Ambient mass spectrometry and machine learning-based diagnosis system for acute coronary syndrome

Ambient mass spectrometry and machine learning-based diagnosis system for acute coronary syndrome - Ambient mass spectrometry and machine learning-based diagnosis system for acute coronary syndrome

Status

Active, not recruiting

Phases

Unknown

Study type

Interventional

Source

JPRN

Registry ID

JPRN-UMIN000053009

Enrollment

Registered

2024-05-12

Start date

2023-01-01

Completion date

Unknown

Last updated

2026-06-29

For informational purposes only — not medical advice. Sourced from public registries and may not reflect the latest updates. Terms

Conditions

The acquired mass spectrum dataset was subsequently analyzed by partial least squares (PLS) regression to find the relationship between two groups. Support vector machine (SVM), a mehod of machine learning, was applied on the dataset to construct the diagnostic algorithm.

Interventions

Myocardial infarction-diagnosed patients/ ACS patients with other diagnoses

Eligibility

Sex/Gender

All

Inclusion criteria

Inclusion criteria: ACS patients

Exclusion criteria

Exclusion criteria: Not enough data

Design outcomes

Primary

Measure	Time frame
The PESI-MS and ML-based diagnosis system is likely an optimal solution to assist physicians in ACS diagnosis with its remarkably predictive accuracy	—

Countries

Japan

Contacts

Public ContactQue Tran

University of Yamanashi Emergency and Critical Care Unit

g19ddm19@yamanashi.ac.jp0552739812

Outcome results

None listed

Source: JPRN (via WHO ICTRP) · Data processed: Jul 3, 2026