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Comparing the anatomy of the intrathoracic systemic central venous system in Iranian pediatrics and adults based on chest CT scan and evaluation of rate of catheter malposition in children
Neda Pak , Fateme Zamani, Sara Naybandi Atashi, Anese Saleh Nia,
Volume 78, Issue 6 (9-2020)
Abstract
Background: Central venous catheterization is a procedure that is being performed frequently especially in critical clinical settings. In such conditions, good knowledge of the surface anatomy of venous structures is vital to avoid possible complications which could result in life-threatening situations such as bleeding and pneumothorax. Considering the difference between venous anatomy of children and adults and even among different age groups of children, and the fact that our recent knowledge of anatomy is based on studies performed on non-Iranian population, we decided to evaluate the anatomy of the intrathoracic systemic venous system in adults and children and assess the rate of catheter malposition in children.
Methods: This was a retrospective cross-sectional study performed in Dr. Shariati Hospital and Children Medical Center of Excellence, Tehran, Iran, from April 2016 to August 2019. In our study, the surface location of brachiocephalic vein (BCV) formation, the junction of superior vena cava (SVC) to right atrium and, formation of SVC were examined in 150 contrast-enhanced chest computed tomography (CT) scans in children. They were classified into three groups based on their age (neonates to three years, three to seven years, and seven to ten years). Also, 100 similar CT scans in adults were being studied. The other category which has been evaluated through 130 pediatric X-rays, was the location of the tip of the central venous catheter.
Results: The formation of BCV was mostly depicted posterior to the sternoclavicular joint in adults while in children it’s located posterior to the medial aspect of the head of clavicle. In adults, the SVC formation was at first intercostal space (ICS) in 52% and second ICS in 29%. In first group of children, SVC was commonly at the level of 2nd costal cartilage (CC), but changed to the first ICS or first CC by increasing age. In adults, junction of right atrium to SVC was at the 3rd CC then 4th CC but in the first group of children was located at the 4th CC that changed to 3rd ICS /3rd CC by increasing age. Also, the tip of central venous catheters was located in the proper position in 74.7% of cases.
Conclusion: This study indicated the different anatomy of central veins in children and adults which could be a cause for malposed central catheter, so knowing this difference and controlling the tip of the catheter by ultrasound during catheterization could help in avoiding this malpositioning.

Diagnosis of pneumothorax using machine learning algorithms and radiomics: Investigating the possibility of replacing simple chest radiography instead of CT scan in order to reduce the radiation dose
Hanieh Alimiri Dehbaghi , Karim Khoshgard, Hamid Sharini, Samira Jafari Khairabadi, Farhad Naleini,
Volume 81, Issue 5 (8-2023)
Abstract
Background: The use of artificial intelligence algorithms to help with accurate diagnosis in medical images is one of the most important applications of this technology in the field of medical imaging. In this research, the possibility of replacing simple chest radiography instead of CT scan using machine learning models to detect pneumothorax was investigated in cases where CT is usually requested.
Methods: This study is analytical and was conducted from November 2022 to May 2023 at Kermanshah University of Medical Sciences. The data used in this research was extracted from the files of 350 patients suspected of pneumothorax. The collected images were pre-processed in MATLAB software. Then, three machine learning algorithms, including Logistic elastic net regression (LENR), Logistic lasso regression (LLR) and Adaptive Boosting (AdaBoost) were used. To evaluate the performance of these models, the criteria of precision, accuracy, sensitivity, specificity, area under the receiver operating characteristic curve (AUC), F1 score, and misclassification were used.
Results: In the AdaBoost model, the accuracy value in radiographic and CT images was calculated as 98.89% and 98.63%, respectively, and the precision value was calculated as 99.17% and 98.27%, respectively. In radiographic images, the AUC value for AdaBoost model was calculated as 100% and in CT scan images as 96.96%. The F1 score for the same model in radiographic was 99% and in CT images was 98.68%. The specificity value for the AdaBoost model was calculated as 99.45% in radiographic images and 94.67% in CT scan images. In the LLR model, the AUC value for radiographic and CT scan images was 99.87% and 99.02%, respectively.
Conclusion: According to the criteria evaluated in the present study, two LLR and AdaBoost models have similar performance in radiographic and CT images in terms of pneumothorax detection ability, so that this complication can also be diagnosed with high precision level using machine learning techniques on the radiographic images and thus receiving higher levels of radiation doses due to CT scan can be avoided in these patients.

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