Diabetic retinopathy retinal imaging -
Wide-field OCT angiograms of diabetic retinopathy using image montaging captured using the Plex Elite swept-source OCT angiography platform Carl Zeiss AG, Oberkochen, Germany. Retinal swept-source OCT-A slabs demonstrate mild non-proliferative diabetic retinopathy with intact retinal vasculature a and moderate non-proliferative diabetic retinopathy and multiple foci of capillary fallout seen in the temporal macula and beyond the vascular arcades b.
Modern digital color fundus cameras, although widely available in developed regions, still typically cost many thousands of dollars, are large and cumbersome, and require specially trained ophthalmic photographers. This often limits their deployment in developing regions and in areas with underserved patient populations who are less likely to attend regular ophthalmic screening.
In recent years, several manufacturers have attempted to address this problem by developing fundus photography attachments for smartphones with digital cameras. Modern smartphone cameras are capable of producing very high-quality digital images the match or exceed the resolution of dedicated clinical-grade retinal cameras, albeit with smaller image sensors.
Fundus photography using a smartphone and handheld diopter condensing lens has been successfully used to capture fundus photos [ 72 ], and since then several commercial developers have released hardware attachments and software to enable convenient fundus photography.
Current smartphone-based imaging solutions are based on the incorporation of additional lens elements in line with the smartphone camera and typically come with custom software that enables capture, labeling, and secure transmission of fundus photos.
Outcomes of recent prospective clinical trials with smartphone-based fundus photography systems used for DR screening are summarized in Table 3.
In most reports the sensitivity for DR using images captured from smartphones is comparable to slit-lamp biomicroscopy or dedicated fundus camera imaging with the benefit of far greater affordability. Given the obvious benefits of low cost and portability, there is clearly excellent potential in smartphone-based DR screening, though the questionable ability of these imaging systems to provide fundus images through undilated pupils poses a problem for screening programs that do not employ routine dilation.
Concerns about acute angle closure precipitated by dilation, particularly in East Asian populations [ 73 ], may render this approach undesirable in certain regions. Portable nonmydriatic fundus cameras are widely available and have been successfully tested for DR screening Table 3 , with diagnostic performance comparable to smartphone-based systems.
These devices offer somewhat better diagnostic performance than fixed fundus cameras in terms of image gradeability and diagnostic accuracy, but the benefit of portability cannot be overstated [ 74 , 75 , 76 , 77 ]. Several large trials have demonstrated the potential of these devices for detecting referable DR in developing regions [ 78 , 79 , 80 , 81 ].
It is likely that the incorporation of both smartphone-based and dedicated portable fundus cameras into DR screening programs will increase dramatically in the coming years, particular in developing countries with large rural areas where clinic review in often not feasible.
Recent incorporation of AI-based automatic image grading to smartphone fundus images [ 82 ] offers potential for even further improvements to the cost-effectiveness and convenience of screening programs.
Over the past few decades, diabetic retinopathy screening programs have been implemented in essentially all developed nations and many developing nations [ 10 , 83 ]. While most current screening programs employ conventional fundus photography with trained grading staff, research over the past few years has demonstrated a role for newer imaging modalities and image analysis software that has the potential to improve screening accuracy and reduce the financial burden of these increasingly expensive programs.
With the dramatic increase in diabetes prevalence currently occurring worldwide, it is clear that more efficient screening programs will be required not only to ensure early detection of disease, but also to reduce inappropriate referrals to ophthalmologists for non-sight-threatening disease that is more appropriately managed with continued observation in a primary healthcare setting.
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Images do not depict the same patient. All four participating students received standardized training from an experienced ophthalmologist, who presented the device and explained how to handle it, in addition to monitoring the recording of the first 10 videos. For the smartphone-based examination, the students captured a high-definition video of the fundus, lasting around two minutes each, using a device that consisted of an iron support where a smartphone in this study, an Apple Iphone 6 ® or a Samsung Galaxy S8 ® was attached to one side and a 20 D lens was attached to the other side.
The device also had an iron adapter on the bottom that allowed its attachment to a slit lamp table. This made image acquisition easier as the patient's head remained fixed by the chin rest, facilitating handling of the camera and adjusting its focus Fig.
Nothing but the inbuilt camera software of each smartphone were used to register the images. All the included patients underwent pharmacological mydriasis prior to the exam.
After posterior pole focus was obtained, recording was started and the patient was asked to look into five directions in the following order: 1 Straight ahead; 2 Temporally; 3 Nasally; 4 Superiorly and 5 Inferiorly. Images obtained by each method were saved on cloud storage Google Drive ® in a randomized manner and organized by codes.
Posteriorly, two independent masked specialists assessed each image individually and classified their findings according to the Airlie-House modified scale [ 4 ] 0—Absence of Retinopathy; 1—Minimal non-proliferative diabetic retinopathy [NPDR]; 2—Mild NPDR; 3—Moderate NPDR; 4—Severe NPDR; 5—Very severe NPDR; 6—Proliferative diabetic retinopathy PDR with no high risk signs; 7—PDR with high risk signs; 8—Advanced PDR; 9—Classification not possible and also according to the presence or absence of hard macular exudates, utilized here as a surrogate marker for diabetic macular edema.
After each individual analysis, the specialists reported the results in an online form created specifically for that purpose on Google Forms®. Both masked specialists independently evaluated and classified all images generated by the standard fundus camera and then evaluated and classified all videos generated by the smartphone-based method.
All images and videos had been completely randomized and identified only by a code, making it impossible for them to identify any patient information. In the same manner, specialist number 1 had no access to the reports produced by specialist number 2 and vice-versa.
A third specialist was asked to evaluate cases where there was disagreement between the specialists 1 and 2. Finally, we calculated the agreement rate, kappa correlation index, sensitivity, specificity and disagreement false positives and false negatives of the reports deriving from the smartphone-based method as compared to those deriving from the gold standard tabletop fundus camera system, as well as interobserver agreement between specialists for each method as further detailed ahead.
Calculations were performed using the numerical calculation software GNU Octave®. Participants had a mean age of Self-declared racial demographic was of Enrolled patients had a previous diagnosis of type 1 DM in Regarding the presence or absence of DR, agreement between the two independent evaluators of the images Interobserver from the smartphone-based device was As for the gold standard fundus photograph, interobserver agreement was Considering reports from the first evaluator Intraobserver 1 , analysis of the smartphone-based device in comparison with the gold standard obtained the agreement of: Considering reports from the second evaluator Intraobserver 2 , smartphone-based device compared to the gold standard showed an agreement of These data are depicted in Tables 2 and 3.
Concerning the classification between proliferative diabetic retinopathy and non-proliferative diabetic retinopathy, interobserver agreement of the images from the smartphone-based device was Intraobserver 1: smartphone-based device analysis compared to gold standard images demonstrated agreement: Intraobserver 2: analysis of the smartphone-based device in comparison with the gold standard images showed agreement: These data are shown in Tables 2 and 4.
For the analysis of the classification of severity of DR, when specialists differed by only one class, we considered only the most severe classification. In this case, interobserver agreement found in the images of the smartphone-based device was In the gold standard images, interobserver agreement was Intraobserver 1: agreement of the reports obtained by the smartphone-based images in comparison with those coming from the gold standard was Intraobserver 2: agreement of the reports obtained by the smartphone-based images in comparison with those coming from the gold standard was Considering a tolerance of up to two classes of divergence, agreement found in the interobserver comparison of the images obtained by the smartphone-based device was Interobserver comparison of the images obtained by the gold standard was Considering the presence or absence of hard macular exudates, agreement of the reports obtained by the smartphone-based images in comparison with those coming from the gold standard was In order to obtain a final analysis between the two methods, results from the two specialists were merged.
On reports from both the smartphone-based and the conventional tabletop camera methods, when the classification attributed by the specialists was consensual in their analysis, the data was kept; when there was no consensus, a third independent masked specialist assessed and assigned the final analysis.
As for the classification between proliferative diabetic retinopathy and nonproliferative diabetic retinopathy, final agreement between the images from the smartphone-based device and those from the gold standard was Regarding the classification of severity of DR, to obtain a final result, when the specialists differed by only 1 class, the most severe classification was assigned, when they differed by up to 2 classes, a third independent masked specialist performed the analysis and attributed the final classification Tables 2 and 5.
Therefore, agreement of the reports obtained by the smartphone-based images in comparison with those coming from the gold standard was Our study was able to verify that retinal images obtained by undergraduate students using a smartphone-based device showed satisfactory performance when compared to the reference standard for the diagnosis of DR.
Recent studies suggest that the diagnostic accuracy of telemedicine using digital images in DR is, in general, high. The high sensitivity of its detection of any clinical level of DR indicates that telemedicine could be widely used for DR screening [ 3 ].
Portable devices for eye fundus image acquisition have shown high levels of agreement with traditional tabletop retinal cameras for the detection and follow-up of DR [ 7 ].
However, the latter tend to perform better compared to smartphone-based devices like the one reported in this study. Russo et al.
The study reported substantial agreement between the methods, with sensitivity and specificity of 0. Toy et al. In the same study, the authors recommended that it would be interesting to compare a smartphone-based device with a tabletop fundus camera, the gold standard for diagnosing DR. In the present study, we found a sensitivity of 0.
We attribute the lower values of sensitivity and specificity in the present study to the fact that the users of the smartphone-based fundus camera were not used to fundus photography, while in the previous studies smartphone-based ophthalmoscopy was performed by a retina specialist [ 8 , 9 ].
In their study, Williams et al. stated that there is level I evidence that single-field fundus photography with interpretation by trained readers can serve as a screening tool to identify patients with diabetic retinopathy for referral for ophthalmologic evaluation and treatment, but it is not a substitute for a comprehensive eye examination [ 11 ].
Ryan ME et al. reported that photographs from smartphones assisted by 20 diopters lenses had a low rate of unclassifiable images, and most of them had at least satisfactory quality. Kappa was 0. Our study, regarding the presence or absence of DR, showed a Kappa of 0.
The smartphone was less sensitive than non-mydriatic photography in detecting the presence of DR at any degree. However, the two methods were similar in detecting vision threatening stages of the disease.
Although both methods have shown robust specificity, smartphone-based teleophthalmology screening represents a much lower cost of implementation, and could be particularly useful as a tool that allows for detection of the disease in patients who may not have proper access to eye care [ 12 ].
Furthermore, considering that artificial intelligence AI systems are currently being developed and gradually implanted worldwide [ 13 , 14 ], it is plausible to assume that the portability of smartphone-generated images could, in a near future, act synergistically with the power of AI in order to amplify access to eye care.
In line with the other studies in literature Russo et al. and Toy et al. High cost and low availability of eye examination, especially when requiring in-site experts, represent an important limitation for DR screening.
Fundus images taken through a smartphone-based method by undergraduate students, here adopted as surrogates for professionals with no previous experience in eye imaging, may favor early diagnosis and severity classification of DR. Implementation of this method in primary healthcare settings such as the basic care units of Brazil's public health system could allow for broader detection and timely referral for intervention in a large population of underserved diabetic patients.
All data generated in this study, including the images obtained through both the analysed method and the gold standard, were saved on private cloud storage Google Drive ® for patient safety and privacy.
We kindly request any interested parts to contact the authors directly for obtaining access to the database when applicable. Klein R, Klein BEK. Epidemiology of eye disease in diabetes. In: Flynn HW Jr, Smiddy WE, editors. Diabetes and ocular Disease: past, present, and future therapies.
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Figure 1. Diabetic retinopathy retinal imaging of Body composition and flexibility training participants Dabetic the study, retinoopathy patients with true-positive results, with true-negative results, 6 with false-negative results, and with false-positive results. Figure 2. Diabetic retinopathy retinal imaging and left eye images of 6 of retinoparhy patients who had false-negative results at the set point set point of 0. Each set of eyes displays the consensus ICDR severity level 0 indicates no diabetic retinopathy [DR]; 1, mild DR; 2, moderate DR; 3, severe DR; and 4, proliferative DR followed by the consensus ICDR diabetic macular edema DME severity level 0 indicates no apparent DME; 1, apparent DME. Figure 3. The area under the curve is 0. International Journal Diabetic retinopathy retinal imaging Retina and Vitreous volume 8 Diabetoc, Article number: 35 Cite this article. Diaberic details. To evaluate the efficacy Diabetic retinopathy retinal imaging rstinal photography retianl by undergraduate students using a smartphone-based device in screening and Probiotic Foods for Candida diagnosing diabetic retinopathy DR. We carried out an open prospective study with ninety-nine diabetic patients eyeswho were submitted to an ophthalmological examination in which undergraduate students registered images of the fundus using a smartphone-based device. At the same occasion, an experienced nurse captured fundus photographs from the same patients using a gold standard tabletop camera system Canon CR-2 Digital Non-Mydriatic Retinal Camerawith a 45º field of view. Two distinct masked specialists evaluated both forms of imaging according to the presence or absence of sings of DR and its markers of severity.
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