BACKGROUND
Authors previously examined deep-learning models to classify the invasion depth (mucosa-confined vs. submucosa-invaded) of gastric neoplasms using endoscopic images. The external-test accuracy reach 77.3%. However, model establishment is labor-intense, requiring high performance. Automated deep-learning (AutoDL), which enable fast searching of optimal neural architectures and hyperparameters without complex coding, have been developed.
OBJECTIVE
To establish AutoDL models in classifying the invasion depth of gastric neoplasms. Additionally, endoscopist-artificial intelligence interactions were explored.
METHODS
The same 2,899 endoscopic images, which 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 analyzed for each image without AutoDL’s support, with faulty AutoDL’s support, and with best performance AutoDL’s support in sequence.
RESULTS
Neuro-T-based model reached 89.3% (95% confidence interval: 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% external-test accuracy. Expert endoscopist decisions were not influenced by AutoDL. The faulty AutoDL has misled the endoscopy trainee and the general physician. However, this was corrected by the support of the best performance AutoDL. The trainee gained the highest benefit from the AutoDL’s 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.
Application of artificial intelligence using a convolutional neural network for detecting gastric cancer in endoscopic images
