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.

Background: Accurate preoperative prediction of cervical lymph node (LN) metastasis in patients with papillary thyroid carcinoma (PTC) provides a basis for surgical decision-making and the extent of tumor resection. This study aimed to develop and validate an ultrasound radiomics nomogram for the preoperative assessment of LN status. Methods: Data from 147 PTC patients at the Wuhan Tongji Hospital and 90 cases at the Hunan Provincial Tumor Hospital between January 2017 and September 2019 were included in our study. They were grouped as the training and external validation set. Radiomics features were extracted from shear-wave elastography (SWE) images and corresponding B-mode ultrasound (BMUS) images. Then, the minimum redundancy maximum relevance algorithm and the least absolute shrinkage and selection operator regression were used to select LN status-related features and construct the SWE and BMUS radiomics score (Rad-score). Multivariate logistic regression was performed using the two radiomics scores together with clinical data, and a nomogram was subsequently developed. The performance of the nomogram was assessed with respect to discrimination, calibration, and clinical usefulness in the training and external validation set. Results: Both the SWE and BMUS Rad-scores were significantly higher in patients with cervical LN metastasis. Multivariate analysis indicated that the SWE Rad-scores, multifocality, and ultrasound (US)-reported LN status were independent risk factors associated with LN status. The radiomics nomogram, which incorporated the three variables, showed good calibration and discrimination in the training set (area under the receiver operator characteristic curve [AUC] 0.851 [CI 0.791-0.912]) and the validation set (AUC 0.832 [CI 0.749-0.916]). The significantly improved net reclassification improvement and index-integrated discrimination improvement demonstrated that SWE radiomics signature was a very useful marker to predict the LN metastasis in PTC. Decision curve analysis indicated that the SWE radiomics nomogram was clinically useful. Furthermore, the nomogram also showed favorable discriminatory efficacy in the US-reported LN-negative (cN0) subgroup (AUC 0.812 [CI 0.745-0.860]). Conclusions: The presented radiomics nomogram, which is based on the SWE radiomics signature, shows a favorable predictive value for LN staging in patients with PTC.

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