Detection of oedema on optical coherence tomography images using deep learning model trained on noisy clinical data.
Ivan PotapenkoMads KristensenBo ThiessonTomas IlginisTorben Lykke SørensenJavad Nouri HajariJosefine FuchsSteffen HamannMorten la CourPublished in: Acta ophthalmologica (2021)
The level of performance shown by the current model might make it valuable in detecting disease activity in automated AMD patient follow-up systems. Our approach demonstrates that high accuracy is not necessarily constrained by incongruent training and validation labels. These results might encourage the use of existing clinical databases for development of deep learning based algorithms without labour-intensive preprocessing in the future.
Keyphrases
- deep learning
- disease activity
- artificial intelligence
- convolutional neural network
- machine learning
- rheumatoid arthritis
- systemic lupus erythematosus
- optical coherence tomography
- big data
- rheumatoid arthritis patients
- ankylosing spondylitis
- case report
- juvenile idiopathic arthritis
- current status
- high throughput
- resistance training
- diabetic retinopathy
- body composition
- single cell
- virtual reality
- real time pcr