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 (σ). Three sets of images, each comprising 100 pairs of scans (100 original & 100 modified), were grouped according to the range of ( σ), including low-, medium- and high-degree of augmentation; σ = 1-6), ( σ = 7-12), and ( σ = 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% ( p> 0.05) in the low-, 73-85% ( p> 0.05 for graders 1 & 2, p = 0.01 for grader 3) in the medium-, and 81-91% ( p < 0.005) in the high-augmentation categories. In the subcategory ( σ = 7-9) the false-negative rates were 93-83%, whereas the rates of correctly identifying original images ranged between 89-99% ( p> 0.05 for all graders). Conclusions: Deformation of low-medium intensity ( σ = 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.