Use of artificial intelligence in paediatric anaesthesia: a systematic review

Abstract

Objectives: Although the development of artificial intelligence (AI) technologies in medicine has been significant, their application to paediatric anaesthesia is not well characterised. As the paediatric operating room is a data-rich environment that requires critical clinical decision-making, this systematic review aims to characterise the current use of AI in paediatric anaesthesia and to identify barriers to the successful integration of such technologies.

Methods: This review was registered with PROSPERO (CRD42022304610), the international registry for systematic reviews. The search strategy was prepared by a librarian and run in five electronic databases (Embase, Medline, Central, Scopus, and Web of Science). Collected articles were screened by two reviewers. Included studies described the use of AI for paediatric anaesthesia (<18 yr old) within the perioperative setting.

Results: From 3313 records identified in the initial search, 40 were included in this review. Identified applications of AI were described for patient risk factor prediction (24 studies; 60%), anaesthetic depth estimation (2; 5%), anaesthetic medication/technique decision guidance (2; 5%), intubation assistance (1; 2.5%), airway device selection (3; 7.5%), physiological variable monitoring (6; 15%), and operating room scheduling (2; 5%). Multiple domains of AI were discussed including machine learning, computer vision, fuzzy logic, and natural language processing.

Conclusion: There is an emerging literature regarding applications of AI for paediatric anaesthesia, and their clinical integration holds potential for ultimately improving patient outcomes. However, multiple barriers to their clinical integration remain including a lack of high-quality input data, lack of external validation/evaluation, and unclear generalisability to diverse settings.

Systematic review protocol: CRD42022304610 (PROSPERO).

Keywords: artificial intelligence; critical care; machine learning; paediatric anaesthesia; perioperative medicine.

Fig 1

PRISMA flow diagram. PRISMA, Preferred Reporting Items for Systematic Reviews and Meta-Analyses.

Fig 2

Mapping of artificial intelligence applications in paediatric anaesthesia by application type. Number of studies describing applications within each category are indicated in parentheses. Percentages represent the proportion of applications within such application category using specified artificial intelligence branch. DLM, dynamic linear model; KMC, K-means classifier; KNN, K-nearest classifier; NLP, natural language processing; SVM, support vector machine.

Fig 3

Identified barriers to the clinical implementation of described artificial intelligence applications within paediatric anaesthesia.

Fig 4

Risk of bias assessment of included studies using the TIDieR checklist for reporting of interventions (left) and the PROBAST checklist for reporting of prediction models (right). PROBAST, Prediction Model Study Risk of Bias Assessment Tool; TIDieR, Template for Intervention Description and Replication.