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

FundusQ-Net: A regression quality assessment deep learning algorithm for fundus images quality grading

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 1245 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 1245 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.

Smartphone Sensors for Health Monitoring and Diagnosis

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.

Automated Fundus Image Quality Assessment in Retinopathy of Prematurity Using Deep Convolutional Neural Networks

PURPOSE

Accurate image-based ophthalmic diagnosis relies on fundus image clarity. This has important implications for the quality of ophthalmic diagnoses and for emerging methods such as telemedicine and computer-based image analysis. The purpose of this study was to implement a deep convolutional neural network (CNN) for automated assessment of fundus image quality in retinopathy of prematurity (ROP).

DESIGN

Experimental study.

PARTICIPANTS

Retinal fundus images were collected from preterm infants during routine ROP screenings.

METHODS

Six thousand one hundred thirty-nine retinal fundus images were collected from 9 academic institutions. Each image was graded for quality (acceptable quality [AQ], possibly acceptable quality [PAQ], or not acceptable quality [NAQ]) by 3 independent experts. Quality was defined as the ability to assess an image confidently for the presence of ROP. Of the 6139 images, NAQ, PAQ, and AQ images represented 5.6%, 43.6%, and 50.8% of the image set, respectively. Because of low representation of NAQ images in the data set, images labeled NAQ were grouped into the PAQ category, and a binary CNN classifier was trained using 5-fold cross-validation on 4000 images. A test set of 2109 images was held out for final model evaluation. Additionally, 30 images were ranked from worst to best quality by 6 experts via pairwise comparisons, and the CNN's ability to rank quality, regardless of quality classification, was assessed.

MAIN OUTCOME MEASURES

The CNN performance was evaluated using area under the receiver operating characteristic curve (AUC). A Spearman's rank correlation was calculated to evaluate the overall ability of the CNN to rank images from worst to best quality as compared with experts.

RESULTS

The mean AUC for 5-fold cross-validation was 0.958 (standard deviation, 0.005) for the diagnosis of AQ versus PAQ images. The AUC was 0.965 for the test set. The Spearman's rank correlation coefficient on the set of 30 images was 0.90 as compared with the overall expert consensus ranking.

CONCLUSIONS

This model accurately assessed retinal fundus image quality in a comparable manner with that of experts. This fully automated model has potential for application in clinical settings, telemedicine, and computer-based image analysis in ROP and for generalizability to other ophthalmic diseases.

Megahertz ultra-wide-field swept-source retina optical coherence tomography compared to current existing imaging devices

BACKGROUND

To investigate the image quality of wide-angle cross-sectional and reconstructed fundus images based on ultra-megahertz swept-source Fourier domain mode locking (FDML) OCT compared to current generation diagnostic devices.

METHODS

A 1,050 nm swept-source FDML OCT system was constructed running at 1.68 MHz A-scan rate covering approximately 70° field of view. Twelve normal eyes were imaged with the device applying an isotropically dense sampling protocol (1,900 × 1,900 A-scans) with a fill factor of 100 %. Obtained OCT scan image quality was compared with two commercial OCT systems (Heidelberg Spectralis and Stratus OCT) of the same 12 eyes. Reconstructed en-face fundus images from the same FDML-OCT data set were compared to color fundus, infrared and ultra-wide-field scanning laser images (SLO).

RESULTS

Comparison of cross-sectional scans showed a high overall image quality of the 15× averaged FDML images at 1.68 MHz [overall quality grading score: 8.42 ± 0.52, range 0 (bad)-10 (excellent)] comparable to current spectral-domain OCTs (overall quality grading score: 8.83 ± 0.39, p = 0.731). On FDML OCT, a dense 3D data set was obtained covering also the central and mid-peripheral retina. The reconstructed FDML OCT en-face fundus images had high image quality comparable to scanning laser ophthalmoscope (SLO) as judged from retinal structures such as vessels and optic disc. Overall grading score was 8.36 ± 0.51 for FDML OCT vs 8.27 ± 0.65 for SLO (p = 0.717).

CONCLUSIONS

Ultra-wide-field megahertz 3D FDML OCT at 1.68 MHz is feasible, and provides cross-sectional image quality comparable to current spectral-domain OCT devices. In addition, reconstructed en-face visualization of fundus images result in a wide-field view with high image quality as compared to currently available fundus imaging devices. The improvement of >30× in imaging speed over commercial spectral-domain OCT technology enables high-density scan protocols leading to a data set for high quality cross-sectional and en-face images of the posterior segment.

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

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 μm 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 κ = 0.69) and moderate for size of deposits (weighted κ = 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.