首页|New Artificial Intelligence Study Findings Have Been Reported from Netherlands C ancer Institute (Artificial Intelligence-based Quantification of Pleural Plaque Volume and Association With Lung Function In Asbestos-exposed Patients)
New Artificial Intelligence Study Findings Have Been Reported from Netherlands C ancer Institute (Artificial Intelligence-based Quantification of Pleural Plaque Volume and Association With Lung Function In Asbestos-exposed Patients)
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By a News Reporter-Staff News Editor at Robotics & Machine Learning Daily News Daily News – Data detailed on Artificial Intelligen ce have been presented. According to news reporting out of Amsterdam, Netherland s, by NewsRx editors, research stated, “Pleural plaques (PPs) are morphologic ma nifestations of long-term asbestos exposure. The relationship between PP and lun g function is not well understood, whereas the time-consuming nature of PP delin eation to obtain volume impedes research.” Our news journalists obtained a quote from the research from Netherlands Cancer Institute, “To automate the laborious task of delineation, we aimed to develop a utomatic artificial intelligence (AI)-driven segmentation of PP. Moreover, we ai med to explore the relationship between pleural plaque volume (PPV) and pulmonar y function tests. Radiologists manually delineated PPs retrospectively in comput ed tomography (CT) images of patients with occupational exposure to asbestos (Ma y 2014 to November 2019). We trained an AI model with a no-new-UNet architecture . The Dice Similarity Coefficient quantified the overlap between AI and radiolog ists. The Spearman correlation coefficient (r) was used for the correlation betw een PPV and pulmonary function test metrics. When recorded, these were vital cap acity (VC), forced vital capacity (FVC), and diffusing capacity for carbon monox ide (DLCO). We trained the AI system on 422 CT scans in 5 folds, each time with a different fold (n = 84 to 85) as a test set. On these independent test sets co mbined, the correlation between the predicted volumes and the ground truth was r = 0.90, and the median overlap was 0.71 Dice Similarity Coefficient. We found w eak to moderate correlations with PPV for VC (n = 80, r = -0.40) and FVC (n = 82 , r = -0.38), but no correlation for DLCO (n = 84, r = -0.09). When the cohort w as split on the median PPV, we observed statistically significantly lower VC (P = 0.001) and FVC (P = 0.04) values for the higher PPV patients, but not for DLCO (P = 0.19). We successfully developed an AI algorithm to automatically segment PP in CT images to enable fast volume extraction.”
AmsterdamNetherlandsEuropeArtifici al IntelligenceAsbestosEmerging TechnologiesInorganic ChemicalsMachine L earningSilicatesSilicon CompoundsSilicon DioxideNetherlands Cancer Insti tute
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