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Purpose: To investigate the anatomical variations and measure the distances between surgically relevant structures in the maxilla associated with Le Fort I osteotomy in patients with maxillary canting using cone beam computed tomography (CBCT).

Methods: CBCT scans of 63 patients (21 males and 42 females) with maxillary canting who were indicated for orthognathic surgical planning were retrospectively investigated and analyzed. The distances of the relevant anatomical structures, including the descending palatine artery, pterygomaxillary junction, infraorbital foramen, and nasolacrimal duct opening, were measured with the key anatomical landmarks to evaluate their variations.

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Objective: To investigate the effects of modified twin-block appliances (MTBA) on obstructive sleep apnea (OSA) and mandibular retrognathia and the changes in the upper airway, hyoid bone position, and hypoxia-related inflammatory marker levels in children with OSA.

Methods: This study included children with OSA and mandibular retrognathia and those with class I without mandibular retrognathia (n = 35 each). The experimental group comprised children with OSA and mandibular retrognathia managed using MTBA.

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Background: Cemento-osseous dysplasia (COD) is the most common apical radiopaque lesion that develops in the tooth-bearing area. However, large, destructive lesions are rare. Herein, we report a case in which COD extended to bilateral condyles, affecting the entire mandible, and was managed with denosumab rather than surgical resection.

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Emergency care units in Hungary treat approximately 140-180 thousand head injuries of varying se'verity each year. These head injuries are mainly caused by traffic accidents, assaults, or domestic accidents. The outpatient care record contains details about the circumstances and underlying mechanisms of the head injury, the results of physical and imaging examinations, and therapeutic recommendations.

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Early prediction of recurrence in high-grade glioma (HGG) is critical due to its aggressive nature and poor prognosis. Distinguishing true recurrence from treatment-related changes, such as radionecrosis, is a major diagnostic challenge. Machine learning (ML) offers a novel approach, leveraging advanced algorithms to analyze complex imaging data with high precision.

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