Thyroid nodules are very common all over the world, and China is no exception. Ultrasound plays an important role in determining the risk stratification of thyroid nodules, which is critical for clinical management of thyroid nodules. For the past few years, many versions of TIRADS (Thyroid Imaging Reporting and Data System) have been put forward by several institutions with the aim to identify whether nodules require fine-needle biopsy or ultrasound follow-up. However, no version of TIRADS has been widely adopted worldwide till date. In China, as many as ten versions of TIRADS have been used in different hospitals nationwide, causing a lot of confusion. With the support of the Superficial Organ and Vascular Ultrasound Group of the Society of Ultrasound in Medicine of the Chinese Medical Association, the Chinese-TIRADS that is in line with China's national conditions and medical status was established based on literature review, expert consensus, and multicenter data provided by the Chinese Artificial Intelligence Alliance for Thyroid and Breast Ultrasound.

A s the most common cancer among women worldwide, breast cancer poses a great challenge to public health on a global scale (1). Identification of the presence of lymph node metastasis is pivotal for the pathologic staging, prognosis, and guidance of treatment in patients with breast cancer (2). Although several histopathologic findings, such as vascular and lymphatic invasion, epithelial hyperplasia, and necrosis, are associated with a higher risk for lymph node metastasis, they are available only postoperatively (3). The preoperative prediction of lymph node metastasis can provide valuable information for determining adjuvant therapy and developing surgical plans, thereby facilitating pretreatment decisions.Preoperative imaging assessment is of great value because of its convenient, comprehensive, and noninvasive properties. US plays a crucial role in detecting breast cancer and predicting lymph node metastasis (4). Most patients with early stage breast cancer who have clinically negative lymph nodes have no suspicious signs at either physical examination or imaging. Although radiologists often cannot find any signs of metastasis on US images of clinically negative lymph nodes, axillary lymph node metastasis is detected with sentinel lymph node biopsy in 15%-20% of patients (5). Several studies have found that numerous breast US characteristics are associated with lymph node metastasis. The distance

Symptomatic patients had more intense contrast agent enhancement in the plaque than asymptomatic patients, suggesting that contrast-enhanced carotid US may be used for plaque risk stratification.

Abstract-Wireless energy harvesting is regarded as a promising energy supply alternative for energy-constrained wireless networks. In this paper, a new wireless energy harvesting protocol is proposed for an underlay cognitive relay network with multiple primary user (PU) transceivers. In this protocol, the secondary nodes can harvest energy from the primary network (PN) while sharing the licensed spectrum of the PN. In order to assess the impact of different system parameters on the proposed network, we first derive an exact expression for the outage probability for the secondary network (SN) subject to three important power constraints: 1) the maximum transmit power at the secondary source (SS) and at the secondary relay (SR), 2) the peak interference power permitted at each PU receiver, and 3) the interference power from each PU transmitter to the SR and to the secondary destination (SD). To obtain practical design insights into the impact of different parameters on successful data transmission of the SN, we derive throughput expressions for both the delay-sensitive and the delay-tolerant transmission modes. We also derive asymptotic closed-form expressions for the outage probability and the delay-sensitive throughput and an asymptotic analytical expression for the delay-tolerant throughput as the number of PU transceivers goes to infinity. The results show that the outage probability improves when PU transmitters are located near SS and sufficiently far from SR and SD. Our results also show that when the number of PU transmitters is large, the detrimental effect of interference from PU transmitters outweighs the benefits of energy harvested from the PU transmitters.Index Terms-Cognitive relay network, energy harvesting, multiple primary user transceivers.

Artificial intelligence (AI), particularly deep learning algorithms, is gaining extensive attention for its excellent performance in image-recognition tasks. They can automatically make a quantitative assessment of complex medical image characteristics and achieve an increased accuracy for diagnosis with higher efficiency. AI is widely used and getting increasingly popular in the medical imaging of the liver, including radiology, ultrasound, and nuclear medicine. AI can assist physicians to make more accurate and reproductive imaging diagnosis and also reduce the physicians’ workload. This article illustrates basic technical knowledge about AI, including traditional machine learning and deep learning algorithms, especially convolutional neural networks, and their clinical application in the medical imaging of liver diseases, such as detecting and evaluating focal liver lesions, facilitating treatment, and predicting liver treatment response. We conclude that machine-assisted medical services will be a promising solution for future liver medical care. Lastly, we discuss the challenges and future directions of clinical application of deep learning techniques.