Published online Oct 14, 2022. doi: 10.3748/wjg.v28.i38.5530
Peer-review started: July 1, 2022
First decision: July 13, 2022
Revised: August 12, 2022
Accepted: September 22, 2022
Article in press: September 22, 2022
Published online: October 14, 2022
Processing time: 103 Days and 0.8 Hours
Artificial intelligence (AI), especially deep learning, is gaining extensive attention for its excellent performance in medical image analysis. It can automatically make a quantitative assessment of complex medical images and help doctors to make more accurate diagnoses. In recent years, AI based on ultrasound has been shown to be very helpful in diffuse liver diseases and focal liver lesions, such as analyzing the severity of nonalcoholic fatty liver and the stage of liver fibrosis, identifying benign and malignant liver lesions, predicting the microvascular invasion of hepatocellular carcinoma, curative transarterial chemoembolization effect, and prognoses after thermal ablation. Moreover, AI based on endoscopic ultrasonography has been applied in some gastrointestinal diseases, such as distinguishing gastric mesenchymal tumors, detection of pancreatic cancer and intraductal papillary mucinous neoplasms, and predicting the preoperative tumor deposits in rectal cancer. This review focused on the basic technical knowledge about AI and the clinical application of AI in ultrasound of liver and gastroenterology diseases. Lastly, we discuss the challenges and future perspectives of AI.
Core Tip: Artificial intelligence (AI) based on ultrasound has been confirmed to be helpful in diagnosing diffuse liver diseases and focal liver lesions, such as analyzing the severity of nonalcoholic fatty liver and the stage of liver fibrosis, identifying benign and malignant liver lesions, predicting microvascular invasion of hepatocellular carcinoma, curative transarterial chemoembolization effect, and prognoses after thermal ablation. AI based on endoscopic ultrasonography has been applied in some gastrointestinal diseases. We focused on basic technical knowledge about AI and the aforementioned clinical application in the ultrasound of liver and gastroenterology. Additionally, we discuss the challenges and future perspectives of AI.