Artificial intelligence in bronchoscopy: a systematic review.
Authors
Affiliations (9)
Affiliations (9)
- Copenhagen Academy for Medical Education and Simulation (CAMES), Center for HR & Education, The Capital Region of Denmark, Copenhagen, Denmark [email protected].
- Department of Clinical Medicine, Faculty of Health and Medical Sciences, University of Copenhagen, Copenhagen, Denmark.
- Copenhagen Academy for Medical Education and Simulation (CAMES), Center for HR & Education, The Capital Region of Denmark, Copenhagen, Denmark.
- Department of Respiratory Medicine, Odense University Hospital, Odense, Denmark.
- Odense Respiratory Research Unit (ODIN), Department of Clinical Research, University of Southern Denmark, Odense, Denmark.
- Gastrounit, Surgical Section, Copenhagen University Hospital - Amager and Hvidovre, Hvidovre, Denmark.
- Chelsea and Westminster Hospital, London, UK.
- Royal Brompton Hospital, London, UK.
- Faculty of Medicine, Imperial College London, London, UK.
Abstract
Artificial intelligence (AI) systems have been implemented to improve the diagnostic yield and operators' skills within endoscopy. Similar AI systems are now emerging in bronchoscopy. Our objective was to identify and describe AI systems in bronchoscopy. A systematic review was performed using MEDLINE, Embase and Scopus databases, focusing on two terms: bronchoscopy and AI. All studies had to evaluate their AI against human ratings. The methodological quality of each study was assessed using the Medical Education Research Study Quality Instrument (MERSQI). 1196 studies were identified, with 20 passing the eligibility criteria. The studies could be divided into three categories: nine studies in airway anatomy and navigation, seven studies in computer-aided detection and classification of nodules in endobronchial ultrasound, and four studies in rapid on-site evaluation. 16 were assessment studies, with 12 showing equal performance and four showing superior performance of AI compared with human ratings. Four studies within airway anatomy implemented their AI, all favouring AI guidance to no AI guidance. The methodological quality of the studies was moderate (mean MERSQI 12.9 points, out of a maximum 18 points). 20 studies developed AI systems, with only four examining the implementation of their AI. The four studies were all within airway navigation and favoured AI to no AI in a simulated setting. Future implementation studies are warranted to test for the clinical effect of AI systems within bronchoscopy.