Image quality characteristics of a novel colour scanning digital ophthalmoscope (SDO) compared with fundus photography

Strauss, Rupert W.; Krieglstein, Tina R.; Priglinger, Siegfried; W, Reis; Ulbig, Michael W.; Neubauer, Aljoscha S.

doi:10.1111/j.1475-1313.2007.00512.x

Cited by 7 publications

(3 citation statements)

References 22 publications

(51 reference statements)

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“…The term subretinal deposits refers to the elevations visible in retro‐mode images, even though some may coincide with drusen on colour fundus photography. Pairs of retro‐mode images from the same imaging session were graded for drusen size and number and according to parameters of image quality (Strauss et al. 2007), for one eye of each patient.…”

Section: Methodsmentioning

confidence: 99%

Drusen detection in retro-mode imaging by a scanning laser ophthalmoscope

Acton

Cubbidge

King

et al. 2011

Acta Ophthalmologica

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ABSTRACT.Purpose: The Nidek F-10 is a scanning laser ophthalmoscope that is capable of a novel fundus imaging technique, so-called 'retro-mode' imaging. The standard method of imaging drusen in age-related macular degeneration (AMD) is by fundus photography. The aim of the study was to assess drusen quantification using retro-mode imaging. Methods: Stereoscopic fundus photographs and retro-mode images were captured in 31 eyes of 20 patients with varying stages of AMD. Two experienced masked retinal graders independently assessed images for the number and size of drusen, using purpose-designed software. Drusen were further assessed in a subset of eight patients using optical coherence tomography (OCT) imaging. Results: Drusen observed by fundus photography (mean 33.5) were significantly fewer in number than subretinal deposits seen in retro-mode (mean 81.6; p < 0.001). The predominant deposit diameter was on average 5 lm smaller in retro-mode imaging than in fundus photography (p = 0.004). Agreement between graders for both types of imaging was substantial for number of deposits (weighted j = 0.69) and moderate for size of deposits (weighted j = 0.42). Retro-mode deposits corresponded to drusen on OCT imaging in all eight patients. Conclusion: The subretinal deposits detected by retro-mode imaging were consistent with the appearance of drusen on OCT imaging; however, a larger longitudinal study would be required to confirm this finding. Retro-mode imaging detected significantly more deposits than conventional colour fundus photography. Retro-mode imaging provides a rapid non-invasive technique, useful in monitoring subtle changes and progression of AMD, which may be useful in monitoring the response of drusen to future therapeutic interventions.

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Section: Methodsmentioning

confidence: 99%

Drusen detection in retro-mode imaging by a scanning laser ophthalmoscope

Acton

Cubbidge

King

et al. 2011

Acta Ophthalmologica

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“…In order to diagnose Diabetic Macular Edema (DME), Diabetic Retinopathy (DR), and most types of age-related macular degeneration (AMD), a good quality fundus image with an adequate field-of-view is critical. A resolution of at least 50 pixels/° along with an imager larger than 1024 × 768 pixels are required [70], and most modern-day smartphone-cameras meet this requirement. The idea of using the smartphone camera for retinal fundus imaging was first presented in Reference [69].…”

Section: Smartphone Sensors For Health Monitoringmentioning

confidence: 99%

Smartphone Sensors for Health Monitoring and Diagnosis

Majumder

Deen

2019

Sensors

302

162

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Over the past few decades, we have witnessed a dramatic rise in life expectancy owing to significant advances in medical science and technology, medicine as well as increased awareness about nutrition, education, and environmental and personal hygiene. Consequently, the elderly population in many countries are expected to rise rapidly in the coming years. A rapidly rising elderly demographics is expected to adversely affect the socioeconomic systems of many nations in terms of costs associated with their healthcare and wellbeing. In addition, diseases related to the cardiovascular system, eye, respiratory system, skin and mental health are widespread globally. However, most of these diseases can be avoided and/or properly managed through continuous monitoring. In order to enable continuous health monitoring as well as to serve growing healthcare needs; affordable, non-invasive and easy-to-use healthcare solutions are critical. The ever-increasing penetration of smartphones, coupled with embedded sensors and modern communication technologies, make it an attractive technology for enabling continuous and remote monitoring of an individual’s health and wellbeing with negligible additional costs. In this paper, we present a comprehensive review of the state-of-the-art research and developments in smartphone-sensor based healthcare technologies. A discussion on regulatory policies for medical devices and their implications in smartphone-based healthcare systems is presented. Finally, some future research perspectives and concerns regarding smartphone-based healthcare systems are described.

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“…Techniques such as automated screening devices and telemedicine have been discussed recently, as a way to provide fast diagnoses without the need of an ophthalmologist on-site [10]. Real world DFIs can be of low quality due to many causes: dirty camera lens, improper flash and gamma adjustment, eye blink and occlusion by eyelashes [11] as well as media opacity [12] and insufficient technician skill. Therefore, to be used in a large scale screening or telemedicine device, the device must be able to automatically identify and handle low quality images.…”

Section: Introductionmentioning

confidence: 99%

FundusQ-Net: a Regression Quality Assessment Deep Learning Algorithm for Fundus Images Quality Grading

Or¹,

Pizem²,

Eijgen³

et al. 2022

Preprint

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Objective: Ophthalmological pathologies such as glaucoma, diabetic retinopathy and age-related macular degeneration are major causes of blindness and vision impairment. There is a need for novel decision support tools that can simplify and speed up the diagnosis of these pathologies. A key step in this process is to automatically estimate the quality of the fundus images to make sure these are interpretable by a human operator or a machine learning model. We present a novel fundus image quality scale and deep learning (DL) model that can estimate fundus image quality relative to this new scale. Methods: A total of 1,245 images were graded for quality by two ophthalmologists within the range 1-10, with a resolution of 0.5. A DL regression model was trained for fundus image quality assessment. The architecture used was Inception-V3. The model was developed using a total of 89,947 images from 6 databases, of which 1,245 were labeled by the specialists and the remaining 88,702 images were used for pre-training and semi-supervised learning. The final DL model was evaluated on an internal test set (n=209) as well as an external test set (n=194). Results: The final DL model, denoted FundusQ-Net, achieved a mean absolute error of 0.61 (0.54-0.68) on the internal test set. When evaluated as a binary classification model on the public DRIMDB database as an external test set the model obtained an accuracy of 99%. Significance: the proposed algorithm provides a new robust tool for automated quality grading of fundus images.

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Image quality characteristics of a novel colour scanning digital ophthalmoscope (SDO) compared with fundus photography

Abstract: Distinct scales to grade image characteristics of different origin were developed and validated. Overall SDO digital imaging was found to provide fundus pictures of a similarly high level of quality as expert photography on slides.

Cited by 7 publications

References 22 publications

Drusen detection in retro-mode imaging by a scanning laser ophthalmoscope

Drusen detection in retro-mode imaging by a scanning laser ophthalmoscope

Smartphone Sensors for Health Monitoring and Diagnosis

FundusQ-Net: a Regression Quality Assessment Deep Learning Algorithm for Fundus Images Quality Grading

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