Publications by authors named "S J Berge"
Objectives: The main objective was to develop and evaluate an artificial intelligence model for tooth segmentation in magnetic resonance (MR) scans.
Methods: MR scans of 20 patients performed with a commercial 64-channel head coil with a T1-weighted 3D-SPACE (Sampling Perfection with Application Optimized Contrasts using different flip angle Evolution) sequence were included. Sixteen datasets were used for model training and 4 for accuracy evaluation.
Objectives: In orthognatic surgery, one of the primary determinants for reliable three-dimensional virtual surgery planning (3D VSP) and an accurate transfer of 3D VSP to the patient in the operation room is the condylar seating. Incorrectly seated condyles would primarily affect the accuracy of maxillary-first bimaxillary osteotomies as the maxillary repositioning is dependent on the positioning of the mandible in the cone-beam computed tomography (CBCT) scan. This study aimed to develop and validate a novel tool by utilizing a deep learning algorithm that automatically evaluates the condylar seating based on CBCT images as a proof of concept.
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- The study aimed to evaluate facial asymmetry following bimaxillary surgery in patients with mild craniofacial microsomia (CFM) compared to those with non-syndromic class II asymmetry.
- Cone-beam CT scans were used to analyze the lower facial asymmetry in three groups: mild CFM, non-syndromic class II asymmetry, and normal controls, revealing that both CFM and class II groups had more significant asymmetry than controls.
- Post-surgery, while no major differences in certain asymmetry metrics were found between CFM and class II groups, CFM patients showed greater vertical contour asymmetry, highlighting ongoing challenges in correcting these features in CFM patients.
After nasal bone fractures, fractures of the mandible are the most frequently encountered injuries of the facial skeleton. Accurate identification of fracture locations is critical for effectively managing these injuries. To address this need, JawFracNet, an innovative artificial intelligence method, has been developed to enable automated detection of mandibular fractures in cone-beam computed tomography (CBCT) scans.
View Article and Find Full Text PDFObjectives: Diagnosing oral potentially malignant disorders (OPMD) is critical to prevent oral cancer. This study aims to automatically detect and classify the most common pre-malignant oral lesions, such as leukoplakia and oral lichen planus (OLP), and distinguish them from oral squamous cell carcinomas (OSCC) and healthy oral mucosa on clinical photographs using vision transformers.
Methods: 4,161 photographs of healthy mucosa, leukoplakia, OLP, and OSCC were included.