Use of Endoscopic Images in the Prediction of Submucosal Invasion of Gastric Neoplasms: Automated Deep Learning Model Development and Usability Study

Abstract

Background: In a previous study, we examined the use of deep learning models to classify the invasion depth (mucosa-confined versus submucosa-invaded) of gastric neoplasms using endoscopic images. The external test accuracy reached 77.3%. However, model establishment is labor intense, requiring high performance. Automated deep learning (AutoDL) models, which enable fast searching of optimal neural architectures and hyperparameters without complex coding, have been developed.

Objective: The objective of this study was to establish AutoDL models to classify the invasion depth of gastric neoplasms. Additionally, endoscopist-artificial intelligence interactions were explored.

Methods: The same 2899 endoscopic images that were employed to establish the previous model were used. A prospective multicenter validation using 206 and 1597 novel images was conducted. The primary outcome was external test accuracy. Neuro-T, Create ML Image Classifier, and AutoML Vision were used in establishing the models. Three doctors with different levels of endoscopy expertise were asked to classify the invasion depth of gastric neoplasms for each image without AutoDL support, with faulty AutoDL support, and with best performance AutoDL support in sequence.

Results: The Neuro-T-based model reached 89.3% (95% CI 85.1%-93.5%) external test accuracy. For the model establishment time, Create ML Image Classifier showed the fastest time of 13 minutes while reaching 82.0% (95% CI 76.8%-87.2%) external test accuracy. While the expert endoscopist's decisions were not influenced by AutoDL, the faulty AutoDL misled the endoscopy trainee and the general physician. However, this was corrected by the support of the best performance AutoDL model. The trainee gained the most benefit from the AutoDL support.

Conclusions: AutoDL is deemed useful for the on-site establishment of customized deep learning models. An inexperienced endoscopist with at least a certain level of expertise can benefit from AutoDL support.

Keywords: artificial intelligence; automated deep learning; convolutional neural network; deep learning; deep learning model; endoscopy; gastric neoplasms; neural network.

Figure 1

Schematic flow for the establishment of automated deep learning models in data construction.

Figure 2

Schematic flow for the establishment of automated deep learning models in performance evaluation.

Figure 3

Schematic flow for the establishment of automated deep learning (AutoDL) models in endoscopist–artificial intelligence interaction test.

Figure 4

Endoscopist–artificial intelligence interactions. AutoDL: automated deep learning.

Figure 5

Representative samples of the attention map (Neuro-T–based model). AutoDL: automated deep learning.