Elastic Deformation of Optical Coherence Tomography Images of Diabetic Macular Edema for Deep-Learning Models Training: How Far to Go?

– Objective: To explore the clinical validity of elastic deformation of optical coherence tomography (OCT) images for data augmentation in the development of deep-learning model for detection of diabetic macular edema (DME). Methods: Prospective evaluation of OCT images of DME (n = 320) subject to elastic transformation, with the deformation intensity represented by ( [Formula: see text]). Three sets of images, each comprising 100 pairs of scans (100 original & 100 modified), were grouped according to the range of ( [Formula: see text]), including low-, medium- and high-degree of augmentation; ( [Formula: see text] = 1-6), ( [Formula: see text] = 7-12), and ( [Formula: see text] = 13-18), respectively. Three retina specialists evaluated all datasets in a blinded manner and designated each image as ’original‘ versus ’modified‘. The rate of assignment of ’original‘ value to modified images (false-negative) was determined for each grader in each dataset. Results: The false-negative rates ranged between 71-77% for the low-, 63-76% for the medium-, and 50-75% for the high-augmentation categories. The corresponding rates of correct identification of original images ranged between 75-85% ( [Formula: see text] 0.05) in the low-, 73-85% ( [Formula: see text] 0.05 for graders 1 & 2, p = 0.01 for grader 3) in the medium-, and 81-91% ( [Formula: see text]) in the high-augmentation categories. In the subcategory ( [Formula: see text] = 7-9) the false-negative rates were 93-83%, whereas the rates of correctly identifying original images ranged between 89-99% ( [Formula: see text] 0.05 for all graders). Conclusions: Deformation of low-medium intensity ( [Formula: see text] = 1-9) may be applied without compromising OCT image representativeness in DME. Clinical and Translational Impact Statement—Elastic deformation may efficiently augment the size, robustness, and diversity of training datasets without altering their clinical value, enhancing the development of high-accuracy algorithms for automated interpretation of OCT images.