Systematic Review of the Implementation of Artificial Intelligence in the Diagnosis of Central Nervous System Infections

Abstract

Despite the increasing use of machine learning (ML) in the early diagnosis and determination of risk factors of various infections, studies evaluating the use of ML in central nervous system infections (CNSI) are limited. The Scopus, Web of Science, and PubMed databases were searched via Ovid using the keywords “Artificial intelligence” OR “Machine learning” OR “Deep learning” AND “Central nervous system infection” OR “Encephalitis” OR “Meningitis”. The last search was performed on July 20, 2022. Studies were selected based on the population, intervention, comparator, outcome(s) of interest, and study design (PICOS). The Joanna Briggs Institute Cohort Studies and Case Control Research Checklist were used to determine the study quality. Studies that included adolescent and adult patients diagnosed with CNSI via cerebrospinal fluid testing or other laboratory examination and imaging methods were reviewed. Five of the 731 identified articles were included. The studies have focused on the role of ML in the following issues: risk factors for developing healthcare-associated ventriculitis/meningitis, assessing treatment failure in patients with spinal epidural abscess (SEA) who were treated non-operatively, prediction of mortality in patients with SEA, differential diagnosis of meningitis, and comparison of differential diagnoses determined using ML methods and that determined by clinicians. Although more studies are needed in this area, ML may soon be used effectively in the diagnosis of CNSI. It is essential to determine the best ML model for each issue. Artificial intelligence applications could potentially contribute to the rapid diagnosis and effective early treatment of diseases.

Keywords

• artificial intelligence
• machine learning
• deep learning
• central nervous system infections
• encephalitis