ObjectiveTo analyze the application and efficacy of continuous positive airway pressure (CPAP) as an initial support measure for respiratory diseases in premature infants. MethodsWe retrospectively studied the clinical data of 160 premature infants hospitalized in the Neonatal Intensive Care Unit from January to December 2014. These infants accepted CPAP as the initial respiratory support. ResultsThe average birth weight and the average gestational age of the 160 premature infants were (1 581±440) g and (31.6±1.9) weeks, respectively. The main diagnosis of the primary diseases in these infants included neonatal pneumonia (81.3%), neonatal respiratory distress syndrome (57.5%), neonatal apnea (53.8%) and neonatal asphyxia (22.5%). The CPAP success rate in those infants whose birth weight was less than 1 000 g was significantly lower than those whose birth weight was equal or greater than 1 000 g (χ2=4.882, P=0.027). The perinatal period analysis showed that premature rupture of membranes, intrauterine fetal distress and maternal pregnancy complications were factors correlating with the effect of CPAP. CPAP treatment analysis showed that early application of CPAP within 24 hours after birth had a success rate of 82.4% (108/131), and initial inhaled oxygen concentration and oxygen pressure were the primary factors affecting CPAP efficacy. ConclusionApplication of CPAP is effective in respiratory support for premature infants and has a high success rate. Early application can reduce the use of mechanical ventilation and intubation. Regulating appropriate parameters helps raise the efficacy of CPAP therapy. Clinically, the standardized application of CPAP and monitoring the failure of CPAP are important for the improvement of the treatment efficacy.
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.