Lung diseases in neonates can be life-threatening condition and may result in respiratory failure and death. Chest X-ray is a traditional diagnostic technique that results in radiation exposure to patients. Lung ultrasound is a user-friendly imaging technique that has been increasingly used in clinical practice in recent years and presents the advantages of real-time imaging and without radiation. Here we review the sonographic appearances of common neonatal lung diseases and present demonstration of typical cases.

Objective. The incidence of superficial organ diseases has increased rapidly in recent years. New methods such as computer-aided diagnosis (CAD) are widely used to improve diagnostic efficiency. Convolutional neural networks (CNNs) are one of the most popular methods, and further improvements of CNNs should be considered. This paper aims to develop a multiorgan CAD system based on CNNs for classifying both thyroid and breast nodules and investigate the impact of this system on the diagnostic efficiency of different preprocessing approaches. Methods. The training and validation sets comprised randomly selected thyroid and breast nodule images. The data were subgrouped into 4 models according to the different preprocessing methods (depending on segmentation and the classification method). A prospective data set was selected to verify the clinical value of the CNN model by comparison with ultrasound guidelines. Diagnostic efficiency was assessed based on receiver operating characteristic (ROC) curves. Results. Among the 4 models, the CNN model using segmented images for classification achieved the best result. For the validation set, the sensitivity, specificity, positive predictive value (PPV), negative predictive value (NPV), accuracy, and area under the curve (AUC) of our CNN model were 84.9%, 69.0%, 62.5%, 88.2%, 75.0%, and 0.769, respectively. There was no statistically significant difference between the CNN model and the ultrasound guidelines. The combination of the two methods achieved superior diagnostic efficiency compared with their use individually. Conclusions. The study demonstrates the probability, feasibility, and clinical value of CAD in the ultrasound diagnosis of multiple organs. The use of segmented images and classification by the nature of the disease are the main factors responsible for the improvement of the CNN model. Moreover, the combination of the CNN model and ultrasound guidelines results in better diagnostic performance, which will contribute to the improved diagnostic efficiency of CAD systems.

Objective Ultrasound imaging is well known to play an important role in the detection of thyroid disease, but the management of thyroid ultrasound remains inconsistent. Both standardized diagnostic criteria and new ultrasound technologies are essential for improving the accuracy of thyroid ultrasound. This study reviewed the global guidelines of thyroid ultrasound and analyzed their common characteristics for basic clinical screening. Advances in the application of a combination of thyroid ultrasound and artificial intelligence (AI) were also presented. Data sources An extensive search of the PubMed database was undertaken, focusing on research published after 2001 with keywords including thyroid ultrasound, guideline, AI, segmentation, image classification, and deep learning. Study selection Several types of articles, including original studies and literature reviews, were identified and reviewed to summarize the importance of standardization and new technology in thyroid ultrasound diagnosis. Results Ultrasound has become an important diagnostic technique in thyroid nodules. Both standardized diagnostic criteria and new ultrasound technologies are essential for improving the accuracy of thyroid ultrasound. In the standardization, since there are no global consensus exists, common characteristics such as a multi-feature diagnosis, the performance of lymph nodes, explicit indications of fine needle aspiration, and the diagnosis of special populations should be focused on. Besides, evidence suggests that AI technique has a good effect on the unavoidable limitations of traditional ultrasound, and the combination of diagnostic criteria and AI may lead to a great promotion in thyroid diagnosis. Conclusion Standardization and development of novel techniques are key factors to improving thyroid ultrasound, and both should be considered in normal clinical use.

Ultrasound (US) can be used to evaluate the brain structure and nervous system damage. Patients with neurologic symptoms need rapid, noninvasive imaging with high spatial resolution and tissue contrast. Magnetic resonance imaging is currently the most sensitive and specific imaging method for evaluating neuropathologic conditions. This approach does present some challenges, such as the need to transport patients who may be seriously ill to the magnetic resonance imaging suite and the need for patients to remain for a considerable time. Cranial US provides a very valuable imaging method for clinicians, which can make a rapid diagnosis and evaluation without ionizing radiation. The main disadvantage of cranial US is its low sensitivity and specificity for subtle/early lesions. In recent years, with the rapid development of anatomic and functional US technology, the practicability of US diagnosis and intervention has been greatly improved. Ultrasound elastography may have the potential to improve the sensitivity and specificity of various cranial nerve conditions. Ultrasound elastography has received considerable critical attention, and an increasing number of studies have recognized its critical role in evaluating brain diseases. At present, US elastography has been applied to the evaluation of traumatic brain injury, ischemic stroke, intraoperative brain tumors, and hypoxic ischemic encephalopathy. The latest animal experiments and human clinical trial developments in the applications of US elastography for brain diseases are summarized in this review.