A deep learning approach to identify blepharoptosis by convolutional neural networks
Journal
International Journal of Medical Informatics
Journal Volume
148
Pages
104402
Date Issued
2021
Author(s)
Abstract
Purpose: Blepharoptosis is a known cause of reversible vision loss. Accurate assessment can be difficult, especially amongst non-specialists. Existing automated techniques disrupt clinical workflow by requiring user input, or placement of reference markers. Neural networks are known to be effective in image classification tasks. We aim to develop an algorithm that can accurately identify blepharoptosis from a clinical photo. Methods: A total of 500 clinical photographs from patients with and without blepharoptosis were sourced from a tertiary ophthalmic center in Taiwan. Images were labeled by two oculoplastic surgeons, with an independent third oculoplastic surgeon to adjudicate disagreements. These images were used to train a series of convolutional neural networks (CNNs) to ascertain the best CNN architecture for this particular task. Results: Of the models that trained on the dataset, most were able to identify ptosis images with reasonable accuracy. We found the best performing model to use the DenseNet121 architecture without pre-training which achieved a sensitivity of 90.1 % with a specificity of 82.4 %, compared to the worst performing model which was used a Resnet34 architecture with pre-training, achieving a sensitivity of 74.1 %, and specificity of 63.6 %. Models with and without pre-training performed similarly (mean accuracy 82.6 % vs. 85.8 % respectively, p = 0.06), though models with pre-training took less time to train (1-minute vs. 16 min, p < 0.01). Conclusions: We report the use of AI to accurately diagnose blepharoptosis from a clinical photograph with no external reference markers or user input requirement. Most current-generation CNN architectures performed reasonably on this task, with the DenseNet121, and Resnet18 architectures without pre-training performing best in our dataset. © 2021 Elsevier B.V.
Subjects
Artificial intelligence; Automated identification; Blepharoptosis; Deep learning models; High accuracy; Novel medical image dataset
SDGs
Other Subjects
Convolution; Convolutional neural networks; Network architecture; Photography; Automated techniques; Clinical photos; Clinical workflow; Current generation; Learning approach; Pre-training; Reasonable accuracy; Vision loss; Deep learning; adult; Article; artificial intelligence; binary classification; consensus; controlled study; convolutional neural network; decision support system; deep learning; diagnostic accuracy; diagnostic test accuracy study; entropy; general practitioner; human; image quality; major clinical study; photography; predictive value; priority journal; ptosis (eyelid); retrospective study; sensitivity and specificity; Taiwan; transfer of learning; algorithm; ptosis; Algorithms; Blepharoptosis; Deep Learning; Humans; Neural Networks, Computer; Taiwan
Publisher
Elsevier Ireland Ltd
Type
journal article