Gurugubelli Sai Dilip Kumar
Sri Karthik Avala
- Assistant Professor, Department of Computer Science and Engineering, Jawaharlal Nehru Technological University, Gurajada, Vizianagaram, Andhra Pradesh, India
- Student, Department of Mechanical Engineering, Andhra University College of Engineering, Visakhapatnam, Andhra Pradesh, India
- Student, Department of Computer Science & Engineering, VIT Chennai University, Chennai, Tamil Nadu, India
Tube misplacement can lead to complications in patients together with serious medical malpractice cases. Intubation and the insertion of various medical tubes were employed to safeguard the airways of critically ill patients. Critically unwell patients underwent intubation, with the insertion of various medical tubes to safeguard their airways. The Nasogastric tube (NGT) serves the purpose of providing nutrition, while the Central Venous Catheter (CVC) finds application in a range of medical procedures. The significant challenge lies in doctors adhering to medical protocols to ensure the correct installation of these tubes. The implementation of medical protocols by physicians to guarantee the correct placement of tubes presents a significant challenge. Misplaced tubes increase the probability of complications in patients and, in the worst of cases, even lead to mortality. So, identifying the proper positioning of tubes before starting the procedure is crucial. In this paper, we propose identifying the misplaced tube and catheters detection using deep learning approach with accurate outcome.
Keywords: Tube Misalignment, Efficient net B7, Central Venous Catheter, deep learning, Nasogastric tube, EfficientNet model
[This article belongs to International Journal of Computer Science Languages(ijcsl)]
1. Mallon DH, McNamara CD, Rahmani GS, O’Regan DP, Amiras DG. Automated detection of enteric tubes misplaced in the respiratory tract on chest radiographs using deep learning with two centre validation. Clinical Radiology. 2022 Oct 1;77(10):e758–64.
2. Jung HC, Kim C, Oh J, Kim TH, Kim B, Lee J, Chung JH, Byun H, Yoon MS, Lee DK. Position Classification of the Endotracheal Tube with Automatic Segmentation of the Trachea and the Tube on Plain Chest Radiography Using Deep Convolutional Neural Network. Journal of Personalized Medicine. 2022 Aug 24;12(9):1363.
3. Elaanba A, Ridouani M, Hassouni L. Automatic detection using deep convolutional neural networks for 11 abnormal positioning of tubes and catheters in chest X-ray Images. In2021 IEEE World AI IoT Congress (AIIoT) 2021 May 10 (pp. 0007–0012). IEEE.
4. Yang C, Qiao S, Yu Q, Yuan X, Zhu Y, Yuille A, Adam H, Chen LC. Moat: Alternating mobile convolution and attention brings strong vision models. arXiv preprint arXiv:2210.01820. 2022 Oct 4.
5. Rapeti D, Reddy VD. A Multi Range Morphological Model on Dermoscopy Images with Edge Based Segmentation for Image Quality Enhancement for Skin Lesion Classification. Revue d’Intelligence Artificielle. 2023 Apr 1;37(2).
6. Mirza MW, Siddiq A, Khan IR. A comparative study of medical image enhancement algorithms and quality assessment metrics on COVID-19 CT images. Signal, Image and Video Processing. 2023 Jun;17(4):915–24.
7. Pham TX. Image segmentation through metaheuristics optimization: application to brain magnetic resonance images (Doctoral dissertation, Paris Est). 2019.
8. Yu TT, Wnorowski AM, Lane BF, Wong-You-Cheong JJ. Performing a basic US examination: road map for radiology residents. RadioGraphics. 2019 Jul;39(4):1075–6.
9. Suh RD, Genshaft SJ, Kirsch J, Kanne JP, Chung JH, Donnelly EF, Ginsburg ME, Heitkamp DE, Henry TS, Kazerooni EA, Ketai LH. ACR Appropriateness Criteria® intensive care unit patients. Journal of thoracic imaging. 2015 Nov 1;30(6):W63–5.
10. Hansen L, Sieren M, Hobe M, Saalbach A, Schulz H, Barkhausen J, Heinrich MP. Radiographic Assessment of CVC Malpositioning: How can AI best support clinicians?. InMedical Imaging with Deep Learning 2021 Apr 14.
|Received||August 28, 2023|
|Accepted||October 9, 2023|
|Published||October 30, 2023|