Evaluating the Accuracy and Repeatability of Mobile 3D Imaging Applications for Breast Phantom Reconstruction

• Published in: Sensors (Basel, Switzerland) Vol. 25; no. 15; p. 4596
• Main Authors: Botti, Elena, Jansen, Bart, Ballen-Moreno, Felipe, Kapila, Ayush, Brahimetaj, Redona
• Format: Journal Article
• Language: English
• Published: Switzerland MDPI AG 24.07.2025; MDPI
• Subjects: 3D imaging technologies; 3D mesh reconstruction; Accuracy; Algorithms; Anthropometry; Breast - diagnostic imaging; Breast - surgery; breast reconstructive surgery; Cameras; Datasets; Female; Humans; Image Processing, Computer-Assisted; Imaging, Three-Dimensional - methods; Mammaplasty - methods; mobile 3D scanning; Mobile Applications; Phantoms, Imaging; Photogrammetry; plastic surgery; Reproducibility; Reproducibility of Results; Scanners; Sensors; Silicones; Smartphones; Software; Three dimensional imaging; mobile 3D scanning; breast reconstructive surgery; plastic surgery; 3D imaging technologies; 3D mesh reconstruction
• ISSN: 1424-8220; 1424-8220
• DOI: 10.3390/s25154596

Three-dimensional imaging technologies are increasingly used in breast reconstructive and plastic surgery due to their potential for efficient and accurate preoperative assessment and planning. This study systematically evaluates the accuracy and consistency of six commercially available 3D scanning applications (apps)—Structure Sensor, 3D Scanner App, Heges, Polycam, SureScan, and Kiri—in reconstructing the female torso. To avoid variability introduced by human subjects, a silicone breast mannequin model was scanned, with fiducial markers placed at known anatomical landmarks. Manual distance measurements were obtained using calipers by two independent evaluators and compared to digital measurements extracted from 3D reconstructions in Blender software. Each scan was repeated six times per application to ensure reliability. SureScan demonstrated the lowest mean error (2.9 mm), followed by Structure Sensor (3.0 mm), Heges (3.6 mm), 3D Scanner App (4.4 mm), Kiri (5.0 mm), and Polycam (21.4 mm), which showed the highest error and variability. Even the app using an external depth sensor (Structure Sensor) showed no statistically significant accuracy advantage over those using only the iPad’s built-in camera (except for Polycam), underscoring that software is the primary driver of performance, not hardware (alone). This work provides practical insights for selecting mobile 3D scanning tools in clinical workflows and highlights key limitations, such as scaling errors and alignment artifacts. Future work should include patient-based validation and explore deep learning to enhance reconstruction quality. Ultimately, this study lays the foundation for more accessible and cost-effective 3D imaging in surgical practice, showing that smartphone-based tools can produce clinically useful scans.