High-resolution manometry (HRM) is the primary procedure used to diagnose
esophageal motility disorders. Its interpretation and classification includes
an initial evaluation of swallow-level outcomes and then derivation of a
study-level diagnosis based on Chicago Classification (CC), using a tree-like
algorithm. This diagnostic approach on motility disordered using HRM was
mirrored using a multi-stage modeling framework developed using a combination
of various machine learning approaches. Specifically, the framework includes
deep-learning models at the swallow-level stage and feature-based machine
learning models at the study-level stage. In the swallow-level stage, three
models based on convolutional neural networks (CNNs) were developed to predict
swallow type, swallow pressurization, and integrated relaxation pressure (IRP).
At the study-level stage, model selection from families of the
expert-knowledge-based rule models, xgboost models and artificial neural
network(ANN) models were conducted, with the latter two model designed and
augmented with motivation from the export knowledge. A simple model-agnostic
strategy of model balancing motivated by Bayesian principles was utilized,
which gave rise to model averaging weighted by precision scores. The averaged
(blended) models and individual models were compared and evaluated, of which
the best performance on test dataset is 0.81 in top-1 prediction, 0.92 in top-2
predictions. This is the first artificial-intelligence-style model to
automatically predict CC diagnosis of HRM study from raw multi-swallow data.
Moreover, the proposed modeling framework could be easily extended to
multi-modal tasks, such as diagnosis of esophageal patients based on clinical
data from both HRM and functional luminal imaging probe panometry (FLIP).