PurposeThe foveal avascular zone (FAZ) is altered in numerous diseases. We assessed factors (axial length, segmentation method, age, sex) impacting FAZ measurements from optical coherence tomography (OCT) angiography images.MethodsWe recruited 116 Caucasian subjects without ocular disease, and acquired two 3 × 3 mm AngioVue scans per each right eye (232 total scans). In images of the superficial plexus, the FAZ was segmented using the AngioVue semiautomatic nonflow measurement tool and ImageJ manual segmentation. In images from the full retinal thickness, the FAZ was segmented using the AngioAnalytics automatic FAZ tool. Repeatability, reliability, and reproducibility were calculated for FAZ measurements (acircularity, area).ResultsFAZ area (mean ± SD) for manual segmentation was 0.257 ± 0.104 mm2, greater than both semiautomatic (0.231 ± 0.0939 mm2) and automatic (0.234 ± 0.0933 mm2) segmentation (P < 0.05). Not correcting for axial length introduced errors up to 31% in FAZ area. Manual area segmentation had better repeatability (0.022 mm2) than semiautomatic (0.046 mm2) or automatic (0.060 mm2). FAZ acircularity had better repeatability with automatic than manual segmentation (0.086 vs. 0.114). Reliability of all area measurements was excellent (intraclass correlation coefficient [ICC] = 0.994 manual, 0.969 semiautomatic, 0.948 automatic). Reliability of acircularity measurements was 0.879 for manual and 0.606 for automatic.ConclusionWe identified numerous factors affecting FAZ measurements. These errors confound comparisons across studies and studies examining factors that may correlate with FAZ measures.Translational RelevanceUsing FAZ measurements as biomarkers for disease progression requires assessing and controlling for different sources of error. Not correcting for ocular magnification can result in significant inaccuracy in FAZ measurements, while choice of segmentation method affects both repeatability and accuracy.

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In recent years interest in the application of Wireless Body Area Network (WBAN) for patient monitoring applications has grown significantly. A WBAN can be used to develop patient monitoring systems which offer flexibility to medical staff and mobility to patients. Patients monitoring could involve a range of activities including data collection from various body sensors for storage and diagnosis, transmitting data to remote medical databases, and controlling medical appliances, etc. Also, WBANs could operate in an interconnected mode to enable remote patient monitoring using telehealth/e-health applications. A WBAN can also be used to monitor athletes' performance and assist them in training activities. For such applications it is very important that a WBAN collects and transmits data reliably, and in a timely manner to a monitoring entity. In order to address these issues, this paper presents WBAN design techniques for medical applications. We examine the WBAN design issues with particular emphasis on the design of MAC protocols and power consumption profiles of WBAN. Some simulation results are presented to further illustrate the performances of various WBAN design techniques.

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In recent years, interests in the application of Wireless Body Area Network (WBAN) have grown considerably. A WBAN can be used to develop a patient monitoring system which offers flexibility and mobility to patients. Use of a WBAN will also allow the flexibility of setting up a remote monitoring system via either the internet or an intranet. For such medical systems it is very important that a WBAN can collect and transmit data reliably, and in a timely manner to the monitoring entity. In this paper we examine the performance of an IEEE802.15.4/Zigbee MAC based WBAN operating in different patient monitoring environment. We study the performance of a remote patient monitoring system using an OPNET based simulation model.

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Some M2M applications such as event monitoring involve a group of devices in a vicinity that act in a coordinated manner. An LTE network can exploit the correlated traffic characteristics of such devices by proactively assigning resources to devices based upon the activity of neighboring devices in the same group. This can reduce latency compared to waiting for each device in the group to request resources reactively per the standard LTE protocol. In this paper, we specify a new low complexity predictive resource allocation algorithm, known as the one way algorithm, for use with delay sensitive event based M2M applications in the LTE uplink. This algorithm requires minimal incremental processing power and memory resources at the eNodeB, yet can reduce the mean uplink latency below the minimum possible value for a non-predictive resource allocation algorithm. We develop mathematical models for the probability of a prediction, the probability of a successful prediction, the probability of an unsuccessful prediction, resource usage/wastage probabilities and mean uplink latency. The validity of these models is demonstrated by comparison with the results from a simulation. The models can be used offline by network operators or online in real time by the eNodeB scheduler to optimize performance.