Machine Learning for Antimicrobial Resistance Prediction: Current Practice, Limitations, and Clinical Perspective

Kim, Jee In; Maguire, Finlay; Tsang, Kara K.; Gouliouris, Theodore; Peacock, Sharon J.; McAllister, Tim A.; McArthur, Andrew G.; Beiko, Robert G.

doi:10.1128/cmr.00179-21

Cited by 64 publications

(42 citation statements)

References 122 publications

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“…88 The vast amount of pathogen genome sequencing being performed and the links to phenotypic antibiotic susceptibility testing have prompted a surge of interest in using statistical methods to predict AST from nucleotide sequencing 52,72,80,[89][90][91][92][93][94] (Table 1). While the applications of machine learning tools are being explored in many bacterial pathogens, [95][96][97] diagnostics? In the case of genome sequencing as a diagnostic, how much of the 2.2 Mb genome of N. gonorrhoeae needs to be evaluated to inform clinical decisions?…”

Section: Rapid Diagnostics That Predict Antimicrobial Susceptibilitymentioning

confidence: 99%

“…The vast amount of pathogen genome sequencing being performed and the links to phenotypic antibiotic susceptibility testing have prompted a surge of interest in using statistical methods to predict AST from nucleotide sequencing 52,72,80,89–94 (Table 1). While the applications of machine learning tools are being explored in many bacterial pathogens, 95–97 this strategy has urgency and relevance in N. gonorrhoeae given the potential clinical and public health benefits of rapid AST diagnostics.…”

Section: Introductionmentioning

confidence: 99%

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Machine learning models for Neisseria gonorrhoeae antimicrobial susceptibility tests

Martin

Mortimer

Grad

2022

Annals of the New York Academy of Sciences

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Neisseria gonorrhoeae is an urgent public health threat due to the emergence of antibiotic resistance. As most isolates in the United States are susceptible to at least one antibiotic, rapid molecular antimicrobial susceptibility tests (ASTs) would offer the opportunity to tailor antibiotic therapy, thereby expanding treatment options. With genome sequence and antibiotic resistance phenotype data for nearly 20,000 clinical N. gonorrhoeae isolates now available, there is an opportunity to use statistical methods to develop sequence-based diagnostics that predict antibiotic susceptibility from genotype. N. gonorrhoeae, therefore, provides a useful example illustrating how to apply machine learning models to aid in the design of sequence-based ASTs. We present an overview of this framework, which begins with establishing the assay technology, the performance criteria, the population in which the diagnostic will be used, and the clinical goals, and extends to the choices that must be made to arrive at a set of features with the desired properties for predicting susceptibility phenotype from genotype. While we focus on the example of N. gonorrhoeae, the framework generalizes to other organisms for which large-scale genotype and antibiotic resistance data can be combined to aid in diagnostics development.

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Section: Rapid Diagnostics That Predict Antimicrobial Susceptibilitymentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

Machine learning models for Neisseria gonorrhoeae antimicrobial susceptibility tests

Martin

Mortimer

Grad

2022

Annals of the New York Academy of Sciences

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“…The key factor for incorporation of these AI technologies in clinical practice is its "explainable" nature. The scientific basis of usage of AI and Machine Learning Technologies in battling AMR should be demonstrated with clear evidence, paving way for its incorporation into clinical practice and public health measures [8].…”

Section: Introductionmentioning

confidence: 99%

Ab.ai – A Novel Automated AI Tool for Reporting Antibiograms

Rajasekar

Thiyagarajan

Ali

2022

Studies in Health Technology and Informatics

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Anti-Microbial Resistance is one of the greatest threats that mankind faces right now due to the inappropriate use of antibiotics. Institution of appropriate antibiotics in right dose for the right patient at right time is the “gamechanger” in fighting AMR. Antibiotic Sensitivity Testing (AST) or antibiogram is done to ascertain the sensitivity profile of the organism. The most widely used method in laboratory practice in India is the Kirby-Bauer’s disk diffusion test. There are few shortcomings in the manual interpretation of antibiograms in the form of high inter-operator variability, mandatory requirement of trained microbiologists – which is difficult in low-resource settings and high degree of interpersonal bias due to various factors like stress, workload, and visual acuity. We propose the Ab.ai tool for automating the AST procedures in laboratory. The Ab.ai tool comprises of 3 phases: first for data collection, second for data processing and the third for generation of antibiotic sensitivity reports. Various software packages like OpenCV and EasyOCR are used for the development of the Ab.ai tool. A total of 50 antibiograms of both GPC and GNB are interpreted both by manual and automated method. The manual method is considered the “gold-standard” and the performance of Ab.ai tool was compared against the manual method. The Ab.ai tool achieved an agreement of 98.4% on susceptibility categorization of GPC antibiotics and 97.6% on that of GNB antibiotics against the gold standard manual method. The proposed Ab.ai tool serves as a perfect candidate for automating AST procedures and would prove to be a “game-changer” in battling AMR.

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“…In the context of microbial molecular ecology studies, machine learning approaches have proven an effective tool for predicting phenotypic features from genomic biomarkers such as antimicrobial resistance, host of isolation, bacterial growth and virulence [23, 24]. The models can predict the features without any prior knowledge about the mechanisms, by learning complex, nonlinear and high-order phylogenetic signals in a training dataset of labeled sequences to be used for rapid detection of the trait in unseen data [25, 26]. Among the various models that were employed in these studies, ensemble methods, e.g.…”

Section: Introductionmentioning

confidence: 99%

Plasmid Permissiveness of Wastewater Microbiomes can be Predicted from 16S rDNA sequences by Machine Learning

Moradigaravand

Dechesne

et al. 2022

Preprint

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Wastewater Treatment Plants (WWTPs) contain a diverse microbial community with high cell density. They constantly receive antimicrobial residues and resistant strains and, therefore, may offer conditions for the Horizontal Gene Transfer (HGT) of antimicrobial resistance determinants, transmitting clinically important genes between, e.g., enteric and environmental bacteria and vice versa. Despite the clinical importance, tools for predicting HGT are still under-developed. In this study, we examined to which extent microbial community composition, as inferred by partial 16S rRNA gene sequences, can predict plasmid permissiveness, i.e., the ability of cells to receive a plasmid through conjugation, for microbial communities in the water cycle, using data from standardized filter mating assays using fluorescent bio-reporter plasmids. We leveraged a range of machine learning models for predicting the permissiveness for each taxon in the community, translating to the range of hosts a plasmid is able to transfer to, for three broad host-range resistance plasmids (pKJK5, pB10, and RP4). Our results indicate that the predicted permissiveness from the best performing model (random forest) showed a moderate-to-strong average correlation of 0.45 for pB10 (95% CI: 0.42-0.52), 0.42 for pKJK5 (0.95% CI: 0.38-0.45) and 0.52 for RP4 (0.95% CI:0.45-0.55) with the experimental permissiveness in the unseen test dataset. Predictive phylogenetic signals occurred despite these being broad host-range plasmids. Our results provide a framework that contributes to assessing the risk of AMR pollution in wastewater systems. The predictive tool is available as a an application under https://github.com/DaneshMoradigaravand/PlasmidPerm.

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Machine Learning for Antimicrobial Resistance Prediction: Current Practice, Limitations, and Clinical Perspective

Abstract: Antimicrobial resistance (AMR) is a global health crisis that poses a great threat to modern medicine. Effective prevention strategies are urgently required to slow the emergence and further dissemination of AMR.

Cited by 64 publications

References 122 publications

Machine learning models for Neisseria gonorrhoeae antimicrobial susceptibility tests

Machine learning models for Neisseria gonorrhoeae antimicrobial susceptibility tests

Ab.ai – A Novel Automated AI Tool for Reporting Antibiograms

Plasmid Permissiveness of Wastewater Microbiomes can be Predicted from 16S rDNA sequences by Machine Learning

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