection in chest radiography for a larger number of nodules of different sizes and densities using a standardized phantom approach. A total of 450 nodules with varying density (d1 to d3) and size (3, 5, 8, 10 and 12 mm) were inserted in a Lungman phantom at various locations. Radiographic images with varying projections were acquired and processed using the AI algorithm for nodule detection.
View Article and Find Full Text PDFUnveiling the truth: Pathogen infections linked to miscarriage: A STROBE-Compliant Mendelian randomization study.
Medicine (Baltimore)
November 2024
Department of General Surgery, The Third Affiliated Hospital of Soochow University, Changzhou, China.
Miscarriage represents a prevalent yet insufficiently studied adverse pregnancy outcome. The definitive causal links between various pathogens and miscarriage remain to be established. To investigate the causal connections between pathogen infections and miscarriage, we utilized a two-sample bidirectional Mendelian randomization (MR) analysis.
View Article and Find Full Text PDFContinuous single-ended depth-of-interaction measurement using highly multiplexed signals and artificial neural networks.
Phys Med Biol
January 2025
Department of Nuclear Medicine, Seoul National University College of Medicine, 103 Daehak-ro, Jongno-gu, Seoul, 03080, Korea (the Republic of).
This study aims to enhance positron emission tomography (PET) imaging systems by developing a continuous depth-of-interaction (DOI) measurement technique using a single-ended readout. Our primary focus is on reducing the number of readout channels in the scintillation detectors while maintaining accurate DOI estimations, using a high-pass filter-based signal multiplexing technique combined with artificial neural networks (ANNs). Approach: Instead of reading out all 64 signals from an 8×8 silicon photomultiplier array for DOI estimation, the proposed method technique reduces the signals into just four channels by applying high-pass filters with different time constants.
View Article and Find Full Text PDFAECuration: Automated event curation for spike sorting.
J Neural Eng
January 2025
Carnegie Mellon University, 5000 Forbes Avenue, Pittsburgh, Pennsylvania, 15213, UNITED STATES.
Spike sorting is a commonly used analysis method for identifying single-units and multi-units from extracellular recordings. The extracellular recordings contain a mixture of signal components, such as neural and non-neural events, possibly due to motion and breathing artifacts or electrical interference. Identifying single and multi-unit spikes using a simple threshold-crossing method may lead to uncertainty in differentiating the actual neural spikes from non-neural spikes.
View Article and Find Full Text PDFMetastatic Lung Lesion Changes in Follow-up Chest CT: The Advantage of Deep Learning Simultaneous Analysis of Prior and Current Scans With SimU-Net.
J Thorac Imaging
September 2024
School of Computer Science and Engineering, The Hebrew University of Jerusalem.
Purpose: Radiological follow-up of oncology patients requires the detection of metastatic lung lesions and the quantitative analysis of their changes in longitudinal imaging studies. Our aim was to evaluate SimU-Net, a novel deep learning method for the automatic analysis of metastatic lung lesions and their temporal changes in pairs of chest CT scans.
Materials And Methods: SimU-Net is a simultaneous multichannel 3D U-Net model trained on pairs of registered prior and current scans of a patient.
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