# Unraveling Functional Dysphagia: A Game-Changing Automated Machine-Learning Diagnostic Approach
## 5 Conclusions  
In this study, we utilized traditional machine learning algorithms, often referred to as AI shallow learners, to achieve novel and accurate differentiation between normal subjects and functional dysphagia patients, obtaining classification accuracies exceeding 90%. Unlike the historical approach of diagnosing functional dysphagia through exclusion, our findings highlight a distinct distension abnormality in these patients, particularly evident in proximal and distal segments. Remarkably, incorporating distension features significantly enhances the classification outcomes compared to using pressure features alone, underscoring the role of the distension phase over the contraction phase in functional dysphagia pathology. However, the limitations of shallow learners lie in their inability to grasp intricate patterns within unstructured or nonlinear data, demanding manual feature engineering and risking overfitting, especially with small datasets. To address this, we emphasize the potential of advanced classifiers like ensemble methods or deep learning in scenarios with larger sample sizes. In our study, careful cross-validation and hyperparameter tuning counteracted limited sample size concerns, resulting in successful classification. Notably, while the origin of the distension anomaly remains unclear, our work underscores the importance of specific characteristics, like peak pressure standard deviation, in distinguishing groups, addressing the limitations of existing classifications. Our method's division of the esophagus into equal compartments circumvents height-related disparities, providing a valuable alternative to the Chicago Classification. The results obtained from this study are highly promising and could pave the way for advancements in the diagnosis and management of patients with functional dysphagia. Based on our findings, we observed that a significant problem in patients with dysphagia is impaired distension of the esophagus during peristalsis, while subtle or no contraction abnormalities were noted in these patients. Future studies may explore the use of pharmacologic approaches, esophageal dilation, and possibly surgical interventions targeting the distension phase of peristalsis. Conceptualization, A.Z. and R.K.M. ; methodology, A.Z. ; software, A.Z. ; validation, A.Z., J.L., Y.B. and Z.P. ; formal analysis, A.Z. ; investigation, A.Z. and R.K.M. ; resources, A.Z. and R.K.M. ; data curation, R.K.M. ; writing--original draft preparation, A.Z. ; writing--review and editing, R.K.M., J.L., Z.P. and Y.B. ; visualization, A.Z. ; supervision, A.Z. ; project administration, A.Z. and R.K.M. ; funding acquisition, A.Z. and R.K.M. All authors have read and agreed to the published version of the manuscript. **Funding:** This work was supported by NIH Grant R01 DK109376. **Institutional Review Board Statement:** The human investigation committee of the University of California San Diego approved the study protocol (IRB # 182156 and 202106X). **Informed Consent Statement:** In the conducted study, the principle of informed consent was diligently adhered to, and consent was obtained from all participating subjects. Prior to their involvement, everyone was provided with comprehensive information about the study's purpose, procedures, potential risks, and benefits. They were given ample time to ask questions and clarify any uncertainties before voluntarily agreeing to participate. The informed consent process aimed to ensure that the subjects fully understood the implications of their involvement and that they willingly and freely consented to be part of the research. This ethical practice ensures that the rights, privacy, and well-being of the subjects are respected and protected throughout the study, upholding the highest standards of research integrity. **Data Availability Statement:** The data presented in this study are available on request from the corresponding author. The data are not publicly available due to IRB.