Purpose: The purpose of this study was to evaluate the intrauterine insemination (IUI) success rate and to define the variables for predicting success. Methods: The secondary data analysis was used with data collected from infertile females who underwent IUI in Fertility and IVF (In Vitro Fertilization) clinics, who benefited from the ‘National Medical-aid Program for ART (assisted reproductive technology) in 2016’, in which the data of 34,920 IUI cases were retrospectively reviewed. The primary outcome measure was the clinical pregnancy rate in elderly and young infertile females. Data were analyzed by descriptive statistics, χ2 test and logistic regression. Results: The pregnancy rate was 12.1% (2,095 cases) in elderly infertile females and 15.6% in young infertile females (2,758 cases) (χ2 = 87.90, p < .001). Using the logistic regression analysis, clinical pregnancy was positively associated with the ovulatory factor (OR= 1.48, p< .001) and male factor (OR= 1.19, p< .05) in elderly infertile females. It was positively associated with the ovulatory factor (OR= 1.30, p= .001) and the peritoneal cavity factor (OR= 0.58, p< .05) in young infertile females. Conclusion Our results indicate that the pregnancy rate in young infertile females was higher than that in old infertile females, and the IUI is the effective option in pregnancies in all ages with infertility due to the ovulatory factor. Additionally, further studies are necessary to fully describe pregnancy experiences for all the infertile females.

Objective: To develop and evaluate a deep learning model for automated segmentation and detection of bone metastasis on spinal MRI. Materials and Methods: We included whole spine MRI scans of adult patients with bone metastasis: 662 MRI series from 302 patients (63.5 ± 11.5 years; male:female, 151:151) from three study centers obtained between January 2015 and August 2021 for training and internal testing (random split into 536 and 126 series, respectively) and 49 MRI series from 20 patients (65.9 ± 11.5 years; male:female, 11:9) from another center obtained between January 2018 and August 2020 for external testing. Three sagittal MRI sequences, including non-contrast T1-weighted image (T1), contrast-enhanced T1-weighted Dixon fat-only image (FO), and contrast-enhanced fat-suppressed T1-weighted image (CE), were used. Seven models trained using the 2D and 3D U-Nets were developed with different combinations (T1, FO, CE, T1 + FO, T1 + CE, FO + CE, and T1 + FO + CE). The segmentation performance was evaluated using Dice coefficient, pixel-wise recall, and pixel-wise precision. The detection performance was analyzed using per-lesion sensitivity and a free-response receiver operating characteristic curve. The performance of the model was compared with that of five radiologists using the external test set. Results: The 2D U-Net T1 + CE model exhibited superior segmentation performance in the external test compared to the other models, with a Dice coefficient of 0.699 and pixel-wise recall of 0.653. The T1 + CE model achieved per-lesion sensitivities of 0.828 (497/600) and 0.857 (150/175) for metastases in the internal and external tests, respectively. The radiologists demonstrated a mean per-lesion sensitivity of 0.746 and a mean per-lesion positive predictive value of 0.701 in the external test. Conclusion The deep learning models proposed for automated segmentation and detection of bone metastases on spinal MRI demonstrated high diagnostic performance.