Artificial intelligence in clinical metagenomic pathogen detection: A critical review of pipeline integrations, challenges, and future directions.
L’essentiel
Metagenomic next-generation sequencing (mNGS) has expanded the scope of clinical diagnostics by enabling culture-independent detection of microorganisms in patient samples. However, mNGS clinical utility remains constrained by substantial computational demands, reference database biases, and the persistent challenge of distinguishing true pathogens from host background, commensal flora and environmental contamination. Traditional alignment and k-mer-based bioinformatics pipelines frequently struggle to balance speed, sensitivity, and the ability to detect highly divergent or novel organisms. This review critically synthesizes the current landscape of Artificial Intelligence (AI) and Machine Learning (ML) applications across the mNGS diagnostic pipeline, examining deep learning architectures-including Convolutional Neural Networks (CNNs), Long Short-Term Memory networks (LSTMs), and Transformers-as integrated into raw read processing, host sequence depletion, primary taxonomic classification, and ancillary detection of antimicrobial resistance (AMR) and virulence factors. While several AI methodologies report high classification accuracy in benchmarking studies, we note that most performance claims derive from simulated datasets or controlled mock communities rather than prospective clinical validation. Significant gaps persist, including limited AI integration in front-end signal optimization, inadequate automated clinical reporting, absence of standardized benchmarking metrics, and unresolved questions regarding data leakage, reproducibility, and generalizability. Successful clinical translation will require addressing the interpretability limitations of current explainable AI approaches, navigating complex and evolving regulatory landscapes for Software as a Medical Device (SaMD), and bridging the gap between computational feasibility and demonstrated patient-outcome benefit. The development of genomic foundation models and multi-modal clinical integration holds promise for advancing mNGS toward real-time, actionable diagnostics, though substantial evidence gaps remain between current proof-of-concept demonstrations and validated clinical deployment.
Synthèse détaillée
Résumé original
Metagenomic next-generation sequencing (mNGS) has expanded the scope of clinical diagnostics by enabling culture-independent detection of microorganisms in patient samples. However, mNGS clinical utility remains constrained by substantial computational demands, reference database biases, and the persistent challenge of distinguishing true pathogens from host background, commensal flora and environmental contamination. Traditional alignment and k-mer-based bioinformatics pipelines frequently struggle to balance speed, sensitivity, and the ability to detect highly divergent or novel organisms. This review critically synthesizes the current landscape of Artificial Intelligence (AI) and Machine Learning (ML) applications across the mNGS diagnostic pipeline, examining deep learning architectures-including Convolutional Neural Networks (CNNs), Long Short-Term Memory networks (LSTMs), and Transformers-as integrated into raw read processing, host sequence depletion, primary taxonomic classification, and ancillary detection of antimicrobial resistance (AMR) and virulence factors. While several AI methodologies report high classification accuracy in benchmarking studies, we note that most performance claims derive from simulated datasets or controlled mock communities rather than prospective clinical validation. Significant gaps persist, including limited AI integration in front-end signal optimization, inadequate automated clinical reporting, absence of standardized benchmarking metrics, and unresolved questions regarding data leakage, reproducibility, and generalizability. Successful clinical translation will require addressing the interpretability limitations of current explainable AI approaches, navigating complex and evolving regulatory landscapes for Software as a Medical Device (SaMD), and bridging the gap between computational feasibility and demonstrated patient-outcome benefit. The development of genomic foundation models and multi-modal clinical integration holds promise for advancing mNGS toward real-time, actionable diagnostics, though substantial evidence gaps remain between current proof-of-concept demonstrations and validated clinical deployment.