ObjectiveTo construct an intelligent ultrasound diagnosis system for breast nodules in patients with thyroid dysfunction using deep learning algorithms. MethodsA retrospective analysis was collected breast ultrasound images of 178 patients with thyroid dysfunction from the ultrasound database of the First Affiliated Hospital of Xinjiang Medical University from January 2023 to February 2024, which served as the training set. The deep learning algorithm was used to construct an intelligent ultrasound diagnosis system for breast nodules in patients with thyroid dysfunction. In addition, a retrospective analysis was collected breast ultrasound images of 81 patients with thyroid dysfunction from the ultrasound database of the First Affiliated Hospital of Xinjiang Medical University from March 2024 to January 2025, which served as the validation set. The above system was used as validation set to diagnose whether patients with thyroid dysfunction had breast nodules, and the diagnostic efficacy of imaging physicians’ diagnosis and the intelligent ultrasound diagnosis system for breast nodules in patients with thyroid dysfunction was analyzed. The consistency between the diagnosis of ultrasound physicians, intelligent ultrasound diagnosis system and the “gold standard” was tested by Kappa test. ResultsThere was no statistically significant difference in age, type of thyroid dysfunction, disease duration, number of breast nodules, and other clinical data between the training set and the validation set (P>0.05). The time required for the training set intelligent ultrasound diagnostic system to diagnose a single breast ultrasound image was (0.04±0.01) min, which was lower than that of an ultrasound specialist [(12.36±2.58) min], t=63.709, P＜0.001. The sensitivity, specificity, accuracy, and area under the curve (AUC) of detecting breast nodules in patients with thyroid dysfunction using an intelligent ultrasound diagnostic system were 97.87% (46/47), 100% (34/34), 98.77% (80/81), and 0.997 [95%CI: (0.951, 1.00)], respectively. The sensitivity, specificity, accuracy, and AUC of detecting breast nodules by ultrasound physicians were 89.36% (42/47), 91.18% (31/34), 90.12% (73/81), and 0.904 [95%CI: (0.818, 0.958)], respectively. The AUC of the intelligent ultrasound diagnosis system was higher than that of the ultrasound physician (Z=2.673, P=0.008). The detection results of breast nodules in patients with thyroid dysfunction diagnosed by ultrasound physicians were generally consistent with the “gold standard” (Kappa value=0.799, P＜0.001), while the intelligent ultrasound diagnosis system was in good agreement with the “gold standard” (Kappa value=0.975, P＜0.001). The confusion matrix results showed that the number of false positives was 3 and 0 for the ultrasound department physicians and the intelligent ultrasound diagnostic system, respectively, while the number of false negatives was 5 and 1. The calibration curve results indicated a high consistency between the diagnostic probability and the actual probability of the intelligent ultrasound diagnostic system, with the calibration curve fitting well with the ideal curve (Hosmer-Lemeshow test: χ2=1.246, P=0.997). ConclusionThe intelligent ultrasound diagnosis system for breast nodules in patients with thyroid dysfunction constructed by deep learning algorithm has good diagnostic efficacy, which can help ultrasound physicians improve screening efficiency and accuracy.
To establ ish a simple and stable cervical ectopic renal transplantation rat model that increase surgical successful rate. Methods A total of 208 male inbred Wistar rats (weighing 220-260 g) were randomly served as donors and recipients. The graft consisting of kidney, renal vein (RV) and renal artery (RA) was obtained, and perfused in situ. The donor RA was end-to-end anastomosed to the recipient left common carotid artery (CCA) by using of “sleeve” anastomosis,and the donor RV to the recipient right external jugular vein by using of “cuff” technique. The distal end of the ureter wasbrought out to form cervical cutaneous stomas. Results A total of 104 ectopic renal transplantations were performed in rats, including stages of the pre-experiment (62 operations) and experiment (42 operations). The success rates of the two stages were 80.6% and 95.2%, respectively. The causes of failure in the pre-experimental stage were anesthesia accidents, thrombosis of the arterial anastomosis, massive hemorrhage, air embol ism and phlebemphraxis. In the experimental stage, 2 rats died due to late anastomotic hemorrhage and thrombosis. The remaining 40 transplanted kidney survived more than 6 months. The time for surgery was (40 ± 6) minutes, the average time for donor surgery was (20 ± 5) minutes, the preparation time for the graft was (8 ± 2) minutes, the operative time for the recipient was (18 ± 3) minutes, including the time for the arterial anastomosis (5 ± 2) minutes and venous anastomosis (2 ± 1) minutes, the cold ischemia time of graft was (15 ± 3) minutes. Conclusion The cervical ectopic renal transplantation technique has the advantages of easy-and fast-to-perform, shorter operation and cold ischemia time, higher successful rate.