首页|University of Bari Aldo Moro Reports Findings in Artificial Intelligence [Early and accurate diagnosis of steatotic liver by artificial intelligence (AI)- supported ultrasonography]
University of Bari Aldo Moro Reports Findings in Artificial Intelligence [Early and accurate diagnosis of steatotic liver by artificial intelligence (AI)- supported ultrasonography]
扫码查看
点击上方二维码区域，可以放大扫码查看
原文链接
NETL
NSTL
By a News Reporter-Staff News Editor at Robotics & Machine Learning Daily News-New research on Artificial Intelligence is the su bject of a report. According to news reporting originating from Bari,Italy,by NewsRx correspondents,research stated,"Steatotic liver disease is the most fre quent chronic liver disease worldwide. Ultrasonography (US) is commonly employed for the assessment and diagnosis." Our news editors obtained a quote from the research from the University of Bari Aldo Moro,"Few information is available on the possible use of artificial intel ligence (AI) to ameliorate the diagnostic accuracy of ultrasonography. An AI-bas ed algorithm was developed using a dataset of US images. We prospectively enroll ed 134 patients for algorithm validation. Patients underwent abdominal US and Pr oton Density Fat Fraction MRI scans (MRI-PDFF),assumed as reference technique. The hepatorenal index was manually calculated (HRIM) by 4 operators. An automati c hepatorenal index (HRIA) was obtained by the algorithm. The accuracy of HRIA t o discriminate steatosis grades was evaluated by ROC analysis using MRI-PDFF cut -offs. Overweight was 40 % of subjects (BMI 26.4 kg/cm). The media n HRIA was 1.11 (IQR 0.32) and the average of 4 manually calculated HRIM was 1.0 8 (IQR 0.26),with a 15 % inter-operator variability. Both HRIA (R = 0.79,P<0.0001) and HRIM (R = 0.69,P<0.0001) significantly correlated with liver fat percentage (MRI-PDFF). Accordin g to MRI-PDFF,32 % of enrolled subjects had steatosis. Discrimina tion capacity by AUC between patient with steatosis and patient without steatosi s was better for HRIA than HRIM (AUC: 0.87 vs. 0.82,respectively). ROC analysis showed an AUC = 0.98 for HRIA with 1.64 cut-off in distinguishing between mild and moderate/severe groups. The use of AI improves accuracy and speed of ultraso nography in the diagnosis of liver steatosis."
BariItalyEuropeAlgorithmsArtific ial IntelligenceDiagnostics and ScreeningEmerging TechnologiesHealth and M edicineMachine LearningRisk and PreventionSteatosis
NETL
NSTL
