Determining which automatic digital blood pressure device performs adequately: a systematic review

The aim of this study is to systematically examine the proportion of accurate readings attained by automatic digital blood pressure (BP) devices in published validation studies. We included studies of automatic digital BP devices using recognized protocols. We summarized the data as mean and s.d. of differences between measured and observed BP, and proportion of measurements within 5 mm Hg. We included 79 articles (10 783 participants) reporting 113 studies from 22 different countries. Overall, 25/31 (81%), 37/41 (90%) and 34/35 (97%) devices passed the relevant protocols [BHS, AAMI and ESH international protocol (ESH-IP), respectively]. For devices that passed the BHS protocol, the proportion of measured values within 5 mm Hg of the observed value ranged from 60 to 86% (AAMI protocol 47–94% and ESH-IP 54–89%). The results for the same device varied significantly when a different protocol was used (Omron HEM-907 80% of readings were within 5 mm Hg using the AAMI protocol compared with 62% with the ESH-IP). Even devices with a mean difference of zero show high variation: a device with 74% of BP measurements within 5 mm Hg would require six further BP measurements to reduce variation to 95% of readings within 5 mm Hg. Current protocols for validating BP monitors give no guarantee of accuracy in clinical practice. Devices may pass even the most rigorous protocol with as few as 60% of readings within 5 mm Hg of the observed value. Multiple readings are essential to provide clinicians and patients with accurate information on which to base diagnostic and treatment decisions

Surface roughness detection of arteries via texture analysis of ultrasound images for early diagnosis of atherosclerosis

There is a strong research interest in identifying the surface roughness of the carotid arterial inner wall via texture analysis for early diagnosis of atherosclerosis. The purpose of this study is to assess the efficacy of texture analysis methods for identifying arterial roughness in the early stage of atherosclerosis. Ultrasound images of common carotid arteries of 15 normal mice fed a normal diet and 28 apoE−/− mice fed a high-fat diet were recorded by a high-frequency ultrasound system (Vevo 2100, frequency: 40 MHz). Six different texture feature sets were extracted based on the following methods: first-order statistics, fractal dimension texture analysis, spatial gray level dependence matrix, gray level difference statistics, the neighborhood gray tone difference matrix, and the statistical feature matrix. Statistical analysis indicates that 11 of 19 texture features can be used to distinguish between normal and abnormal groups (p<0.05). When the 11 optimal features were used as inputs to a support vector machine classifier, we achieved over 89% accuracy, 87% sensitivity and 93% specificity. The accuracy, sensitivity and specificity for the k-nearest neighbor classifier were 73%, 75% and 70%, respectively. The results show that it is feasible to identify arterial surface roughness based on texture features extracted from ultrasound images of the carotid arterial wall. This method is shown to be useful for early detection and diagnosis of atherosclerosis.Lili Niu, Ming Qian, Wei Yang, Long Meng, Yang Xiao, Kelvin K. L. Wong, Derek Abbott, Xin Liu, Hairong Zhen