Optimizing AI models to predict esophageal squamous cell carcinoma risk by incorporating small datasets of soft palate images

Optimizing AI models to predict esophageal squamous cell carcinoma risk by incorporating small datasets of soft palate images

Abstract There is a currently an unmet need for non-invasive methods to predict the risk of esophageal squamous cell carcinoma (ESCC). Previously, we found that specific soft palate morphologies are strongly associated with increased ESCC risk. However, there is currently no artificial intelligence...

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Bibliographic Details
Main Authors: Kotaro Waki, Katsuya Nagaoka, Keishi Okubo, Masato Kiyama, Ryosuke Gushima, Kento Ohno, Munenori Honda, Akira Yamasaki, Kenshi Matsuno, Yoki Furuta, Hideaki Miyamoto, Hideaki Naoe, Motoki Amagasaki, Yasuhito Tanaka
Format: Article
Language:English
Published: Nature Portfolio 2025-02-01
Series:Scientific Reports
Subjects:
Esophageal squamous cell carcinoma
Artificial intelligence
Convolutional neural network
Soft palate
Non-invasive risk assessment
Online Access:https://doi.org/10.1038/s41598-025-86829-8
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