This paper aimed to investigate the application of ultrasound image segmentation technology based on the back propagation neural network (BPNN) artificial intelligence algorithm in the diagnosis of breast cancer axillary lymph node metastasis, thereby providing a theoretical basis for clinical diagnosis. In this study, 90 breast cancer patients with axillary lymph node metastasis were selected as the research objects and rolled randomly into an experimental group and a control group. Besides, all of them were examined by ultrasound. The BPNN algorithm for the ultrasound image segmentation diagnosis method was applied to the patiens from the experimental group, while the control group was given routine ultrasound diagnosis. Thus, the value of this algorithm in ultrasonic diagnosis was compared and explored. The results showed that when the number of hidden layer nodes based on the BPNN artificial intelligence algorithm was 2, 3, 4, 5, 6, 7, and 8, the corresponding segmentation accuracy was 97.3%, 96.5%, 94.8%, 94.8%, and 94.1% in turn. Among them, the segmentation accuracy was the highest when the number of hidden layer nodes was 2. The correlation of independent variable bubble plot analysis showed that the presence or absence of capsules, the presence of crab feet or burrs in breast cancer lesions was critical influencing factors for the occurrence of axillary lymph node metastasis, and the standardized importance was 99.7% and 70.8%, respectively. Besides, the area under the two-dimensional receiver operating characteristic (ROC) curve of the BPNN artificial intelligence algorithm model classification was always greater than the area under the curve of manual segmentation, and the segmentation accuracy was 90.31%, 94.88%, 95.48%, 95.44%, and 97.65% in sequence. In addition, the segmentation specificity of different running times was higher than that of manual segmentation. In conclusion, the BPNN artificial intelligence algorithm had high accuracy, sensitivity, and specificity for ultrasound image segmentation, with a better segmentation effect. Therefore, it had a better diagnostic effect for breast cancer axillary lymph node metastasis.

The present study aimed to investigate the correlation between ultrasonographic features, basic fibroblast growth factor (bFGF), and the local invasiveness of papillary thyroid carcinoma (PTC). A total of 350 samples of thyroid nodules were collected. Routine ultrasonography was performed before the operation and routine pathological diagnosis and bFGF detection were performed after the operation.’ These 350 samples of thyroid nodules included 90 samples of nodular goiter, 36 samples of focal thyroiditis, and 224 samples of PTC. A total of 326 thyroid nodules were examined for bFGF. The results revealed that the difference in the expression of bFGF between the benign and malignant groups was statistically significant (P < .05) and the difference in the positive expression of bFGF between the invasive and non-invasive PTC groups was statistically significant (P < .05). Whether the shape of PTC is regular or not and whether there is micro-calcification in PTC and other ultrasonographic features, the size and location of the lesions and the age of the patient help make a preliminary prediction of local invasiveness before the operation. Postoperative detection of bFGF is helpful for further risk assessments of PTC.