简介：AbstractBackground:Reports evaluating the efficacy of transcranial sonography (TCS) for the differential diagnosis of Parkinson disease (PD) and other movement disorders in China are scarce. Therefore, this study aimed to assess the application of TCS for the differential diagnosis of PD, multiple system atrophy (MSA), progressive supranuclear palsy (PSP), and essential tremor (ET) in Chinese individuals.Methods:From 2017 to 2019, 500 inpatients treated at the Department of Dyskinesia, Beijing Tiantan Hospital, Capital Medical University underwent routine transcranial ultrasound examination. The cross-sections at the midbrain and thalamus levels were scanned, and the incidence rates of substantia nigra (SN) positivity and the incidence rates of lenticular hyperechoic area were recorded. The echo of the SN was manually measured.Results:Of the 500 patients, 125 were excluded due to poor signal in temporal window sound transmission. Among the 375 individuals with good temporal window sound transmission, 200 were diagnosed with PD, 90 with ET, 50 with MSA, and 35 with PSP. The incidence rates of SN positivity differed significantly among the four patient groups (χ2 = 121.061, P < 0.001). Between-group comparisons were performed, and the PD group showed a higher SN positivity rate than the ET (χ2 = 94.898, P < 0.017), MSA (χ2 = 57.619, P < 0.017), and PSP (χ2 = 37.687, P < 0.017) groups. SN positivity showed a good diagnostic value for differentiating PD from the other three movement diseases, collectively or individually. The incidences of lenticular hyperechoic area significantly differed among the four patient groups (χ2 = 38.904, P < 0.001). Next, between-group comparisons were performed. The lenticular hyperechoic area was higher in the PD group than in the ET (χ2 = 6.714, P < 0.017) and MSA (χ2= 18.680, P < 0.017) groups but lower than that in the PSP group (χ2 = 0.679, P > 0.017).Conclusion:SN positivity could effectively differentiate PD from ET, PSP, and MSA in a Chinese population.

简介：AbstractBackground:The current deep learning diagnosis of breast masses is mainly reflected by the diagnosis of benign and malignant lesions. In China, breast masses are divided into four categories according to the treatment method: inflammatory masses, adenosis, benign tumors, and malignant tumors. These categorizations are important for guiding clinical treatment. In this study, we aimed to develop a convolutional neural network (CNN) for classification of these four breast mass types using ultrasound (US) images.Methods:Taking breast biopsy or pathological examinations as the reference standard, CNNs were used to establish models for the four-way classification of 3623 breast cancer patients from 13 centers. The patients were randomly divided into training and test groups (n = 1810 vs. n = 1813). Separate models were created for two-dimensional (2D) images only, 2D and color Doppler flow imaging (2D-CDFI), and 2D-CDFI and pulsed wave Doppler (2D-CDFI-PW) images. The performance of these three models was compared using sensitivity, specificity, area under receiver operating characteristic curve (AUC), positive (PPV) and negative predictive values (NPV), positive (LR+) and negative likelihood ratios (LR-), and the performance of the 2D model was further compared between masses of different sizes with above statistical indicators, between images from different hospitals with AUC, and with the performance of 37 radiologists.Results:The accuracies of the 2D, 2D-CDFI, and 2D-CDFI-PW models on the test set were 87.9%, 89.2%, and 88.7%, respectively. The AUCs for classification of benign tumors, malignant tumors, inflammatory masses, and adenosis were 0.90, 0.91, 0.90, and 0.89, respectively (95% confidence intervals [CIs], 0.87-0.91, 0.89-0.92, 0.87-0.91, and 0.86-0.90). The 2D-CDFI model showed better accuracy (89.2%) on the test set than the 2D (87.9%) and 2D-CDFI-PW (88.7%) models. The 2D model showed accuracy of 81.7% on breast masses ≤1 cm and 82.3% on breast masses >1 cm; there was a significant difference between the two groups (P < 0.001). The accuracy of the CNN classifications for the test set (89.2%) was significantly higher than that of all the radiologists (30%).Conclusions:The CNN may have high accuracy for classification of US images of breast masses and perform significantly better than human radiologists.Trial registration:Chictr.org, ChiCTR1900021375; http://www.chictr.org.cn/showproj.aspx?proj=33139.