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| http://www.ncbi.nlm.nih.gov/pmc/articles/PMC7006800 | PMC |
| http://dx.doi.org/10.1148/rg.352154006 | DOI Listing |
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AI-generated CT body composition biomarkers associated with increased mortality risk in socioeconomically disadvantaged individuals.
Abdom Radiol (NY)
April 2024
Department of Radiology, University of Wisconsin School of Medicine & Public Health, 600 Highland Ave, Madison, WI, 53792, USA.
Article Synopsis
- This study aims to explore how socioeconomic disadvantage, measured using the national area deprivation index (ADI), affects body composition metrics obtained through advanced AI tools in CT scans, potentially linking these measures to the risk of mortality and cardiovascular issues.
- Participants included 7,785 asymptomatic adults, analyzed based on their ADI rankings, showing that higher ADI ranks correlated with increased levels of abdominal aortic calcium, visceral fat, and the visceral-to-subcutaneous fat ratio, while muscle attenuation decreased.
- Results indicated that the most disadvantaged socioeconomic group had significantly higher risk body composition markers compared to the least disadvantaged group, highlighting the association between economic status and health risks.
Current implications and challenges of artificial intelligence technologies in therapeutic intervention of colorectal cancer.
Explor Target Antitumor Ther
December 2023
Department of Biotechnology, Jaypee Institute of Information Technology, Noida 201309, Uttar Pradesh, India.
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- * Early detection of CRC is crucial and several screening methods are available, including endoscopy and various non-invasive tests, but they come with challenges like cost and invasiveness.
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Automated detection of incidental abdominal aortic aneurysms on computed tomography.
Abdom Radiol (NY)
February 2024
Radiology and Imaging Sciences, Clinical Center, National Institutes of Health, Bethesda, MD, 20892-1182, USA.
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Automated Assessment of Renal Calculi in Serial Computed Tomography Scans.
Appl Med Artif Intell (2022)
September 2022
Imaging Biomarkers and Computer-Aided Diagnosis Laboratory, Department of Radiology and Imaging Sciences, National Institutes of Health Clinical Center, Bethesda, MD, USA.
An automated pipeline is developed for the serial assessment of renal calculi using computed tomography (CT) scans obtained at multiple time points. This retrospective study included 722 scans from 330 patients chosen from 8544 asymptomatic patients who underwent two or more CTC (CT colonography) or non-enhanced abdominal CT scans between 2004 and 2016 at a single medical center. A pre-trained deep learning (DL) model was used to segment the kidneys and the calculi on the CT scans at each time point.
View Article and Find Full Text PDFComputer-aided Detection of Polyps Does Not Improve Colonoscopist Performance in a Pragmatic Implementation Trial.
Gastroenterology
March 2023
Division of Gastroenterology and Hepatology, Department of Medicine, Stanford University School of Medicine, Stanford, California.
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