Artificial Intelligence in Laboratory Technologies for Early Detection and Prognostication of Sepsis: A Systematic Review

Mohsen Bakouri; Nasser M. Alqahtani; Othman M. Alhussain; Nawaf Alrashidi; Sulaiman N. Almutairi; Ahmed O. Alabdulwahab; Badr S. Alaskar; Megren A. Alqahtani

doi:10.61091/jpms202312310

Background: Sepsis a complex clinical syndrome represents life-threatening organ dysfunction instigated by an infection's dysregulated host response. Early detection and accurate prognostication of sepsis are crucial; they pave the way for timely intervention, ultimately enhancing patient outcomes. The rise in interest towards Artificial Intelligence (AI) applications within laboratory technologies is directly related to its potential for improving early detection and prognosis forecasting in sepsis cases; this interest comes as AI continues its advancement. Methods: We conducted a systematic review of studies utilizing AI algorithms in laboratory settings for early sepsis detection and prognostication: our methods entailed searching relevant databases for research published until October 2023. Our inclusion criteria spanned original articles; these applied machine learning (ML) and deep learning (DL) techniques to laboratory data with the aim being sepsis prediction. We assessed the quality of the studies, extracted and synthesized data on AI model performance metrics - including: area under receiver operating characteristic curve (AUROC), sensitivity, specificity, and accuracy. Results: The review encompassed eight studies meeting the inclusion criteria; AI models showcased exceptional predictive capabilities evidenced by a range of AUROC values from 0.799 to 0.9213, signifying noticeably acceptable performance. However, there was wide variation in sensitivity and specificity among these analyses; an indicator of heterogeneity in model performance. Superior prognostic accuracy and potential for real-time monitoring of patients' early sepsis signs emerged in several models; notably, within the first 12 hours of patient admission their highest predictive period. The models frequently outperformed traditional scoring systems. Conclusion: Laboratory technology's AI applications significantly promise sepsis' early detection and prognostication. Reviewed studies suggest AI models may surpass traditional methods, offering potential integration into clinical workflows for rapid sepsis identification aid. Nevertheless, we also acknowledged both the variability in model performance and necessity of additional validation across diverse clinical settings. Future research: it must concentrate on two key aspects-the refinement of AI algorithms to enhance sensitivity and precision; furthermore, it should delve into evaluating the clinical impact of tools for sepsis prediction that are assisted by AI.