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    Deep learning model-assisted detection of kidney stones on computed tomography
    (Brazilian Soc Urol, 2022) Çaglayan, Alper; Horsanalı, Mustafa Ozan; Kocadurdu, Kenan; İsmailoğlu, Eren; Güneyli, Serkan
    Introduction: The aim of this study was to investigate the success of a deep learning model in detecting kidney stones in different planes according to stone size on unenhanced computed tomography (CT) images. Materials and Methods: This retrospective study included 455 patients who underwent CT scanning for kidney stones between January 2016 and January 2020; of them, 405 were diagnosed with kidney stones and 50 were not. Patients with renal stones of 0-1 cm, 1-2 cm, and >2 cm in size were classified into groups 1, 2, and 3, respectively. Two radiologists reviewed 2,959 CT images of 455 patients in three planes. Subsequently, these CT images were evaluated using a deep learning model. The accuracy rate, sensitivity, specificity, and positive and negative predictive values of the deep learning model were determined. Results: The training group accuracy rates of the deep learning model were 98.2%, 99.1%, and 97.3% in the axial plane; 99.1%, 98.2%, and 97.3% in the coronal plane; and 98.2%, 98.2%, and 98.2% in the sagittal plane, respectively. The testing group accuracy rates of the deep learning model were 78%, 68% and 70% in the axial plane; 63%, 72%, and 64% in the coronal plane; and 85%, 89%, and 93% in the sagittal plane, respectively. Conclusions: The use of deep learning algorithms for the detection of kidney stones is reliable and effective. Additionally, these algorithms can reduce the reporting time and cost of CT-dependent urolithiasis detection, leading to early diagnosis and management.
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    How does the type of delivery affect pelvic floor structure? Magnetic resonance imaging parameter-based anatomical study
    (Via Medica, 2023) Şenkaya, Ayşe Rabia; İsmailoğlu, Eren; Arı, Sabahattin Anıl; Karaca, İbrahim
    Objectives: The aim of this study is to examine the effects of delivery type and birth weight on pelvic floor structure using muscle defects, uterus-vagina angles and landmarks in pelvic magnetic resonance imaging (MRI). Material and methods: This is a retrospective study. Pelvic MR images of 38 vaginal deliveries and 62 cesarean section patients who met the study criteria were analyzed. Pubococcygeal line, H line, M line were marked on MR images, uterus cervix, cervix upper vagina, upper and middle vagina, middle and lower vagina angles, urogenital hiatus width, levator hiatus width, obturator internus muscle area, levator ani defect was measured. The urinary incontinence and pelvic organ prolapse examination findings were recorded. The patients' age, body mass index (BMI), parity, delivery type, maximum birth weight questions were asked. The data of both groups were compared. Results: Uterocervical angle and levator ani muscle defect was significantly higher in the vaginal delivery group (p < 0.001). In the vaginal delivery group, a significant positive correlation was found between the parity and the levator ani muscle defect (r = 0.552), (p = 0.000). A significant negative correlation was found between the parity and the uterocervical angle (r = -0.337), (p = 0.039). A significant negative correlation was found between maximum birth weight and cervix upper vagina angle (r = -0.365) (p = 0.024). In the vaginal delivery group, a negative significant correlation was found between birth weight and obturator internus muscle area (r = -0.378), (p = 0.019). Conclusions: These results show that cesarean section exposes the pelvic floor to less trauma and suggest that cesarean section may protect the pelvic floor.