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Cost-effectiveness and environmental impact of artificial intelligence-assisted colonoscopy with the
Lyndon V Hernandez1, Michael B Wallace2, Daniel von Renteln3
1GI Associates, LLC, Gastroenterology, Wisconsin, United States, Kenosha.
Background:
Artificial intelligence (AI)-assisted colonoscopy reduces costs in colorectal cancer screening, but its incremental value relative to non-AI resect-and-discard strategies for diminutive polyps (≤5 mm) remains unclear. We aimed to compare the clinical, economic, and environmental outcomes of resect-and-discard strategies both with and without AI assistance with the standard of care (SoC).
Methods:
In our Markov model simulating 6 million individuals aged ≥45 years undergoing screening colonoscopy, four strategies were evaluated: SoC; non-AI resect and discard (nonAI-RD); computer-aided detection (CADe)-assisted resect and discard (CADe-RD); and combined CADe and computer-aided diagnosis (CADx)-assisted resect and discard (CADe+CADx-RD). The primary outcome was total quality-adjusted life years (QALYs), with secondary outcomes including cost and carbon dioxide (CO2) emissions.
Results:
NonAI-RD achieved the lowest total cost ($36.6 billion) and carbon emissions (0.90 million kgCO2while yielding the highest QALYs (51.37 million), although differences in QALYs across strategies were small (<0.01%). CADe-RD and CADe+CADx-RD remained cost-saving relative to SoC ($36.9 billion and $37.4 billion vs. $37.8 billion, respectively), but were associated with higher costs than nonAI-RD. CADe modestly increased adenoma detection without improving QALYs, while the addition of CADx increased costs and emissions and resulted in slightly lower QALYs. Sensitivity analysis showed that CADe+CADx-RD matched or exceeded the QALYs for CADe-RD only within a limited region of high diagnostic performance. At currently reported CADx performance levels, CADe+CADx-RD yielded lower QALYs and higher costs than CADe-RD. CADe+CADx-RD achieved noninferiority in QALYs relative to CADe-RD only within a limited region of high diagnostic performance.
Conclusions:
In our nondeterministic model, nonAI-RD provided the optimal balance of clinical benefit, cost savings, and lower environmental impact.
