A deep-learning-based unsupervised model on esophageal manometry using variational autoencoder.

ABSTRACT: High-resolution manometry (HRM) is the primary method for diagnosing esophageal motility disorders and its interpretation and classification are based on variables (features) from data of each swallow. Modeling and learning the semantics directly from raw swallow data could not only help automate the feature extraction, but also alleviate the bias from pre-defined features. With more than 32-thousand raw swallow data, a generative model using the approach of variational auto-encoder (VAE) was developed, which, to our knowledge, is the first deep-learning-based unsupervised model on raw esophageal manometry data. The VAE model was reformulated to include different types of loss motivated by domain knowledge and tuned with different hyper-parameters. Training of the VAE model was found sensi

SUBMITTER: Kou W 

PROVIDER: S-EPMC7901248 | biostudies-literature | 2021 Feb

REPOSITORIES: biostudies-literature