Objectives: The preoperative differentiation of thyroid nodules into benign thyroid nodules (BTN), non-metastatic malignant thyroid nodules (NMTN), and metastatic malignant thyroid nodules (MMTN) is critical for guiding clinical management strategies. Ultrasound (US) examinations frequently exhibit diagnostic inconsistencies due to operator-dependent variability. Computer-assisted diagnosis (CAD), an artificial intelligence (AI) model based on convolutional neural networks (CNNs), can help overcome inconsistencies in US examination outcomes by leveraging large-scale ultrasound imaging datasets to improve classification accuracy. Our study aimed to establish and validate this AI-powered ultrasound diagnostic model for precise preoperative discrimination among BTN, NMTN, and MMTN. Methods: A total of 209 patients (BTN = 66, NMTN = 15, and MMTN = 128) were consecutively identified and enrolled from a multi-center database. A subset of 195 patients (BTN = 60, NMTN = 15, and MMTN = 120) was selected for final analysis. These patients were divided into two groups: a training set (BTN = 50, NMTN = 11, and MMTN = 100) and a testing set (BTN = 10, NMTN = 4, and MMTN = 20). A total of 3,537 ultrasound images from the 195 patients were preprocessed by normalizing grayscale values and reducing noise. The processed images were then input into the AI model, which was trained to classify thyroid nodules. The model’s performance was evaluated using the testing set and assessed through receiver operating characteristic (ROC) curve analysis and the confusion matrix. Finally, the diagnostic accuracy of the AI model was compared with that of radiologists to determine its clinical utility in ultrasound-based diagnosis. Results: Compared to junior and senior radiologists, the AI model achieved near-perfect AUC values of 0.97 (BTN), 0.99 (NMTN), and 0.96 (MMTN), significantly outperforming the senior radiologist’s AUCs (0.88 for NMTN) and the junior radiologist’s weaker discrimination. In addition, the accuracy of this model was higher than all ultrasound radiologists (95% vs. 73 and 84% for the junior radiologist and senior radiologist, respectively). Conclusion: The AI-based ultrasound imaging diagnostic model showed excellent performance in differentiating BTN, NMTN, and MMTN, supporting its value as a diagnostic tool for the clinical decision-making process.

Previous studies have shown that patients with pre-existing chronic obstructive pulmonary diseases (COPD) were more likely to be infected with coronavirus disease (COVID-19) and lead to more severe lung lesions. However, few studies have explored the severity and prognosis of COVID-19 patients with different phenotypes of COPD. The aim of this study is to investigate the value of the deep learning and radiomics features for the severity evaluation and the nucleic acid turning-negative time prediction in COVID-19 patients with COPD including two phenotypes of chronic bronchitis predominant patients and emphysema predominant patients. A total of 281 patients were retrospectively collected from Hohhot First Hospital between October 2022 and January 2023. They were divided to three groups: COVID-19 group of 95 patients, COVID-19 with emphysema group of 94 patients, COVID-19 with chronic bronchitis group of 92 patients. All patients underwent chest computed tomography (CT) scans and recorded clinical data. The U-net model was pretrained to segment the pulmonary involvement area on CT images and the severity of pneumonia were evaluated by the percentage of pulmonary involvement volume to lung volume. The 107 radiomics features were extracted by pyradiomics package. The Spearman method was employed to analyze the correlation of the data and visualize it through a heatmap. Then we establish a deep learning model (model 1) and a fusion model (model 2) combined deep learning with radiomics features to predict nucleic acid turning-negative time. COVID-19 patients with emphysema was lowest in the lymphocyte count compared to COVID-19 patients and COVID-19 companied with chronic bronchitis, and they have the most extensive range of pulmonary inflammation. The lymphocyte count was significantly correlated with pulmonary involvement and the time for nucleic acid turning negative (r=-0.145, P < 0.05). Importantly, our results demonstrated that model 2 achieved an accuracy of 80.9% in predicting nucleic acid turning-negative time. The pre-existing emphysema phenotype of COPD severely aggravated the pulmonary involvement of COVID-19 patients. Deep learning and radiomics features may provide more information to accurately predict the nucleic acid turning-negative time, which is expected to play an important role in clinical practice.