Deep learning based lesion detection from anterior segment optical coherence tomography images and its application in the diagnosis of keratoconus

Objective: To developed an image analysis system of anterior segment optical coherence tomography (AS-OCT) examination results based on deep learning technology, and to evaluate its effect in identifying various types of corneal pathologies and quantified indices. Methods: A total of 4 026 patients (5 617 eyes), including 1 977 males and 2 049 females, aged (45±23) years, were enrolled in Qingdao Eye Hospital from January 2011 to August 2019. The AS-OCT images were used as a training dataset, which were labeled with location information of 16 corneal pathologies (including corneal epithelial defect, corneal epithelial thickening, corneal thinning and so on) by clinical experts, as well as the tissue stratification of the corneal epithelium and stroma. The labeled AS-OCT images were used to train the corneal pathology detection model and corneal stratification model based on deep convolutional neural network algorithm. Then 1 709 AS-OCT images of the affected eyes were collected as a validation dataset. Compared with the artificial labeling results, the accuracy, sensitivity and specificity were evaluated in the corneal pathology detection model, and the overlapping rate (Dice coefficient) between the labeled area of the model and the artificial labeling area was used to evaluate the corneal stratification model. Results: The results of 5 617 training sets showed that there were 1 472 cases of corneal epithelial defect, 2 416 cases of corneal epithelial thickening, 2 001 cases of corneal thinning, 780 cases of corneal lordosis, 2 064 cases of corneal thickening, 358 cases of subepithelial blisters, 486 cases of subepithelial opacity, 1 010 cases of corneal ulcer, 3 635 cases of stromal opacity, 1 060 cases of posterior elastic layer fold, 137 cases of posterior elastic layer detachment, 665 cases of keratic precipitate, 176 cases of corneal perforation, 127 cases of corneal foreign body, 299 cases of after lamellar keratoplasty (LKP) and 234 cases of after penetrating keratoplasty (PKP). Among 1 709 images, 1 596 were manually labeled. The average sensitivity and specificity of the corneal pathology detection model were 96.5% and 96.1% compared with the results of manual labeling. Fifteen samples were missed for detection, and the rate was 0.93%. The average Dice coefficients of the corneal stratification model for the corneal epithelium and stroma were 0.985 and 0.917, respectively. Conclusions: Our artificial intelligence-based diagnosis system with AS-OCT is able to give quantified information and location information of corneal lesions with high accuracy, which can help ophthalmologists improve the efficiency and accuracy of diagnosis. (Chin J Ophthalmol, 2021, 57: 447-453).