Contribution of machine learning for subspecies identification from Mycobacterium abscessus with MALDI‐TOF MS in solid and liquid media

Mycobacterium abscessus (MABS) displays differential subspecies susceptibility to macrolides. Thus, identifying MABS's subspecies (M. abscessus, M. bolletii and M. massiliense) is a clinical necessity for guiding treatment decisions. We aimed to assess the potential of Machine Learning (ML)‐based classifiers coupled to Matrix‐Assisted Laser Desorption/Ionization Time‐of‐Flight (MALDI‐TOF) MS to identify MABS subspecies. Two spectral databases were created by using 40 confirmed MABS strains. Spectra were obtained by using MALDI‐TOF MS from strains cultivated on solid (Columbia Blood Agar, CBA) or liquid (MGIT®) media for 1 to 13 days. Each database was divided into a dataset for ML‐based pipeline development and a dataset to assess the performance. An in‐house programme was developed to identify discriminant peaks specific to each subspecies. The peak‐based approach successfully distinguished M. massiliense from the other subspecies for strains grown on CBA. The ML approach achieved 100% accuracy for subspecies identification on CBA, falling to 77.5% on MGIT®. This study validates the usefulness of ML, in particular the Random Forest algorithm, to discriminate MABS subspecies by MALDI‐TOF MS. However, identification in MGIT®, a medium largely used in mycobacteriology laboratories, is not yet reliable and should be a development priority. Machine Learning (ML) enhances Mycobacterium abscessus (MABS) subspecies identification using Matrix‐Assisted Laser Desorption/Ionization Time‐of‐Flight (MALDI‐TOF) Mass Spectrometry (MS). The ML method demonstrated 100% accuracy in identifying subspecies on the Columbia Blood Agar (CBA) solid medium, with a reduction to 77.5% on the MGIT® liquid medium.