Artificial Intelligence Algorithm-Based CTA Imaging for Diagnosing Ischemic Type Biliary Lesions after Orthotopic Liver Transplantation

• Published in: Computational and mathematical methods in medicine Vol. 2022; pp. 1 - 8
• Main Authors: Yu, Zhenxing, Ou, Guixue, Wang, Ruihua, Zhang, Qinghua
• Format: Journal Article
• Language: English
• Published: United States Hindawi 04.01.2022
• Subjects: Adult; Algorithms; Angiography, Digital Subtraction - statistics & numerical data; Artificial Intelligence; Biliary Tract - blood supply; Biliary Tract - diagnostic imaging; Computational Biology; Computed Tomography Angiography - statistics & numerical data; Female; Humans; Ischemia - diagnostic imaging; Ischemia - etiology; Liver Transplantation - adverse effects; Liver Transplantation - methods; Liver Transplantation - statistics & numerical data; Male; Middle Aged; Neural Networks, Computer
• ISSN: 1748-670X; 1748-6718; 1748-6718
• DOI: 10.1155/2022/3399892

The study focused on the clinical application value of artificial intelligence-based computed tomography angiography (CTA) in the diagnosis of orthotopic liver transplantation (OLT) after ischemic type biliary lesions (ITBL). A total of 66 patients receiving OLT in hospital were selected. Convolutional neural network (CNN) algorithm was used to denoise and detect the edges of CTA images of patients. At the same time, the quality of the processed image was subjectively evaluated and quantified by Hmax, Ur, Cr, and other indicators. Then, the digital subtraction angiography (DSA) diagnosis and CTA diagnosis based on CNN were compared for the sensitivity, specificity, positive predictive value, negative predictive value, and patient classification results. It was found that CTA can clearly reflect the information of hepatic aorta lesions and thrombosis in patients with ischemic single-duct injury after liver transplantation. After neural network algorithm processing, the image quality is obviously improved, the lesions are more prominent, and the details of lesion parts are also well displayed. ITBL occurred in 40 (71%) of 56 patients with abnormal CTA at early stage. ITBL occurred in only 8 (12.3%) of 65 patients with normal CTA at early stage. Early CTA manifestations had high sensitivity (72.22%), specificity (87.44%), positive predictive value (60.94%), and negative predictive value (92.06%) for the diagnosis of ITBL. It was concluded that artificial intelligence-based CTA had high clinical application value in the diagnosis of ITBL after OLT.