Published online May 16, 2022. doi: 10.4253/wjge.v14.i5.311
Peer-review started: May 13, 2021
First decision: July 4, 2021
Revised: July 15, 2021
Accepted: April 27, 2022
Article in press: April 27, 2022
Published online: May 16, 2022
Computer vision allied with deep learning, especially through the use of deep convolutional neural networks, has been increasingly employed in the automation of medical image analysis. Among these are endoscopic images, which are of great importance in the evaluation of a number of gastroenterological diseases.
Endoscopic findings constitute the diagnostic definition for esophagitis, a multietiological condition with significant impacts on quality of life and the possibility of evolution to a series of complications. Automating the identification of findings suggestive of esophageal inflammation using artificial intelligence could add great value to the evaluation and management of this clinical condition.
To identify whether a densely connected convolutional neural network with pre-trained and fine-tuned weights is able to binary classify esophageal Z-line endoscopic images according to the presence or absence of esophagitis.
Endoscopic images of 1932 patients with a diagnosis of esophagitis and 1663 patients were splitted in training (80%) and test (20%) and used to develop and evaluate a binary deep learning classifier built using a pre-trained DenseNet-201 architecture. The classifier model performance was evaluated in the test set according to accuracy, sensitivity, specificity and area under the receiver operating characteristic curve.
The proposed model was able to diagnose esophagitis in the validation set with sensitivity of 93.18 and specificity of 93.46, demonstrating the feasibility of using deep transfer learning to discriminate normal from damaged mucosa in endoscopic images of the same anatomical segment. It remains to be investigated whether, by means of a more diverse set of images, this technique can be proposed to identify different types of esophageal abnormalities, and potentially in other organs.
Convolutional neural networks with transfer learning for automated analysis of endoscopic images, as proposed in this study, demonstrate potential for incorporation into clinical practice as a clinical decision support tool, mainly benefiting scarce resources settings.
Sets of endoscopic images representative of various clinical conditions should be published, in order to allow the findings of this study to be externally validated and for new models with different classificatory approaches to emerge.