Applied Sciences

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Special Issue Editors

Dr. Dariusz Radomski

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Faculty of Electronics and Information Technology, Warsaw University of Technology, Nowowiejska 15/19, 00-665 Warsaw, Poland
Interests: system identification; physiological modeling; pregnancy and fetus monitoring; physiological measurements of sexual reactions; causal modeling

Prof. Dr. Artur Przelaskowski

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Guest Editor

Faculty of Mathemetics and Information Sciences, Warsaw University of Technology, Nowowiejska 15/19, 00-665 Warsaw, Poland
Interests: medical image; medical image compression methods in both reversible and lossy cases; computer-aided diagnosis (mammography, 3D-US, brain CT); wavelet-based image approximants; diagnostic accuracy estimation of processed images

Special Issue Information

Dear Colleagues,

Modern measurement and information technologies enable the collection of large amounts of data about health at population and individual levels. Therefore, management of health and clinical decision-making is hardly difficult. Therefore, the application of artificial intelligence in healthcare dynamically increases. So, many journals and papers are presenting novel algorithms for biosignal and medical image processing. However, in my humble opinion, they have at least two drawbacks. The published methods mainly concern ECG, EEG, rarely EMG signals, and brain images. Secondly, the biosignal and image parametrization methods and the proposed classifiers usually belong to the black models class, i.e., parameters do not have biological or clinical meaning, and classifier decisions are not understandable for healthcare providers. However, WHO and UE recommend that clinical decision systems be based on explainable artificial intelligence. This is a new trend in health intelligence supported by quantitative or qualitative models of physiological or pathological causal or dynamical systems. Therefore, this Special Issue will focus on biologically or clinically explainable advanced signal and image processing and explainable machine learning in biomedical and healthcare applications, including but not limited to screening and diagnostics, patient monitoring, telehealth, health risk assessment, medical prognosis, and survival analysis. The processing data can be derived at molecular, cellular, tissue, organ, or organism (individual) levels. Original research contributions will be prioritized, but critical reviews about the state of the art, current limitations, and future directions are welcome. The higher priority will receive applications in gynecology, obstetrics, urology, sexology, pelvic floor physiotherapy, or gastrology.

Dr. Dariusz Radomski
Prof. Dr. Artur Przelaskowski
Guest Editors

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Keywords

physiological system identification
multiscale signal processing
bioinformatics
medical image analyses
explainable artificial intelligence
clinical decision support systems
pregnancy monitoring
fetus monitoring
pelvic floor muscle monitoring and therapy support
physiological measurements of sexual reactions
nonlinear signal processing and system identification
causal modeling
Bayesian nets
probabilistic nets
health intelligence

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Research

17 pages, 1500 KB

Open AccessArticle

A Physiologically Explainable Classifier for Labour Prediction Based on Electrohysterographical Signals

by Dariusz S. Radomski, Zuzanna Oscik, Rafal Jozwiak and Ewa Dmoch-Gajzlerska

Appl. Sci. 2025, 15(24), 12960; https://doi.org/10.3390/app152412960 - 9 Dec 2025

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Abstract

BACKGROUND. Managing women in pregnancy or labour is becoming a serious challenge because of delayed conception age and higher morbidity. The main negative factor is increasing numbers of overweight and obese women. Fatty tissue significantly biases the detection of uterine contractions by tocography, which is routinely used in obstetrical wards. Thus, the FDA approved an alternative method called electrohysterography (EHG) and recommended it for women with an over-normal BMI. However, almost all published methods of labour prediction based on EHG signals use a “black-box model” approach, i.e., increasingly numerically complex signal features and classification algorithms that are chosen a priori, without any physiological rationale behind them. This makes using these algorithms difficult in obstetrical practice. AIM. The aim of the study was to show that a simple classifier based on a single and physiologically interpretable parameter can predict uterine contractions during labour with an accuracy comparable to advanced classifiers. METHODS. An obstetrical interpretable EHG parameter was introduced called the uterine activity index. To avoid the influence of confounding factors associated with preterm labour and imbalanced signal sets, this classifier was evaluated using the private, retrospective database of EHG signals registered for 45 women in the third trimester of a pregnancy, and 31 women in the second stage of labour with a normal BMI. The classifier, based on the logistic regression model, was tested using the bootstrap method. RESULTS. The bootstrapping mean (95% confidence interval) of the AUC ROC estimated for the 200 bootstrap samples was 0.96 (0.91–0.99). This accuracy was slightly better for EHG signals in comparison to predictions based on classical tocography. CONCLUSIONS. The obtained results confirm that a simple physiologically explained classifier can be considered in commercial applications of electrohysterography. However, its clinical significance should be evaluated through properly designed randomised clinical trials. Full article

(This article belongs to the Special Issue Novel Advances in Biomedical Signal and Image Processing)

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16 pages, 15460 KB

Open AccessArticle

Visual Hull-Based Approach for Coronary Vessel Three-Dimensional Reconstruction

by Dominik Bernard Lau and Tomasz Dziubich

Appl. Sci. 2025, 15(19), 10450; https://doi.org/10.3390/app151910450 - 26 Sep 2025

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Abstract

This paper addresses the problem of automatically reconstructing three-dimensional coronary vessel trees from a series of X-ray angiography images, a task which remains difficult, particularly with respect to solutions requiring no additional user input. This study analyses the performance of a visual hull-based algorithm, producing the actual positions of heart arteries in the coordinate system, which is an approach not sufficiently explored in XRA images analysis. The proposed algorithm first creates a bounding cube using a novel heuristic and then iteratively projects the cube onto preprocessed 2D images, removing points too far from the depicted arteries. The method performance is first evaluated on a synthetic dataset through a series of experiments, and for a set of common clinical angles, 3D Dice of 75.25% and 78.61% reprojection Dice is obtained, which rivals the state-of-the-art machine learning methods. The findings suggest that the method offers a promising and interpretable alternative to black box methods on the synthethic dataset in question. Full article

(This article belongs to the Special Issue Novel Advances in Biomedical Signal and Image Processing)

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