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Dynamic Digital Biomarkers of Motor and Cognitive Function in Parkinson's Disease
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Pattern Identification in Patients with Functional Dyspepsia Using Brain-Body Bio-Signals: Protocol of a Clinical

Won-Joon Koh1, Junsuk Kim2, Younbyoung Chae3

  • 1Department of Korean Medicine, Graduate School, Kyung Hee University, Seoul 02453, Republic of Korea.

Journal of Clinical Medicine
|February 26, 2025
PubMed
Summary
This summary is machine-generated.

This study develops an AI algorithm using brain-body bio-signals to improve the diagnosis of functional dyspepsia (FD). Integrating artificial intelligence with bio-signals enhances the objectivity and reliability of traditional Korean medicine diagnostics for FD patients.

Keywords:
Korean medicineartificial intelligencebio-signalsbrain–body interactionfunctional dyspepsiapulse diagnosis

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Area of Science:

  • Integrative Medicine
  • Biomedical Engineering
  • Artificial Intelligence in Healthcare

Background:

  • Functional dyspepsia (FD) is a prevalent gastrointestinal disorder affecting 8-46% of the population, causing significant socioeconomic burdens.
  • Current traditional medicine diagnostics for FD rely on subjective methods like observation, questioning, abdominal examination, and pulse diagnosis, lacking standardization and objectivity.
  • There is a need for enhanced diagnostic tools to improve the accuracy and reliability of FD assessment in traditional medicine.

Purpose of the Study:

  • To develop an artificial intelligence (AI)-based algorithm for predicting identified patterns in functional dyspepsia (FD) patients.
  • To integrate multi-modal bio-signal data, including brain activity, pulse wave, skin conductance, and electrocardiography, for enhanced diagnostic capabilities.
  • To improve the objectivity and reliability of traditional Korean medicine diagnostic protocols for FD.

Main Methods:

  • An observational cross-sectional study involving 100 patients diagnosed with FD according to Rome IV criteria.
  • Collection of integrated brain-body bio-signal data using functional near-infrared spectroscopy (fNIRS), pulse wave analysis, skin conductance response (SCR), and electrocardiography (ECG).
  • Development of AI algorithms to analyze bio-signal patterns and correlate them with differential diagnoses provided by licensed Korean medicine doctors.

Main Results:

  • AI algorithms were created by integrating multi-modal bio-signal data from FD patients.
  • The study protocol was approved by the Institutional Review Board and registered in the Korean Clinical Trial Registry.
  • The integration of AI-based bio-signal analysis is expected to enhance the objectivity and reliability of traditional diagnostic methods.

Conclusions:

  • The integration of bio-signal analysis and AI offers a promising approach to improve clinical practices for diagnosing FD.
  • This approach has the potential to enhance the acceptance and standardization of traditional medicine diagnostic processes within broader healthcare systems.
  • AI-driven bio-signal analysis can lead to more objective and reliable diagnoses for functional dyspepsia patients.