Development of 3D-printed universal adapter in enhancing retinal imaging accessibility.

BACKGROUND: The revolutionary technology of smartphone-based retinal imaging has been consistently improving over the years. Smartphone-based retinal image acquisition devices are designed to be portable, easy to use, and cost-efficient, which enables eye care to be more widely accessible especially in geographically remote areas. This enables early disease detection for those who are in low- and middle- income population or just in general has very limited access to eye care. This study investigates the limitation of smartphone compatibility of existing smartphone-based retinal image acquisition devices. Additionally, this study aims to propose a universal adapter design that is usable with an existing smartphone-based retinal image acquisition device known as the PanOptic ophthalmoscope. This study also aims to simulate the reliability, validity, and performance overall of the developed prototype. METHODS: A literature review has been conducted that identifies the limitation of smartphone compatibility among existing smartphone-based retinal image acquisition devices. Designing and modeling of proposed adapter were performed using the software AutoCAD 3D. For the proposed performance evaluation, finite element analysis (FEA) in the software Autodesk Inventor and 5-point scale method were demonstrated. RESULTS: Published studies demonstrate that most of the existing smartphone-based retinal imaging devices have compatibility limited to specific older smartphone models. This highlights the benefit of a universal adapter in broadening the usability of existing smartphone-based retinal image acquisition devices. A functional universal adapter design has been developed that demonstrates its compatibility with a variety of smartphones regardless of the smartphone dimension or the position of the smartphone's camera lens. The proposed performance evaluation method generates an efficient stress analysis of the proposed adapter design. The end-user survey results show a positive overall performance of the developed universal adapter. However, a significant difference between the expert's views on the developed adapter and the quality of images is observed. CONCLUSION: The compatibility of existing smartphone-based retinal imaging devices is still mostly limited to specific smartphone models. Besides this, the concept of a universal and suitable adapter for retinal imaging using the PanOptic ophthalmoscope was presented and validated in this paper. This work provides a platform for future development of smartphone-based ophthalmoscope that is universal.

A Review on the Extraction of Quantitative Retinal Microvascular Image Feature.

Digital image processing is one of the most widely used computer vision technologies in biomedical engineering. In the present modern ophthalmological practice, biomarkers analysis through digital fundus image processing analysis greatly contributes to vision science. This further facilitates developments in medical imaging, enabling this robust technology to attain extensive scopes in biomedical engineering platform. Various diagnostic techniques are used to analyze retinal microvasculature image to enable geometric features measurements such as vessel tortuosity, branching angles, branching coefficient, vessel diameter, and fractal dimension. These extracted markers or characterized fundus digital image features provide insights and relates quantitative retinal vascular topography abnormalities to various pathologies such as diabetic retinopathy, macular degeneration, hypertensive retinopathy, transient ischemic attack, neovascular glaucoma, and cardiovascular diseases. Apart from that, this noninvasive research tool is automated, allowing it to be used in large-scale screening programs, and all are described in this present review paper. This paper will also review recent research on the image processing-based extraction techniques of the quantitative retinal microvascular feature. It mainly focuses on features associated with the early symptom of transient ischemic attack or sharp stroke.

Degree of contribution (DoC) feature selection algorithm for structural brain MRI volumetric features in depression detection.

PURPOSE: Accurate detection of depression at an individual level using structural magnetic resonance imaging (sMRI) remains a challenge. Brain volumetric changes at a structural level appear to have importance in depression biomarkers studies. An automated algorithm is developed to select brain sMRI volumetric features for the detection of depression. METHODS: A feature selection (FS) algorithm called degree of contribution (DoC) is developed for selection of sMRI volumetric features. This algorithm uses an ensemble approach to determine the degree of contribution in detection of major depressive disorder. The DoC is the score of feature importance used for feature ranking. The algorithm involves four stages: feature ranking, subset generation, subset evaluation, and DoC analysis. The performance of DoC is evaluated on the Duke University Multi-site Imaging Research in the Analysis of Depression sMRI dataset. The dataset consists of 115 brain sMRI scans of 88 healthy controls and 27 depressed subjects. Forty-four sMRI volumetric features are used in the evaluation. RESULTS: The DoC score of forty-four features was determined as the accuracy threshold (Acc_Thresh) was varied. The DoC performance was compared with that of four existing FS algorithms. At all defined Acc_Threshs, DoC outperformed the four examined FS algorithms for the average classification score and the maximum classification score. CONCLUSION: DoC has a good ability to generate reduced-size subsets of important features that could yield high classification accuracy. Based on the DoC score, the most discriminant volumetric features are those from the left-brain region.

An algorithm for determination of rank and degree of contribution of sMRI volumetric features in depression detection.

Brain volume changes at structural level appear to have utmost importance in depression biomarkers studies. However, these brain volumetric findings have very minimal utilization in depression detection studies at individual level. Thus, this paper presents an evaluation of volumetric features to identify the relevant/optimal features for the detection of depression. An algorithm is presented for determination of rank and degree of contribution (DoC) of structural magnetic resonance imaging (sMRI) volumetric features. The algorithm is based on the frequencies of each feature contribution toward the desired accuracy limit. Forty-four volumetric features from various brain regions were adopted for evaluation. From DoC analysis, the DoC of each volumetric feature for depression detection is calculated and the features that dominate the contribution are determined.

Towards automated detection of depression from brain structural magnetic resonance images.

INTRODUCTION: Depression is a major issue worldwide and is seen as a significant health problem. Stigma and patient denial, clinical experience, time limitations, and reliability of psychometrics are barriers to the clinical diagnoses of depression. Thus, the establishment of an automated system that could detect such abnormalities would assist medical experts in their decision-making process. This paper reviews existing methods for the automated detection of depression from brain structural magnetic resonance images (sMRI). METHODS: Relevant sources were identified from various databases and online sites using a combination of keywords and terms including depression, major depressive disorder, detection, classification, and MRI databases. Reference lists of chosen articles were further reviewed for associated publications. RESULTS: The paper introduces a generic structure for representing and describing the methods developed for the detection of depression from sMRI of the brain. It consists of a number of components including acquisition and preprocessing, feature extraction, feature selection, and classification. CONCLUSION: Automated sMRI-based detection methods have the potential to provide an objective measure of depression, hence improving the confidence level in the diagnosis and prognosis of depression.