Triage of Persons With Tuberculosis Symptoms Using Artificial Intelligence-Based Chest Radiograph Interpretation: A Cost-Effectiveness Analysis

Ntwali Placide Nsengiyumva; Hamidah Hussain; Olivia Oxlade; Arman Majidulla; Ahsana Nazish; Aamir J Khan; Dick Menzies; Faiz Ahmad Khan; Kevin Schwartzman

doi:10.1093/ofid/ofab567

Triage of Persons With Tuberculosis Symptoms Using Artificial Intelligence-Based Chest Radiograph Interpretation: A Cost-Effectiveness Analysis

Open Forum Infect Dis. 2021 Dec 15;8(12):ofab567. doi: 10.1093/ofid/ofab567. eCollection 2021 Dec.

Authors

Affiliations

¹ McGill International Tuberculosis Centre, Research Institute of the McGill University Health Centre, Montreal, Canada.
² Interactive Research and Development Global, Singapore.
³ Ghori Tuberculosis Clinic, Indus Hospital, Karachi, Pakistan.
⁴ Department of Medicine and Department of Epidemiology, Biostatistics and Occupational Health, McGill University, Montreal, Canada.

Abstract

Background: In settings without access to rapid expert radiographic interpretation, artificial intelligence (AI)-based chest radiograph (CXR) analysis can triage persons presenting with possible tuberculosis (TB) symptoms, to identify those who require additional microbiological testing. However, there is limited evidence of the cost-effectiveness of this technology as a triage tool.

Methods: A decision analysis model was developed to evaluate the cost-effectiveness of triage strategies with AI-based CXR analysis for patients presenting with symptoms suggestive of pulmonary TB in Karachi, Pakistan. These strategies were compared to the current standard of care using microbiological testing with smear microscopy or GeneXpert, without prior triage. Positive triage CXRs were considered to improve referral success for microbiologic testing, from 91% to 100% for eligible persons. Software diagnostic accuracy was based on a prospective field study in Karachi. Other inputs were obtained from the Pakistan TB Program. The analysis was conducted from the healthcare provider perspective, and costs were expressed in 2020 US dollars.

Results: Compared to upfront smear microscopy for all persons with presumptive TB, triage strategies with AI-based CXR analysis were projected to lower costs by 19%, from $23233 per 1000 persons, and avert 3%-4% disability-adjusted life-years (DALYs), from 372 DALYs. Compared to upfront GeneXpert, AI-based triage strategies lowered projected costs by 37%, from $34346 and averted 4% additional DALYs, from 369 DALYs. Reinforced follow-up for persons with positive triage CXRs but negative microbiologic tests was particularly cost-effective.

Conclusions: In lower-resource settings, the addition of AI-based CXR triage before microbiologic testing for persons with possible TB symptoms can reduce costs, avert additional DALYs, and improve TB detection.

Keywords: artificial intelligence; chest radiography; cost-effectiveness; deep learning; tuberculosis.