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Background/Aim: The worldwide population is increasingly aging. Maxillofacial fractures of the geriatric population have been increased. Evaluation of the demographic variables, causes and the patterns of maxillofacial traumas in the elderly population is the main aim of this study. Materials and Methods: Seven hundred thirteen maxillofacial tomography images which were scanned between 2010 and 2019 were evaluated. Data from 50 patients aged 65 years old and/or older, who were treated for maxillofacial fracture at the Department of Otorhinolaryngology, Gaziantep University, were retrospectively analyzed. Two groups were created according to the facial fracture pattern. Facial fractures were reclassified into 2 groups; mandibula, orbital, zygomaticomaxillary complex group fractures and the other group of frontal, naso-orbito-ethmoid fractures and were used as a comparison. Results: The mean age of the patients was 72.5 (min 65- max 93). The gender distribution was 17 females (34%) and 33 males (66%). The most common fractured bone was the nasal bone and the least one is the frontal bone. Approximately one-quarter of 50 fractures were seen in 70 to 79 years old. Falling is more common in females and men are more prone to work-related accidents than home-related accidents. Conclusion: Facial fractures in the elderly often seen in midface location. Falling is the common etiology of facial fracture in all genders at elderly. However, male dominance is seen in other etiological factors. Additional diseases in the elderly seem to increase the severity of facial fracture.

Nations Development Programme has been regularly publishing the Human Development Index, which attempts to include all countries, in the form of Human Development Reports since 1990. This study aims to compare the status of Turkey in terms of human development and its performance over the years with those of nearby blocs by using Human Development Reports. Human Development Index scenarios created for Turkey are also introduced in the study.

Background: This study was retrospectively conducted to evaluate the postoperative surgical results of our patients with anterior skull base mass, defect, and/or cerebrospinal fluid rhinorrhea who underwent reconstruction via endoscopic endonasal approach and to share our experiences. Methods: Sociodemographic features of patients who had undergone endoscopic surgery in our clinic due to anterior skull base mass, defect, or rhinorrhea were evaluated in terms of etiological factors, surgical method, pathology, postoperative complications, need for revision surgery and comorbid disease. Results: A total of 131 patients were included; 76 were male and mean age was 36.2 years. Endoscopic endonasal surgery was performed for nasal mass (70.2%) in 92 cases, rhinorrhea (17.6%) in 23 cases, chronic sinusitis (7.6%) in 10 cases, and gunshot injury (4.6%) in 6 cases. After surgery, benign mass pathology was detected in 75 patients and malignant mass pathology was detected in 23 patients. Osteoma was the most common among benign formations, and squamous cell carcinoma was the most common among malignant formations. The most common cause of surgical revision was nasal masses (25 cases, 77.4%). Conclusion: Endoscopic intracranial interventions and increasedanterior skull base surgery are garnering increased interest of physicians as endoscopic approaches are gaining popularity in recent years. Successful results are achieved through appropriate diagnostic methods and endoscopic approaches. Success rates will be further increased due to developing technology and imaging methods, while the risk of complications and revision surgery will be further reduced.

Deep neural network-based diagnostic tools have gained state-of-the-art performance in the medical field in recent years. Diagnostic accuracy has become very critical for medical treatments. This paper proposes a simple and novel deep learning-based system for the analysis of paranasal sinuses conditions. In this work, we focus on analysing the paranasal sinuses on CT images automatically, providing physicians with high-accuracy diagnosis. The proposed system enables one to reduce the number of images to be searched in a CT scan for a patient automatically, and also it provides automatic segmentation for marking and cropping the paranasal sinuses region. Thus, the proposed system significantly decreases the data required in the training phase with a gain in computational efficiency while maintaining high-accuracy performance. The proposed algorithm also makes the required segmentation automatically without manual cropping and yields outstanding performance on detecting abnormalities in the sinuses. The proposed approach has been tested on real CT images and achieved an accuracy rate of 98.52 % with a sensitivity of 100 %.