Deep learning to detect macular atrophy in wet age-related macular degeneration using optical coherence tomography.
Sci Rep
; 13(1): 8296, 2023 05 22.
Article
en En
| MEDLINE
| ID: mdl-37217770
ABSTRACT
Here, we have developed a deep learning method to fully automatically detect and quantify six main clinically relevant atrophic features associated with macular atrophy (MA) using optical coherence tomography (OCT) analysis of patients with wet age-related macular degeneration (AMD). The development of MA in patients with AMD results in irreversible blindness, and there is currently no effective method of early diagnosis of this condition, despite the recent development of unique treatments. Using OCT dataset of a total of 2211 B-scans from 45 volumetric scans of 8 patients, a convolutional neural network using one-against-all strategy was trained to present all six atrophic features followed by a validation to evaluate the performance of the models. The model predictive performance has achieved a mean dice similarity coefficient score of 0.706 ± 0.039, a mean Precision score of 0.834 ± 0.048, and a mean Sensitivity score of 0.615 ± 0.051. These results show the unique potential of using artificially intelligence-aided methods for early detection and identification of the progression of MA in wet AMD, which can further support and assist clinical decisions.
Texto completo:
1
Banco de datos:
MEDLINE
Asunto principal:
Degeneración Macular Húmeda
/
Aprendizaje Profundo
Tipo de estudio:
Prognostic_studies
/
Screening_studies
Límite:
Humans
Idioma:
En
Revista:
Sci Rep
Año:
2023
Tipo del documento:
Article
País de afiliación:
Reino Unido