Assessing Visualization Quality of Bile Duct Stones: Supine Vs Semi-Prone Position
Nayab Mustansar*
Department of Radiology, CMH PESHAWAR, Pakistan
*Corresponding Author: Nayab Mustansar, Department of Radiology, CMH PESHAWAR, Pakistan.
Received:
May 17, 2024; Published: July 19, 2024
Abstract
Objectives: This study aimed to compare the visualization quality of bile duct stones between the supine and semi-prone positions using ultrasound.
Study design: It is a cross-sectional prospective study carried out in the Radiology department of CMH Peshawar for a span of five months from January 2024-May 2024.
Setting: Radiology department of CMH Peshawar.
Study duration: 1st January 2024- 15th May 2024.
Methodology: A total of 100 patients with suspected choledocholithiasis were included using a non-probability purposive sampling method for this study. Each patient underwent a comprehensive assessment including medical history and physical examination. Subsequently each patient underwent ultrasound in both the supine and semi-prone positions. The visualization quality of bile duct stones was assessed by experienced radiologist (5 years post specialization experience). Comparative analysis in both positions was calculated. Statistics patient demographics in both positions (supine and semi-prone) was calculated. Results: The visualization quality of bile duct stones was significantly higher in the semi-prone position compared to the supine position in ultrasound.
Conclusion: In conclusion, our study demonstrates that the semi-prone position provides better visualization of bile duct stones compared to the supine position.
Keywords: Common Bile Duct (CBD); Ultrasound; Supine; Semi-Prone Position
References
- C Molvar and B “Choledocholithiasis: Evaluation, treatment, and outcomes”. in Proceedings. Seminars in Interventional Radiology 33 (2016): 268-276.
- R Costi., et al. “Diagnosis and management of choledocholithiasis in the golden age of imaging, endoscopy and laparoscopy”. World Journal of Gastroenterology 37 (2014): 13382.
- Ahmed RC Cheung and EB Keeffe. “Management of gallstones and their complications”. Family Physician 61.6 (2000): 1673-1680.
- S Tazuma., et al. “Evidence-based clinical practice guidelines for cholelithiasis 2016”. Journal of Gastroenterology 3 (2017): 276-300.
- , et al. “Incidence rates of post-ERCP complications: A systematic survey of prospective studies”. The American Journal of Gastroenterology 102.8 (2007): 1781-1788.
- T , et al. “Patterns and predictive factors of complications after endoscopic retrograde cholangiopancreatography”. British Journal of Surgery 100.3 (2013): 373-380.
- UB Kuzu., et al. “Management of suspected common bile duct stone: Diagnostic yield of current guidelines”. HPB2 (2017): 126-132.
- RM Narvaez-Rivera., et al. “Accuracy of ASGE criteria for the prediction of choledocholithiasis”. Revista Espanola de Enfermedades Digestivas6 (2016): 309- 314.
- H He., et al. “Accuracy of ASGE high-risk criteria in evaluation of patients with suspected common bile duct stones”. Gastrointestinal Endoscopy3 (2017): 525-532.
- SW , et al. “Accuracy of MDCT in the diagnosis of choledocholithiasis”. American Journal of Roentgenology 187.1 (2006): 174-180.
- CW Tseng., et al. “Can computed tomography with coronal reconstruction improve the diagnosis of choledocholithiasis?’’ Journal of Gastroenterology and Hepatology 10 (2008): 1586-1589.
- X Wang., et al. “A weakly-supervised framework for COVID-19 classification and lesion localization from chest CT”. IEEE Transactions on Medical Imaging 8 (2020): 2615-2625.
- TH Lin., et al. “Deep ensemble feature network for gastric section classification”. IEEE Journal of Biomedical and Health Informatics 25.1 (2021): 77-87.
- JY Jhang., et al. “Gastric section correlation network for gastric precancerous lesion diagnosis”. IEEE Open Journal of Engineering in Medicine and Biology (2023): 1-9.
- F Yu and V “Multi-scale context aggregation by dilated convolutions”. in Proceedings. The International Conference on Learning Representations (2016): 1-13.
- S Woo., et al. “CBAM: Convolutional block attention module”. in Proceedings. European Conference on Computer Vision (2018): 3-19.
- B Zhou., et al. “Learning deep features for discriminative localization”. in Proceedings. IEEE conference on computer vision and pattern Recognition (CVPR) (2016): 2921-2929.
- RR Selvaraju., et al. “Grad- CAM: Visual explanations from deep networks via gradient-based localization”. in Proceedings. IEEE International Conference on Computer Vision (ICCV) (2017): 618-626.
- O , et al. “U-Net: Convolutional networks for biomedical image segmentation”. in Proceedings. International Conference on Medical Image Computing and Computer-Assisted Intervention (2015): 234-241.
- X Li., et al. “H-DenseUNet: Hybrid densely connected UNet for liver and tumor segmentation from CT volumes”. IEEE Transactions on Medical Imaging 12 (2018): 2663-2674.
- S Park., et al. “Autoencoder inspired convolutional network-based super-resolution method in MRI”. IEEE Journal of Translational Engineering in Health and Medicine 9 (2017): 1-13.
- J Zhang., et al. “MLBF-Net: A multi-lead-branch fusion network for multiclass arrhythmia classification using 12-lead ECG”. IEEE Journal of Translational Engineering in Health and Medicine 9 (2021): 1-11.
- MA Ottom., et al. “ZNet: Deep learning approach for 2D MRI brain tumor segmentation”. IEEE Journal of Translational Engineering in Health and Medicine 10 (2022): 1-8.
- S Huang., et al. “Evaluations of deep learning methods for pathology image classification”. in Proceedings. IEEE Biomedical Circuits and Systems Conference (2022): 95-99.
- KK Singh and YJ Lee. “Hide-and-seek: Forcing a network to be meticulous for weakly-supervised object and action localization”. in Proceedings. IEEE International Conference on Computer Vision (ICCV) (2017): 3544-3553.
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