The discovery of antibiotics in 1928 and their subsequent large-scale production are considered to be one of the most important achievements in the history of medicine \[[@bb0005]\].  One of the most important discoveries after that of β-lactams was streptomycin, the first aminoglycoside discovered.  The history of aminoglycosides was then marked by the successive introduction of a series of compounds (kanamycin, gentamicin, and tobramycin) for the treatment of infections due to Gram-negative bacilli \[[@bb0010]\]. 

The recent expansion of extensively drug-resistant (XDR) pathogens and particularly that of carbapenem-producing *Enterobacteriaceae* (CRE) has brought into light aminoglycosides, which may retain activity even in XDR isolates \[[@bb0015]\].  Specific indications for aminoglycoside therapy include amikacin and gentamicin administered intravenously for infections caused by MDR Gram-negative organisms \[[@bb0020]\]. 

Interpretative reading of antimicrobial susceptibility test results allows to analyze the susceptibility pattern and to predict the underlying resistance mechanisms \[[@bb0025]\].  Contrary to β-lactams antibiotics, correlations between resistance to aminoglycosides inferred based on the European Committee on Antimicrobial Susceptibility Testing (EUCAST) clinical breakpoints and expert rules are generally poor \[[@bb0030]\].  Therefore, the need for improvement of detection of aminoglycosides resistance mechanisms in routine is of great importance. 

Recently in *EBioMedicine*, Mancini and colleagues presented an Aminoglycoside Resistance Mechanism Inference Algorithm (ARMIA) for the inference of resistance mechanisms from inhibition zone diameters \[[@bb0035]\].  This algorithm uses ECOFFs for gentamicin, tobramycin and kanamycin as well as a working separator cut-off for amikacin.  They compared the performance of ARMIA and EUCAST CBPs/expert rules with that of whole-genome sequencing (WGS) in predicting aminoglycoside resistance.  The results of this study showed that ARMIA-based inference of resistance mechanisms and WGS data were congruent in 96·3%.  In contrast, there was a poor correlation between resistance mechanisms inferred using EUCAST CBPs/expert rules and WGS data (85·6%) \[[@bb0035]\]. 

When assessing the accuracy of various susceptibility testing methods as compared to standard reference methods, the terms very major errors (vME) have been used to describe false susceptible \[[@bb0040]\].  Thus, in the comparison made by Mancini and colleagues, they reported that EUCAST produced 63 (12·9%) vME, compared to only 2 (0·4%) vME with ARMIA \[[@bb0035]\]. 

*In vitro* susceptibility rates may vary significantly, depending on the aminoglycoside resistance mechanisms, which are frequently co-transferred along with other resistance genes on mobile genetic elements \[[@bb0015]\].  It is reported that aminoglycoside resistance mechanisms, such as 16S rRNA methylase, coexist with other resistance mechanisms including extended-spectrum β-lactamase, carbapenemase, and plasmid-mediated quinolone resistance determinants \[[@bb0045]\].  Thus, it is important to detect the underlying aminoglycosides resistance mechanisms to prevent co-selection of these resistance mechanisms.  The ARMIA developed by Mancini and colleagues would be useful for this purpose to avoid misidentification of the aminoglycoside resistance mechanisms. 

Declaration of Competing Interest
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None to declare.