首页|Shanghai Jiao Tong University School of Medicine Reports Findings in Artificial Intelligence (Experimental and clinical validation of an artificial intelligence metal artifact correction algorithm for low-dose following up CT of percutaneou s ...)
Shanghai Jiao Tong University School of Medicine Reports Findings in Artificial Intelligence (Experimental and clinical validation of an artificial intelligence metal artifact correction algorithm for low-dose following up CT of percutaneou s ...)
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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 Shanghai, People ’s Republic of China, by NewsRx correspondents, research stated, “Lowdose follo wing up computed tomography (CT) of percutaneous vertebroplasty (PVP) that invol ves the use of bone cement usually suffers from lightweight metal artifacts, whe re conventional techniques for CT metal artifact reduction are often not suffici ently effective. This study aimed to validate an artificial intelligence (AI)-ba sed metal artifact correction (MAC) algorithm for use in low-dose following up C T for PVP.” Our news editors obtained a quote from the research from the Shanghai Jiao Tong University School of Medicine, “In experimental validation, an ovine vertebra ph antom was designed to simulate the clinical scenario of PVP. With routine-dose i mages acquired prior to the cement introduction as the reference, low-dose CT sc ans were taken on the cemented phantom and processed with conventional MAC and A I-MAC. The resulting image quality was compared in CT attenuation, image noise, signal-to-noise ratio (SNR), and contrast-to-noise ratio (CNR), followed by a qu antitative evaluation of the artifact correction accuracy based on adaptive segm entation of the paraspinal muscle. In clinical validation, ten cases of low-dose following up CT after PVP were enrolled to test the performance of diagnosing s arcopenia with measured CT attenuation per cemented vertebral segment, via recei ver operating characteristic (ROC) analysis. With respect to the reference image , no significant difference was found for AI-MAC in CT attenuation, image noise, SNRs, and CNR (all P>0.05). The paraspinal muscle segme nted on the AIMAC image was 18.6% and 8.3% more com plete to uncorrected and MAC images. Higher area under the curve (AUC) of the RO C analysis was found for AI-MAC (AUC =0.92) compared to the uncorrected (AUC =0. 61) and MAC images (AUC =0.70).”
ShanghaiPeople’s Republic of ChinaAs iaAlgorithmsArtificial IntelligenceEmerging TechnologiesHealth and Medic ineMachine LearningOrthopedic ProceduresVertebroplasty
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