The increasing prevalence of the aging population, and inadequate and uneven distribution of medical resources, have led to a growing demand for telemedicine services. Gait disturbance is a primary symptom of neurological disorders such as Parkinson’s disease (PD). This study proposed a novel approach for the quantitative assessment and analysis of gait disturbance from two-dimensional (2D) videos captured using smartphones. The approach used a convolutional pose machine to extract human body joints and a gait phase segmentation algorithm based on node motion characteristics to identify the gait phase. Moreover, it extracted features of the upper and lower limbs. A height ratio-based spatial feature extraction method was proposed that effectively captures spatial information. The proposed method underwent validation via error analysis, correction compensation, and accuracy verification using the motion capture system. Specifically, the proposed method achieved an extracted step length error of less than 3 cm. The proposed method underwent clinical validation, recruiting 64 patients with Parkinson’s disease and 46 healthy controls of the same age group. Various gait indicators were statistically analyzed using three classic classification methods, with the random forest method achieving a classification accuracy of 91%. This method provides an objective, convenient, and intelligent solution for telemedicine focused on movement disorders in neurological diseases.
ObjectiveTo systematically review the diagnostic value of automatic breast volume scanner (ABVS) and handheld ultrasound (HHUS) for benign and malignant breast lesions.MethodsPubMed, EMbase, Web of Science, Biosis Preview, The Cochrane Library, WanFang Data, CNKI, VIP and SinoMed databases were electronically searched to collect studies on HHUS versus ABVS for benign and malignant breast lesions from inception to May 31st, 2019. Two reviewers independently screened literature, extracted data and assessed the risk of bias of included studies, and then, meta-analysis was performed by using Meta-Disc software.ResultsA total of 24 studies were included. The pooled sensitivity, specificity, positive likelihood ratio, negative likelihood ratio and diagnostic odds ratio for HHUS were 0.83 (95%CI 0.82 to 0.85), 0.81 (95%CI 0.79 to 0.82), 19.71 (95%CI 14.93 to 26.01), 4.05 (95%CI 3.49 to 4.69), 0.22 (95%CI 0.18 to 0.26), and for ABVS were 0.90 (95%CI 0.89 to 0.92), 0.88 (95%CI 0.87 to 0.89), 76.86(95%CI 55.13 to 107.17), 7.40 (95%CI 6.07 to 9.04), 0.11 (95%CI 0.09 to 0.14), respectively. The areas under the summary receiver operating characteristic curves in the differentiation of benign and malignant breast lesions were 0.88 and 0.96 for ABVS and HHUS, respectively.ConclusionThe current evidence shows that ABVS has higher value than HHUS in the diagnosis of benign and malignant breast tumor. Due to limited quality and quantity of the included studies, more high quality studies are needed to verify above conclusion.