Artificial intelligence-assisted grading for tear trough deformity

Abstract

BackgroundVarious classification systems for tear trough deformity (TTD) have been published, but their complexity can pose challenges in clinical use, especially for less experienced surgeons. It is believed that artificial intelligence (AI) technology can address some of these challenges by reducing inadvertent errors and improving the accuracy of medical practice. In this study, we aimed to establish a reliable and precise digital image grading model for TTD using smartphone-based photography, enhanced by AI deep learning technology. This model is designed to aid and guide surgeons, particularly those who are less experienced or from younger generations, during clinical examinations and in making decisions about further surgical interventions. Materials and MethodsA total of 504 patients and 983 photos were included in the study. We adopted the Barton’s grading system for TTD. All photos were taken with the same smartphone and processed and analyzed using the medical AI assistant (MAIATM) software. The photos were then randomly divided into two groups to establish training and testing models. ResultsThe confusion matrix for the training model demonstrated a sensitivity of 56%, specificity of 87.3%, an F1 score of 0.57, and an area under the curve (AUROC) of 0.85. For the testing group, the sensitivity was 49.3%, specificity was 85%, with an F1 score of 0.49, and an AUROC of 0.83. Representative heatmaps were also generated. ConclusionOur study is the first to demonstrate that tear trough deformities can be easily categorized using a built-in smartphone camera in conjunction with an AI deep learning program. This approach can reduce errors during clinical patient evaluations, particularly for less experienced practitioners.