The development of liquid chromatography tandem mass spectrometry (LC-MS/MS) allows clinical laboratory to propose targeted and quantitative steroidomics analysis for diagnostic and follow-up explorations. Quantitative profiles of circulating steroids are now available in one single run. However, the screening and diagnostic thresholds for LC-MS/MS are yet to be completely defined in both basal and post synacthen states. These thresholds are likely to differ from those established with immunoassays due to the enhanced specificity and sensitivity of LC-MS/MS, a method less prone to cross-reactivity and interferences (Fiet et al., 2017). The concept of profiling or multiplexing the circulating steroidome is a paradigm shift as it appears to be way more accurate than single specie immunoassay quantitation to stratify patients. In subnormal or atypical cases, we showed that an exhaustive profile in a basal state (i.e. without ACTH stimulation test) could differentiate 21-hydroxylase deficiency from other enzyme defects and establish a more accurate diagnosis (Fiet et al., 2017). In parallel to these analytical progresses, machine learning (ML) approaches are emerging as essential tools in diagnosis thank to its classification strength compare to classical univariate statistics. Our main hypothesis is that these statistical models can be all the more powerful as, when combined with the metabolic profiling of the steroid biosynthesis pathway, it will be possible to avoid, or at least, limit the use of ACTH stimulation tests. In our experience, ML is indeed, particularly adapted to convert these metabolomic signatures, potentially concatenated with other bio-clinical parameters, into straightforward diagnostic outputs for clinicians.