ABSTRACT
Background: Artificial intelligence (AI) leverages today's exceptional computational powers and algorithmic abilities to learn from large data sets and solve complex problems. The aim of this study was to construct an AI model that can intelligently and reliably recognize the anatomy of cleft lip and nasal deformity and automate placement of nasolabial markings that can guide surgical design. Methods: We adopted the high-resolution net architecture, a recent family of convolutional neural networks-based deep learning architecture specialized in computer-vision tasks to train an AI model, which can detect and place the 21 cleft anthropometric points on cleft lip photographs and videos. The model was tested by calculating the Euclidean distance between hand-marked anthropometric points placed by an expert cleft surgeon to ones generated by our cleft AI model. A normalized mean error (NME) was calculated for each point. Results: All NME values were between 0.029 and 0.055. The largest NME was for cleft-side cphi. The smallest NME value was for cleft-side alare. These errors were well within standard AI benchmarks. Conclusions: We successfully developed an AI algorithm that can identify the 21 surgically important anatomic landmarks of the unilateral cleft lip. This model can be used alone or integrated with surface projection to guide various cleft lip/nose repairs. Having demonstrated the feasibility of creating such a model on the complex three-dimensional surface of the lip and nose, it is easy to envision expanding the use of AI models to understand all of human surface anatomy-the full territory and playground of plastic surgeons.
