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.

Diabetic Retinopathy Screening Using Artificial Intelligence and Handheld Smartphone-Based Retinal Camera.

BACKGROUND: Portable retinal cameras and deep learning (DL) algorithms are novel tools adopted by diabetic retinopathy (DR) screening programs. Our objective is to evaluate the diagnostic accuracy of a DL algorithm and the performance of portable handheld retinal cameras in the detection of DR in a large and heterogenous type 2 diabetes population in a real-world, high burden setting. METHOD: Participants underwent fundus photographs of both eyes with a portable retinal camera (Phelcom Eyer). Classification of DR was performed by human reading and a DL algorithm (PhelcomNet), consisting of a convolutional neural network trained on a dataset of fundus images captured exclusively with the portable device; both methods were compared. We calculated the area under the curve (AUC), sensitivity, and specificity for more than mild DR. RESULTS: A total of 824 individuals with type 2 diabetes were enrolled at Itabuna Diabetes Campaign, a subset of 679 (82.4%) of whom could be fully assessed. The algorithm sensitivity/specificity was 97.8 % (95% CI 96.7-98.9)/61.4 % (95% CI 57.7-65.1); AUC was 0·89. All false negative cases were classified as moderate non-proliferative diabetic retinopathy (NPDR) by human grading. CONCLUSIONS: The DL algorithm reached a good diagnostic accuracy for more than mild DR in a real-world, high burden setting. The performance of the handheld portable retinal camera was adequate, with over 80% of individuals presenting with images of sufficient quality. Portable devices and artificial intelligence tools may increase coverage of DR screening programs.

Clinical validation of a smartphone-based handheld fundus camera for the evaluation of optic nerve head / Validação clínica de um retinógrafo portátil acoplado a smartphone para avaliação da cabeça do nervo óptico

ABSTRACT Purpose: To compare the quality of retinal images captured with a smartphone-based, handheld fundus camera with that of retinal images captured with a commercial fundus camera and to analyze their agreement in determining the cup-to-disc ratio for a cohort of ophthalmological patients. Methods: A total of 50 patients from a secondary ophthalmic outpatient service center underwent a bilateral fundus examination under mydriasis with a smartphone-based, handheld fundus camera and with a commercial fundus camera (4 images/patient by each). Two experienced ophthalmologists evaluated all the fundus images and graded them on the Likert 1-5 scale for quality. Multivariate regression analyses was then performed to evaluate the factors associated with the image quality. Two masked ophthalmologists determined the vertical cup-to-disc ratio of each fundus image, and both the intraobserver (between devices) and interobserver agreement between them was calculated. Results: Ninety-eight images from 49 patients were processed in this study for their quality analysis. Ten images from five patients (four from commercial fundus camera and one from smartphone-based, handheld fundus camera) were not included in the analyses due to their extremely poor quality. The medians [interquartile interval] of the image quality were not significantly different between those from the smartphone-based, handheld fundus camera and from the commercial fundus camera (4 [4-5] versus 4 [3-4] respectively, p=0.06); however, both the images captured with the commercial fundus camera and the presence of media opacity presented a significant negative correlation with the image quality. Both the intraobserver [intraclass correlation coefficient (ICC)=0.82, p<0.001 and 0.83, p<0.001, for examiners 1 and 2, respectively] and interobserver (ICC=0.70, p=0.001 and 0.81; p<0.001, for smartphone-based handheld fundus camera and commercial fundus camera, respectively) agreements were excellent and statistically significant. Conclusions: Our results thus indicate that the smartphone-based, handheld fundus camera yields an image quality similar to that from a commercial fundus camera, with significant agreement in the cup-to-disc ratios between them. In addition to the good outcomes recorded, the smartphone-based, handheld fundus camera offers the advantages of portability and low-cost to serve as an alternative for fundus documentation for future telemedicine approaches in medical interventions.

RESUMO Objetivo: Comparar a qualidade das imagens da retina capturadas com um retinógrafo portátil acoplado a um smartphone com aquelas adquiridas com um retinógrafo comercial padrão e analisar a concordância na determinação da relação escavação/ cabeça do nervo óptico em um coorte de pacientes de um serviço oftalmológico. Métodos: Cinquenta pacientes de um serviço oftalmológico secundário foram submetidos a uma avaliação do fundo de olho bilateral, sob midríase, utilizando o retinógrafo portátil acoplado a um smartphone e o retinógrafo comercial padrão (4 imagens por paciente). Dois oftalmologistas experientes avaliaram a qualidade de todas as imagens e atribuíram a elas uma pontuação entre 1 e 5, de acordo com a escala Likert. Os fatores relacionados a qualidade das imagens foram avaliados utilizando uma análise de regressão multivariada. Dois oftalmologistas determinaram de forma mascarada a relação da escavação/ cabeça do nervo óptico de cada imagem e a concordância intra e interobservador foi calculada. Resultados: Noventa e oito imagens de 49 pacientes foram utilizadas neste estudo para análise de qualidade. Dez imagens de cinco pacientes (quatro do retinógrafo comercial padrão e um do retinógrafo portátil acoplado a um smartphone) foram excluídas das análises de concordância devido à baixa qualidade das mesmas, mas foram considerados nas análises de qualidade. Dos cinco pacientes com imagens excluídas, quatro foram capturadas pelo retinógrafo comercial padrão e uma pelo retinógrafo portátil acoplado a um smartphone. As medianas (intervalo interquartil) da qualidade das imagens não apresentaram diferença estatística entre o retinógrafo portátil acoplado a um smartphone e o retinógrafo comercial padrão (4 [4-5] versus 4 [3-4] respectivamente, p=0.06). As imagens obtidas com o retinógrafo comercial padrão e o diagnóstico de opacidade de meios apresentou uma correlação negativa com a qualidade da imagem. As concordâncias intraobservador (ICC=0,82, p<0,001 e 0,83, p<0,001, para o examinador 1 e 2, respectivamente) e interobservador (ICC = 0,70, p=0,001 e 0,81, p<0.001, para o retinógrafo portátil acoplado a um smartphone e retinógrafo comercial padrão, respectivamente) foram excelentes e estatisticamente significativas. Conclusões: Nossos resultados sugerem que o retinógrafo portátil acoplado a um smartphone apresenta uma qualidade de imagem semelhante ao retinógrafo comercial padrão, com concordância significativa na análise da relação escavação-cabeça do nervo óptico. Além dos bons resultados apresentados, o retinógrafo portátil acoplado a um smartphone pode ser considerado uma alternativa portátil de baixo custo para documentação de retina em cenários futuros de telemedicina.

Clinical validation of a smartphone-based handheld fundus camera for the evaluation of optic nerve head.

PURPOSE: To compare the quality of retinal images captured with a smartphone-based, handheld fundus camera with that of retinal images captured with a commercial fundus camera and to analyze their agreement in determining the cup-to-disc ratio for a cohort of ophthalmological patients. METHODS: A total of 50 patients from a secondary ophthalmic outpatient service center underwent a bilateral fundus examination under mydriasis with a smartphone-based, handheld fundus camera and with a commercial fundus camera (4 images/patient by each). Two experienced ophthalmologists evaluated all the fundus images and graded them on the Likert 1-5 scale for quality. Multivariate regression analyses was then performed to evaluate the factors associated with the image quality. Two masked ophthalmologists determined the vertical cup-to-disc ratio of each fundus image, and both the intraobserver (between devices) and interobserver agreement between them was calculated. RESULTS: Ninety-eight images from 49 patients were processed in this study for their quality analysis. Ten images from five patients (four from commercial fundus camera and one from smartphone-based, handheld fundus camera) were not included in the analyses due to their extremely poor quality. The medians [interquartile interval] of the image quality were not significantly different between those from the smartphone-based, handheld fundus camera and from the commercial fundus camera (4 [4-5] versus 4 [3-4] respectively, p=0.06); however, both the images captured with the commercial fundus camera and the presence of media opacity presented a significant negative correlation with the image quality. Both the intraobserver [intraclass correlation coefficient (ICC)=0.82, p<0.001 and 0.83, p<0.001, for examiners 1 and 2, respectively] and interobserver (ICC=0.70, p=0.001 and 0.81; p<0.001, for smartphone-based handheld fundus camera and commercial fundus camera, respectively) agreements were excellent and statistically significant. CONCLUSIONS: Our results thus indicate that the smartphone-based, handheld fundus camera yields an image quality similar to that from a commercial fundus camera, with significant agreement in the cup-to-disc ratios between them. In addition to the good outcomes recorded, the smartphone-based, handheld fundus camera offers the advantages of portability and low-cost to serve as an alternative for fundus documentation for future telemedicine approaches in medical interventions.

Imaging the photoinduced charge injection in CdS/TiO2 nanoparticles by the sequential fluorescence mapping method.

The combination of a sensitizer and TiO2 nanoparticles forming a photocatalytic material is a central issue in many fields of applied photochemistry. The charge injection of emissive sensitizers into the conduction band of the semiconductor TiO2 may form a photoactive region that becomes dark, or it has a very low emission signal due to the generation of sensitizer radicals. However, by sequential coupling of a selected photoredox dye, such as resazurin, the dark region may become fluorescent at the interfaces where the charge injection has taken place due to the concomitant formation of fluorescent resorufin by cascade electron transfer. Using this strategy and a total internal reflection fluorescence microscopy (TIRFM) image, the charge injection in TiO2/CdS and SiO2/TiO2/CdS nanoparticles is investigated The method allows the charge injection efficiency of the excited CdS into TiO2 to be evaluated qualitatively, explaining the differences observed for these photocatalytic materials in H2 generation.

The spatial distribution of the photostability of thionine in zeolite L nanochannels investigated by Photobleaching Lifetime Imaging Microscopy.

Dye photobleaching is a photochemical reaction that can be investigated locally using fluorescence microscopy techniques. In this study, a user-friendly computational tool to assist photobleaching experiments called Photobleaching Lifetime Imaging Microscopy (PbLIM) is presented. With this tool it is possible to recover the photobleaching kinetics spatially, where a photobleaching lifetime is generated for each pixel of the image. Our model was applied to the photobleaching process of thionine encapsulated into the one-dimensional nano-channels of Zeolite L (ZL), from where we gained insight into the molecular oxygen distribution inside the ZL channels, as well as the detailed photobleaching of the confined thionine.