Sessão de Encontro com o Autor – Trabalhos Científicos - Apresentação Oral


Código

TL16

Área Técnica

Retina

Instituição onde foi realizado o trabalho

  • Principal: Roski Eye Institute, Keck School of Medicine, University of Southern California, Los Angeles, USA.
  • Secundaria: Fundação Universidade Regional de Blumenau

Autores

  • GABRIELA DALMEDICO RICHTER (Interesse Comercial: NÃO)

Título

Clinical validation of AirDoc camera and automated system for diabetic retinopathy screening in a Brazilian population

Objetivo

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. Portable retinal cameras with embedded artificial intelligence (AI) algorithms for automated image classification offer a promising alternative for DR screening. Our objective was to evaluate the performance of Airdoc portable cameras with embedded AI for DR screening, in a Brazilian sample.

Método

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 each image's quality and graded DR classification. The performance of each system for the detection of DR was assessed, and a comparison was performed between both devices regarding image quality and automated diagnostic accuracy for DR.

Resultado

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. Eyer's ground-truth images showed a DR prevalence of 16.8%, 14 (11.2%) mild non-proliferative, 7 (5.6%) moderate non-proliferative, and 3 (2.4%) diabetic macular edema (DME). Analysis revealed 87 (82.86%) concordant DR classifications between cameras, with AirDoc underestimating 12 (11.43%) and overestimating 6 (5.71%) cases. For DME, 108 (98.1%) images had concordant reports. The AirDoc AI system showed 100% specificity in detecting referable diabetic retinopathy, but a 33% sensitivity and 66.7% false negative rate.

Conclusão

AirDoc images displayed higher rates of ungradable and low-quality images, affecting the DR grading. Additionally, the AirDoc AI system exhibited low sensitivity and elevated rates of false negatives.

Promotor

Realização - CBO

Mídia de apoio

Universo Visual

Organização

Organizadora

Agência Web

Sistema de Gerenciamento desenvolvido por Inteligência Web

Patrocínio Platina

CRISTALIA
GENOM
JOHNSON&JOHNSON

Patrocínio Ouro

ALCON
OFTA

Patrocínio Prata

ABBVIE
EssilorLuxottica

Patrocínio Bronze

ADAPT
SOLÓTICA

Patrocínio

ROCHE

68º Congresso Brasileiro de Oftalmologia

04 a 07 de setembro de 2024 | Brasília/DF

Política de privacidade

Número de protocolo de comunicação à Anvisa: 2024023032
Responsável Técnica Médica: Wilma Lelis Barboza | CRM 69998-SP