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An alternative approach to code, store, and regenerate 3D data in dental medicine using open-source software: A
Mahmoud E Elbashti1, Marta Macarena Paz-Cortes2, Giovanni Giovannini2
1Department of Advanced Prosthodontics, Graduate School of Medical and Dental Sciences, Institute of Science Tokyo, Tokyo, Japan and Faculty of Dentistry, University of Zawia, Zawia, Libya.
Purpose:
To develop a scripting-based technique for managing three-dimensional (3D) dental data and evaluate the regenerated standard tessellation language (STL) data in terms of file size, accuracy (trueness and precision), and processing time.
Materials And Methods:
Ten STL dental and maxillofacial models were obtained from various imaging technologies, including intraoral scanners, computer-aided design (CAD) software, and cone-beam computed tomography (CBCT), and saved as STL files. ChatGPT was used to generate Python scripts in Blender for mesh simplification and data compression, which were then saved as .py files. The models were regenerated from these scripts in Blender, and their accuracy was assessed using GOM Inspect software, comparing trueness and precision. Statistical analysis, including Kruskal-Wallis and Mann-Whitney tests, was conducted to evaluate differences in file sizes between the original, Python-generated, and regenerated STL files, with statistical analyses performed at a level of significance α=0.05.
Results:
The scripting-based technique was successfully utilized in ChatGPT to generate Python script code for accessing comprehensive data on STL models, utilizing Blender's scripting functionality. This approach enabled the generation, regeneration, and visualization of STL models, resulting in significantly smaller file sizes for both the Python script and regenerated STL files compared to the original STL files (p < 0.001). No significant differences in trueness were observed, with deviations ranging from 0.0 µm to 6.8 µm, and all regenerated STL models demonstrated perfect precision. Additionally, a proportional relationship was noted between the original STL file sizes and processing times.
Conclusions:
The scripting-based approach proved to be effective in coding, storing, and regenerating STL dental data with reduced file sizes and efficient processing times without compromising the accuracy.
Clinical Significance:
Various STL dental models of patients can be coded, stored, and regenerated to be used again within efficient processing time without affecting the accuracy.
