#OCT-trained deep learning algorithm helpful for assessing glaucomatous neural damage This study describes a new approach that uses data from spectral-domain (SD) OCT to train a deep-learning (DL) algorithm for quantifying glaucomatous structural damage. Study design The authors trained a DL neural network on 32,820 pairs of disc photos and RNFL scans to predict SD-OCT average RNFL thickness. The sample was divided into a validation plus training set and a test set. The DL performance was assessed in the test sample by evaluating correlation and agreement between the predicted and actual OCT measurements. Outcomes There was a strong correlation between predicted and observed mean average RNFL thickness (r=0.832), with a low mean absolute error of prediction (7.39 microns). After training on disc photographs, the DL determined normal vs. abnormal RNFL thickness with 83.7% accuracy. Limitations Disc photo quality was not assessed or controlled for. Additionally, the algorithm was trained to identify average RNFL thickness; thus, segmental loss in the face of normal average thickness could potentially misclassify #glaucomatous nerves. Clinical significance This is an innovative approach that has the advantage of removing the subjectivity and poor reproducibility of human grading. It has potentially wide applications for #glaucoma screening and assessment of optic nerve changes over time in practices that do not have access to SD-OCT. #ai #deeplearning #algorithm #glaucomatest #glaucomascreening #opticnerve #ophthalmology #optometry #optometrist #ophthalmologist #oftalmologia #oftalmólogo #optometria Ref https://www.aao.org/editors-choice/oct-trained-deep-learning-algorithm-helpful-assess?hootPostID=c586544f440d15045a1b35c07b38ba55 @tonometerdiaton https://www.instagram.com/p/BwzitUGgh4v/?utm_source=ig_tumblr_share&igshid=pvvx2p1oxp65
