Table 7 A summary on the details of ML, strengths and limitations of the studies on pathogens of periprosthetic joint infection

Nguyen, M. (2018) [17]

Used ML-based NGS-based pAST method to predict the MICs for Klebsiella pneumoniae in order to determine the degree of resistance status on 20 antibiotics

Gradient-boosting tree

Average accuracy: 92.0%

No prior knowledge of the gene content was needed; the continuous phenotype (MIC) was predicted

No external validation

Khaledi, A. (2020) [18]

Used ML-based NGS-based pAST method to predict the antibiotic susceptibility of Pseudomonas aeruginosa to 4 antibiotics (ceftazidime, meropenem, ciprofloxacin and tobramycin)

Support vector machine

-Using single-nucleotide polymorphism’s information or gene expression data alone:

Sensitivity and Predictive value: 0.8–0.9

-In combination:

Sensitivity and Predictive value: >0.9

No prior knowledge of molecular mechanisms of the resistance was needed

No external validation

Aun, E. (2018) [19]

Used ML-based NGS-based pAST method to predict the antibiotic susceptibility of Pseudomonas aeruginosa to ciprofloxacin

Logistic regression for binary phenotypes (susceptible or resistant); Linear regression for the continuous phenotype (MIC value)

-Logistic regression:

Accuracy: 88.0%. F1-measure: 0.88. Sensitivity: 0.90. Specificity: 0.87

-Linear regression:

Coefficient of determination (R²): 0.42. Pearson correlation coefficient: 0.68. Spearman correlation coefficient: 0.84

A simple software ‘PhenotypeSeeker’ was developed; the continuous phenotype (MIC) was predicted

No external validation