Smart Pleural Effusion Drainage Monitoring System Establishment for Rapid Effusion Volume Estimation and Safety Confirmation

Pleural effusion is the pathologic accumulation of body fluids around the unilateral or bilateral lungs that is primarily caused by heart disease. A chest radiograph is a rapid examination technique used to provide a preliminary diagnosis of lung and heart diseases. Computer-aided diagnosis with the digitalized image is an automated approach that addresses the drawbacks of manual inspection. In this study, two corner detectors along with a two-dimensional convolution process are used to enhance the chest X-ray image for an accurate extrapolation of the bilateral lung cavities. Based on bounding box pixel analysis, the pixel ratios of the lung anatomy between normal and abnormal conditions can be estimated to identify the pleural effusion size. Next, a smart drainage monitoring system is developed to improve the current functions of the traditional drainage tool and confirm the drainage safety, including (a) drainage volume and required time detection, (b) unplanned removal warning, and (c) physiological status monitoring. The experimental result will be used to determine the feasibility of the proposed effusion volume estimation algorithm and the efficiency of the smart drainage monitoring prototyping tool. The proposed smart drainage monitoring system and the computer-aided method with digitalized images can be further applied in real clinical practice in the intensive care unit.

[1]  Bulat Ibragimov,et al.  Deep neural network ensemble for pneumonia localization from a large-scale chest x-ray database , 2019, Comput. Electr. Eng..

[2]  M. Vargas,et al.  Utility of pleural effusion drainage in the ICU: An updated systematic review and META-analysis. , 2019, Journal of critical care.

[3]  Chia-Hung Lin,et al.  Application of two-dimensional fractional-order convolution and bounding box pixel analysis for rapid screening of pleural effusion. , 2019, Journal of X-Ray Science and Technology.

[4]  E. Bignami,et al.  An easier and safe affair, pleural drainage with ultrasound in critical patient: a technical note , 2018, Critical Ultrasound Journal.

[5]  V. S. Patil,et al.  A Review on Functions of Rakt Dhatu and Prana Vayu to Establish Lung Function Capacity , 2018 .

[6]  E. Bignami,et al.  Thoracic ultrasound for pleural effusion in the intensive care unit: a narrative review from diagnosis to treatment , 2017, Critical Care.

[7]  B. Balachander,et al.  Drainage Monitoring System Using Iot (Dms) , 2017 .

[8]  Ming-Jui Wu,et al.  Bilateral Photoplethysmography Analysis for Peripheral Arterial Stenosis Screening With a Fractional-Order Integrator and Info-Gap Decision-Making , 2016, IEEE Sensors Journal.

[9]  A. Abbosh,et al.  Review of systems for the detection and monitoring of accumulated fluids in the human torso , 2015, 2015 International Symposium on Antennas and Propagation (ISAP).

[10]  Hamed Minaei Zaeim,et al.  Evaluation of the use of frequency response in the diagnosis of pleural effusion on a phantom model of the human lungs , 2014, 2014 36th Annual International Conference of the IEEE Engineering in Medicine and Biology Society.

[11]  Liming Chen,et al.  HSOG: A Novel Local Image Descriptor Based on Histograms of the Second-Order Gradients , 2014, IEEE Transactions on Image Processing.

[12]  H. Kawasaki,et al.  An analysis of and new risk factors for reexpansion pulmonary edema following spontaneous pneumothorax. , 2014, Journal of thoracic disease.

[13]  Chia-Hung Lin,et al.  Combining fractional-order edge detection and chaos synchronisation classifier for fingerprint identification , 2014, IET Image Process..

[14]  Reinhard Klette,et al.  Concise Computer Vision: An Introduction into Theory and Algorithms , 2014 .

[15]  F. Čtvrtlík,et al.  Quantification of pleural effusion on CT by simple measurement. , 2012, Biomedical papers of the Medical Faculty of the University Palacky, Olomouc, Czechoslovakia.

[16]  V. Jain,et al.  A new, simple method for estimating pleural effusion size on CT scans. , 2013, Chest.

[17]  N. Eizenberg,et al.  Anatomy and pathophysiology of the pleura and pleural space. , 2013, Thoracic surgery clinics.

[18]  Hyeon Yu,et al.  Management of pleural effusion, empyema, and lung abscess. , 2011, Seminars in interventional radiology.

[19]  F. Irani,et al.  Re-expansion pulmonary edema following thoracentesis , 2010, Canadian Medical Association Journal.

[20]  Gerhard Ziemer,et al.  Ultrasound estimation of volume of postoperative pleural effusion in cardiac surgery patients. , 2010, Interactive cardiovascular and thoracic surgery.

[21]  Yi-Fei Pu,et al.  Fractional Differential Mask: A Fractional Differential-Based Approach for Multiscale Texture Enhancement , 2010, IEEE Transactions on Image Processing.

[22]  Jiliu Zhou,et al.  Construction of Fractional differential Masks Based on Riemann-Liouville Definition , 2010 .

[23]  Chen Shu-zhi An Improved Corner Detection Algorithm Based on Harris , 2010 .

[24]  Liu Fu-Bing Yu Le Cai Zhi-Gang Deng Chao Zhang Deng-Rong AN AUTO-ADAPTED FEATURES EXTRACTION METHOD BASED ON HARRIS OPERATOR , 2009 .

[25]  Ronald M. Summers,et al.  Computer aided evaluation of pleural effusion using chest CT images , 2009, 2009 IEEE International Symposium on Biomedical Imaging: From Nano to Macro.

[26]  P. Radhakrishnan,et al.  Non-invasive Studies on Age Related Parameters Using a Blood Volume Pulse Sensor , 2008 .

[27]  Max A. Viergever,et al.  Vessel Axis Tracking Using Topology Constrained Surface Evolution , 2007, IEEE Transactions on Medical Imaging.

[28]  Westgate Road,et al.  Photoplethysmography and its application in clinical physiological measurement , 2007 .

[29]  K Sembulingam,et al.  Essentials of Medical Physiology , 2006 .

[30]  Peter Rockett,et al.  Performance assessment of feature detection algorithms: a methodology and case study on corner detectors , 2003, IEEE Trans. Image Process..

[31]  Farzin Mokhtarian,et al.  Robust Image Corner Detection Through Curvature Scale Space , 1998, IEEE Trans. Pattern Anal. Mach. Intell..

[32]  Junaed Sattar Snakes , Shapes and Gradient Vector Flow , 2022 .