Energy and carbon cost of training dental image classifiers: how model size affects efficiency

Leodanis Hernández Cabrera, Leodanishernandezcabrera1@gmail.com, Maikro Osvaldo Chávez Moya, Maikroilv@gmail.com, Neisy Pérez Ramos and Neisyperezramos@gmail.com

SAP Artificial Intelligence in Dentistry, 2025

Abstract: Introduction: Artificial intelligence in dentistry is growing, but the energy and carbon cost of training dental image classifiers is rarely reported, so researchers and clinicians have no basis for choosing a model size that balances accuracy against environmental cost. Methods: Four pretrained models of increasing size (MobileNetV2, ResNet18, ResNet34, ResNet50) were fine-tuned on the same 182-tooth, four-class diagnosis task from the DENTEX dataset under an identical protocol. Training energy and carbon emissions were measured with the CodeCarbon library, and accuracy was evaluated on a held-out set. Results: Energy increased from 0.26 mWh for MobileNetV2 to 0.86 mWh for ResNet50, a 3.30-fold difference across a tenfold range in parameters. Accuracy did not rise with size: ResNet18 reached 62.20% accuracy, matching ResNet50 at 62.20% while using roughly one third of its energy. ResNet18 achieved the highest accuracy per unit energy. Conclusions: For dental image classification at this scale, larger models add energy and carbon without adding accuracy, and the smallest adequate model is the efficient choice. Reporting energy alongside accuracy should become routine when dental AI models are developed.

Date: 2025
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Persistent link: https://EconPapers.repec.org/RePEc:cwf:aidart:aid202543

DOI: 10.62486/aid202543

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