Smartphone Scanning and Machine Learning for Automated Presurgical 3D-printed Plate Fabrication From Cleft Impressions.

Abstract

Capturing accurate palatal impressions in newborns with cleft lip and palate is essential for early intervention, yet traditional methods using plaster casts are often challenging and uncomfortable for infants. We present an innovative digital workflow utilizing a smartphone scanning application and a machine learning tool for presurgical plate generation. This protocol employs an open-source mobile app to capture high-resolution 3-dimensional scans of the infant's palate directly at the point of care. The process includes positioning of the infant, scanning technique, and file exportation. The resulting 3-dimensional model is then processed through a custom machine learning tool, which automates the design of a presurgical orthopedic plate with nasal stents. The method is simple, portable, and cost-effective, making it feasible in outpatient settings and especially advantageous for resource-limited environments. By eliminating the need for stone models and expensive intraoral scanners, this workflow streamlines the production of presurgical plates while enhancing patient comfort and safety. This digital approach introduces a new standard in early cleft care, offering a reproducible, accessible, and efficient protocol for clinicians aiming to modernize and simplify presurgical treatment.