Artificial Intelligence Based Clustering Algorithm for Pulse Diagnosis.

Background: In traditional medicine, pulse palpation is a unique diagnostic technique focusing on identifying patterns of symptoms through subjective assessment of bio-signals. However, its reliability and objectivity have been questioned. We developed an artificial intelligence-based algorithm for clustering doctors' diagnostic results using unsupervised clustering techniques on pulse waveform signals. Methods: Raw pulse signals were recorded from both wrists of healthy individuals and were then analyzed, with diagnoses provided by a Korean Medicine doctor. To measure pairwise pulse similarity, Dynamic Time Warping (DTW) was used, and Multidimensional Scaling (MDS) was applied for dimensionality reduction, enabling the clustering and validation of data-driven diagnostic patterns. Results: Our findings revealed discrepancies between traditional pulse diagnosis and automated diagnoses, yet the clustering algorithm showed high alignment between data-driven groupings and expert diagnoses. Notably, pulse signals from the left wrist had better alignment in several categories than those from the right wrist (cosine similarity: left hand 0.56 ± 0.13; right hand 0.54 ± 0.15). The "Floating-Sinking" pattern was particularly identifiable, achieving the highest Cosine similarity (0.83). Conclusion: The results suggest significant alignment between data-driven pattern identification and expert diagnoses, especially for the "Floating-Sinking" pattern. Further refinement with diverse populations is necessary, but data-driven diagnostic tools hold potential for standardizing and quantifying traditional pulse diagnosis, moving it toward a scientifically robust practice. Trial Registration: Clinical Research Information Service KCT0007655 (registered on 2024-02-29).