MOSAICS VERSUS EARLY TREATMENT DIABETIC RETINOPATHY SEVEN STANDARD FIELDS FOR EVALUATION OF DIABETIC RETINOPATHY SEVERITY

A survey on medical image analysis in diabetic retinopathy

Diabetic Retinopathy (DR) represents a highly-prevalent complication of diabetes in which individuals suffer from damage to the blood vessels in the retina. The disease manifests itself through lesion presence, starting with microaneurysms, at the nonproliferative stage before being characterized by neovascularization in the proliferative stage. Retinal specialists strive to detect DR early so that the disease can be treated before substantial, irreversible vision loss occurs. The level of DR severity indicates the extent of treatment necessary - vision loss may be preventable by effective diabetes management in mild (early) stages, rather than subjecting the patient to invasive laser surgery. Using artificial intelligence (AI), highly accurate and efficient systems can be developed to help assist medical professionals in screening and diagnosing DR earlier and without the full resources that are available in specialty clinics. In particular, deep learning facilitates diagnosis earlier and with higher sensitivity and specificity. Such systems make decisions based on minimally handcrafted features and pave the way for personalized therapies. Thus, this survey provides a comprehensive description of the current technology used in each step of DR diagnosis. First, it begins with an introduction to the disease and the current technologies and resources available in this space. It proceeds to discuss the frameworks that different teams have used to detect and classify DR. Ultimately, we conclude that deep learning systems offer revolutionary potential to DR identification and prevention of vision loss.

Comparison of montage with conventional stereoscopic seven-field photographs for assessment of ETDRS diabetic retinopathy severity

Background

The ETDRS stereoscopic seven-field (7F) has been a standard imaging and grading protocol for assessment of diabetic retinopathy (DR) severity score in many clinical trials. To the best of our knowledge, the comparison between montage and stereoscopic 7F has not been reported in the literature. Therefore, the main purpose of this study is to compare agreement between montage and stereoscopic seven-field (7F) photographs in the assessment of DR severity.

Methods

Stereoscopic 7F photographs were captured from subjects with DR. Montages of monoscopic 7F images were created using Adobe Photoshop CS6 Extended©. The best quality image of each stereo pair was selected and placed on a 150 × 125-inch canvas field according to the standard location from field 1 to 7. All the fields were aligned following the vessels and overlaid using the built-in blending tool. The resulting montage was utilized for grading and compared with grading on stereoscopic 7F photographs. Three independent graders were asked to assess DR severity on stereoscopic 7F photographs and montage. Severity level agreement between stereo 7F and montage was cross-tabulated and the agreement of DR severity levels between stereoscopic 7-field images and montage was analyzed using κ intergrader agreement; statistical significance was set at p < 0.05.

Results

A total of 50 eyes were included in the study. There was a substantial agreement between stereoscopic 7F and montage (κ = 0.745, κ_weighted = 0.867) in assessment of DR severity. Of 50 eyes, 80% of the cases showed complete agreement, and 100% of the cases had agreement within one-step. There was a moderate agreement among graders, and κ-value ranged from 0.4705 to 0.5803.

Conclusion

In this study, we found a substantial agreement in assessing DR severity score employing non-stereoscopic montage and stereoscopic 7F photographs.

Automated retinal image analysis for diabetic retinopathy in telemedicine

There will be an estimated 552 million persons with diabetes globally by the year 2030. Over half of these individuals will develop diabetic retinopathy, representing a nearly insurmountable burden for providing diabetes eye care. Telemedicine programmes have the capability to distribute quality eye care to virtually any location and address the lack of access to ophthalmic services. In most programmes, there is currently a heavy reliance on specially trained retinal image graders, a resource in short supply worldwide. These factors necessitate an image grading automation process to increase the speed of retinal image evaluation while maintaining accuracy and cost effectiveness. Several automatic retinal image analysis systems designed for use in telemedicine have recently become commercially available. Such systems have the potential to substantially improve the manner by which diabetes eye care is delivered by providing automated real-time evaluation to expedite diagnosis and referral if required. Furthermore, integration with electronic medical records may allow a more accurate prognostication for individual patients and may provide predictive modelling of medical risk factors based on broad population data.

Telemedicine for detecting diabetic retinopathy: a systematic review and meta-analysis

Objective

To determine the diagnostic accuracy of telemedicine in various clinical levels of diabetic retinopathy (DR) and diabetic macular oedema (DME).

Methods

PubMed, EMBASE and Cochrane databases were searched for telemedicine and DR. The methodological quality of included studies was evaluated using the Quality Assessment for Diagnostic Accuracy Studies (QUADAS-2). Measures of sensitivity, specificity and other variables were pooled using a random effects model. Summary receiver operating characteristic curves were used to estimate overall test performance. Meta-regression and subgroup analyses were used to identify sources of heterogeneity. Publication bias was evaluated using Stata V.12.0.

Results

Twenty articles involving 1960 participants were included. Pooled sensitivity of telemedicine exceeded 80% in detecting the absence of DR, low- or high-risk proliferative diabetic retinopathy (PDR), it exceeded 70% in detecting mild or moderate non-proliferative diabetic retinopathy (NPDR), DME and clinically significant macular oedema (CSME) and was 53% (95% CI 45% to 62%) in detecting severe NPDR. Pooled specificity of telemedicine exceeded 90%, except in the detection of mild NPDR which reached 89% (95% CI 88% to 91%). Diagnostic accuracy was higher with digital images obtained through mydriasis than through non-mydriasis, and was highest when a wide angle (100–200°) was used compared with a narrower angle (45–60°, 30° or 35°) in detecting the absence of DR and the presence of mild NPDR. No potential publication bias was detected.

Conclusions

The diagnostic accuracy of telemedicine using digital imaging in DR is overall high. It can be used widely for DR screening. Telemedicine based on the digital imaging technique that combines mydriasis with a wide angle field (100–200°) is the best choice in detecting the absence of DR and the presence of mild NPDR.

Feasibility of telemedicine in detecting diabetic retinopathy and age-related macular degeneration

Age-related macular degeneration and diabetic retinopathy are important causes of visual impairment and blindness in the world. Because of recent advances and newly available treatment modalities along with the devastating consequences associated with late stages of these diseases, much attention has been paid to the importance of early detection and improving patient access to specialist care. Telemedicine or, more specifically, digital retinal imaging utilizing telemedical technology has been proposed as an important alternative screening and management strategy to help meet this demand. In this paper, we perform a literature review and analysis that evaluates the validity and feasibility of telemedicine in detecting diabetic retinopathy and age-related macular degeneration. Understanding both the progress and barriers to progress that have been demonstrated in these two areas is important for future telemedicine research projects and innovations in telemedicine technology.

Observer performance using virtual pathology slides: impact of LCD color reproduction accuracy

The use of color LCDs in medical imaging is growing as more clinical specialties use digital images as a resource in diagnosis and treatment decisions. Telemedicine applications such as telepathology, teledermatology, and teleophthalmology rely heavily on color images. However, standard methods for calibrating, characterizing, and profiling color displays do not exist, resulting in inconsistent presentation. To address this, we developed a calibration, characterization, and profiling protocol for color-critical medical imaging applications. Physical characterization of displays calibrated with and without the protocol revealed high color reproduction accuracy with the protocol. The present study assessed the impact of this protocol on observer performance. A set of 250 breast biopsy virtual slide regions of interest (half malignant, half benign) were shown to six pathologists, once using the calibration protocol and once using the same display in its "native" off-the-shelf uncalibrated state. Diagnostic accuracy and time to render a decision were measured. In terms of ROC performance, Az (area under the curve) calibrated = 0.8570 and Az uncalibrated = 0.8488. No statistically significant difference (p = 0.4112) was observed. In terms of interpretation speed, mean calibrated = 4.895 s; mean uncalibrated = 6.304 s which is statistically significant (p = 0.0460). Early results suggest a slight advantage diagnostically for a properly calibrated and color-managed display and a significant potential advantage in terms of improved workflow. Future work should be conducted using different types of color images that may be more dependent on accurate color rendering and a wider range of LCDs with varying characteristics.

Nonmydriatic ultrawide field retinal imaging compared with dilated standard 7-field 35-mm photography and retinal specialist examination for evaluation of diabetic retinopathy

PURPOSE

To compare nonmydriatic stereoscopic Optomap ultrawide field images with dilated stereoscopic Early Treatment Diabetic Retinopathy Study 7-standard field 35-mm color 30-degree fundus photographs (ETDRS photography) and clinical examination for determining diabetic retinopathy (DR) and diabetic macular edema (DME) severity.

DESIGN

Single-site, prospective, comparative, instrument validation study.

METHODS

One hundred three diabetic patients (206 eyes) representing the full spectrum of DR severity underwent nonmydriatic ultrawide field 100-degree and 200-degree imaging, dilated ETDRS photography, and dilated fundus examination by a retina specialist. Two independent readers graded images to determine DR and DME severity. A third masked retina specialist adjudicated discrepancies.

RESULTS

Based on ETDRS photography (n = 200), the results were as follows: no DR (n = 25 eyes [12.5%]), mild nonproliferative DR (NPDR; 47 [23.5%]), moderate NPDR (61 [30.5%]), severe NPDR (11 [5.5%]), very severe NPDR (3 [1.5%]), and proliferative DR (52 [2.5%]). One (0.5%) eye was ungradable and 6 eyes did not complete ETDRS photography. No DME was found in 114 eyes (57.0%), DME was found in 28 eyes (14.0%), and clinically significant DME was found in 47 eyes (23.5%), and 11 (5.5%) eyes were ungradable. Exact DR severity agreement between ultrawide field 100-degree imaging and ETDRS photography occurred in 84%, with agreement within 1 level in 91% (K(W) = 0.85; K = 0.79). Nonmydriatic ultrawide field images exactly matched clinical examination results for DR in 70% and were within 1 level in 93% (K(W) = 0.71; K = 0.61). Nonmydriatic ultrawide field imaging acquisition time was less than half that of dilated ETDRS photography (P < .0001).

CONCLUSIONS

Nonmydriatic ultrawide field images compare favorably with dilated ETDRS photography and dilated fundus examination in determining DR and DME severity; however, they are acquired more rapidly. If confirmed in broader diabetic populations, nonmydriatic ultrawide field imaging may prove to be beneficial in DR evaluation in research and clinical settings.