首页|University of Melbourne Reports Findings in Artificial Intelligence (Using a new artificial intelligence-aided method to assess body composition CT segmentation in colorectal cancer patients)
University of Melbourne Reports Findings in Artificial Intelligence (Using a new artificial intelligence-aided method to assess body composition CT segmentation in colorectal cancer patients)
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By a News Reporter-Staff News Editor at Robotics & Machine Learning Daily News Daily News-New research on Artificial Intelligenc e is the subject of a report. According to news reporting originating from Victo ria, Australia, by NewsRx correspondents, research stated, "This study aimed to evaluate the accuracy of our own artificial intelligence (AI)-generated model to assess automated segmentation and quantification of body composition-derived co mputed tomography (CT) slices from the lumber (L3) region in colorectal cancer ( CRC) patients. A total of 541 axial CT slices at the L3 vertebra were retrospect ively collected from 319 patients with CRC diagnosed during 2012-2019 at a singl e Australian tertiary institution, Western Health in Melbourne." Our news editors obtained a quote from the research from the University of Melbo urne, "A twodimensional U-Net convolutional network was trained on 338 slices t o segment muscle, visceral adipose tissue (VAT) and subcutaneous adipose tissue (SAT). Manual reading of these same slices of muscle, VAT and SAT was created to serve as ground truth data. The Dice similarity coefficient was used to assess the U-Net-based segmentation performance on both a validation dataset (68 slices ) and a test dataset (203 slices). The measurement of cross-sectional area and H ounsfield unit (HU) density of muscle, VAT and SAT were compared between two met hods. The segmentation for muscle, VAT and SAT demonstrated excellent performanc e for both the validation (Dice similarity coefficients > 0.98, respectively) and test (Dice similarity coefficients > 0.97, respectively) datasets. There was a strong positive correlation between ma nual and AI segmentation measurements of body composition for both datasets (Spe arman's correlation coefficients: 0.944-0.999, P<0.001)."
VictoriaAustraliaAustralia and New Z ealandArtificial IntelligenceCancerColon CancerColorectal ResearchEmer ging TechnologiesGastroenterologyHealth and MedicineMachine LearningOnco logy
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