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Updated: Sep 11, 2026

Accuracy in Dental Medicine, A New Way to Measure Trueness and Precision
Published on: April 29, 2014
Trueness and scanning time of smartphone-based photogrammetry applications: an in vitro comparative study
Gennaro Ruggiero1, Vincenzo Vallefuoco1, Roberto Sorrentino1
1Department of Neurosciences, Reproductive and Odontostomatological Sciences, Division of Prosthodontics, Scientific Unit of Digital Dentistry, University "Federico II" of Naples, Naples, 80131, Italy.
Objective:
To compare the global and regional trueness, native-scale usability, and full-face scanning time of three smartphone-based photogrammetry applications.
Methods:
Thirty mannequin-head scans (n=10/application) were acquired with an iPhone 17 Pro using Polycam v.5.2.0, Qlone Dental v.1.0.14, and KIRI Engine v.4.1.1 under an identical 60-frame protocol. An ATOS Q scan served as the metrological reference. Global and facial-third RMS cloud-to-mesh deviations were calculated after scale-adjusted iterative closest point registration. Scanning time and the need for external rescaling were recorded (α=.05; Tukey pairwise tests).
Results:
Global RMS trueness did not differ significantly among applications (P=.059; η²=.189). Qlone Dental showed lower RMS deviations than Polycam in the upper (0.45 ±0.17 mm versus 0.76 ±0.37 mm; P=.025) and lower facial thirds (0.68 ±0.07 mm versus 0.97 ±0.30 mm; P=.009). Only Qlone Dental in the upper third had a 95% confidence interval entirely below the 0.6-mm virtual-facebow benchmark. All Qlone Dental exports retained metric scale, whereas all Polycam and KIRI Engine exports required rescaling. Qlone Dental had the shortest scanning time (13.17 ±1.08 seconds; P<.001).
Conclusions:
Under standardized static conditions, none of the applications reliably achieved global RMS values below the 0.6-mm benchmark. The tested applications should not presently be considered standalone metric references for virtual facebow transfer; their more defensible role is in visualization, documentation, and communication workflows.
Clinical Significance:
A controlled same-device comparison identified application-dependent trade-offs in regional reconstruction, native-scale usability, and capture speed.

