The Architecture of an Automatic eHealth Platform With Mobile Client for Cerebrovascular Disease Detection

Abstract

BackgroundIn recent years, cerebrovascular disease has been the leading cause of death and adult disability in the world. This study describes an efficient approach to detect cerebrovascular disease.ObjectiveIn order to improve cerebrovascular treatment, prevention, and care, an automatic cerebrovascular disease detection eHealth platform is designed and studied.MethodsWe designed an automatic eHealth platform for cerebrovascular disease detection with a four-level architecture: object control layer, data transmission layer, service supporting layer, and application service layer. The platform has eight main functions: cerebrovascular database management, preprocessing of cerebral image data, image viewing and adjustment model, image cropping compression and measurement, cerebrovascular segmentation, 3-dimensional cerebrovascular reconstruction, cerebrovascular rendering, cerebrovascular virtual endoscope, and automatic detection. Several key technologies were employed for the implementation of the platform. The anisotropic diffusion model was used to reduce the noise. Statistics segmentation with Gaussian-Markov random field model (G-MRF) and Stochastic Estimation Maximization (SEM) parameter estimation method were used to realize the cerebrovascular segmentation. Ball B-Spline curve was proposed to model the cerebral blood vessels. Compute unified device architecture (CUDA) based on ray-casting volume rendering presented by curvature enhancement and boundary enhancement were used to realize the volume rendering model. We implemented the platform with a network client and mobile phone client to fit different users.ResultsThe implemented platform is running on a common personal computer. Experiments on 32 patients’ brain computed tomography data or brain magnetic resonance imaging data stored in the system verified the feasibility and validity of each model we proposed. The platform is partly used in the cranial nerve surgery of the First Hospital Affiliated to the General Hospital of People's Liberation Army and radiology of Beijing Navy General Hospital. At the same time it also gets some applications in medical imaging specialty teaching of Tianjin Medical University. The application results have also been validated by our neurosurgeon and radiologist.ConclusionsThe platform appears beneficial in diagnosis of the cerebrovascular disease. The long-term benefits and additional applications of this technology warrant further study. The research built a diagnosis and treatment platform of the human tissue with complex geometry and topology such as brain vessel based on the Internet of things.

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“…One of the most central roles in this layer is the business process. Consequently, it is important that healthcare organizations pay more attention to determining policies and requirements regarding the data management, recourse and service management, equipment management and facilities management [18,22,31,32,37,61,65,67,93,96]. However, many reviewed studies ignored the importance of business activities and provided no solution for business technology algorithms.…”

“…A few of the selected studies have considered IoT security aspects; however, these aspects are only limited to some layers of the IoT architecture [4,7,32,58,61,67,68,71]. In this regard, Bilal et al [106] have remarked that all layers of IoT architecture face security challenges and threats.…”

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Automated Modular Magnetic Resonance Imaging Clinical Decision Support System (MIROR): An Application in Pediatric Cancer Diagnosis (Preprint)

“…In the past decade, clinical decision support (CDS) systems have increasingly gained attention, and the routine uptake of these intelligent systems is becoming more common [10][11][12][13][14][15][16][17]. Introduction of CDSs has provided clinicians and health care investigators with a platform for extraction of relevant information to aid decision making, prevent errors, and enhance health care.…”