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Algorithm for Detection and Quantification of Hyperreflective Dots on Optical Coherence Tomography in Diabetic Macular Edema.
Huang, Haifan; Zhu, Liangjiu; Zhu, Weifang; Lin, Tian; Los, Leonoor Inge; Yao, Chenpu; Chen, Xinjian; Chen, Haoyu.
Afiliación
  • Huang H; Joint Shantou International Eye Center, Shantou University and the Chinese University of Hong Kong, Shantou, China.
  • Zhu L; Department of Ophthalmology, University Medical Center Groningen, University of Groningen, Groningen, Netherlands.
  • Zhu W; School of Electronics and Information Engineering, Soochow University, Suzhou, China.
  • Lin T; School of Electronics and Information Engineering, Soochow University, Suzhou, China.
  • Los LI; Joint Shantou International Eye Center, Shantou University and the Chinese University of Hong Kong, Shantou, China.
  • Yao C; Department of Ophthalmology, University Medical Center Groningen, University of Groningen, Groningen, Netherlands.
  • Chen X; School of Electronics and Information Engineering, Soochow University, Suzhou, China.
  • Chen H; School of Electronics and Information Engineering, Soochow University, Suzhou, China.
Front Med (Lausanne) ; 8: 688986, 2021.
Article en En | MEDLINE | ID: mdl-34485331
Purpose: To develop an algorithm to detect and quantify hyperreflective dots (HRDs) on optical coherence tomography (OCT) in patients with diabetic macular edema (DME). Materials and Methods: Twenty OCTs (each OCT contains 128 b scans) from 20 patients diagnosed with DME were included in this study. Two types of HRDs, hard exudates and small HRDs (hypothesized to be activated microglia), were identified and labeled independently by two raters. An algorithm using deep learning technology was developed based on input (in total 2,560 OCT b scans) of manual labeling and differentiation of HRDs from rater 1. 4-fold cross-validation was used to train and validate the algorithm. Dice coefficient, intraclass coefficient (ICC), correlation coefficient, and Bland-Altman plot were used to evaluate agreement of the output parameters between two methods (either between two raters or between one rater and proposed algorithm). Results: The Dice coefficients of total HRDs, hard exudates, and small HRDs area of the algorithm were 0.70 ± 0.10, 0.72 ± 0.11, and 0.46 ± 0.06, respectively. The correlations between rater 1 and proposed algorithm (range: 0.95-0.99, all p < 0.001) were stronger than the correlations between the two raters (range: 0.84-0.96, all p < 0.001) for all parameters. The ICCs were higher for all the parameters between rater 1 and proposed algorithm (range: 0.972-0.997) than those between the two raters (range: 0.860-0.953). Conclusions: Our proposed algorithm is a good tool to detect and quantify HRDs and can provide objective and repeatable information of OCT for DME patients in clinical practice and studies.
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Texto completo: 1 Colección: 01-internacional Base de datos: MEDLINE Tipo de estudio: Diagnostic_studies / Guideline / Prognostic_studies Idioma: En Revista: Front Med (Lausanne) Año: 2021 Tipo del documento: Article País de afiliación: China Pais de publicación: Suiza
Texto completo
Añadir a Mi BVS
Imprimir
XML
PubMed Links
Buscar en Google

Texto completo: 1 Colección: 01-internacional Base de datos: MEDLINE Tipo de estudio: Diagnostic_studies / Guideline / Prognostic_studies Idioma: En Revista: Front Med (Lausanne) Año: 2021 Tipo del documento: Article País de afiliación: China Pais de publicación: Suiza