Thyroid nodule, as a common clinical endocrine disease, has become increasingly prevalent worldwide. Ultrasound, as the premier method of thyroid imaging, plays an important role in accurately diagnosing and managing thyroid nodules. However, there is a high degree of inter- and intra-observer variability in image interpretation due to the different knowledge and experience of sonographers who have huge ultrasound examination tasks everyday. Artificial intelligence based on computer-aided diagnosis technology maybe improve the accuracy and time efficiency of thyroid nodules diagnosis. This study introduced an artificial intelligence software called SW-TH01/II to evaluate ultrasound image characteristics of thyroid nodules including echogenicity, shape, border, margin, and calcification. We included 225 ultrasound images from two hospitals in Shanghai, respectively. The sonographers and software performed characteristics analysis on the same group of images. We analyzed the consistency of the two results and used the sonographers' results as the gold standard to evaluate the accuracy of SW-TH01/II. A total of 449 images were included in the statistical analysis. For the seven indicators, the proportions of agreement between SW-TH01/II and sonographers' analysis results were all greater than 0.8. For the echogenicity (with very hypoechoic), aspect ratio and margin, the kappa coefficient between the two methods were above 0.75 (P < 0.001). The kappa coefficients of echogenicity (echotexture and echogenicity level), border and calcification between the two methods were above 0.6 (P < 0.001). The median time it takes for software and sonographers to interpret an image were 3 (2, 3) seconds and 26.5 (21.17, 34.33) seconds, respectively, and the difference were statistically significant (z = -18.36, P < 0.001). SW-TH01/II has a high degree of accuracy and great time efficiency benefits in judging the characteristics of thyroid nodule. It can provide more objective results and improve the efficiency of ultrasound examination. SW-TH01/II can be used to assist the sonographers in characterizing the thyroid nodule ultrasound images.

Breast cancer is the most common malignant tumor among women worldwide, and early diagnosis is crucial for reducing mortality rates. Traditional diagnostic methods have significant limitations in terms of accuracy and consistency. Imaging is a common technique for diagnosing and predicting breast cancer, but human error remains a concern. Increasingly, artificial intelligence (AI) is being employed to assist physicians in reducing diagnostic errors. We developed an intelligent diagnostic model combining deep learning and radiomics to enhance breast tumor diagnosis. The model integrates MobileNet with ResNeXt-inspired depthwise separable and grouped convolutions, improving feature processing and efficiency while reducing parameters. Using AI-Dhabyani and TCIA breast ultrasound datasets, we validated the model internally and externally, comparing it to VGG16, ResNet, AlexNet, and MobileNet. Results: The internal validation set achieved an accuracy of 83.84% with an AUC of 0.92, outperforming other models. The external validation set showed an accuracy of 69.44% with an AUC of 0.75, demonstrating high robustness and generalizability. Conclusions: We developed an intelligent diagnostic model using deep learning and radiomics to improve breast tumor diagnosis. The model combines MobileNet with ResNeXt-inspired depthwise separable and grouped convolutions, enhancing feature processing and efficiency while reducing parameters. It was validated internally and externally using the AI-Dhabyani and TCIA breast ultrasound datasets and compared with VGG16, ResNet, AlexNet, and MobileNet.