A review of artificial intelligence applications for antimicrobial resistance

Lv, Ji; Deng, Senyi; Zhang, Le

doi:10.1016/j.bsheal.2020.08.003

Cited by 90 publications

(85 citation statements)

References 115 publications

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“…The first approach is the one that could be applied clinically to detect AMR in pathogens. For example, the Typewriter method uses a database called BLASTn to compare genome sequence with WGS data of 24 ARG and their mutations in S. aureus species [189,192]. This approach requires adequate resource allocation, interdisciplinary effort, funding, and teamwork to provide early diagnosis and increase the quality of care in the era of drug resistance.…”

Section: Capitalizing On New Technologiesmentioning

confidence: 99%

“…AI techniques used different methods to improve AST that include the combination of flow cytometer-assisted antimicrobial susceptibility test (FAST) and machine learning techniques [203] and IR-spectrometer method that combines infrared (IR) spectroscopy with the artificial neural network [208]. For WGS-AST, the Support Vector Machine (SVM) and the Set Covering Machine (SCM) models are used to learn and predict AMR phenotypes [179,209].The SCM model allows genotype-to-phenotype predictions [192]. The SVM model uses the number of co-occurring k-mers between the genome of the isolates and the reference genes to learn and predict the phenotypes of the bacteria to a specific antimicrobial [194].…”

Section: Capitalizing On New Technologiesmentioning

confidence: 99%

See 1 more Smart Citation

Surveillance of antimicrobial resistance in low- and middle-income countries: a scattered picture

Iskandar

Molinier

Hallit

et al. 2021

Antimicrob Resist Infect Control

232

144

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Data on comprehensive population-based surveillance of antimicrobial resistance is lacking. In low- and middle-income countries, the challenges are high due to weak laboratory capacity, poor health systems governance, lack of health information systems, and limited resources. Developing countries struggle with political and social dilemma, and bear a high health and economic burden of communicable diseases. Available data are fragmented and lack representativeness which limits their use to advice health policy makers and orientate the efficient allocation of funding and financial resources on programs to mitigate resistance. Low-quality data means soaring rates of antimicrobial resistance and the inability to track and map the spread of resistance, detect early outbreaks, and set national health policy to tackle resistance. Here, we review the barriers and limitations of conducting effective antimicrobial resistance surveillance, and we highlight multiple incremental approaches that may offer opportunities to strengthen population-based surveillance if tailored to the context of each country.

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Section: Capitalizing On New Technologiesmentioning

confidence: 99%

Section: Capitalizing On New Technologiesmentioning

confidence: 99%

Surveillance of antimicrobial resistance in low- and middle-income countries: a scattered picture

Iskandar

Molinier

Hallit

et al. 2021

Antimicrob Resist Infect Control

232

144

View full text Add to dashboard Cite

show abstract

“…In this regard, computational techniques can speedily and cost-effectively assess the impact of mutations on the function of proteins and their evolution. Recently, methods that depend on mathematical modeling and artificial intelligence were used to predict the occurrence and fate of AMR without the requirement of laborious, expensive, and sometimes inconvenient in-situ analyses [ 63 ]. In addition, big data analytics such as artificial neural networks and artificial intelligence already showed great promise in microbiological diagnostic testing and assisting in clinical decision making [ 64 , 65 ].…”

Section: Antimicrobial Resistancementioning

confidence: 99%

“…In addition, big data analytics such as artificial neural networks and artificial intelligence already showed great promise in microbiological diagnostic testing and assisting in clinical decision making [ 64 , 65 ]. The main strength of these techniques is their capability to generate and handle large amounts of data and predict the prevalence of AMR from historical data [ 63 , 65 ]. They can, thus, be used to select the best intervention strategies or experimental conditions that generate data for developing preventive and remediation approaches.…”

Section: Antimicrobial Resistancementioning

confidence: 99%

Insects, Rodents, and Pets as Reservoirs, Vectors, and Sentinels of Antimicrobial Resistance

et al. 2021

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This paper reviews the occurrence of antimicrobial resistance (AMR) in insects, rodents, and pets. Insects (e.g., houseflies, cockroaches), rodents (rats, mice), and pets (dogs, cats) act as reservoirs of AMR for first-line and last-resort antimicrobial agents. AMR proliferates in insects, rodents, and pets, and their skin and gut systems. Subsequently, insects, rodents, and pets act as vectors that disseminate AMR to humans via direct contact, human food contamination, and horizontal gene transfer. Thus, insects, rodents, and pets might act as sentinels or bioindicators of AMR. Human health risks are discussed, including those unique to low-income countries. Current evidence on human health risks is largely inferential and based on qualitative data, but comprehensive statistics based on quantitative microbial risk assessment (QMRA) are still lacking. Hence, tracing human health risks of AMR to insects, rodents, and pets, remains a challenge. To safeguard human health, mitigation measures are proposed, based on the one-health approach. Future research should include human health risk analysis using QMRA, and the application of in-silico techniques, genomics, network analysis, and ’big data’ analytical tools to understand the role of household insects, rodents, and pets in the persistence, circulation, and health risks of AMR.

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“…In particular, single-cell analysis platforms are highly promising for providing high resolution diagnosis with a quick turnaround time. For example, automated single-cell morphological analysis platforms with machine learning algorithms provide cost-effective and accurate antimicrobial susceptibility data in non-traditional healthcare settings [6][7][8][9]. A nanoarray digital polymerase chain reaction with high resolution melt curve analysis enables rapid broad bacteria identification and phenotypic AST [10,11].…”

Section: Introductionmentioning

confidence: 99%

A Rapid Single-Cell Antimicrobial Susceptibility Testing Workflow for Bloodstream Infections

et al. 2021

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Bloodstream infections are a significant cause of morbidity and mortality worldwide. The rapid initiation of effective antibiotic treatment is critical for patients with bloodstream infections. However, the diagnosis of bloodborne pathogens is largely complicated by the matrix effect of blood and the lengthy blood tube culture procedure. Here we report a culture-free workflow for the rapid isolation and enrichment of bacterial pathogens from whole blood for single-cell antimicrobial susceptibility testing (AST). A dextran sedimentation step reduces the concentration of blood cells by 4 orders of magnitude in 20–30 min while maintaining the effective concentration of bacteria in the sample. Red blood cell depletion facilitates the downstream centrifugation-based enrichment step at a sepsis-relevant bacteria concentration. The workflow is compatible with common antibiotic-resistant bacteria and does not influence the minimum inhibitory concentrations. By applying a microfluidic single-cell trapping device, we demonstrate the workflow for the rapid determination of bacterial infection and antimicrobial susceptibility testing at the single-cell level. The entire workflow from blood to categorical AST result can be completed in less than two hours.

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A review of artificial intelligence applications for antimicrobial resistance

Cited by 90 publications

References 115 publications

Surveillance of antimicrobial resistance in low- and middle-income countries: a scattered picture

Surveillance of antimicrobial resistance in low- and middle-income countries: a scattered picture

Insects, Rodents, and Pets as Reservoirs, Vectors, and Sentinels of Antimicrobial Resistance

A Rapid Single-Cell Antimicrobial Susceptibility Testing Workflow for Bloodstream Infections

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