Clinical trial NCT05630248: Visualization Engineering Platform for TCM Pulse Diagnosis - Pulse Diagnosis Based on Federated Learning to Diagnose Slippery and Choppy and Other Pulses Waveform Image Features to Assist in the Study of TCM Pathological Logic Analysis
Official title: Visualization Engineering Platform for TCM Pulse Diagnosis - Pulse Diagnosis Based on Federated Learning to Diagnose Slippery and Choppy and Other Pulses Waveform Image Features to Assist in the Study of TCM Pathological Logic Analysis
Study type: OBSERVATIONAL
Overall status: UNKNOWN
Conditions: Slippery Pulse
Choppy Pulse
Keywords: Artificial intelligence
Recurrent Neural Network
Wrist artery pulse diagnosis instrument
TCM pulse diagnosis
Feature extraction
Lead sponsor: China Medical University Hospital
Sponsor class: OTHER
First posted: 2022-11-29
Enrollment: 240
Sex: ALL
Minimum age (years): 20
Maximum age (years): 90
Standard ages: ADULT
OLDER_ADULT
Accepts healthy volunteers: yes
Observational model: OTHER
Time perspective: PROSPECTIVE
Sampling method: NON_PROBABILITY_SAMPLE
Study population: outpatient

Brief summary: The diagnoses processes of Traditional Chinese Medicine (TCM) focus on the following four main types of diagnoses methods consisting of inspection, olfaction, inquiry, and palpation. The most important one is palpation also called pulse diagnosis which is to measure wrist artery pulse by TCM doctor's fingers to detect patient's health state. The pulse diagnosis has three parts, namely 'Chun', 'Guan' and 'Chy', with the location. Wrist measurements correspond to different parts of the body's organs. In this project, it is to classify pulse types by using specialized pulse measuring instruments. The measured pulse wave (Measured Pulse Wave, MPW) was segmented into arterial pulse wave curves (APWC) by the image suggestion method. The research object of this project is to collect and group patients diagnosed by traditional Chinese medicine practitioners, namely slippery pulse, choppy pulse group and normal pulse control group, with at least 80 cases for each group. The research purpose of this project is mainly to carry out the visualization engineering platform of TCM pulse diagnosis - based on the pulse diagnosis of federated learning to diagnose the pulse waveform image features such as slippery pulse and choppy pulse to provide auxiliary TCM pathological logic analysis research and back-end cross-federal learning of TCM pulse diagnosis Implementation of the node system. In other words, it is expected that the pulse wave characteristics measured by TCM physicians who cooperate with experts in the field can be collected from many TCM pulse diagnosis federated learning nodes, and analyzed by the Multiple-Expert Repertory Grid Elicitation (MERGE) method. Finally, the artificial intelligence model based on FL is trained to carry out TCM pathological logic analysis and related research. The results will be provided to TCM physicians as an important reference to assist clinical diagnosis.


Eligibility criteria: Inclusion Criteria:

1. Already sign test consent permit.
2. More than 20-year-old.

Exclusion Criteria:

1. There is a wound or inflammation at the wrist skin measurement.

Primary outcomes:
measure: Slippery Pulse
timeFrame: 30 minutes in duration
Secondary outcomes:
Other outcomes:
Locations:
city: Taichung
country: Taiwan
facility: China Medical University Hospital
state: North District
Study officials:
References: