Image-based classification of bladder state using electrical impedance tomography

Abstract

Objective: In this study, we examine the potential of using machine learning classification to determine the bladder state (‘not full’, ‘full’) with electrical impedance tomography (EIT) images of the pelvic region. Accurate classification of these states would enable urinary incontinence (UI) monitoring to alert the patient, before involuntary voiding occurs, in a low-cost and discrete manner. Approach: Using both numerical and experimental data, we form datasets that contain diverse observations with varying clinical parameters such as bladder volume, urine conductivity, and the reference used for time-difference imaging. We then classify the bladder state using both pixel-wise and feature extraction-based classification techniques. We employ principal component analysis, wavelets, and image segmentation to help create features. Main results: The performance was compared across several classifier algorithms. The minimum accuracy was 77.50%. The highest accuracy observed was 100%, and was found by combining principal component analysis and the Gaussian radial based function kernel support vector machine. This combination also offered the best trade-off between classification performance and the costs of training time and memory space. The biggest challenge in bladder state classification is classifying volumes near the separation volume of not full and full, in which choosing the most suitable classifier combination can minimize this error. Significance: We performed the first machine learning classification of bladder EIT images, achieving high classification accuracies with both numerical and experimental data. This work highlights the potential of using image-based machine learning with an EIT device to support bladder monitoring for those suffering from UI.

Investigation of Bladder Volume Measurement Based on Fringe Effect of Electrical Impedance Tomography Sensors

“…Dunne et al suggested that the urination of a patient can be determined by the bladder status. They proposed a method based on machine learning by training the measured voltages [3] and the reconstructed EIT images [8] to determine the bladder status. Schlebusch et al found that urine conductivity significantly affects the measurement accuracy of bladder volume by EIT [9].…”

Advances of deep learning in electrical impedance tomography image reconstruction

“…In particular, in terms of medical diagnosis and decision-making, deep learning can be used to provide doctors with intelligent auxiliary diagnosis information for quick decision-making. Candiani et al used neural networks to achieve effective classification of brain stroke from EIT results ( Candiani and Santacesaria, 2022 ), whereas Dunne et al used image-based machine learning that provides intelligent monitoring for urinary incontinence patients ( Dunne et al, 2018 ). Moreover, Lee et al (2020) proposed EIT abdominal fat estimation based on deep learning.…”

Brain Haemorrhage Detection Through SVM Classification of Electrical Impedance Tomography Measurements

“…Alternatively, unsupervised filter methods can use the entire dataset in order to rank the features, without biasing the classification result. An unsupervised feature selection algorithm, the Laplacian Score algorithm [55,56], was used in this work to rank the features on the measurement sets (datasets). Specifically, the Laplacian Score algorithm works on the assumption that if two data points are close, then the data points most likely share a label [55].…”