[Determination of anthropometric measurements in obstructive sleep apnea syndrome in Turkish population]. / Türk popülasyonunda obstrüktif uyku apne sendromunda rol oynayan antropometrik ölçümlerin belirlenmesi.

INTRODUCTION: In this study, we aimed to determine the values of anthropometric measurements and rates used in the evaluation of obstructive sleep apnea syndrome (OSAS) in our country. MATERIALS AND METHODS: Twenty accredited sleep centers in thirteen provinces participated in this multicenter prospective study. OSAS symptoms and polysomnographic examination and apnea-hypopnea index (AHI) ≥ 5 cases OSAS study group; patients with AHI < 5 and STOP-Bang < 2 were included as control group. Demographic characteristics (age, sex, body mass index-BMI) and anthropometric measurements (neck, waist and hip circumference, waist/hip ratio) of the subjects were recorded. RESULT: The study included 2684 patients (81.3% OSAS) with a mean age of 50.50 ± 0.21 years from 20 centers. The cases were taken from six geographical regions of the country (Mediterranean, Eastern Anatolia, Aegean, Central Anatolia, Black Sea and Marmara Region). Demographic characteristics and anthropometric measurements; age, neck, waist, hip circumference and waist/ hip ratios and BMI characteristics when compared with the control group; when compared according to regions, age, neck, waist, hip circumference and waist/hip ratios were found to be statistically different (p< 0.001, p< 0.001, p< 0.05, respectively). When compared by sex, age, neck and hip circumference, waist/hip ratio, height, weight and BMI characteristics were statistically different (p< 0.001, respectively). Neck circumference and waist/hip ratio were respectively 42.58 ± 0.10 cm, 0.99 ± 0.002, 39.24 ± 0.16 cm, 0.93 ± 0.004 were found in women. CONCLUSIONS: The neck circumference was lower than the standard value in men, but higher in women. The waist/hip ratio was above the ideal measurements in both men and women. In this context, the determination of the country values will allow the identification of patients with the possibility of OSAS and referral to sleep centers for polysomnography.

Deep Learning-Assisted Segmentation and Classification of Brain Tumor Types on Magnetic Resonance and Surgical Microscope Images.

OBJECTIVE: The primary aim of this research was to harness the capabilities of deep learning to enhance neurosurgical procedures, focusing on accurate tumor boundary delineation and classification. Through advanced diagnostic tools, we aimed to offer surgeons a more insightful perspective during surgeries, improving surgical outcomes and patient care. METHODS: The study deployed the Mask R-convolutional neural network (CNN) architecture, leveraging its sophisticated features to process and analyze data from surgical microscope videos and preoperative magnetic resonance images. Resnet101 and Resnet50 backbone networks are used in the Mask R-CNN method, and experimental results are given. We subsequently tested its performance across various metrics, such as accuracy, precision, recall, dice coefficient (DICE), and Jaccard index. Deep learning models were trained from magnetic resonance imaging and surgical microscope images, and the classification result obtained for each patient was combined with the weighted average. RESULTS: The algorithm exhibited remarkable capabilities in distinguishing among meningiomas, metastases, and high-grade glial tumors. Specifically, for the Mask R-CNN Resnet 101 architecture, precision, recall, DICE, and Jaccard index values were recorded as 96%, 93%, 91%, and 84%, respectively. Conversely, for the Mask R-CNN Resnet 50 architecture, these values stood at 94%, 89%, 89%, and 82%. Additionally, the model achieved an impressive DICE score range of 94%-95% and an accuracy of 98% in pathology estimation. CONCLUSIONS: As illustrated in our study, the confluence of deep learning with neurosurgical procedures marks a transformative phase in medical science. The results are promising but underscore diverse data sets' significance for training and refining these deep learning models.

Factors Affecting Development of Chronic Obstructive Pulmonary Disease in Pneumoconiosis Cases: A Cross-sectional Study Between 2017 and 2022 in Turkey.

OBJECTIVES: It was aimed to determine the factors affecting the development of chronic obstructive pulmonary disease (COPD) in pneumoconiosis cases. METHODS: Pneumoconiosis cases were divided into two groups as those with only pneumoconiosis and those with coexistence of pneumoconiosis and COPD. Demographic data, smoking habits, pulmonary function test, radiological findings, and occupational risk factors of the cases were compared. RESULTS: Chronic obstructive pulmonary disease was detected in 134 of 465 pneumoconiosis cases (28.8%) included in the study. It was determined that patients who developed COPD were statistically significantly older, had longer exposure duration, had lower forced expiratory volume in 1 second, forced vital capacity, and forced expiratory volume in 1 second/forced vital capacity values, and had more pulmonary symptoms. Chronic obstructive pulmonary disease development was more common in sandblasting workers, dental technicians, and miners than in other occupations. CONCLUSION: It has been shown that the risk of developing COPD is high in cases of pneumoconiosis, independent of smoking, especially in certain occupational groups.