Schwendicke, Falk; Mertens, Sarah; Cantu, Anselmo Garcia; Chaurasia, Akhilanand; Meyer-Lueckel, Hendrik; Krois, Joachim (2022). Cost-effectiveness of AI for caries detection: randomized trial. Journal of dentistry, 119, p. 104080. Elsevier 10.1016/j.jdent.2022.104080
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OBJECTIVES
We assessed the cost-effectiveness of AI-supported detection of proximal caries in a randomized controlled clustered cross-over superiority trial.
METHODS
Twenty-three dentists were sampled to assess 20 bitewings; 10 were randomly evaluated supported by an AI-based software (dentalXrai Pro 1.0.4, dentalXrai Ltd, Berlin, Germany) and the other 10 without AI support. The reference test had been established by four independent experts and an additional review. We evaluated the proportion of true and false positive and negative detections and the treatment decisions assigned to each detection (non-invasive, micro-invasive, invasive). Cost-effectiveness was assessed using a mixed public-private-payer perspective in German healthcare. Using the accuracy and treatment decision data from the trial, a Markov simulation model was populated and posterior permanent teeth in initially 31-years old individuals followed over their lifetime. The model allowed extrapolation from the initial detection and therapy to treatment success, re-treatments and, eventually, tooth loss and replacement, capturing long-term effectiveness (tooth retention) and costs (cumulative in Euro). Costs were estimated using the German public and private fee catalogues. Monte-Carlo microsimulations were used and incremental cost-effectiveness at different willingness-to-pay ceiling thresholds assessed.
RESULTS
In the trial, AI-supported detection was significantly more sensitive than detection without AI. However, in the AI group, lesions were more often treated invasively. As a result, AI and no AI showed identical effectiveness (tooth retention for a mean (2.5-97.5%) 49 (48-51) years) and nearly identical costs (AI: 330 (250-409) Euro, no AI: 330 (248-410) Euro). 41% simulations found AI and 43% no AI to be more cost-effective. The resulting cost-effectiveness remained uncertain regardless of a payer's willingness-to-pay.
CONCLUSIONS
Higher accuracy of AI did not lead to higher cost-effectiveness, as more invasive treatment approaches generated costs and diminished possible effectiveness advantages.
CLINICAL SIGNIFICANCE
The cost-effectiveness of AI could be improved by supporting not only caries detection, but also subsequent management.
Item Type: |
Journal Article (Original Article) |
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Division/Institute: |
04 Faculty of Medicine > School of Dental Medicine > Department of Preventive, Restorative and Pediatric Dentistry |
UniBE Contributor: |
Meyer-Lückel, Hendrik |
Subjects: |
600 Technology > 610 Medicine & health |
ISSN: |
1879-176X |
Publisher: |
Elsevier |
Language: |
English |
Submitter: |
Daniela Zesiger |
Date Deposited: |
20 Dec 2022 12:02 |
Last Modified: |
25 Dec 2022 02:12 |
Publisher DOI: |
10.1016/j.jdent.2022.104080 |
PubMed ID: |
35245626 |
Uncontrolled Keywords: |
Artificial Intelligence Caries detection/diagnosis/prevention Computer Simulation Decision-Making Dental Economic Evaluation Radiology |
BORIS DOI: |
10.48350/176064 |
URI: |
https://boris.unibe.ch/id/eprint/176064 |