Obtaining 3D craniofacial morphometric data is essential in a variety of medical and educational disciplines. In this study, we explore smartphone-based photogrammetry with photos and video recordings as an effective tool to create accurate and accessible metrics from head 3D models. The research involves the acquisition of craniofacial 3D models on both volunteers and head mannequins using a Samsung Galaxy S22 smartphone. For the photogrammetric processing, Agisoft Metashape v 1.7 and PhotoMeDAS software v 1.7 were used. The Academia 50 white-light scanner was used as reference data (ground truth). A comparison of the obtained 3D meshes was conducted, yielding the following results: 0.22 ± 1.29 mm for photogrammetry with camera photos, 0.47 ± 1.43 mm for videogrammetry with video frames, and 0.39 ± 1.02 mm for PhotoMeDAS. Similarly, anatomical points were measured and linear measurements extracted, yielding the following results: 0.75 mm for photogrammetry, 1 mm for videogrammetry, and 1.25 mm for PhotoMeDAS, despite large differences found in data acquisition and processing time among the four approaches. This study suggests the possibility of integrating photogrammetry either with photos or with video frames and the use of PhotoMeDAS to obtain overall craniofacial 3D models with significant applications in the medical fields of neurosurgery and maxillofacial surgery.

The assessment of cranial deformation is relevant in the field of medicine dealing with infants, especially in paediatric neurosurgery and paediatrics. To address this demand, the smartphone-based solution PhotoMeDAS has been developed, harnessing mobile devices to create three-dimensional (3D) models of infants’ heads and, from them, automatic cranial deformation reports. Therefore, it is crucial to examine the accuracy achievable with different mobile devices under similar conditions so prospective users can consider this aspect when using the smartphone-based solution. This study compares the linear accuracy obtained from three smartphone models (Samsung Galaxy S22 Ultra, S22, and S22+). Twelve measurements are taken with each mobile device using a coded cap on a head mannequin. For processing, three different bundle adjustment implementations are tested with and without self-calibration. After photogrammetric processing, the 3D coordinates are obtained. A comparison is made among spatially distributed distances across the head with PhotoMeDAS vs. ground truth established with a Creaform ACADEMIA 50 while-light 3D scanner. With a homogeneous scale factor for all the smartphones, the results showed that the average accuracy for the S22 smartphone is −1.15 ± 0.53 mm, for the S22+, 0.95 ± 0.40 mm, and for the S22 Ultra, −1.8 ± 0.45 mm. Worth noticing is that a substantial improvement is achieved regardless of whether the scale factor is introduced per device.

Facial asymmetry presents a significant challenge for health practitioners, including physicians, dentists, and physical therapists. Manual measurements often lack the precision needed for accurate assessments, highlighting the appeal of imaging technologies like structured light scanners and photogrammetric systems. However, high-end commercial systems remain cost prohibitive, especially for public health services in developing countries. This study aims to evaluate cell-phone-based photogrammetric methods for generating 3D facial models to detect facial asymmetries. For this purpose, 15 patients had their faces scanned with the ACADEMIA 50 3D scanner, as well as with cell phone images and videos using photogrammetry and videogrammetry, resulting in 3D facial models. Each 3D model (coming from a 3D scanner, photogrammetry, and videogrammetry) was half-mirrored to analyze dissimilarities between the two ideally symmetric face sides using Hausdorff distances between the two half-meshes. These distances were statistically analyzed through various measures and hypothesis tests. The results indicate that, in most cases, both photogrammetric and videogrammetric approaches are as reliable as 3D scanning for detecting facial asymmetries. The benefits and limitations of using images, videos, and 3D scanning are also presented.

Cranial 3D models are essential for the analysis and early detection of deformities in infants. While they have traditionally been obtained using manual methods or 3D scanners, mobile photogrammetry offers an accessible and precise alternative. This study examines the use of PhotoMeDAS (Photogrammetric Medical Deformation Assessment Solutions), a mobile-based solution for generating 3D models with the accuracy required to assess and plan corrective interventions. Additionally, the potential of Artificial Intelligence (AI) for classifying cranial deformities is explored, although its implementation faces the challenge of limited data availability. To address this limitation, the generation of semi-synthetic data from 3D point clouds obtained through mobile photogrammetry is proposed, using a total of 60 cases (actual data). These data are classified according to different cranial pathologies, introducing controlled variations along the X, Y, and Z axes to simulate morphological changes. As a result, 2700 cases were generated, enriching the available datasets and facilitating the evaluation and application of classification algorithms such as decision trees, Random Forest, and multilayer perceptron neural networks. Preliminary results highlight the potential of PhotoMeDAS as a practical and cost-effective tool, provided that AI is efficiently integrated into future versions. However, further validation is required to confirm its applicability in actual clinical environments.