Artificial intelligence (AI) is playing an increasingly important role in medicine, especially in the field of medical imaging. It can be used to diagnose diseases and predict certain statuses and possible events that may happen. Recently, more and more studies have confirmed the value of AI based on ultrasound in the evaluation of diffuse liver diseases and focal liver lesions. It can assess the severity of liver fibrosis and nonalcoholic fatty liver, differentially diagnose benign and malignant liver lesions, distinguish primary from secondary liver cancers, predict the curative effect of liver cancer treatment and recurrence after treatment, and predict microvascular invasion in hepatocellular carcinoma. The findings from these studies have great clinical application potential in the near future. The purpose of this review is to comprehensively introduce the current status and future perspectives of AI in liver ultrasound.
In the study of vegetable price forecast, as the price is subject to various uncertain factors (weather, supply and demand, etc.), it has attributes such as high nonlinear, randomness and high noise, which would lead to the difficulty in forecasting. But grasping the law of price development and understanding the development trend of price, would help farmers grow the vegetable reasonably, and reduce unbalanced supply and demand. Therefore, we will make use of the characteristics of neural networks such as self-adapt,self-study and high fault tolerance, to build up the model of BP neural network with the training function of L-M for forecasting the vegetable prices. Finally, numerical example proves that the method is effective.
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