Background: Three-dimensional facial scan technologies, such as stereophotogrammetry, are frequently used in oral and maxillofacial surgery, dentistry, and plastic surgery to assess patient outcomes and plan surgical procedures. Most facial scanners rely on stationary clinical systems, which provide high accuracy and reliability in generating 3D representations of the human face; however, they are cost-intensive and immobile. Recently, smartphone-based facial scan applications that use stereophotogrammetry have been developed. These applications are cost-effective and more accessible than existing stationary systems. Methods: In this study, we compared the accuracy of three different smartphone applications—EM3D, Polycam, and ScandyPro—on an iPhone 14 Pro, which utilizes Apple’s LiDAR (Light Detection and Ranging) technology, to a stationary system by 3DMD, which is considered a gold standard in many studies. Results: The applications included in the study have demonstrated the capability to perform accurately in clinical settings, with mean surface distances of 1.46 mm for EM3D, 1.66 mm for Polycam, and 1.61 mm for ScandyPro. Conclusions: These results suggest that smartphone-based facial scan systems could be a viable alternative, particularly in resource-limited settings. However, further research and collaboration between academia and industry are necessary to fully integrate these technologies into clinical practice.

Temporal hollowing, which is a depression in the temple region, often results from trauma, surgical interventions, or neurological conditions. This condition is frequently observed after the resection of encephaloceles, where it can cause esthetic and functional challenges due to temporalis muscle atrophy and nerve palsy. We present a case of a 21-year-old female patient who developed temporal hollowing and complete atrophy of the right temporalis muscle following an encephalocele resection in childhood. The patient also suffered from right-sided frontal nerve branch palsy. To address this complex deformity, a patient-specific implant (PSI) made of hydroxyapatite (HA) was digitally designed and produced using 3D printing technology. The postoperative course was uneventful, with the implant securely positioned and the esthetic result highly satisfactory. This case highlights the potential of 3D printed PSIs in craniofacial reconstruction, offering an optimal solution for both functional restoration and esthetic enhancement. HA further ensures the long-term stability and integration of the implant, providing a promising approach for addressing complex craniofacial defects.

Skull segmentation in magnetic resonance imaging (MRI) is essential for cranio-maxillofacial (CMF) surgery planning, yet manual approaches are time-consuming and error-prone. Computed tomography (CT) provides superior bone contrast but exposes patients to ionizing radiation, which is particularly concerning in pediatric care. This study presents an AI-based workflow that enables skull segmentation directly from routine MRI. Using 186 paired CT–MRI datasets, CT-based segmentations were transferred to MRI via multimodal registration to train dedicated deep learning models. Performance was evaluated against manually segmented CT ground truth using Dice Similarity Coefficient (DSC), Mean Surface Distance (MSD), and Hausdorff Distance (HD). AI achieved higher performance on CT (DSC 0.981) than MRI (DSC 0.864), with MSD and HD also favoring CT. Despite lower absolute accuracy on MRI, the approach substantially improved segmentation quality compared with manual MRI methods, particularly in clinically relevant regions. This automated method enables accurate skull modeling from standard MRI without radiation exposure or specialized sequences. While CT remains more precise, the presented framework enhances MRI utility in surgical planning, reduces manual workload, and supports safer, patient-specific treatment, especially for pediatric and trauma cases.