AIMC Topic: Spleen
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Heatstroke death identification using ATR-FTIR spectroscopy combined with a novel multi-organ machine learning approach.
Spectrochimica acta. Part A, Molecular and biomolecular spectroscopy
Aug 27, 2024
With global warming, the number of deaths due to heatstroke has drastically increased. Nevertheless, there are still difficulties with the forensic assessment of heatstroke deaths, including the absence of particular organ pathological abnormalities ...
Exposure Pathways of Ambient Magnetite Nanoparticles Revealed by Machine Learning-Aided Single-Particle Mass Spectrometry.
Nano letters
Jul 2, 2024
Nanosized ultrafine particles (UFPs) from natural and anthropogenic sources are widespread and pose serious health risks when inhaled by humans. However, tracing the inhaled UFPs is extremely difficult, and the distribution, translocation, and metab...
ISLE: An Intelligent Streaming Framework for High-Throughput AI Inference in Medical Imaging.
Journal of imaging informatics in medicine
Jun 27, 2024
As the adoption of artificial intelligence (AI) systems in radiology grows, the increase in demand for greater bandwidth and computational resources can lead to greater infrastructural costs for healthcare providers and AI vendors. To that end, we de...
Personalized Composite Dosimetric Score-Based Machine Learning Model of Severe Radiation-Induced Lymphopenia Among Patients With Esophageal Cancer.
International journal of radiation oncology, biology, physics
May 24, 2024
PURPOSE: Radiation-induced lymphopenia (RIL) is common among patients undergoing radiation therapy (RT)' Severe RIL has been linked to adverse outcomes. The severity and risk of RIL can be predicted from baseline clinical characteristics and dosimetr...
Deep Learning Models for Abdominal CT Organ Segmentation in Children: Development and Validation in Internal and Heterogeneous Public Datasets.
AJR. American journal of roentgenology
May 1, 2024
Deep learning abdominal organ segmentation algorithms have shown excellent results in adults; validation in children is sparse. The purpose of this article is to develop and validate deep learning models for liver, spleen, and pancreas segmentation...
Deep learning-aided 3D proxy-bridged region-growing framework for multi-organ segmentation.
Scientific reports
Apr 29, 2024
Accurate multi-organ segmentation in 3D CT images is imperative for enhancing computer-aided diagnosis and radiotherapy planning. However, current deep learning-based methods for 3D multi-organ segmentation face challenges such as the need for labor-...
Fully automated deep learning based auto-contouring of liver segments and spleen on contrast-enhanced CT images.
Scientific reports
Feb 26, 2024
Manual delineation of liver segments on computed tomography (CT) images for primary/secondary liver cancer (LC) patients is time-intensive and prone to inter/intra-observer variability. Therefore, we developed a deep-learning-based model to auto-cont...
Deep Learning for Automated Detection and Localization of Traumatic Abdominal Solid Organ Injuries on CT Scans.
Journal of imaging informatics in medicine
Feb 16, 2024
Computed tomography (CT) is the most commonly used diagnostic modality for blunt abdominal trauma (BAT), significantly influencing management approaches. Deep learning models (DLMs) have shown great promise in enhancing various aspects of clinical pr...
Deep Learning Auto-Segmentation Network for Pediatric Computed Tomography Data Sets: Can We Extrapolate From Adults?
International journal of radiation oncology, biology, physics
Jan 19, 2024
PURPOSE: Artificial intelligence (AI)-based auto-segmentation models hold promise for enhanced efficiency and consistency in organ contouring for adaptive radiation therapy and radiation therapy planning. However, their performance on pediatric compu...
Machine Learning Detection and Characterization of Splenic Injuries on Abdominal Computed Tomography.
Canadian Association of Radiologists journal = Journal l'Association canadienne des radiologistes
Jan 8, 2024
BACKGROUND: Multi-detector contrast-enhanced abdominal computed tomography (CT) allows for the accurate detection and classification of traumatic splenic injuries, leading to improved patient management. Their effective use requires rapid study inter...
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