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Updated: Aug 5, 2026
Accuracy in Dental Medicine, A New Way to Measure Trueness and Precision
Published on: April 29, 2014
Advancing 3D dental scanning: The use of photogrammetry with light detection and ranging for edentulous arches
Mohammad Ali Saghiri1, Ali Mohammad Saghiri2, Elham Samadi3
1Associate Professor, Biomaterial and Prosthodontic Laboratory, Department of Restorative Dentistry, Rutgers School of Dental Medicine, Rutgers Biomedical and Health Sciences, Rutgers University, Newark, NJ; and Adjunct Associate Professor, Department of Endodontics, Arthur A. Dugoni School of Dentistry, University of the Pacific, San Francisco, Calif.
Statement Of Problem:
The advent of computer-aided design and computer-aided manufacturing (CAD-CAM) has necessitated the acquisition of digital scans. However, there are limitations and problems with acquiring accurate 3-dimensional (3D) casts from edentulous patients, especially in the presence of saliva.
Purpose:
The purpose of this in vitro study was to develop a novel approach for obtaining 3D casts of edentulous arches by using 2-dimensional (2D) images as an alternative to traditional 3D scanners with and without light detection and ranging (LiDAR).
Material And Methods:
This study comprised 6 groups, each consisting of 10 specimens. For the control group, 3D casts were generated by scanning edentulous mandibular molds using a dental laboratory scanner. Experimental groups included photogrammetry with and without LiDAR under various conditions (Groups PG360, PG120, LPG120, PG360S, LPG120S). For Groups PG120, LPG120, and LPG120S, a custom-made manikin was used. In all photogrammetry groups, images of each mold were captured with a mobile phone (iPhone 14 Pro Max). The casts from the experimental groups were superimposed onto those from the control group using the Blender Foundation software program (Version 3.6.1). The mean distances were calculated and statistically analyzed using 1-way ANOVA followed by the post hoc Tukey test (α=.05).
Results:
The mean distances between the experimental groups and the control group varied significantly. The PG360 and PG120 groups showed a statistically significant difference from the control group (P<.001, 95% CI), with mean distances of 1.54 ±0.31 mm and 4.54 ±1.65 mm, respectively. The LPG120S group, which combined photogrammetry with LiDAR in the presence of artificial saliva, achieved a mean distance of 2.03 ±0.46 mm, which was not significantly different from the control group (P=.501, 95% CI).
Conclusions:
The successful scanning of edentulous mandibular molds using a mobile phone was achieved through a combination of 2D images and LiDAR, covering a limited access angle of 120 degrees. Compared with other techniques, the method developed the most accurate 3D casts and was less susceptible to interference from saliva, a significant issue for intraoral scanners.
