Computer-Aided Diagnosis Software for Hypertensive Risk Determination Through Fundus Image Processing

Morales, Sandra; Naranjo, Valery; Alcañíz, Mariano

doi:10.1109/jbhi.2014.2337960

Cited by 12 publications

(15 citation statements)

References 25 publications

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“…With software assistance, the reader's sensitivity to retinopathy detection was improved by 30%. To put these numbers in perspective, a recent analogous study by Morales et al [22] showed that their software system assistance was able to improve reader's sensitivity to retinopathy (HR) detection from 38% (without software) to 56% (with software), which is an 18% improvement. Moreover, their software used only two parameters, branching angle and vessel width.…”

Section: Discussionmentioning

confidence: 99%

Clinical utilization of automated image analysis software for improving retinal reader's performance

Joshi

Agurto

Barriga

et al. 2016

2016 IEEE Southwest Symposium on Image Analysis and Interpretation (SSIAI)

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The incidence of cardiovascular disease (CVD) is on the rise and reported to be the world's leading cause of death. Retinal vascular abnormalities and lesions of hypertensive retinopathy (HR) have been shown to be highly predictive risk factors for CVD. However, the inter-reader agreement on detection of HR abnormalities by trained retinal readers is only fair to moderate, such as κ=0.56 for artery-venous nicking and κ=0.42 for arterial narrowing, indicating a significant inconsistency. We have developed a system for automated analysis of retinal photographs for HR abnormalities, which can assist a retinal reader in the grading process and provide additional insight into CVD risk. This includes a set of algorithms for retinal vessel network analysis and detection of HR abnormalities. Three retinal readers graded a set of 120 retinal images with and without the assistance of the system. Assistance resulted in an average 30% improvement in the reader's sensitivity to retinopathy detection, with a 32% reduction in average reading time. The inter-reader agreement using this system increased by an average of 54%, which indicates improvement in grading standardization. The system increases the efficiency of the grading process, and demonstrates increased reproducibility of the retinal grading that can standardize the diagnosis.

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Section: Discussionmentioning

confidence: 99%

Clinical utilization of automated image analysis software for improving retinal reader's performance

Joshi

Agurto

Barriga

et al. 2016

2016 IEEE Southwest Symposium on Image Analysis and Interpretation (SSIAI)

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“…Some well-known CAD systems include screening systems for diabetic retinopathy [7], hypertensive risk [8] and pathology detected using digital chest radiographs [9]. However, no screening system has been developed for anemia-related risk assessment till date.…”

Section: Introductionmentioning

confidence: 99%

Azure-Based Smart Monitoring System for Anemia-Like Pallor

2017

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Increasing costs of diagnostic healthcare have necessitated the development of hardware independent non-invasive Point of Care (POC) systems. Although anemia prevalence rates in global populations vary between 10% and 60% in various demographic groups, smart monitoring systems have not yet been developed for screening and tracking anemia-like pallor. In this work, we present two cloud platform-hosted POC applications that are directed towards smart monitoring of anemia-like pallor through eye and tongue pallor site images. The applications consist of a front-end graphical user interface (GUI) module and two different back-end image processing and machine learning modules. Both applications are hosted on a browser accessible tomcat server ported to an Azure Virtual Machine (VM). We observe that the first application spatially segments regions of interest from pallor site images with higher pallor classification accuracy and relatively longer processing times when compared to the lesser accurate yet faster second application. Also, both applications achieve 65-98% accuracy in separating normal images from images with pallor or abnormalities. The optimized front-end module is significantly light-weight with a run-through time ratio of 10 −5 with respect to the back-end modules. Thus, the proposed applications are portable and hardware independent, allowing for their use in pallor monitoring and screening tasks.

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“…These features are used in a computer-aided diagnosis software for hypertensive risk determination which increases the percentage of cases detected if it is compared with the diagnosis obtained from the visual inspection of the fundus (Morales et al, 2014b).…”

Section: Main Contributionsmentioning

confidence: 99%

Fundus characterization for automatic disease screening through retinal image processing

Martínez¹

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Computer-Aided Diagnosis Software for Hypertensive Risk Determination Through Fundus Image Processing

Cited by 12 publications

References 25 publications

Clinical utilization of automated image analysis software for improving retinal reader's performance

Clinical utilization of automated image analysis software for improving retinal reader's performance

Azure-Based Smart Monitoring System for Anemia-Like Pallor

Fundus characterization for automatic disease screening through retinal image processing

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