Pub Date : 2026-01-13DOI: 10.1016/j.irbm.2026.100931
Anna Wargel , Andreas Mayr , Steven van Andel , Elke Pucks-Faes , Peter Federolf
Background
Inertial motion capture systems like Xsens Awinda are increasingly used for assessing movement in clinical populations. However, it remains unclear how stroke-related motor impairments affect the quality of motion capture calibration, which is critical for obtaining reliable data.
Research question
Does motor impairment severity in stroke patients influence calibration quality when using the Xsens Awinda system?
Methods
Forty-eight individuals with a primary stroke (median age 66 years; 21 female; FAC ≥3) performed a total of 117 motion capture assessments using the Xsens Awinda system. Calibration quality was rated using Xsens' internal quality metrics. Kruskal-Wallis tests were conducted to compare functional motor abilities, assessed using the BOOMER test and 10-meter walk time, across calibration quality levels.
Results
Of the 109 valid trials, 55% achieved good, 29% acceptable, and 16% poor calibration quality. Neither the BOOMER scores nor the 10-meter walk time was significantly associated with calibration quality levels.
Significance
These findings suggest that the Xsens Awinda system can be used across a range of motor impairment levels without introducing bias due to changes in calibration quality. Nonetheless, calibration may be challenging for severely affected individuals, a limitation that warrants further practical refinement of current procedures.
{"title":"IMU Calibration for Post-Stroke Movement Assessment: Does Calibration Quality Depend on Motor Impairment Severity?","authors":"Anna Wargel , Andreas Mayr , Steven van Andel , Elke Pucks-Faes , Peter Federolf","doi":"10.1016/j.irbm.2026.100931","DOIUrl":"10.1016/j.irbm.2026.100931","url":null,"abstract":"<div><h3>Background</h3><div>Inertial motion capture systems like Xsens Awinda are increasingly used for assessing movement in clinical populations. However, it remains unclear how stroke-related motor impairments affect the quality of motion capture calibration, which is critical for obtaining reliable data.</div></div><div><h3>Research question</h3><div>Does motor impairment severity in stroke patients influence calibration quality when using the Xsens Awinda system?</div></div><div><h3>Methods</h3><div>Forty-eight individuals with a primary stroke (median age 66 years; 21 female; FAC ≥3) performed a total of 117 motion capture assessments using the Xsens Awinda system. Calibration quality was rated using Xsens' internal quality metrics. Kruskal-Wallis tests were conducted to compare functional motor abilities, assessed using the BOOMER test and 10-meter walk time, across calibration quality levels.</div></div><div><h3>Results</h3><div>Of the 109 valid trials, 55% achieved <em>good</em>, 29% <em>acceptable</em>, and 16% <em>poor</em> calibration quality. Neither the BOOMER scores nor the 10-meter walk time was significantly associated with calibration quality levels.</div></div><div><h3>Significance</h3><div>These findings suggest that the Xsens Awinda system can be used across a range of motor impairment levels without introducing bias due to changes in calibration quality. Nonetheless, calibration may be challenging for severely affected individuals, a limitation that warrants further practical refinement of current procedures.</div></div>","PeriodicalId":14605,"journal":{"name":"Irbm","volume":"47 2","pages":"Article 100931"},"PeriodicalIF":4.2,"publicationDate":"2026-01-13","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"145963404","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":4,"RegionCategory":"医学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Pub Date : 2026-01-09DOI: 10.1016/j.irbm.2026.100928
Ying Jiang , Huiping Luo , Mrzayan Yerbolat , Jun Tao
Obstructive Sleep Apnea (OSA) is the most commonly diagnosed sleep-related breathing disorder, affecting over one billion individuals globally. While polysomnography (PSG) remains the gold standard for diagnosis, it is resource-intensive and limited in accessibility. Consequently, the development of automated diagnostic systems has emerged as a vital research area, particularly those leveraging machine learning (ML) and deep learning (DL) techniques.
Objectives
This narrative review aims to provide a comprehensive comparison of ML and DL-based methods for computer-assisted diagnosis of OSA in adults. The review emphasizes model architectures, performance metrics, application scenarios, and real-world deployment challenges. Special attention is given to advanced DL architectures—such as hybrid models and Transformers—as well as the potential role of wearable technologies in scalable diagnosis.
Material and methods
The literature search was conducted using Web of Science, IEEE Xplore, and PubMed to identify peer-reviewed articles published between 2008 and 2024. Search terms included Obstructive Sleep Apnea (OSA), k-Nearest Neighbor (k-NN), Support Vector Machine (SVM), Linear Discriminant Analysis (LDA), Logistic Regression (LR), Ensemble Algorithm, Artificial Neural Network (ANN), Convolution Neural Network (CNN), Recurrent Neural Network (RNN), Deep Belief Network (DBN), hybrid neural network, and Transformers.
Results
DL-based models have demonstrated superior performance over conventional ML approaches, particularly in their ability to perform automated, hierarchical feature extraction and model complex physiological patterns. Hybrid and Transformer-based networks stand out for their diagnostic accuracy and scalability. However, most models remain limited to benchmark dataset validation and lack hardware-level implementation. Key challenges include data heterogeneity, poor model interpretability, and limited clinical generalizability.
Conclusion
DL-driven diagnostic frameworks—especially those incorporating multimodal signals and wearable data—represent the most promising direction for achieving accurate, scalable, and accessible OSA detection. Future research should prioritize clinical validation across diverse populations, integration of explainable AI techniques, and real-world deployment through user-centered design and IoT-based wearable platforms.
阻塞性睡眠呼吸暂停(OSA)是最常见的与睡眠有关的呼吸障碍,全球有超过10亿人受到影响。虽然多导睡眠图(PSG)仍然是诊断的金标准,但它是资源密集型的,而且可及性有限。因此,自动诊断系统的开发已经成为一个重要的研究领域,特别是那些利用机器学习(ML)和深度学习(DL)技术的研究领域。目的对基于ML和基于dl的成人OSA计算机辅助诊断方法进行综合比较。回顾强调了模型架构、性能度量、应用程序场景,以及真实世界的部署挑战。特别关注先进的深度学习架构,如混合模型和转换器,以及可穿戴技术在可扩展诊断中的潜在作用。材料和方法文献检索是通过Web of Science、IEEE explore和PubMed进行的,以确定2008年至2024年间发表的同行评审文章。搜索词包括阻塞性睡眠呼吸暂停(OSA)、k-最近邻(k-NN)、支持向量机(SVM)、线性判别分析(LDA)、逻辑回归(LR)、集成算法、人工神经网络(ANN)、卷积神经网络(CNN)、循环神经网络(RNN)、深度信念网络(DBN)、混合神经网络和变压器。结果基于dl的模型表现出优于传统ML方法的性能,特别是在执行自动化、分层特征提取和复杂生理模式建模的能力方面。混合和基于变压器的网络以其诊断准确性和可扩展性而脱颖而出。然而,大多数模型仍然局限于基准数据集验证,缺乏硬件级实现。主要的挑战包括数据的异质性、较差的模型可解释性和有限的临床推广。dl驱动的诊断框架,特别是那些结合多模态信号和可穿戴数据的诊断框架,代表了实现准确、可扩展和可访问的OSA检测的最有希望的方向。未来的研究应优先考虑在不同人群中进行临床验证,整合可解释的人工智能技术,并通过以用户为中心的设计和基于物联网的可穿戴平台在现实世界中进行部署。
{"title":"Computer-Assisted Diagnosis of Obstructive Sleep Apnea in Adults: A Narrative Review","authors":"Ying Jiang , Huiping Luo , Mrzayan Yerbolat , Jun Tao","doi":"10.1016/j.irbm.2026.100928","DOIUrl":"10.1016/j.irbm.2026.100928","url":null,"abstract":"<div><div>Obstructive Sleep Apnea (OSA) is the most commonly diagnosed sleep-related breathing disorder, affecting over one billion individuals globally. While polysomnography (PSG) remains the gold standard for diagnosis, it is resource-intensive and limited in accessibility. Consequently, the development of automated diagnostic systems has emerged as a vital research area, particularly those leveraging machine learning (ML) and deep learning (DL) techniques.</div></div><div><h3>Objectives</h3><div>This narrative review aims to provide a comprehensive comparison of ML and DL-based methods for computer-assisted diagnosis of OSA in adults. The review emphasizes model architectures, performance metrics, application scenarios, and real-world deployment challenges. Special attention is given to advanced DL architectures—such as hybrid models and Transformers—as well as the potential role of wearable technologies in scalable diagnosis.</div></div><div><h3>Material and methods</h3><div>The literature search was conducted using Web of Science, IEEE Xplore, and PubMed to identify peer-reviewed articles published between 2008 and 2024. Search terms included Obstructive Sleep Apnea (OSA), k-Nearest Neighbor (k-NN), Support Vector Machine (SVM), Linear Discriminant Analysis (LDA), Logistic Regression (LR), Ensemble Algorithm, Artificial Neural Network (ANN), Convolution Neural Network (CNN), Recurrent Neural Network (RNN), Deep Belief Network (DBN), hybrid neural network, and Transformers.</div></div><div><h3>Results</h3><div>DL-based models have demonstrated superior performance over conventional ML approaches, particularly in their ability to perform automated, hierarchical feature extraction and model complex physiological patterns. Hybrid and Transformer-based networks stand out for their diagnostic accuracy and scalability. However, most models remain limited to benchmark dataset validation and lack hardware-level implementation. Key challenges include data heterogeneity, poor model interpretability, and limited clinical generalizability.</div></div><div><h3>Conclusion</h3><div>DL-driven diagnostic frameworks—especially those incorporating multimodal signals and wearable data—represent the most promising direction for achieving accurate, scalable, and accessible OSA detection. Future research should prioritize clinical validation across diverse populations, integration of explainable AI techniques, and real-world deployment through user-centered design and IoT-based wearable platforms.</div></div>","PeriodicalId":14605,"journal":{"name":"Irbm","volume":"47 1","pages":"Article 100928"},"PeriodicalIF":4.2,"publicationDate":"2026-01-09","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"145976882","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":4,"RegionCategory":"医学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Pub Date : 2026-01-07DOI: 10.1016/j.irbm.2026.100929
Ayda Karimi Dastgerdi , Amir Esrafilian , Christopher P. Carty , Alireza Y. Bavil , Rami K. Korhonen , Ivan Astori , Wayne Hall , David John Saxby
The meniscus plays a vital role in knee biomechanics, contributing to shock absorption, joint stability, proprioception, and lubrication. Anterior cruciate ligament reconstruction (ACLR) aims to restore knee stability after anterior cruciate ligament (ACL) injury; however, 30–60% of patients experience concurrent or subsequent meniscal damage. Despite this, the influence of ACLR surgical parameters on meniscal biomechanics remains largely unexplored. This study investigates how four key ACLR surgical parameters—graft type, graft size, tunnel location, and pre-tensioning—affect meniscal contact forces and stress using a coupled neuromusculoskeletal-finite element (NMSKFE) modeling approach during simulated walking. NMSK-FE simulations were conducted in six participants to assess changes in meniscal-tibial contact forces and stress distributions under various ACLR configurations. While most surgical conditions restored meniscal mechanics to near-intact levels (normalized root mean square error (nRMSE) < 10%), substantial deviations were observed in certain cases, particularly in anteroposterior and mediolateral contact forces and maximum principal stress (nRMSE > 10%). Notably, posterior graft placement with zero pre-tensioning increased medial meniscus stress, potentially elevating the risk of degeneration or injury. These findings highlight the individualized nature of ACLR outcomes, influenced not only by surgical parameters but also by patient-specific factors such as knee morphology and gait patterns. This study underscores the need for pre-surgical assessments that integrate patient-specific biomechanics to optimize ACLR strategies, enhance meniscal preservation, and improve long-term knee health. By incorporating meniscal mechanics and dynamic gait analysis, this research advances personalized ACLR approaches, addressing a critical gap in the field.
{"title":"The Effects of Surgical Parameters on Meniscal Mechanics Following Anterior Cruciate Ligament Reconstruction: An Exploratory In-Silico Study","authors":"Ayda Karimi Dastgerdi , Amir Esrafilian , Christopher P. Carty , Alireza Y. Bavil , Rami K. Korhonen , Ivan Astori , Wayne Hall , David John Saxby","doi":"10.1016/j.irbm.2026.100929","DOIUrl":"10.1016/j.irbm.2026.100929","url":null,"abstract":"<div><div>The meniscus plays a vital role in knee biomechanics, contributing to shock absorption, joint stability, proprioception, and lubrication. Anterior cruciate ligament reconstruction (ACLR) aims to restore knee stability after anterior cruciate ligament (ACL) injury; however, 30–60% of patients experience concurrent or subsequent meniscal damage. Despite this, the influence of ACLR surgical parameters on meniscal biomechanics remains largely unexplored. This study investigates how four key ACLR surgical parameters—graft type, graft size, tunnel location, and pre-tensioning—affect meniscal contact forces and stress using a coupled neuromusculoskeletal-finite element (NMSKFE) modeling approach during simulated walking. NMSK-FE simulations were conducted in six participants to assess changes in meniscal-tibial contact forces and stress distributions under various ACLR configurations. While most surgical conditions restored meniscal mechanics to near-intact levels (normalized root mean square error (nRMSE) < 10%), substantial deviations were observed in certain cases, particularly in anteroposterior and mediolateral contact forces and maximum principal stress (nRMSE > 10%). Notably, posterior graft placement with zero pre-tensioning increased medial meniscus stress, potentially elevating the risk of degeneration or injury. These findings highlight the individualized nature of ACLR outcomes, influenced not only by surgical parameters but also by patient-specific factors such as knee morphology and gait patterns. This study underscores the need for pre-surgical assessments that integrate patient-specific biomechanics to optimize ACLR strategies, enhance meniscal preservation, and improve long-term knee health. By incorporating meniscal mechanics and dynamic gait analysis, this research advances personalized ACLR approaches, addressing a critical gap in the field.</div></div>","PeriodicalId":14605,"journal":{"name":"Irbm","volume":"47 2","pages":"Article 100929"},"PeriodicalIF":4.2,"publicationDate":"2026-01-07","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"145981530","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":4,"RegionCategory":"医学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Pub Date : 2025-12-31DOI: 10.1016/j.irbm.2025.100927
Taohua Liu , Alphonse Houssou Hounye , Xiucao Yin , Qian Liu , Jichu Wu , Xiongzhi Li
BACKGROUNDS
Vascular calcification (VC) is an actively regulated dynamic process characterizing by abnormal deposition of calcium phosphate mineral in the extracellular matrix and in cells of the arterial wall. Significant advances have been made in comprehending the ferroptosis linked to VC, yet the precise molecular mechanism is still not fully understood. Interpretability and explainability of machine learning models are crucial for incorporating them into decision-making processes. We used the Shapley additive explanation (SHAP) and Local Interpretable Model-agnostic Explanations (LIME) methods in this study to interpret and explain a random forest model in order to discover the significant attributes.
METHODS
This paper employed the GEO tools to get a VC dataset. The DEGs were discovered using the EdgeR package in R to identify potential ferroptosis-associated hub genes that could be used for VC diagnosis. We used qRT-PCR and western blotting techniques to confirm the DEGs associated with ferroptosis that were discovered in the microarray data. Finally, we suggest two innovative strategies, using SHAP and LIME, to enhance interpretation. We evaluated the explanatory outcomes of the SHAP scheme with other approaches using GEO datasets.
RESULTS
We uncovered 49 ferroptosis DEGs in VC, including 31 upregulated and 18 downregulated genes. The outputs obtained from the GSEA and the study of the KEGG using WebGestalt revealed that the differentially expressed genes (DEGs) related to ferroptosis are found to be involved in six paths, one of which was the Ferroptosis signaling pathway. SHAP and LIME interpretation aligned well with the interpretations provided by the current methodologies. We demonstrated the significance of TP63 and GPX2 as crucial predictive factors for VC using of suggested methodologies. Lastly, we examined the three genes identified by two machine learning models in vitro and observed that the mRNA and protein profile levels of FTH1 exhibited an elevated level and the levels of SLC3A2 and SLC7A11 exhibited a reduced level in the β-GP-treated class in comparison to the normal class. The nomogram and 5 potential hub genes exhibited excellent predictive performance, with AUC values ranging from 0.724 to 0.969.
CONCLUSIONS
Our investigation found three ferroptosis-associated potential hub genes by comprehensive exploration (FTH1, SLC3A2, and SLC7A11). In addition, we created a nomogram for VC diagnosis utilising bioinformatics and machine learning approaches (SHAP and LIME). Our methods are effective for analyzing machine learning models and may reveal the fundamental connections among variables and outputs.
{"title":"Development and Validation of Predictive Factors for Vascular Calcification via Interpretable Machine Learning","authors":"Taohua Liu , Alphonse Houssou Hounye , Xiucao Yin , Qian Liu , Jichu Wu , Xiongzhi Li","doi":"10.1016/j.irbm.2025.100927","DOIUrl":"10.1016/j.irbm.2025.100927","url":null,"abstract":"<div><h3>BACKGROUNDS</h3><div>Vascular calcification (VC) is an actively regulated dynamic process characterizing by abnormal deposition of calcium phosphate mineral in the extracellular matrix and in cells of the arterial wall. Significant advances have been made in comprehending the ferroptosis linked to VC, yet the precise molecular mechanism is still not fully understood. Interpretability and explainability of machine learning models are crucial for incorporating them into decision-making processes. We used the Shapley additive explanation (SHAP) and Local Interpretable Model-agnostic Explanations (LIME) methods in this study to interpret and explain a random forest model in order to discover the significant attributes.</div></div><div><h3>METHODS</h3><div>This paper employed the GEO tools to get a VC dataset. The DEGs were discovered using the EdgeR package in R to identify potential ferroptosis-associated hub genes that could be used for VC diagnosis. We used qRT-PCR and western blotting techniques to confirm the DEGs associated with ferroptosis that were discovered in the microarray data. Finally, we suggest two innovative strategies, using SHAP and LIME, to enhance interpretation. We evaluated the explanatory outcomes of the SHAP scheme with other approaches using GEO datasets.</div></div><div><h3>RESULTS</h3><div>We uncovered 49 ferroptosis DEGs in VC, including 31 upregulated and 18 downregulated genes. The outputs obtained from the GSEA and the study of the KEGG using WebGestalt revealed that the differentially expressed genes (DEGs) related to ferroptosis are found to be involved in six paths, one of which was the Ferroptosis signaling pathway. SHAP and LIME interpretation aligned well with the interpretations provided by the current methodologies. We demonstrated the significance of TP63 and GPX2 as crucial predictive factors for VC using of suggested methodologies. Lastly, we examined the three genes identified by two machine learning models in vitro and observed that the mRNA and protein profile levels of FTH1 exhibited an elevated level and the levels of SLC3A2 and SLC7A11 exhibited a reduced level in the <em>β</em>-GP-treated class in comparison to the normal class. The nomogram and 5 potential hub genes exhibited excellent predictive performance, with AUC values ranging from 0.724 to 0.969.</div></div><div><h3>CONCLUSIONS</h3><div>Our investigation found three ferroptosis-associated potential hub genes by comprehensive exploration (FTH1, SLC3A2, and SLC7A11). In addition, we created a nomogram for VC diagnosis utilising bioinformatics and machine learning approaches (SHAP and LIME). Our methods are effective for analyzing machine learning models and may reveal the fundamental connections among variables and outputs.</div></div>","PeriodicalId":14605,"journal":{"name":"Irbm","volume":"47 1","pages":"Article 100927"},"PeriodicalIF":4.2,"publicationDate":"2025-12-31","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"145938958","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":4,"RegionCategory":"医学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
The imminent performance of multimodal and heterogeneous modalities to forecast the advance of Alzheimer's disease (AD) precisely is one of the key problems. Current models are usually not interpretable, time-consistent, and multimodal, making them less useful in clinical forecasting.
Objective
The objective of the study is to develop a hybrid generative approach to simulate the individualized AD progression process, which can generate future anatomical and clinical states, model latent over-time dynamics, and measure the uncertainty.
Methods
The proposed study suggests using a multimodal paradigm that enables a combination of Conditional Latent Diffusion Models (cLDM) and Neural Ordinary Differential Equations (ODEs). The model permits the generation of plausible future MRI, cognitive scoring, and biomarker trajectories for a patient at baseline. The ADNI dataset was evaluated with structural similarity (SSIM), clinical prediction error, and classification accuracy.
Key Findings
The model provided an SSIM equal to 0.86 on synthesizing future MRI, and the MAE of MMSE prediction was equal to 1.5. It exceeded baselines in all the imaging, cognitive, and biomarker settings. The conversion of AD resulted in an accuracy of the classification of 88% with stable multimodal generalization at calibrated output of probability.
Conclusion
The proposed model offers a feasible and explainable approach to the forecast of an AD trajectory, allowing realistic simulations of a digital twin and projecting its progress within a multi-year perspective. It also supports early detection, custom intervention, and uncertainty-conscious clinical decision-making.
{"title":"cLDM-ODE: A Multimodal Generative Framework for Uncertainty-Aware Forecasting of Alzheimer's Disease Progression","authors":"Rishabh Sharma , Vinay Kukreja , Shanmugasundaram Hariharan , Shih-Yu Chen","doi":"10.1016/j.irbm.2025.100926","DOIUrl":"10.1016/j.irbm.2025.100926","url":null,"abstract":"<div><h3>Context</h3><div>The imminent performance of multimodal and heterogeneous modalities to forecast the advance of Alzheimer's disease (AD) precisely is one of the key problems. Current models are usually not interpretable, time-consistent, and multimodal, making them less useful in clinical forecasting.</div></div><div><h3>Objective</h3><div>The objective of the study is to develop a hybrid generative approach to simulate the individualized AD progression process, which can generate future anatomical and clinical states, model latent over-time dynamics, and measure the uncertainty.</div></div><div><h3>Methods</h3><div>The proposed study suggests using a multimodal paradigm that enables a combination of Conditional Latent Diffusion Models (cLDM) and Neural Ordinary Differential Equations (ODEs). The model permits the generation of plausible future MRI, cognitive scoring, and biomarker trajectories for a patient at baseline. The ADNI dataset was evaluated with structural similarity (SSIM), clinical prediction error, and classification accuracy.</div></div><div><h3>Key Findings</h3><div>The model provided an SSIM equal to 0.86 on synthesizing future MRI, and the MAE of MMSE prediction was equal to 1.5. It exceeded baselines in all the imaging, cognitive, and biomarker settings. The conversion of AD resulted in an accuracy of the classification of 88% with stable multimodal generalization at calibrated output of probability.</div></div><div><h3>Conclusion</h3><div>The proposed model offers a feasible and explainable approach to the forecast of an AD trajectory, allowing realistic simulations of a digital twin and projecting its progress within a multi-year perspective. It also supports early detection, custom intervention, and uncertainty-conscious clinical decision-making.</div></div>","PeriodicalId":14605,"journal":{"name":"Irbm","volume":"47 1","pages":"Article 100926"},"PeriodicalIF":4.2,"publicationDate":"2025-12-08","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"145748791","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":4,"RegionCategory":"医学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Pub Date : 2025-11-13DOI: 10.1016/j.irbm.2025.100922
Eng-Keat Kwa , Soon-Keng Cheong , Poh-Foong Lee
Introduction
Visual impairment can significantly affect psychological and physiological well-being, potentially due to autonomic imbalance, and while deep breathing has been shown to improve autonomic modulation as measured by heart rate variability (HRV), its impact on individuals with visual impairment remains underexplored, prompting this study to investigate its immediate and long-term effects on HRV in this population compared to normally sighted individuals.
Materials and methods
A total of 98 participants with visually impaired (VI) individuals (n = 49) and normally sighted (NS) individuals (n = 49) were recruited. The HRV, including standard deviation of the normal-to-normal intervals (SDNN), root mean square of successive differences, normalized low frequency (nLF), normalized high frequency (nHF), and low frequency to high-frequency ratio (LF/HF), was measured at baseline (BL), immediate post intervention (IPI) and post-intervention (POST) after 2 weeks daily audio-guided deep breathing.
Results
Kruskal-Wallis tests revealed significant phase effects for nLF (p = 0.002), nHF (p = 0.002), and LF/HF (p = 0.024) in the VI group, with post hoc analyses indicating significantly higher nLF (p = 0.004), LF/HF (p = 0.007), and lower nHF (p = 0.004) at IPI compared to BL. While the NS group showed no significant changes across phases. Between-group comparisons revealed significantly higher nLF (p = 0.034), LF/HF (p = 0.007), and lower nHF (p = 0.034) at IPI in the VI group compared to the NS group.
Conclusion
Deep breathing led to immediate increases in nLF and LF/HF, and a decrease in nHF, in individuals with visual impairment compared to sighted individuals, suggesting baroreflex resonance at 0.1 Hz. However, the absence of significant SDNN changes limits conclusions about parasympathetic modulation. Further research is needed to assess the potential long-term benefits.
{"title":"Exploring Autonomic Modulation Through Deep Breathing: Immediate and Long-Term Effects on Heart Rate Variability in Visually Impaired Individuals","authors":"Eng-Keat Kwa , Soon-Keng Cheong , Poh-Foong Lee","doi":"10.1016/j.irbm.2025.100922","DOIUrl":"10.1016/j.irbm.2025.100922","url":null,"abstract":"<div><h3>Introduction</h3><div>Visual impairment can significantly affect psychological and physiological well-being, potentially due to autonomic imbalance, and while deep breathing has been shown to improve autonomic modulation as measured by heart rate variability (HRV), its impact on individuals with visual impairment remains underexplored, prompting this study to investigate its immediate and long-term effects on HRV in this population compared to normally sighted individuals.</div></div><div><h3>Materials and methods</h3><div>A total of 98 participants with visually impaired (VI) individuals (n = 49) and normally sighted (NS) individuals (n = 49) were recruited. The HRV, including standard deviation of the normal-to-normal intervals (SDNN), root mean square of successive differences, normalized low frequency (nLF), normalized high frequency (nHF), and low frequency to high-frequency ratio (LF/HF), was measured at baseline (BL), immediate post intervention (IPI) and post-intervention (POST) after 2 weeks daily audio-guided deep breathing.</div></div><div><h3>Results</h3><div>Kruskal-Wallis tests revealed significant phase effects for nLF (p = 0.002), nHF (p = 0.002), and LF/HF (p = 0.024) in the VI group, with post hoc analyses indicating significantly higher nLF (p = 0.004), LF/HF (p = 0.007), and lower nHF (p = 0.004) at IPI compared to BL. While the NS group showed no significant changes across phases. Between-group comparisons revealed significantly higher nLF (p = 0.034), LF/HF (p = 0.007), and lower nHF (p = 0.034) at IPI in the VI group compared to the NS group.</div></div><div><h3>Conclusion</h3><div>Deep breathing led to immediate increases in nLF and LF/HF, and a decrease in nHF, in individuals with visual impairment compared to sighted individuals, suggesting baroreflex resonance at 0.1 Hz. However, the absence of significant SDNN changes limits conclusions about parasympathetic modulation. Further research is needed to assess the potential long-term benefits.</div></div>","PeriodicalId":14605,"journal":{"name":"Irbm","volume":"47 1","pages":"Article 100922"},"PeriodicalIF":4.2,"publicationDate":"2025-11-13","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"145584574","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":4,"RegionCategory":"医学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Pub Date : 2025-11-07DOI: 10.1016/j.irbm.2025.100921
Veli Avci , Mehmet Tahir Huyut , Andrei Velichko , Maksim Belyaev
Introduction
Acute appendicitis is the commonest cause of surgical abdominal pain, yet diagnosis in children remains challenging; delays increase the risk of perforation, peritonitis and sepsis. We sought to develop a rapid, inexpensive and interpretable clinical-decision support system (CDSS) that leverages routine blood tests (RBT) to assist early paediatric triage.
Materials and Methods
In this retrospective single-centre study (January 2020–December 2024) we analysed 275 emergency-department encounters for abdominal pain (75 histology-confirmed appendicitis, 200 controls). The six-stage pipeline comprised (1) cohort selection; (2) exploratory logistic-regression screening of RBT variables; (3) training of Random Forest, Gradient Boosting and LightGBM ensembles (with/without SMOTE) under 10 × 10 stratified cross-validation; (4) SHAP-based feature interpretation; (5) exhaustive generation of every two- and three-parameter arithmetic biomarker from seven RBT features; and (6) derivation of probability-threshold curves and a three-zone rule tree for the top biomarker. Performance was reported with accuracy (ACC), Matthews correlation coefficient (MCC), AUC-ROC, sensitivity, specificity, F1-Score PPV and NPV.
Results
Logistic regression and SHAP confirmed CRP, WBC and neutrophil count as strong positive predictors, whereas MPV and PDW were protective; PLT remained non-informative. All three ensemble classifiers surpassed 97% accuracy, 98% AUC-ROC and 0.93 MCC, with no gain from SMOTE. An extensive formula search, the best two-parameter marker was Neutrophil ÷ PDW (MCC = 0.73, specificity 95%). Its ensemble curve crosses P = 0.5 five times; practical cut-offs of < 0.633 (strongly indicate healthy) and > 0.794 (strongly indicate appendicitis) retain high NPV (∼91%) and PPV (∼86%). Among triple formulas that do not rely on PLT, the leading biomarker was CRP+WBC+Neutrophil (MCC = 0.85, PPV 92%, NPV 95%). The ensemble curve intersects at P = 0.5 at three points; values >27 strongly predict appendicitis, <23 indicates a healthy state, and values 23–27 leave a small uncertain band. A rule-based CDSS built on these two biomarkers correctly classified all controls (specificity 100%), sensitivity 95%, achieved 91% overall accuracy, and offers interpretable, electronic health records (EHRs)-ready cut-offs for paediatric appendicitis triage.
Conclusion
Routine haematology-biochemistry data, interpreted through ensemble learning and engineered biomarkers, can deliver fast, transparent and highly accurate support for paediatric appendicitis triage. Given its zero false-positive rate, the proposed CDSS is best suited to in-hospital monitoring, where minimising false negatives is critical. Prospective multi-centre validation is warranted.
{"title":"A Novel Biomarker-Based Decision Support System for Pediatric Appendicitis Diagnosis: A Comparative Study of Ensemble Models Algorithms","authors":"Veli Avci , Mehmet Tahir Huyut , Andrei Velichko , Maksim Belyaev","doi":"10.1016/j.irbm.2025.100921","DOIUrl":"10.1016/j.irbm.2025.100921","url":null,"abstract":"<div><h3>Introduction</h3><div>Acute appendicitis is the commonest cause of surgical abdominal pain, yet diagnosis in children remains challenging; delays increase the risk of perforation, peritonitis and sepsis. We sought to develop a rapid, inexpensive and interpretable clinical-decision support system (CDSS) that leverages routine blood tests (RBT) to assist early paediatric triage.</div></div><div><h3>Materials and Methods</h3><div>In this retrospective single-centre study (January 2020–December 2024) we analysed 275 emergency-department encounters for abdominal pain (75 histology-confirmed appendicitis, 200 controls). The six-stage pipeline comprised (1) cohort selection; (2) exploratory logistic-regression screening of RBT variables; (3) training of Random Forest, Gradient Boosting and LightGBM ensembles (with/without SMOTE) under 10 × 10 stratified cross-validation; (4) SHAP-based feature interpretation; (5) exhaustive generation of every two- and three-parameter arithmetic biomarker from seven RBT features; and (6) derivation of probability-threshold curves and a three-zone rule tree for the top biomarker. Performance was reported with accuracy (ACC), Matthews correlation coefficient (MCC), AUC-ROC, sensitivity, specificity, F1-Score PPV and NPV.</div></div><div><h3>Results</h3><div>Logistic regression and SHAP confirmed CRP, WBC and neutrophil count as strong positive predictors, whereas MPV and PDW were protective; PLT remained non-informative. All three ensemble classifiers surpassed 97% accuracy, 98% AUC-ROC and 0.93 MCC, with no gain from SMOTE. An extensive formula search, the best two-parameter marker was Neutrophil ÷ PDW (MCC = 0.73, specificity 95%). Its ensemble curve crosses P = 0.5 five times; practical cut-offs of < 0.633 (strongly indicate healthy) and > 0.794 (strongly indicate appendicitis) retain high NPV (∼91%) and PPV (∼86%). Among triple formulas that do not rely on PLT, the leading biomarker was CRP+WBC+Neutrophil (MCC = 0.85, PPV 92%, NPV 95%). The ensemble curve intersects at P = 0.5 at three points; values >27 strongly predict appendicitis, <23 indicates a healthy state, and values 23–27 leave a small uncertain band. A rule-based CDSS built on these two biomarkers correctly classified all controls (specificity 100%), sensitivity 95%, achieved 91% overall accuracy, and offers interpretable, electronic health records (EHRs)-ready cut-offs for paediatric appendicitis triage.</div></div><div><h3>Conclusion</h3><div>Routine haematology-biochemistry data, interpreted through ensemble learning and engineered biomarkers, can deliver fast, transparent and highly accurate support for paediatric appendicitis triage. Given its zero false-positive rate, the proposed CDSS is best suited to in-hospital monitoring, where minimising false negatives is critical. Prospective multi-centre validation is warranted.</div></div>","PeriodicalId":14605,"journal":{"name":"Irbm","volume":"47 1","pages":"Article 100921"},"PeriodicalIF":4.2,"publicationDate":"2025-11-07","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"145622682","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":4,"RegionCategory":"医学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Pub Date : 2025-11-06DOI: 10.1016/j.irbm.2025.100919
Alireza Y. Bavil , Emmanuel Eghan-Acquah , Rod Barrett , Laura E. Diamond , Liam Johnson , Stefanie Feih , David J. Saxby , Christopher P. Carty
Background
Proximal femoral osteotomy (PFO) is a surgical correction of proximal femoral deformity. Surgical choices, notably the postoperative neck-shaft angle (NSA), can affect postoperative stability and healing. While NSA's role in femoral mechanics is recognized, its impact on bone healing remains unclear.
Objective
To determine the influence of postoperative NSA on bone healing; to investigate the interaction of healing-related parameters and mechanical safety.
Methods
Medical imaging, gait data, and surgical information from nine patients (10 femurs) were used to build personalized finite element models of PFO-implanted femurs. Three postoperative neck-shaft angles (128°, 135°, 143°) were tested. During simulated walking, interfragmentary movement, deviatoric strain, mechanical stimulus, bone-implant micromotion, and peak von Mises stress (PVMS) were evaluated. Healing mode (primary vs. secondary) was classified based on interfragmentary movement thresholds.
Results
Mono-modal healing (primary in four and secondary in three) was observed in seven femurs, independent of postoperative NSA. In three femurs, a transition from primary to secondary healing occurred with increased NSAs. The PVMS for the implant and the bone exceeded critical values across all NSAs for two femurs, and micromotion was deemed critical only at 128° in two femurs.
Conclusion
This study highlights the value of integrating patient-specific modelling into preoperative planning. Bone healing modes were sensitive to postoperative NSA in 30% of cases, while 70% exhibited a single healing mode across the tested angles. Overall, findings suggest the need to simultaneously consider the complex interaction between NSA and subject-specific factors on mechanical safety and healing outcomes following PFO.
{"title":"Selected Postoperative Neck-Shaft Angle in Proximal Femoral Osteotomy Can Affect the Bone Healing: A Finite Element Study","authors":"Alireza Y. Bavil , Emmanuel Eghan-Acquah , Rod Barrett , Laura E. Diamond , Liam Johnson , Stefanie Feih , David J. Saxby , Christopher P. Carty","doi":"10.1016/j.irbm.2025.100919","DOIUrl":"10.1016/j.irbm.2025.100919","url":null,"abstract":"<div><h3>Background</h3><div>Proximal femoral osteotomy (PFO) is a surgical correction of proximal femoral deformity. Surgical choices, notably the postoperative neck-shaft angle (NSA), can affect postoperative stability and healing. While NSA's role in femoral mechanics is recognized, its impact on bone healing remains unclear.</div></div><div><h3>Objective</h3><div>To determine the influence of postoperative NSA on bone healing; to investigate the interaction of healing-related parameters and mechanical safety.</div></div><div><h3>Methods</h3><div>Medical imaging, gait data, and surgical information from nine patients (10 femurs) were used to build personalized finite element models of PFO-implanted femurs. Three postoperative neck-shaft angles (128°, 135°, 143°) were tested. During simulated walking, interfragmentary movement, deviatoric strain, mechanical stimulus, bone-implant micromotion, and peak von Mises stress (PVMS) were evaluated. Healing mode (primary vs. secondary) was classified based on interfragmentary movement thresholds.</div></div><div><h3>Results</h3><div>Mono-modal healing (primary in four and secondary in three) was observed in seven femurs, independent of postoperative NSA. In three femurs, a transition from primary to secondary healing occurred with increased NSAs. The PVMS for the implant and the bone exceeded critical values across all NSAs for two femurs, and micromotion was deemed critical only at 128° in two femurs.</div></div><div><h3>Conclusion</h3><div>This study highlights the value of integrating patient-specific modelling into preoperative planning. Bone healing modes were sensitive to postoperative NSA in 30% of cases, while 70% exhibited a single healing mode across the tested angles. Overall, findings suggest the need to simultaneously consider the complex interaction between NSA and subject-specific factors on mechanical safety and healing outcomes following PFO.</div></div>","PeriodicalId":14605,"journal":{"name":"Irbm","volume":"47 1","pages":"Article 100919"},"PeriodicalIF":4.2,"publicationDate":"2025-11-06","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"145622683","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":4,"RegionCategory":"医学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Pub Date : 2025-10-31DOI: 10.1016/j.irbm.2025.100920
Pierre Rumeau , Marc Dupui
User Centered Design (UCD) is widely used in computer science and service provision, not so in public health administration. As a regulatory body (e.santé Occitanie) we applied UCD to design, deploy and assess a new medical service (ambulatory ECG at the local medical practice) at the request of a local general practitioner (GP). Method. We used semi-directed interviews starting with the GP to define the user groups profiles, as-is scenario and personae. We organized a focus group with the local stakeholders and representatives of Social Security, Regional Health Agency and e.santé Occitanie to refine the expression of needs. We used a SWOT to categorize the internal and external factors impacting the service. We designed a first specification prototype and a preliminary assessment of additional costs from the point of view of the Social Security. When the service started we arranged an 8-week prospective survey to verify the medical indications adhered to best practices. We ran an observational costs analysis on the first full year of operation. Results. As the study started in 2018 we had to cope with COVID 19 induced delays and changes. End of 2021 we could observe the start of the operation of the optimized version of the service with 4 steps instead of 9 in the as-is version. The 8-week medical indication survey showed full compliance with best practices. In 2022, first full year of operation, 39 patients had an AECG, less than expected, probably due to a waiting list effect. The additional costs analysis gave a benefit for Social Security of 12,397.05 € at 95% of expectations. After absorption of investment costs, expected benefit is 16,479.45 € at 98%. Conclusion. As a regulatory body, we successfully implemented the full UCD cycle on a bottom-up medical service improvement proposal. The service is still operating and has been spontaneously adopted elsewhere. UCD applied to selected local proposals could unveil a wealth of quality of care improvement solutions while keeping the costs down.
{"title":"User Centered Design May Apply to Public Health Issues: A Case Study on Arrhythmia Detection Service in Rural General Practice","authors":"Pierre Rumeau , Marc Dupui","doi":"10.1016/j.irbm.2025.100920","DOIUrl":"10.1016/j.irbm.2025.100920","url":null,"abstract":"<div><div>User Centered Design (UCD) is widely used in computer science and service provision, not so in public health administration. As a regulatory body (e.santé Occitanie) we applied UCD to design, deploy and assess a new medical service (ambulatory ECG at the local medical practice) at the request of a local general practitioner (<em>GP</em>). Method. We used semi-directed interviews starting with the GP to define the user groups profiles, as-is scenario and personae. We organized a focus group with the local stakeholders and representatives of Social Security, Regional Health Agency and e.santé Occitanie to refine the expression of needs. We used a SWOT to categorize the internal and external factors impacting the service. We designed a first specification prototype and a preliminary assessment of additional costs from the point of view of the Social Security. When the service started we arranged an 8-week prospective survey to verify the medical indications adhered to best practices. We ran an observational costs analysis on the first full year of operation. Results. As the study started in 2018 we had to cope with COVID 19 induced delays and changes. End of 2021 we could observe the start of the operation of the optimized version of the service with 4 steps instead of 9 in the as-is version. The 8-week medical indication survey showed full compliance with best practices. In 2022, first full year of operation, 39 patients had an AECG, less than expected, probably due to a waiting list effect. The additional costs analysis gave a benefit for Social Security of 12,397.05 € at 95% of expectations. After absorption of investment costs, expected benefit is 16,479.45 € at 98%. Conclusion. As a regulatory body, we successfully implemented the full UCD cycle on a bottom-up medical service improvement proposal. The service is still operating and has been spontaneously adopted elsewhere. UCD applied to selected local proposals could unveil a wealth of quality of care improvement solutions while keeping the costs down.</div></div>","PeriodicalId":14605,"journal":{"name":"Irbm","volume":"46 6","pages":"Article 100920"},"PeriodicalIF":4.2,"publicationDate":"2025-10-31","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"145462854","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":4,"RegionCategory":"医学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Pub Date : 2025-10-29DOI: 10.1016/j.irbm.2025.100918
Kalthoum Belghith, Ali Aghaei, Wael Maktouf, Mustapha Zidi
This study aims to evaluate stress distributions in the medial gastrocnemius muscle (GM) of patients with spastic myopathy after stroke. Shear wave elastography was employed to measure the shear modulus in three specific regions (upper, middle, and lower) of the muscle in six participants (three healthy and three post-stroke). Shear modulus measurements served as inputs for a finite element model to estimate stress distributions during uniform muscle stretching. The skeletal muscle was modeled as a hyperelastic, incompressible, and inhomogeneous material. The results showed that the stress distribution tends to increase in the post-stroke group, particularly in the middle (+60%) and lower regions (+13%). These results demonstrate the feasibility of estimating stress distributions using SWE data in post-stroke conditions, highlighting potential for further optimization of both experimental protocols and numerical models. These advancements could ultimately provide valuable insights into the clinical challenges associated with understanding spastic myopathy pathologies.
{"title":"Assessment of Stress Distributions in a Skeletal Muscle Affected by Post-Stroke Spastic Myopathy","authors":"Kalthoum Belghith, Ali Aghaei, Wael Maktouf, Mustapha Zidi","doi":"10.1016/j.irbm.2025.100918","DOIUrl":"10.1016/j.irbm.2025.100918","url":null,"abstract":"<div><div>This study aims to evaluate stress distributions in the medial gastrocnemius muscle (GM) of patients with spastic myopathy after stroke. Shear wave elastography was employed to measure the shear modulus in three specific regions (upper, middle, and lower) of the muscle in six participants (three healthy and three post-stroke). Shear modulus measurements served as inputs for a finite element model to estimate stress distributions during uniform muscle stretching. The skeletal muscle was modeled as a hyperelastic, incompressible, and inhomogeneous material. The results showed that the stress distribution tends to increase in the post-stroke group, particularly in the middle (+60%) and lower regions (+13%). These results demonstrate the feasibility of estimating stress distributions using SWE data in post-stroke conditions, highlighting potential for further optimization of both experimental protocols and numerical models. These advancements could ultimately provide valuable insights into the clinical challenges associated with understanding spastic myopathy pathologies.</div></div>","PeriodicalId":14605,"journal":{"name":"Irbm","volume":"46 6","pages":"Article 100918"},"PeriodicalIF":4.2,"publicationDate":"2025-10-29","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"145462855","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":4,"RegionCategory":"医学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}