Optimising scan body enhances accuracy of full-arch implant scan using a smartphone video with deep learning model: An in vitro study

Objective. A deep learning (DL) model was used to convert smartphone videos of a complete arch implant cast into 3D scans. The aim of current study was to determine if a custom scan body (SB) with geometric features and coating would outperform regular PEEK stock SB in this DL scenario. The DL-derived scan outcomes were compared with those obtained from a conventional splinted open-tray impression and from photogrammetry. Materials and Methods. A maxillary edentulous model with six implants and multi-unit abutment analogs was scanned using four protocols: conventional splinted open-tray impression (CO), photogrammetry (PG; Icam4D), DL using stock SBs (DLS) and DL using custom SBs (DLC). Each protocol was repeated for 10 times. The DL scans were produced from smartphone videos with a high-fidelity, multi-view 3D construction AI model (Neuralangelo). The custom designed SB incorporated geometric features and was fabricated via 3D printing followed by a spray coating. Accuracy (trueness and precision) was assessed using three measurements: Root Mean Square (RMS), linear deviation, and angular deviation. Results. DLC outperformed DLS in both trueness and precision regarding RMS and linear measurements (p<0.001). CO and PG demonstrated the highest RMS and linear trueness, with no significant difference between them (RMS: p=0.93; linear: p=0.663). PG achieved the best precision across RMS, linear and angular measurements. Conclusion. The optimised SB significantly improves the accuracy of DL-based approach for full-arch implant scan comparing to regular PEEK stock scan bodies. While early stage, neural surface reconstruction has potential as a viable option for full-arch implant rehabilitation.