The Application Value of Ultrasound in the Diagnosis of Ovarian Torsion
DOI:
https://doi.org/10.54097/nnvdz490Keywords:
Ovarian Torsion, Abdominal Ultrasound, Vaginal Ultrasound, Gynecological Diseases, Imaging ExaminationAbstract
In a study of 80 patients suspected of ovarian torsion and necrosis admitted to our hospital between January and December 2023, we analyzed the results of abdominal and vaginal ultrasounds with surgical diagnoses as the reference. Of the 80 cases, 74 were confirmed as ovarian torsion, accounting for 92.50%, while the remaining diagnoses included ruptured corpus luteum cysts, periapendiceal abscesses, and ruptured ectopic pregnancies, each with 2 cases. Abdominal ultrasound identified 63 cases of ovarian torsion and 17 cases of non-ovarian torsion, whereas vaginal ultrasound identified 73 cases of ovarian torsion and 7 cases of non-ovarian torsion. The sensitivity, specificity, and accuracy of abdominal ultrasound were 82.43%, 66.67%, and 78.75%, respectively, while those of vaginal ultrasound were 94.59%, 50.00%, and 91.25%. Although there was no statistical difference in specificity between the two methods (P > 0.05), vaginal ultrasound demonstrated significantly higher sensitivity and accuracy compared to abdominal ultrasound (P < 0.05). Thus, vaginal ultrasound is a more reliable diagnostic tool for ovarian torsion, providing valuable information for clinicians and improving diagnostic accuracy.
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[1] Kameda, T., & Taniguchi, N. (2016). Overview of point-of-care abdominal ultrasound in emergency and critical care. Journal of Intensive Care, 4(1), 53.
[2] Akbar, A., Peoples, N., Xie, H., Sergot, P., Hussein, H., Peacock IV, W. F., & Rafique, Z.. (2022). Thrombolytic Administration for Acute Ischemic Stroke: What Processes can be Optimized?. McGill Journal of Medicine, 20(2).
[3] Dietrich, C. F. (2009). Significance of abdominal ultrasound in inflammatory bowel disease. Digestive Diseases, 27(4), 482-493.
[4] Hübner, U., Schlicht, W., Outzen, S., Barthel, M., & Halsband, H. (2000). Ultrasound in the diagnosis of fractures in children. The Journal of Bone & Joint Surgery British Volume, 82(8), 1170-1173.
[5] Lei, H., Wang, B., Shui, Z., Yang, P., & Liang, P. (2024). Automated Lane Change Behavior Prediction and Environmental Perception Based on SLAM Technology. arXiv preprint arXiv:2404.04492.
[6] Haowei, Ma, et al. "CRISPR/Cas-based nanobiosensors: A reinforced approach for specific and sensitive recognition of mycotoxins." Food Bioscience 56 (2023): 103110.
[7] Li, J., Wang, Y., Xu, C., Liu, S., Dai, J., & Lan, K. (2024). Bioplastic derived from corn stover: Life cycle assessment and artificial intelligence-based analysis of uncertainty and variability. Science of The Total Environment, 174349.
[8] Petersen, J. K., Fjaellegaard, K., Rasmussen, D. B., Alstrup, G., Høegholm, A., Sidhu, J. S., ... & Bodtger, U. (2024). Ultrasound in the Diagnosis of Non-Expandable Lung: A Prospective Observational Study of M-Mode, B-Mode, and 2D-Shear Wave Elastography. Diagnostics, 14(2), 204.
[9] Shi J, Shang F, Zhou S, et al. Applications of Quantum Machine Learning in Large-Scale E-commerce Recommendation Systems: Enhancing Efficiency and Accuracy [J]. Journal of Industrial Engineering and Applied Science, 2024, 2(4): 90-103.
[10] Wang, S., Zheng, H., Wen, X., & Fu, S. (2024). DISTRIBUTED HIGH-PERFORMANCE COMPUTING METHODS FOR ACCELERATING DEEP LEARNING TRAINING. Journal of Knowledge Learning and Science Technology ISSN: 2959-6386 (online), 3(3), 108-126.
[11] Fruehwirth, Jane Cooley, Alex Xingbang Weng, and Krista MPerreira."The effect of social media use on mental health ofcollege students during the pandemic." Health Economics (2024).
[12] Jiang, Yanhui, Jin Cao, and Chang Yu. "Dog Heart Rate and Blood Oxygen Metaverse Interaction System." arXiv preprint arXiv:2406.04466 (2024).
[13] Shi, Y., Shang, F., Xu, Z., & Zhou, S. (2024). Emotion-Driven Deep Learning Recommendation Systems: Mining Preferences from User Reviews and Predicting Scores. Journal of Artificial Intelligence and Development, 3(1), 40-46.
[14] Wang, B., He, Y., Shui, Z., Xin, Q., & Lei, H. (2024). Predictive Optimization of DDoS Attack Mitigation in Distributed Systems using Machine Learning. Applied and Computational Engineering, 64, 95-100.
[15] EL-Fattah, N. M. A., El-Mahdy, H. S., Hamisa, M. F., & Ibrahim, A. M. (2024). Thoracic fluid content (TFC) using electrical cardiometry versus lung ultrasound in the diagnosis of transient tachypnea of newborn. European Journal of Pediatrics, 183(6), 2597-2603.
[16] Li, S., Xu, H., Lu, T., Cao, G., & Zhang, X. (2024). Emerging Technologies in Finance: Revolutionizing Investment Strategies and Tax Management in the Digital Era. Management Journal for Advanced Research, 4(4), 35-49.
[17] Zhang, M., Yuan, B., Li, H., & Xu, K. (2024). LLM-Cloud Complete: Leveraging Cloud Computing for Efficient Large Language Model-based Code Completion. Journal of Artificial Intelligence General science (JAIGS) ISSN: 3006-4023, 5(1), 295-326.
[18] Wang, B., Zheng, H., Qian, K., Zhan, X., & Wang, J. (2024). Edge computing and AI-driven intelligent traffic monitoring and optimization. Applied and Computational Engineering, 77, 225-230.
[19] Xu, H., Niu, K., Lu, T., & Li, S. (2024). Leveraging artificial intelligence for enhanced risk management in financial services: Current applications and future prospects. Engineering Science & Technology Journal, 5(8), 2402-2426.
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