Abstract
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.
Highlights
Before the 1990s, the incidence of biliary tract complications after liver transplantation was as high as 30-50%, and the fatality rate was 20-30%, which was one of the main factors affecting the prognosis and long-term survival of liver transplantation patients [1,2,3]
It was noted that computed tomography angiography (CTA) can clearly reflect the information of hepatic aortic lesions and thrombosis in patients with ischemic singleduct injury after orthotopic liver transplantation (OLT)
ischemic type biliary syndrome (ITBL) patients were taken as the research subjects, and the Convolutional neural network (CNN) algorithm was used to process CTA images of patients to analyze the application value of the CNN-based CTA in the diagnosis of ITBL patients
Summary
Before the 1990s, the incidence of biliary tract complications after liver transplantation was as high as 30-50%, and the fatality rate was 20-30%, which was one of the main factors affecting the prognosis and long-term survival of liver transplantation patients [1,2,3]. With the continuous progress and improvement of organ preservation and surgical techniques, the incidence and mortality of biliary tract complications after orthotopic liver transplantation (OLT) have decreased, but are still as high as 8-25% and 15%, respectively, accounting for 15-34% of the causes of death after OLT [4, 5]. The main risk factors for biliary tract injury after liver transplantation are summarized as follows: heat ischemic injury, ischemic injury during cold preservation, chronic rejection, ABO blood group discrepancy, cytomegalovirus infection, and cytotoxic effect of bile salt. Generally speaking, risk factors leading to biliary tract injury are different in different transplant centers, so specific risk factors need to be analyzed on a case-by-case basis [9,10,11]
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