Artificial intelligence diagnostic system predicts multiple Lugol-voiding lesions in the esophagus and patients at high risk for esophageal squamous cell carcinoma.

Yohei Ikenoyama Toshiyuki YoshioJunki TokuraSakiko NaitoKen NamikawaYoshitaka TokaiShoichi YoshimizuYusuke HoriuchiAkiyoshi IshiyamaToshiaki HirasawaTomohiro TsuchidaNaoyuki KatayamaTomohiro TadaJunko Fujisaki

Published in: Endoscopy (2021)

The AI system could predict multiple LVLs with high sensitivity from images without iodine staining. The system could enable endoscopists to apply iodine staining more judiciously.

Keyphrases

artificial intelligence
deep learning
machine learning
big data
end stage renal disease
newly diagnosed
ejection fraction
prognostic factors
convolutional neural network
magnetic resonance imaging
optical coherence tomography
flow cytometry
computed tomography
contrast enhanced