Prof. Dr. Didier Stricker
Dr. Alain Pagani
Dr. Gerd Reis
Eric Thil
Keonna Cunningham
Dr. Oliver Wasenmüller
Dr. Gabriele Bleser
Dr. Bruno Mirbach
Dr. Jason Raphael Rambach
Dr. Bertram Taetz
Dr. Muhammad Zeshan Afzal
Sk Aziz Ali
Mhd Rashed Al Koutayni
Murad Almadani
Alaa Alshubbak
Yuriy Anisimov
Jilliam Maria Diaz Barros
Ramy Battrawy
Hammad Butt
Mahdi Chamseddine
Steve Dias da Cruz
Fangwen Shu
Torben Fetzer
Ahmet Firintepe
Sophie Folawiyo
David Michael Fürst
Kamalveerkaur Garewal
Christiano Couto Gava
Leif Eric Goebel
Tewodros Amberbir Habtegebrial
Simon Häring
Khurram Hashmi
Jigyasa Singh Katrolia
Andreas Kölsch
Onorina Kovalenko
Stephan Krauß
Paul Lesur
Muhammad Jameel Nawaz Malik
Michael Lorenz
Markus Miezal
Mina Ameli
Nareg Minaskan Karabid
Mohammad Minouei
Pramod Murthy
Mathias Musahl
Peter Neigel
Manthan Pancholi
Qinzhuan Qian
Engr. Kumail Raza
Dr. Nadia Robertini
María Alejandra Sánchez Marín
Dr. Kripasindhu Sarkar
Alexander Schäfer
Pascal Schneider
René Schuster
Mohamed Selim
Lukas Stefan Staecker
Dennis Stumpf
Yongzhi Su
Xiaoying Tan
Yaxu Xie
Dr. Vladislav Golyanik
Dr. Aditya Tewari
André Luiz Brandão
Convolutional Recurrent Neural Network for Bubble Detection in a Portable Continuous Bladder Irrigation Monitor
Convolutional Recurrent Neural Network for Bubble Detection in a Portable Continuous Bladder Irrigation Monitor
David Riaño, Szymon Wilk, Annette ten Teije (Hrsg.). Artificial Intelligence in Medicine. Conference on Artificial Intelligence in Medicine (AIME-2019) June 26-29 Poznan Poland Seiten 57-66 ISBN 678-3-030-21641-2 Springer 2019 .
- Abstract:
- Continuous bladder irrigation (CBI) is commonly used to prevent urinary problems after prostate or bladder surgery. Nowadays, the irrigation flow rate is regulated manually based on the color (qualitative estimation of the blood concentration) of the drainage fluid. To monitor the blood concentration quantitatively and continuously, we have developed a portable CBI monitor based on the Lambert-Beer law. It measures transmitted light intensity via a camera sensor and deduces the blood concentration. To achieve high reliability, we need to guarantee that the measurement is conducted when there is no air bubble passing through the view of the camera. To detect bubble occurrences, we propose a convolutional recurrent neural network with a sequence of images as input: the convolutional layers extract spatial features from 2D images; the recurrent layers capture temporal features in the image sequence. Our experimental results show that our network has smaller scale and higher accuracy compared with conventional convolutional and recurrent neural networks.