Image quality comparison of AirDoc portable retina camera versus eyer in a diabetic retinopathy screening program.
Diabetic retinopathy
Image quality
Portable retinal cameras
Retina
Journal
International journal of retina and vitreous
ISSN: 2056-9920
Titre abrégé: Int J Retina Vitreous
Pays: England
ID NLM: 101677897
Informations de publication
Date de publication:
14 Jun 2024
14 Jun 2024
Historique:
received:
14
04
2024
accepted:
27
05
2024
medline:
15
6
2024
pubmed:
15
6
2024
entrez:
14
6
2024
Statut:
epublish
Résumé
Diabetic retinopathy (DR) stands as the foremost cause of preventable blindness in adults. Despite efforts to expand DR screening coverage in the Brazilian public healthcare system, challenges persist due to various factors including social, medical, and financial constraints. Our objective was to evaluate the quality of images obtained with the AirDoc, a novel device, compared to Eyer portable camera which has already been clinically validated. Images were captured by two portable retinal devices: AirDoc and Eyer. The included patients had their fundus images obtained in a screening program conducted in Blumenau, Santa Catarina. Two retina specialists independently assessed image's quality. A comparison was performed between both devices regarding image quality and the presence of artifacts. The analysis included 129 patients (mean age of 61 years), with 29 (43.28%) male and an average disease duration of 11.1 ± 8 years. In Ardoc, 21 (16.28%) images were classified as poor quality, with 88 (68%) presenting artifacts; in Eyer, 4 (3.1%) images were classified as poor quality, with 94 (72.87%) presenting artifacts. Although both Eyer and AirDoc devices show potential as screening tools, the AirDoc images displayed higher rates of ungradable and low-quality images, that may directly affect the DR and DME grading. We must acknowledge the limitations of our study, including the relatively small sample size. Therefore, the interpretations of our analyses should be approached with caution, and further investigations with larger patient cohorts are warranted to validate our findings.
Sections du résumé
BACKGROUND
BACKGROUND
Diabetic retinopathy (DR) stands as the foremost cause of preventable blindness in adults. Despite efforts to expand DR screening coverage in the Brazilian public healthcare system, challenges persist due to various factors including social, medical, and financial constraints. Our objective was to evaluate the quality of images obtained with the AirDoc, a novel device, compared to Eyer portable camera which has already been clinically validated.
METHODS
METHODS
Images were captured by two portable retinal devices: AirDoc and Eyer. The included patients had their fundus images obtained in a screening program conducted in Blumenau, Santa Catarina. Two retina specialists independently assessed image's quality. A comparison was performed between both devices regarding image quality and the presence of artifacts.
RESULTS
RESULTS
The analysis included 129 patients (mean age of 61 years), with 29 (43.28%) male and an average disease duration of 11.1 ± 8 years. In Ardoc, 21 (16.28%) images were classified as poor quality, with 88 (68%) presenting artifacts; in Eyer, 4 (3.1%) images were classified as poor quality, with 94 (72.87%) presenting artifacts.
CONCLUSIONS
CONCLUSIONS
Although both Eyer and AirDoc devices show potential as screening tools, the AirDoc images displayed higher rates of ungradable and low-quality images, that may directly affect the DR and DME grading. We must acknowledge the limitations of our study, including the relatively small sample size. Therefore, the interpretations of our analyses should be approached with caution, and further investigations with larger patient cohorts are warranted to validate our findings.
Identifiants
pubmed: 38877585
doi: 10.1186/s40942-024-00559-z
pii: 10.1186/s40942-024-00559-z
doi:
Types de publication
Letter
Langues
eng
Pagination
43Informations de copyright
© 2024. The Author(s).
Références
Salongcay RP, Aquino LAC, Salva CMG, Saunar AV, Alog GP, Sun JK et al. Comparison of Handheld Retinal Imaging with ETDRS 7-Standard Field Photography for Diabetic Retinopathy and Diabetic Macular Edema. Ophthalmol Retina [Internet]. 2022;6(7):548–56. https://doi.org/10.1016/j.oret.2022.03.002 .
Piyasena MMPN, Yip JLY, MacLeod D, Kim M, Gudlavalleti VSM. Diagnostic test accuracy of diabetic retinopathy screening by physician graders using a hand-held non-mydriatic retinal camera at a tertiary level medical clinic. BMC Ophthalmol [Internet]. 2019;19(1):89. https://doi.org/10.1186/s12886-019-1092-3 .
Midena E, Zennaro L, Lapo C, Torresin T, Midena G, Pilotto E et al. Handheld Fundus Camera for Diabetic Retinopathy Screening: A Comparison Study with Table-Top Fundus Camera in Real-Life Setting. J Clin Med Res [Internet]. 2022;11(9). https://doi.org/10.3390/jcm11092352 .
de Oliveira JAE, Nakayama LF, Zago Ribeiro L, de Oliveira TVF, Choi SNJH, Neto EM et al. Clinical validation of a smartphone-based retinal camera for diabetic retinopathy screening. Acta Diabetol [Internet]. 2023;60(8):1075–81. https://doi.org/10.1007/s00592-023-02105-z .
Nakayama LF, Zago Ribeiro L, Novaes F, Miyawaki IA, Miyawaki AE, de Oliveira JAE et al. Artificial intelligence for telemedicine diabetic retinopathy screening: a review. Ann Med [Internet]. 2023;55(2). https://doi.org/10.1080/07853890.2023.2258149 .
Penha FM, Priotto BM, Hennig F, Przysiezny B, Wiethorn BA, Orsi J et al. Single retinal image for diabetic retinopathy screening: performance of a handheld device with embedded artificial intelligence. Int J Retina Vitreous [Internet]. 2023;9(1). https://doi.org/10.1186/s40942-023-00477-6 .
Nakayama LF, Gonçalves MB, Ribeiro LZ, Malerbi FK, Regatieri CVS. Diabetic Retinopathy Labeling Protocol for the Brazilian Multilabel Ophthalmological Dataset [Internet]. 2023. https://doi.org/10.31219/osf.io/puznm .
Zhang W, Nicholas P, Schuman SG, Allingham MJ, Faridi A, Suthar T et al. Screening for Diabetic Retinopathy Using a Portable, Noncontact, Nonmydriatic Handheld Retinal Camera. J Diabetes Sci Technol [Internet]. 2017;11(1):128–34. https://doi.org/10.1177/1932296816658902 .
Caceres J, Zhang Y, Boe L, Zhou Y, Besirli C, Paulus YM et al. Diabetic Retinopathy Screening Using a Portable Retinal Camera in Vanuatu. Clin Ophthalmol [Internet]. 2023;17:2919–27. https://doi.org/10.2147/OPTH.S410425 .
Jin K, Lu H, Su Z, Cheng C, Ye J, Qian D. Telemedicine screening of retinal diseases with a handheld portable non-mydriatic fundus camera. BMC Ophthalmol [Internet]. 2017;17(1):89. https://doi.org/10.1186/s12886-017-0484-5 .
Li HK, Horton M, Bursell SE, Cavallerano J, Zimmer-Galler I, Tennant M et al. Telehealth practice recommendations for diabetic retinopathy, second edition. Telemed J E Health [Internet]. 2011;17(10):814–37. https://doi.org/10.1089/tmj.2011.0075 .
Davila JR, Sengupta SS, Niziol LM, Sindal MD, Besirli CG, Upadhyaya S et al. Predictors of Photographic Quality with a Handheld Nonmydriatic Fundus Camera Used for Screening of Vision-Threatening Diabetic Retinopathy. Ophthalmologica [Internet]. 2017;238(1–2):89–99. https://doi.org/10.1159/000475773 .
Malerbi FK, Andrade RE, Morales PH, Stuchi JA, Lencione D, de Paulo JV et al. Diabetic Retinopathy Screening Using Artificial Intelligence and Handheld Smartphone-Based Retinal Camera. J Diabetes Sci Technol [Internet]. 2021;1932296820985567. https://doi.org/10.1177/1932296820985567 .
Ruamviboonsuk P, Tiwari R, Sayres R, Nganthavee V, Hemarat K, Kongprayoon A et al. Real-time diabetic retinopathy screening by deep learning in a multisite national screening programme: a prospective interventional cohort study. Lancet Digit Health [Internet]. 2022;4(4):e235–44. https://doi.org/10.1016/S2589-7500(22)00017-6 .
Brennan IG, Kelly SR, McBride E, Garrahy D, Acheson R, Harmon J et al. Addressing Technical Failures in a Diabetic Retinopathy Screening Program. OPTH [Internet]. 2024 Feb 9 [cited 2024 Feb 12];18:431–40. https://www.dovepress.com/addressing-technical-failures-in-a-diabetic-retinopathy-screening-prog-peer-reviewed-fulltext-article-OPTH .