This technology combines MALDI-TOF MSmachine learning models to search for the biomarkers for rapid identification of drug-resistant bacteria strains. Through the mass spectrometry signals generated by the clinical bacterial identification process, the risk of drug-resistant bacteria can be predicted, which will effectively reduce the laborious process of clinical medical examiners, assist clinicians to use antibiotics accurately, reduce the incidence of severe diseases,reduce the problems caused by the abuse of antibiotics. This technology integrates clinical laboratory medicinemachine learning technologies to accelerate the digital transformation of healthcare.

"This technology can be directly applied to the department of laboratory medicine that currently use MALDI-TOF to identify bacterial species, without the need to purchase additional instrumentsadd new operators.
 
 For routine clinical practice, after MALDI-TOF analysis of cultured clinical strains, the MALDI-TOF spectra can be rapidly identified within 10 s as antibiotics-resistancenon-antibiotics-resistance using our ML model instead of using conventional AST methods that spend ~24 h to 48 h. This technology allows physicians to immediately obtain the correct antibiotic dosage guidelinesadminister drugs. This technology has also been embedded in the clinical system of our hospital for testing."

By cooperating with five hospitals, our MALDI-TOF spectra library should be the largest database we know so far. We combined the rapid bacterial sample preparation method, MALDI-TOFmachine learning to obtain a rapid bacterial resistance prediction system. This technology can further discover novel protein biomarkers for drug-resistant bacteria strainshelp to explore possible drug resistance mechanism. This technology can be directly applied to the department of laboratory medicine of a hospital for early drug administrationmedical-cost savings. In addition, protein biomarkers developedidentified from this technology have the potential to be applied in the development of rapid chip assaysantibody-based antibiotics.