Robustness of a deep-learning dental radiograph classifier to compression and resolution loss for teledentistry

Introduction: Teledentistry transmits radiographs through channels that compress and downsample images, but classifiers are almost always evaluated on uncompressed images. The gap between benchmark conditions and real transmission is rarely quantified. Methods: A tooth-level diagnostic classifier was built with transfer learning on 182 annotated teeth (four diagnosis classes) from 46 panoramic radiographs of the open DENTEX dataset, then re-evaluated after degrading the source images with JPEG compression (quality 75 to 10), downsampling (by 1/2 to 1/8) and Gaussian noise (sigma 10 to 50). Results: The classifier reached 67.30% accuracy on uncompressed images. Mild degradation was benign, and downsampling by half slightly improved accuracy to 72.70%. Severe degradation reduced accuracy, reaching 52.70% under JPEG quality 10, 54.50% under downsampling by 1/8, and 54.50% under the highest noise, corresponding to absolute losses of 14.50, 12.70 and 12.70 percentage points, respectively. Conclusions: A dental radiograph classifier tolerates the mild degradation typical of routine transmission but loses substantial accuracy under heavy compression, strong downsampling, and high noise. Transmission settings should therefore be treated as part of model specification rather than an afterthought.