Deep learning-based algorithm for the detection of idiopathic full thickness macular holes in spectral domain optical coherence tomography.
Carolina C S ValentimAnna K WuSophia YuNiranchana ManivannanQinqin ZhangJessica CaoWeilin SongVictoria WangHannah KangAneesha KalurAmogh I IyerThais ContiRishi P SinghKatherine E TalcottPublished in: International journal of retina and vitreous (2024)
The DL-based algorithm was able to accurately detect IFTMHs features on individual SD-OCT B-scans in both test sets. However, there was a low correlation between the algorithm's probability score and IFTMH severity stages. The algorithm may serve as a clinical decision support tool that assists with the identification of IFTMHs. Further training is necessary for the algorithm to identify stages of IFTMHs.
Keyphrases
- deep learning
- optical coherence tomography
- machine learning
- clinical decision support
- diabetic retinopathy
- artificial intelligence
- convolutional neural network
- neural network
- computed tomography
- optic nerve
- magnetic resonance imaging
- magnetic resonance
- electronic health record
- sensitive detection
- age related macular degeneration
- real time pcr