Automated Identification of Different Severity Levels of Diabetic Retinopathy Using a Handheld Fundus Camera and Single-Image Protocol.

Purpose: To evaluate the performance of artificial intelligence (AI) systems embedded in a mobile, handheld retinal camera, with a single retinal image protocol, in detecting both diabetic retinopathy (DR) and more-than-mild diabetic retinopathy (mtmDR). Design: Multicenter cross-sectional diagnostic study, conducted at 3 diabetes care and eye care facilities. Participants: A total of 327 individuals with diabetes mellitus (type 1 or type 2) underwent a retinal imaging protocol enabling expert reading and automated analysis. Methods: Participants underwent fundus photographs using a portable retinal camera (Phelcom Eyer). The captured images were automatically analyzed by deep learning algorithms retinal alteration score (RAS) and diabetic retinopathy alteration score (DRAS), consisting of convolutional neural networks trained on EyePACS data sets and fine-tuned using data sets of portable device fundus images. The ground truth was the classification of DR corresponding to adjudicated expert reading, performed by 3 certified ophthalmologists. Main Outcome Measures: Primary outcome measures included the sensitivity and specificity of the AI system in detecting DR and/or mtmDR using a single-field, macula-centered fundus photograph for each eye, compared with a rigorous clinical reference standard comprising the reading center grading of 2-field imaging protocol using the International Classification of Diabetic Retinopathy severity scale. Results: Of 327 analyzed patients (mean age, 57.0 ± 16.8 years; mean diabetes duration, 16.3 ± 9.7 years), 307 completed the study protocol. Sensitivity and specificity of the AI system were high in detecting any DR with DRAS (sensitivity, 90.48% [95% confidence interval (CI), 84.99%-94.46%]; specificity, 90.65% [95% CI, 84.54%-94.93%]) and mtmDR with the combination of RAS and DRAS (sensitivity, 90.23% [95% CI, 83.87%-94.69%]; specificity, 85.06% [95% CI, 78.88%-90.00%]). The area under the receiver operating characteristic curve was 0.95 for any DR and 0.89 for mtmDR. Conclusions: This study showed a high accuracy for the detection of DR in different levels of severity with a single retinal photo per eye in an all-in-one solution, composed of a portable retinal camera powered by AI. Such a strategy holds great potential for increasing coverage rates of screening programs, contributing to prevention of avoidable blindness. Financial Disclosures: F.K.M. is a medical consultant for Phelcom Technologies. J.A.S. is Chief Executive Officer and proprietary of Phelcom Technologies. D.L. is Chief Technology Officer and proprietary of Phelcom Technologies. P.V.P. is an employee at Phelcom Technologies.

Single retinal image for diabetic retinopathy screening: performance of a handheld device with embedded artificial intelligence.

BACKGROUND: Diabetic retinopathy (DR) is a leading cause of blindness. Our objective was to evaluate the performance of an artificial intelligence (AI) system integrated into a handheld smartphone-based retinal camera for DR screening using a single retinal image per eye. METHODS: Images were obtained from individuals with diabetes during a mass screening program for DR in Blumenau, Southern Brazil, conducted by trained operators. Automatic analysis was conducted using an AI system (EyerMaps™, Phelcom Technologies LLC, Boston, USA) with one macula-centered, 45-degree field of view retinal image per eye. The results were compared to the assessment by a retinal specialist, considered as the ground truth, using two images per eye. Patients with ungradable images were excluded from the analysis. RESULTS: A total of 686 individuals (average age 59.2 ± 13.3 years, 56.7% women, diabetes duration 12.1 ± 9.4 years) were included in the analysis. The rates of insulin use, daily glycemic monitoring, and systemic hypertension treatment were 68.4%, 70.2%, and 70.2%, respectively. Although 97.3% of patients were aware of the risk of blindness associated with diabetes, more than half of them underwent their first retinal examination during the event. The majority (82.5%) relied exclusively on the public health system. Approximately 43.4% of individuals were either illiterate or had not completed elementary school. DR classification based on the ground truth was as follows: absent or nonproliferative mild DR 86.9%, more than mild (mtm) DR 13.1%. The AI system achieved sensitivity, specificity, positive predictive value, and negative predictive value percentages (95% CI) for mtmDR as follows: 93.6% (87.8-97.2), 71.7% (67.8-75.4), 42.7% (39.3-46.2), and 98.0% (96.2-98.9), respectively. The area under the ROC curve was 86.4%. CONCLUSION: The portable retinal camera combined with AI demonstrated high sensitivity for DR screening using only one image per eye, offering a simpler protocol compared to the traditional approach of two images per eye. Simplifying the DR screening process could enhance adherence rates and overall program coverage.

A new handheld fundus camera combined with visual artificial intelligence facilitates diabetic retinopathy screening.

AIM: To explore the performance in diabetic retinopathy (DR) screening of artificial intelligence (AI) system by evaluating the image quality of a handheld Optomed Aurora fundus camera in comparison to traditional tabletop fundus cameras and the diagnostic accuracy of DR of the two modalities. METHODS: Overall, 630 eyes were included from three centers and screened by a handheld camera (Aurora, Optomed, Oulu, Finland) and a table-top camera. Image quality was graded by three masked and experienced ophthalmologists. The diagnostic accuracy of the handheld camera and AI system was evaluated in assessing DR lesions and referable DR. RESULTS: Under nonmydriasis status, the handheld fundus camera had better image quality in centration, clarity, and visible range (1.47, 1.48, and 1.40) than conventional tabletop cameras (1.30, 1.28, and 1.18; P<0.001). Detection of retinal hemorrhage, hard exudation, and macular edema were comparable between the two modalities, in principle, with the area under the curve of the handheld fundus camera slightly lower. The sensitivity and specificity for the detection of referable DR with the handheld camera were 82.1% (95%CI: 72.1%-92.2%) and 97.4% (95%CI: 95.4%-99.5%), respectively. The performance of AI detection of DR using the Phoebus Algorithm was satisfactory; however, Phoebus showed a high sensitivity (88.2%, 95%CI: 79.4%-97.1%) and low specificity (40.7%, 95%CI: 34.1%-47.2%) when detecting referable DR. CONCLUSION: The handheld Aurora fundus camera combined with autonomous AI system is well-suited in DR screening without mydriasis because of its high sensitivity of DR detection as well as its image quality, but its specificity needs to be improved with better modeling of the data. Use of this new system is safe and effective in the detection of referable DR in real world practice.

Handheld fundus camera performance, image quality and outcomes of diabetic retinopathy grading in a pilot screening study.

PURPOSE: To compare the performance and image quality of the handheld fundus camera to standard table-top fundus cameras in diabetic retinopathy (DR) screening. The reliability and diagnostic accuracy of DR grading performed by an ophthalmologist and a photographer reader were evaluated. MATERIALS AND METHODS: 157 patients with diabetes, attending screening or follow-up of DR, were evaluated by fundus photographs taken in mydriasis by Optomed Aurora and Canon or Zeiss Visucam fundus cameras. The image quality and the severity of DR were evaluated independently by an ophthalmologist and experienced photographer. The sensitivity, specificity and reliability of the assessments were determined. RESULTS: 1884 fundus images from 314 eyes were analysed. In 53% of all eyes, DR was not present. 10% had mild non-proliferative diabetic retinopathy (NPDR), 16% moderate NPDR, 6% severe NPDR and 16% proliferative diabetic retinopathy (PDR). The DR grading outcomes by Aurora highly equalled to those of Canon or Zeiss (κ = 0.93, 95% CI 0.91 to 0.94), and there was almost perfect agreement in grading between the ophthalmologist and photographer (κ = 0.96, 95% CI 0.95 to 0.97). The image quality of Aurora was sufficient for reliable assessment according to both graders in 84-88% of the cases. CONCLUSION: The Optomed Aurora fundus camera seems appropriate for DR screening. The sufficient image quality and high diagnostic accuracy for DR grading are supportive for a less expensive and easily transportable screening system for DR. Immediate image grading carried out by a photographer would further improve and speed up the screening process in all settings.

Quality and learning curve of handheld versus stand-alone non-mydriatic cameras.

PURPOSE: Nowadays, complex digital imaging systems allow detailed retinal imaging without dilating patients' pupils. These so-called non-mydriatic cameras have advantages in common circumstances (eg, for screening or emergency purposes) but present limitations in terms of image quality and field of view. We compare the usefulness of two non-mydriatic camera systems (ie, a handheld versus a stand-alone device) for fundus imaging. The primary outcome was image quality. The secondary outcomes were learning effects and quality grade-influencing factors. METHODS: The imaging procedures followed standard protocol and were all performed by the same investigator. Camera 1 (DRS®) was a stand-alone system, while Camera 2 (Smartscope® PRO) was a mobile system. In order to evaluate possible learning effects, we selected an examiner with no prior training in the use of these systems. The images were graded separately by two experienced and "blinded" ophthalmologists following a defined protocol. RESULTS: In total, 211 people were enrolled. Quality grade comparisons showed significantly better grades for Camera 1. Both systems achieved better quality grades for macular images than for disc-centered images. No remarkable learning effects could be demonstrated. CONCLUSIONS: Both camera systems are useful for fundus imaging. The greater mobility of Camera 2 was associated with lower image quality. For screening scenarios or telemedicine, it must be determined whether image quality or mobility is more important.

Screening for Diabetic Retinopathy Using a Portable, Noncontact, Nonmydriatic Handheld Retinal Camera.

BACKGROUND: Diabetic retinopathy (DR) is a leading cause of low vision and blindness. We evaluated the feasibility of using a handheld, noncontact digital retinal camera, Pictor, to obtain retinal images in dilated and undilated eyes for DR screening. We also evaluated the accuracy of ophthalmologists with different levels of training/experience in grading these images to identify eyes with vision-threatening DR. METHODS: A prospective study of diabetic adults scheduled to have dilated eye exams at Duke Eye Center from January to May 2014 was conducted. An imager acquired retinal images pre- and postdilation with Pictor and selected 1 pre- and 1 postdilation image per eye. Five masked ophthalmologists graded images for gradability (based on image focus and centration) and the presence of no, mild, moderate, or severe nonproliferative DR (NPDR) or proliferative DR (PDR). Referable disease was defined as moderate or severe NPDR or PDR on image grading. We evaluated feasibility based on the graders' evaluation of image gradability. We evaluated accuracy of identifying vision-threatening disease (severe NPDR or PDR documented on dilated clinical examination) based on the graders' sensitivity and specificity of grading referable disease. RESULTS: Images were gradable in 86-94% of predilation and 94-97% of postdilation photos. Compared to the dilated clinical exam, overall sensitivity for identifying vision-threatening DR was 64-88% and specificity was 71-90%. CONCLUSIONS: Pictor can capture retinal images of sufficient quality to screen for DR with and without dilation. Single retinal images obtained using Pictor can identify eyes with vision-threatening DR with high sensitivity and acceptable specificity compared to clinical exam.

Development of a handheld fluorescence imaging device to investigate the characteristics of protoporphyrin IX fluorescence in healthy and diseased skin.

BACKGROUND: Topical Photodynamic therapy (PDT) is an effective treatment for superficial non-melanoma skin cancers (NMSC) and dysplasia. During PDT light activates the photosensitiser (PpIX), metabolised from a topical pro-drug. A combination of PpIX, light and molecular oxygen results in inflammation and cell death. However, the outcomes of the treatment could be better. Insufficient biosynthesis of PpIX may be one of the causes of incomplete response or recurrence. Measuring surface fluorescence is usually employed as a means of studying PpIX formation. The aim of this work was to develop a device and a method for convenient fluorescence imaging in clinical settings to gather information on PpIX metabolism in healthy skin and NMSC with a view to improving PDT regimes. METHODS: A handheld fluorescence camera and a time course imaging method was developed and used in healthy volunteers and patients diagnosed with basal cell carcinoma (BCC) and actinic keratosis (AK). The photosensitiser (precursor) creams used were 5-aminolaevulinic acid (ALA; Ameluz(®)) and methyl aminolevulinate (MAL; Metvix(®)). Pain was assessed using a visual analogue score immediately after the PDT. RESULTS: Fluorescence due to PpIX increases over three hours incubation in healthy skin and in lesional BCC and AK. Distribution of PpIX fluorescence varies between the lesion types and between subjects. There was no significant correlation between PpIX fluorescence characteristics and pro-drug, diagnosis or pain experienced. However, there was a clear dependence on body site. CONCLUSION: The device and the method developed can be used to assess the characteristics of PpIX fluorescence, quantitative analysis and time course. Our findings show that body site influences PpIX fluorescence which we suggest may be due to the difference in skin temperature at different body sites.