Acupuncture therapy needs to cause enough somatosensory stimulation to achieve therapeutic effect.How to identify the site and depth before acupuncture,assess therapeutic effect after acupuncture and ensure the safety of treatment are the points for clinical application of acupuncture therapy,but no standard objective and quantitative access has been achieved.Under assistance of ultrasound,precise positioning before acupuncture,visualization of anatomical structures and quantification of the depth during acupuncture became feasible.The research progresses of ultrasound for assisting acupuncture therapy were reviewed in this article.

Genitourinary syndrome of menopause (GSM),is a common disease in postmenopausal women.It is reported that the incidence of GSM in breast cancer patients (79％-95％) was higher than that in normal counterparts (more than 50％).GSM significantly reduced the quality of life in breast cancer patients,but was not given enough attention,and most of breast cancer patients with GSM were not diagnosed and treated.In addition,chemotherapy and endocrine therapy for breast cancer may increase the incidence and severity of GSM.Hence,prevention and treatment of GSM should be strengthened in breast cancer patients.

Objective: To evaluate the performance of a convolutional neural network (CNN) model that can automatically detect and classify rib fractures, and output structured reports from computed tomography (CT) images. Materials and Methods: This study included 1079 patients (median age, 55 years; men, 718) from three hospitals, between January 2011 and January 2019, who were divided into a monocentric training set (n = 876; median age, 55 years; men, 582), five multicenter/multiparameter validation sets (n = 173; median age, 59 years; men, 118) with different slice thicknesses and image pixels, and a normal control set (n = 30; median age, 53 years; men, 18). Three classifications (fresh, healing, and old fracture) combined with fracture location (corresponding CT layers) were detected automatically and delivered in a structured report. Precision, recall, and F1-score were selected as metrics to measure the optimum CNN model. Detection/diagnosis time, precision, and sensitivity were employed to compare the diagnostic efficiency of the structured report and that of experienced radiologists. Results: A total of 25054 annotations (fresh fracture, 10089; healing fracture, 10922; old fracture, 4043) were labelled for training (18584) and validation (6470). The detection efficiency was higher for fresh fractures and healing fractures than for old fractures (F1-scores, 0.849, 0.856, 0.770, respectively, p = 0.023 for each), and the robustness of the model was good in the five multicenter/multiparameter validation sets (all mean F1-scores > 0.8 except validation set 5 [512 x 512 pixels; F1-score = 0.757]). The precision of the five radiologists improved from 80.3% to 91.1%, and the sensitivity increased from 62.4% to 86.3% with artificial intelligence-assisted diagnosis. On average, the diagnosis time of the radiologists was reduced by 73.9 seconds. Conclusion Our CNN model for automatic rib fracture detection could assist radiologists in improving diagnostic efficiency, reducing diagnosis time and radiologists’ workload.