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Objective:To explore the application value of artificial intelligence (AI) model based on deep learning in breast nodules classification of Breast Imaging Reporting and Data System of ultrasound (BI-RADS-US).Methods:The ultrasound images of 2 426 breast nodules from 1 558 female patients with breast diseases at Beijing Tongren Hospital, Capital Medical University between December 2006 and December 2019 were collected . The image data sets were divided into training (63%), verification (7%), and test (30%) subsets for the construction of AI model. The diagnostic efficiencies of AI model, doctors' arbitration results and doctors' diagnosis with or without AI model assistance were analyzed by using receiver operating characteristic (ROC) curve. The Cohen weighted Kappa statistic was used to compare the consistency of BI-RADS-US classification among 5 ultrasound doctors' diagnosis with or without AI model assistance. And the changes of BI-RADS-US classification were analyzed before and after each doctor adopted AI model assistance.Results:The differences in diagnostic efficiencies of AI model, doctors' arbitration results and doctors' diagnosis with or without AI model assistance were statistically significant (all P > 0.05). The consistency among 5 ultrasound doctors was improved due to AI model assistance and Kappa value was increased from 0.433 (category 3), 0.600 (category 4a), 0.614 (category 4b), 0.570 (category 4c) and 0.495 (category 5) to 0.812, 0.704, 0.823, 0.690 and 0.509 (all P < 0.05), respectively. The upgrade and downgrade of BI-RADS-US classification occurred in 5 doctors after the classification of AI model assistance. Downgrade from category 4 to 3 in benign nodules of 56.6% (47/76) and upgrade from category 4 to 5 in malignant nodules of 69.4% (34/49) were mostly observed. Conclusions:AI-assisted BI-RADS-US classification can effectively improve the consistency of classification among the doctors without reducing the diagnostic efficiency. AI model shows clinical values in reducing unnecessary biopsy of partial benign lesions and increasing diagnostic accuracy of partial malignant lesions through the adjustment of breast nodule classification.
RESUMO
Objective To investigate the effect and mechanism of electroacupuncture at acupoints on radiation-induced hippocampal neuronal apoptosis in C57 mice. Methods Forty one-month-old C 57/6J mice were equally and randomly divided into control group, model group(8 Gy), acupoint-electroacupuncture group, and non-acupoint-electroacupuncture group. In the acupoint-electroacupuncture group,mice received electroacupuncture intervention at Baihui, Fengfu, and Shenshu points. The novel object recognition test was used to evaluate the cognitive, learning, and memory abilities in mice. Immunohistochemistry was used to determine the levels of caspase-3 and apoptosis-inducing factor(AIF). TUNEL assay was used to detect DNA breaks. The data were analyzed by one-way analysis of variance and the Tukey's multiple comparison. Results Compared with the control group, the model group had a significantly reduced recognition index(P= 0.040);compared with the model group, the acupoint-electroacupuncture group had a significantly increased recognition index(P=0.043),shown by significantly longer time spent exploring novel objects(P=0.035).According to the results of immunohistochemistry,the model group had significantly higher apoptosis rate and levels of caspase-3 and AIF in hippocampal neurons than the control group(P= 0.002, 0.003, 0.023), while the acupoint-electroacupuncture group had significantly lower apoptosis rate and levels of caspase-3 and AIF in hippocampal neurons than the model group(P=0.027, 0.005, 0.004). Conclusions Radiation can induce hippocampal neuronal apoptosis and damage cognitive function in mice. Electroacupuncture intervention can, to some degree, reduce above damages.