iDEAS: A web-based system for dry eye assessment

Remeseiro, Beatriz; Barreira, N.; García-Resúa, Carlos; Lira, Madalena; Giráldez, María Jesús; Yebra‐Pimentel, Eva; Penedo, Manuel G.

doi:10.1016/j.cmpb.2016.02.015

Cited by 16 publications

(18 citation statements)

References 34 publications

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“…When light is projected over cornea, a pattern is formed over lipid layer [3,[8][9]. This pattern is captured using instrument called Tearscope Plus [23][24]. As shown in Figure 5, it looks like a grid on lipid layer.…”

Section: Lipid Layer Interference Patternmentioning

confidence: 99%

“…Two techniques have been proposed under this category. They are a Computer Aided System to Support Dry Eye Diagnosis based on tear film maps (CASDES) [23] and iDEAS [24]. Input to both these techniques is image captured using tearscope plus device.…”

Section: Techniques Based On Interference Pattern For Lipid Layermentioning

confidence: 99%

“…In [24], authors have presented a web based system for dry eye disease assessment. As shown in Figure 11, the system consists of four main parts: remote user interface, web application server, image processing module and data storage.…”

Section: Ideas: a Web Based System For Dry Eye Assessmentmentioning

confidence: 99%

“…In this paper, we summarize them along with corresponding clinical tests. Based on our study, we classify the techniques into following three major categories subject to the approach used to identify the dry eye disease: 1) Techniques based on tear film stability [15][16][17][18][19][20][21][22][23][24][25], 2) Techniques based on blink rate detection [26] and 3) Techniques based on amount of tear produced [27]. DED detection techniques based on tear film stability further differ from each other with respect to imaging modality.…”

Section: Introductionmentioning

confidence: 99%

“…DED detection techniques based on tear film stability further differ from each other with respect to imaging modality. Hence, based on imaging modality used, we further classify them into three subcategories: techniques using fluorescein tear film video [15][16][17][18][19][20][21][22], techniques using lipid layer interference patterns [23][24] and techniques using thermal infrared images [25]. From our rigorous study on DED detection techniques, we have identified several parameters to evaluate them.…”

Section: Introductionmentioning

confidence: 99%

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A Comprehensive Survey on Image Modality based Computerized Dry Eye Disease Detection Techniques

Vyas¹,

Mehta²

2020

Adv. sci. technol. eng. syst. j.

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Dry Eye Disease (DED) is one of the commonly occurring chronic disease today, affecting the vision of eye. It causes severe discomfort in eye, visual disturbance and blurred vision impacting the quality of life of patients. Due to recent advancements in Artificial Intelligence (AI) and rapid progress of analytics techniques, several image modality based computerized DED detection techniques are available in literature. These techniques transform image data into real and actionable insights and thus, provide new understanding and ways for better and quicker treatment. In addition, they assist ophthalmologist to diagnose dry eye disease precisely as well as to reduce the cost of healthcare. In this paper, we present a review on computerized DED detection techniques covering all available techniques. In addition, we present a parametric evaluation of these techniques based on our identified set of parameters. Though surveys on clinical DED detection techniques are available, this is the first attempt in literature to log down all computerized DED detection techniques at one place. Our survey will assist researchers and ophthalmologists to understand various technological advancements in the field of DED detection. As a little amount of work has been done in the field of automatizing dry eye disease detection, the field provides plenty of research opportunities to develop an automated efficient DED detection technique.

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