Pub Date : 2025-02-03DOI: 10.1109/OJEMB.2025.3537768
Mattia Di Florio;Yannick Bornat;Marta Carè;Vinicius Rosa Cota;Stefano Buccelli;Michela Chiappalone
Goal: This study addresses the inherent difficulties in the creation of neuroengineering devices for real-time neural signal processing, a task typically characterized by intricate and technically demanding processes. Beneath the substantial hardware advancements in neurotechnology, there is often rather complex low-level code that poses challenges in terms of development, documentation, and long-term maintenance. Methods: We adopted an alternative strategy centered on Model-Based Design (MBD) to simplify the creation of neuroengineering systems and reduce the entry barriers. MBD offers distinct advantages by streamlining the design workflow, from modelling to implementation, thus facilitating the development of intricate systems. A spike detection algorithm has been implemented on a commercially available system based on a Field-Programmable Gate Array (FPGA) that combines neural probe electronics with configurable integrated circuit. The entire process of data handling and data processing was performed within the Simulink environment, with subsequent generation of hardware description language (HDL) code tailored to the FPGA hardware. Results: The validation was conducted through in vivo experiments involving six animals and demonstrated the capability of our MBD-based real time processing (latency <=>Conclusions: This methodology can have a significant impact in the development of neuroengineering systems by speeding up the prototyping of various system architectures. We have made all project code files open source, thereby providing free access to fellow scientists interested in the development of neuroengineering systems.
{"title":"Enabling Model-Based Design for Real-Time Spike Detection","authors":"Mattia Di Florio;Yannick Bornat;Marta Carè;Vinicius Rosa Cota;Stefano Buccelli;Michela Chiappalone","doi":"10.1109/OJEMB.2025.3537768","DOIUrl":"https://doi.org/10.1109/OJEMB.2025.3537768","url":null,"abstract":"<italic>Goal</i>: This study addresses the inherent difficulties in the creation of neuroengineering devices for real-time neural signal processing, a task typically characterized by intricate and technically demanding processes. Beneath the substantial hardware advancements in neurotechnology, there is often rather complex low-level code that poses challenges in terms of development, documentation, and long-term maintenance. <italic>Methods</i>: We adopted an alternative strategy centered on Model-Based Design (MBD) to simplify the creation of neuroengineering systems and reduce the entry barriers. MBD offers distinct advantages by streamlining the design workflow, from modelling to implementation, thus facilitating the development of intricate systems. A spike detection algorithm has been implemented on a commercially available system based on a Field-Programmable Gate Array (FPGA) that combines neural probe electronics with configurable integrated circuit. The entire process of data handling and data processing was performed within the Simulink environment, with subsequent generation of hardware description language (HDL) code tailored to the FPGA hardware. <italic>Results</i>: The validation was conducted through in vivo experiments involving six animals and demonstrated the capability of our MBD-based real time processing (latency <=>Conclusions</i>: This methodology can have a significant impact in the development of neuroengineering systems by speeding up the prototyping of various system architectures. We have made all project code files open source, thereby providing free access to fellow scientists interested in the development of neuroengineering systems.","PeriodicalId":33825,"journal":{"name":"IEEE Open Journal of Engineering in Medicine and Biology","volume":"6 ","pages":"312-319"},"PeriodicalIF":2.7,"publicationDate":"2025-02-03","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://ieeexplore.ieee.org/stamp/stamp.jsp?tp=&arnumber=10870096","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143583136","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Pub Date : 2025-01-31DOI: 10.1109/OJEMB.2025.3537560
Hamza Rasaee;Maryia Samuel;Bahareh Behboodi;Jonathan Afilalo;Hassan Rivaz
Ultrasound imaging is crucial in medical diagnostics, offering real-time visualization of internal anatomical structures. However, accurate automatic segmentation of ultrasound images remains challenging, particularly in scenarios with limited labeled data. In this paper, we propose a semi-supervised learning approach for ultrasound image segmentation, leveraging the statistics of data in unlabeled images to enhance segmentation accuracy. Our method builds upon the encoder-decoder architecture and incorporates innovative semi-supervised learning techniques based on contrastive learning. We have collected ultrasound images from 80 patients and 34 healthy volunteers, focusing on applications in sarcopenia assessment and emergency response scenarios. We demonstrate the effectiveness of our approach through extensive experiments on expert segmentations in this dataset.Our results demonstrate the superior performance of the proposed method across various training data splits (i.e., 1%, 5%, 10%, 20%, 30%, and 100%). While U-NET performed the best with 100% of the training data (i.e., 154 annotated images), the proposed method achieved comparable performance with only 10% of the data (i.e., 16 annotated images). Furthermore, statistical analysis confirmed that our method significantly outperforms existing models, including U-NET, CCT, and UniMatch, in most scenarios (i.e., training set splits). These findings highlight the robustness and efficiency of the proposed method, especially in environments where labeled data is scarce
{"title":"Ultrasound Segmentation Using Semi-Supervised Learning: Application in Point-of-Care Sarcopenia Assessment","authors":"Hamza Rasaee;Maryia Samuel;Bahareh Behboodi;Jonathan Afilalo;Hassan Rivaz","doi":"10.1109/OJEMB.2025.3537560","DOIUrl":"https://doi.org/10.1109/OJEMB.2025.3537560","url":null,"abstract":"Ultrasound imaging is crucial in medical diagnostics, offering real-time visualization of internal anatomical structures. However, accurate automatic segmentation of ultrasound images remains challenging, particularly in scenarios with limited labeled data. In this paper, we propose a semi-supervised learning approach for ultrasound image segmentation, leveraging the statistics of data in unlabeled images to enhance segmentation accuracy. Our method builds upon the encoder-decoder architecture and incorporates innovative semi-supervised learning techniques based on contrastive learning. We have collected ultrasound images from 80 patients and 34 healthy volunteers, focusing on applications in sarcopenia assessment and emergency response scenarios. We demonstrate the effectiveness of our approach through extensive experiments on expert segmentations in this dataset.Our results demonstrate the superior performance of the proposed method across various training data splits (i.e., 1%, 5%, 10%, 20%, 30%, and 100%). While U-NET performed the best with 100% of the training data (i.e., 154 annotated images), the proposed method achieved comparable performance with only 10% of the data (i.e., 16 annotated images). Furthermore, statistical analysis confirmed that our method significantly outperforms existing models, including U-NET, CCT, and UniMatch, in most scenarios (i.e., training set splits). These findings highlight the robustness and efficiency of the proposed method, especially in environments where labeled data is scarce","PeriodicalId":33825,"journal":{"name":"IEEE Open Journal of Engineering in Medicine and Biology","volume":"6 ","pages":"322-331"},"PeriodicalIF":2.7,"publicationDate":"2025-01-31","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://ieeexplore.ieee.org/stamp/stamp.jsp?tp=&arnumber=10869339","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143706642","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Pub Date : 2025-01-16DOI: 10.1109/OJEMB.2025.3530841
Benjamin P. Veasey;Amir A. Amini
Goal: This paper investigates using Low-Rank Adaptation (LoRA) to adapt large vision models (LVMs) pretrained with self-supervised learning (SSL) for lung nodule malignancy classification. Inspired by LoRA's success in the field of Natural Language Processing, we hypothesized that such an adaptation technique can significantly improve classification performance, parameter efficiency, and training speed for the novel application of lung image cancer diagnostic. Methods: Utilizing two comprehensive lung nodule datasets, NLSTx and LIDC, which together encompass a diverse array of biopsy- and radiologist-confirmed lung CT scans, our rigorous experimental setup demonstrates that LoRA-adapted models markedly surpass traditional fine-tuning methods. Results: The best LoRA-adapted model achieved a 3% increase in ROC AUC over the state-of-the-art model, utilized 89.9% fewer parameters, and reduced training times by 36.5%. Conclusions: Integrating LoRA with out-of-domain pretrained LVMs offers a promising avenue for enhancing performance of lung nodule malignancy classification. The annotations for the NLSTx dataset are also released with this paper on GitHub at https://github.com/benVZ/NLSTx.
{"title":"Low-Rank Adaptation of Pre-Trained Large Vision Models for Improved Lung Nodule Malignancy Classification","authors":"Benjamin P. Veasey;Amir A. Amini","doi":"10.1109/OJEMB.2025.3530841","DOIUrl":"https://doi.org/10.1109/OJEMB.2025.3530841","url":null,"abstract":"<italic>Goal:</i> This paper investigates using Low-Rank Adaptation (LoRA) to adapt large vision models (LVMs) pretrained with self-supervised learning (SSL) for lung nodule malignancy classification. Inspired by LoRA's success in the field of Natural Language Processing, we hypothesized that such an adaptation technique can significantly improve classification performance, parameter efficiency, and training speed for the novel application of lung image cancer diagnostic. <italic>Methods:</i> Utilizing two comprehensive lung nodule datasets, NLSTx and LIDC, which together encompass a diverse array of biopsy- and radiologist-confirmed lung CT scans, our rigorous experimental setup demonstrates that LoRA-adapted models markedly surpass traditional fine-tuning methods. <italic>Results:</i> The best LoRA-adapted model achieved a 3% increase in ROC AUC over the state-of-the-art model, utilized 89.9% fewer parameters, and reduced training times by 36.5%. <italic>Conclusions:</i> Integrating LoRA with out-of-domain pretrained LVMs offers a promising avenue for enhancing performance of lung nodule malignancy classification. The annotations for the NLSTx dataset are also released with this paper on GitHub at <uri>https://github.com/benVZ/NLSTx</uri>.","PeriodicalId":33825,"journal":{"name":"IEEE Open Journal of Engineering in Medicine and Biology","volume":"6 ","pages":"296-304"},"PeriodicalIF":2.7,"publicationDate":"2025-01-16","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://ieeexplore.ieee.org/stamp/stamp.jsp?tp=&arnumber=10843806","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143361179","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Pub Date : 2025-01-14DOI: 10.1109/OJEMB.2025.3526457
Vasileios Skaramagkas;Ioannis Kyprakis;Georgia S. Karanasiou;Dimitris I. Fotiadis;Manolis Tsiknakis
Quality of Life (QoL) assessment has evolved over time, encompassing diverse aspects of human existence beyond just health. This paper presents a comprehensive review of the integration of Deep Learning (DL) techniques in QoL assessment, focusing on the analysis of wearable data. QoL, as defined by the World Health Organisation, encompasses physical, mental, and social well-being, making it a multifaceted concept. Traditional QoL assessment methods, often reliant on subjective reports or informal questioning, face challenges in quantification and standardization. To address these challenges, DL, a branch of machine learning inspired by the human brain, has emerged as a promising tool. DL models can analyze vast and complex datasets, including patient-reported outcomes, medical images, and physiological signals, enabling a deeper understanding of factors influencing an individual's QoL. Notably, wearable sensory devices have gained prominence, offering real-time data on vital signs and enabling remote healthcare monitoring. This review critically examines DL's role in QoL assessment through the use of wearable data, with particular emphasis on the subdomains of physical and psychological well-being. By synthesizing current research and identifying knowledge gaps, this review provides valuable insights for researchers, clinicians, and policymakers aiming to enhance QoL assessment with DL. Ultimately, the paper contributes to the adoption of advanced technologies to improve the well-being and QoL of individuals from diverse backgrounds.
{"title":"A Review on Deep Learning for Quality of Life Assessment Through the Use of Wearable Data","authors":"Vasileios Skaramagkas;Ioannis Kyprakis;Georgia S. Karanasiou;Dimitris I. Fotiadis;Manolis Tsiknakis","doi":"10.1109/OJEMB.2025.3526457","DOIUrl":"https://doi.org/10.1109/OJEMB.2025.3526457","url":null,"abstract":"Quality of Life (QoL) assessment has evolved over time, encompassing diverse aspects of human existence beyond just health. This paper presents a comprehensive review of the integration of Deep Learning (DL) techniques in QoL assessment, focusing on the analysis of wearable data. QoL, as defined by the World Health Organisation, encompasses physical, mental, and social well-being, making it a multifaceted concept. Traditional QoL assessment methods, often reliant on subjective reports or informal questioning, face challenges in quantification and standardization. To address these challenges, DL, a branch of machine learning inspired by the human brain, has emerged as a promising tool. DL models can analyze vast and complex datasets, including patient-reported outcomes, medical images, and physiological signals, enabling a deeper understanding of factors influencing an individual's QoL. Notably, wearable sensory devices have gained prominence, offering real-time data on vital signs and enabling remote healthcare monitoring. This review critically examines DL's role in QoL assessment through the use of wearable data, with particular emphasis on the subdomains of physical and psychological well-being. By synthesizing current research and identifying knowledge gaps, this review provides valuable insights for researchers, clinicians, and policymakers aiming to enhance QoL assessment with DL. Ultimately, the paper contributes to the adoption of advanced technologies to improve the well-being and QoL of individuals from diverse backgrounds.","PeriodicalId":33825,"journal":{"name":"IEEE Open Journal of Engineering in Medicine and Biology","volume":"6 ","pages":"261-268"},"PeriodicalIF":2.7,"publicationDate":"2025-01-14","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://ieeexplore.ieee.org/stamp/stamp.jsp?tp=&arnumber=10841411","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143105936","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Pub Date : 2025-01-10DOI: 10.1109/OJEMB.2025.3528194
Megan Mendieta;Maryam Hatami;Manmohan Singh;Sajedeh Saeidi Fard;Mohammad Dehshiri;Alexander Schill;Dmitry Nevozhay;Salavat Aglyamov;Bulent Ozpolat;Konstantin V. Sokolov;Yasemin M. Akay;Kirill V. Larin;Metin Akay
Goal: In this research, we investigated the changes in elasticity of in vitro glioblastoma multiforme (GBM) spheroids when treated with the gold standard chemotherapy for GBM, Temozolomide (TMZ). Additionally, we aimed to use this alternative biomarker to assess how modifying the tumor microenvironment (TME) with the addition of human astrocytes (HA) would influence treatment efficacy. Methods: Spheroid stiffness was investigated using advanced non-invasive optical techniques, nanobomb optical coherence elastography (nb-OCE) and Brillouin microscopy to obtain new biomechanical insights by assessing local tumor progression or response to therapy using GBM cells (LN229). Results: The treated monocultured GBM groups showed a significant decrease in stiffness and increased sensitivity to treatment with TMZ. Treated HA groups across approaches remained relatively unchanged in stiffness. Treated co-culture groups demonstrated significant resistance to treatment with TMZ, where stiffness decreased less than that of the treated LN229 cells. Conclusions: These results confirm earlier findings using cell viability as a biomarker for treatment efficacy, making nb-OCE and Brillouin promising options to probe 3D tumor models in vitro non-invasively.
{"title":"Non-Invasive Measurement of Elasticity in Glioblastoma Multiforme Validates Decreased TMZ Sensitivity in Astrocyte Co-Culture","authors":"Megan Mendieta;Maryam Hatami;Manmohan Singh;Sajedeh Saeidi Fard;Mohammad Dehshiri;Alexander Schill;Dmitry Nevozhay;Salavat Aglyamov;Bulent Ozpolat;Konstantin V. Sokolov;Yasemin M. Akay;Kirill V. Larin;Metin Akay","doi":"10.1109/OJEMB.2025.3528194","DOIUrl":"https://doi.org/10.1109/OJEMB.2025.3528194","url":null,"abstract":"<italic>Goal:</i> In this research, we investigated the changes in elasticity of in vitro glioblastoma multiforme (GBM) spheroids when treated with the gold standard chemotherapy for GBM, Temozolomide (TMZ). Additionally, we aimed to use this alternative biomarker to assess how modifying the tumor microenvironment (TME) with the addition of human astrocytes (HA) would influence treatment efficacy. <italic>Methods:</i> Spheroid stiffness was investigated using advanced non-invasive optical techniques, nanobomb optical coherence elastography (nb-OCE) and Brillouin microscopy to obtain new biomechanical insights by assessing local tumor progression or response to therapy using GBM cells (LN229). <italic>Results:</i> The treated monocultured GBM groups showed a significant decrease in stiffness and increased sensitivity to treatment with TMZ. Treated HA groups across approaches remained relatively unchanged in stiffness. Treated co-culture groups demonstrated significant resistance to treatment with TMZ, where stiffness decreased less than that of the treated LN229 cells. <italic>Conclusions:</i> These results confirm earlier findings using cell viability as a biomarker for treatment efficacy, making nb-OCE and Brillouin promising options to probe 3D tumor models in vitro non-invasively.","PeriodicalId":33825,"journal":{"name":"IEEE Open Journal of Engineering in Medicine and Biology","volume":"6 ","pages":"287-295"},"PeriodicalIF":2.9,"publicationDate":"2025-01-10","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://ieeexplore.ieee.org/stamp/stamp.jsp?tp=&arnumber=10836794","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"145078602","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Goal: Effective preoperative planning for shoulder joint replacement requires accurate glenohumeral joint (GH) digital surfaces and reliable clinical staging. Methods: xCEL-UNet was designed as a dual-task deep network for humerus and scapula bone reconstruction in CT scans, and assessment of three GH joint clinical conditions, namely osteophyte size (OS), joint space reduction (JS), and humeroscapular alignment (HSA). Results: Trained on a dataset of 571 patients, the model optimized segmentation and classification through transfer learning. It achieved median root mean squared errors of 0.31 and 0.24 mm, and Hausdorff distances of 2.35 and 3.28 mm for the humerus and scapula, respectively. Classification accuracy was 91 for OS, 93 for JS, and 85% for HSA. GradCAM-based activation maps validated the network's interpretability. Conclusions: this framework delivers accurate 3D bone surface reconstructions and dependable clinical assessments of the GH joint, offering robust support for therapeutic decision-making in shoulder arthroplasty.
{"title":"Context-Aware Dual-Task Deep Network for Concurrent Bone Segmentation and Clinical Assessment to Enhance Shoulder Arthroplasty Preoperative planning","authors":"Luca Marsilio;Andrea Moglia;Alfonso Manzotti;Pietro Cerveri","doi":"10.1109/OJEMB.2025.3527877","DOIUrl":"https://doi.org/10.1109/OJEMB.2025.3527877","url":null,"abstract":"<italic>Goal:</i> Effective preoperative planning for shoulder joint replacement requires accurate glenohumeral joint (GH) digital surfaces and reliable clinical staging. <italic>Methods:</i> xCEL-UNet was designed as a dual-task deep network for humerus and scapula bone reconstruction in CT scans, and assessment of three GH joint clinical conditions, namely osteophyte size (OS), joint space reduction (JS), and humeroscapular alignment (HSA). <italic>Results:</i> Trained on a dataset of 571 patients, the model optimized segmentation and classification through transfer learning. It achieved median root mean squared errors of 0.31 and 0.24 mm, and Hausdorff distances of 2.35 and 3.28 mm for the humerus and scapula, respectively. Classification accuracy was 91 for OS, 93 for JS, and 85% for HSA. GradCAM-based activation maps validated the network's interpretability. <italic>Conclusions:</i> this framework delivers accurate 3D bone surface reconstructions and dependable clinical assessments of the GH joint, offering robust support for therapeutic decision-making in shoulder arthroplasty.","PeriodicalId":33825,"journal":{"name":"IEEE Open Journal of Engineering in Medicine and Biology","volume":"6 ","pages":"269-278"},"PeriodicalIF":2.7,"publicationDate":"2025-01-09","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://ieeexplore.ieee.org/stamp/stamp.jsp?tp=&arnumber=10835174","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143106057","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Pub Date : 2024-12-30DOI: 10.1109/OJEMB.2024.3523442
Kavit R. Amin;Samuel R. Smith;Amit N. Pujari;Syed Ali Raza Zaidi;Robert Horne;Atif Shahzad;Christopher Walshaw;Christy Holland;Stephen Halpin;Rory J. O'Connor
Spasticity is disabling feature of long-term neurological conditions that has substantial impact on people’ quality of life. Assessing spasticity and determining the efficacy of current treatments is limited by the measurement tools available in clinical practice. We convened an expert panel of clinicians and engineers to identify a solution to this urgent clinical need. We established that a reliable ambulatory spasticity monitoring system that collates clinically meaningful data remotely would be useful in the management of this complex condition. This paper provides an overview of current practices in managing and monitoring spasticity. Then, the paper describes how a remote monitoring system can help in managing spasticity and identifies challenges in development of such a system. Finally the paper proposes a monitoring system solution that exploits recent advancements in low-energy wearable systems comprising of printable electronics, a personal area network (PAN) to low power wide area networks (LPWAN) alongside back-end cloud infrastructure. The proposed technology will make an important contribution to patient care by allowing, for the first time, longitudinal monitoring of spasticity between clinical follow-up, and thus has life altering and cost-saving implications. This work in spasticity monitoring and management serves as an exemplar for other areas of rehabilitation.
{"title":"Remote Monitoring for the Management of Spasticity: Challenges, Opportunities and Proposed Technological Solution","authors":"Kavit R. Amin;Samuel R. Smith;Amit N. Pujari;Syed Ali Raza Zaidi;Robert Horne;Atif Shahzad;Christopher Walshaw;Christy Holland;Stephen Halpin;Rory J. O'Connor","doi":"10.1109/OJEMB.2024.3523442","DOIUrl":"https://doi.org/10.1109/OJEMB.2024.3523442","url":null,"abstract":"Spasticity is disabling feature of long-term neurological conditions that has substantial impact on people’ quality of life. Assessing spasticity and determining the efficacy of current treatments is limited by the measurement tools available in clinical practice. We convened an expert panel of clinicians and engineers to identify a solution to this urgent clinical need. We established that a reliable ambulatory spasticity monitoring system that collates clinically meaningful data remotely would be useful in the management of this complex condition. This paper provides an overview of current practices in managing and monitoring spasticity. Then, the paper describes how a remote monitoring system can help in managing spasticity and identifies challenges in development of such a system. Finally the paper proposes a monitoring system solution that exploits recent advancements in low-energy wearable systems comprising of printable electronics, a personal area network (PAN) to low power wide area networks (LPWAN) alongside back-end cloud infrastructure. The proposed technology will make an important contribution to patient care by allowing, for the first time, longitudinal monitoring of spasticity between clinical follow-up, and thus has life altering and cost-saving implications. This work in spasticity monitoring and management serves as an exemplar for other areas of rehabilitation.","PeriodicalId":33825,"journal":{"name":"IEEE Open Journal of Engineering in Medicine and Biology","volume":"6 ","pages":"279-286"},"PeriodicalIF":2.7,"publicationDate":"2024-12-30","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://ieeexplore.ieee.org/stamp/stamp.jsp?tp=&arnumber=10817570","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143105935","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Pub Date : 2024-12-17DOI: 10.1109/OJEMB.2024.3387891
{"title":"IEEE Engineering in Medicine and Biology Society Information","authors":"","doi":"10.1109/OJEMB.2024.3387891","DOIUrl":"https://doi.org/10.1109/OJEMB.2024.3387891","url":null,"abstract":"","PeriodicalId":33825,"journal":{"name":"IEEE Open Journal of Engineering in Medicine and Biology","volume":"5 ","pages":"C2-C2"},"PeriodicalIF":2.7,"publicationDate":"2024-12-17","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://ieeexplore.ieee.org/stamp/stamp.jsp?tp=&arnumber=10805082","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142843067","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Pub Date : 2024-12-12DOI: 10.1109/OJEMB.2024.3516320
Christoforos Meliadis;Emily Feng;Ezekiel Johnson;Wendy Zhu;Paramesh Gopi;Vivek Mohan;Peter H. Hwang;Jacob Johnson;Bryant Y. Lin
Goal: This study aims to introduce a novel method for estimating maxillary sinus volume using smartphone technology, providing an accessible alternative to traditional imaging techniques. Methods: We recruited 40 participants to conduct a comparative analysis between Computed Tomography (CT) and face scans obtained using an Apple iPhone. Utilizing Apple's ARKit for 3D facial mesh modeling, we estimated sinus dimensions based on established craniofacial landmarks and calculated the volume through a geometric approximation of the maxillary sinus. Results: We demonstrated a high degree of agreement between CT and face scans, with Mean Absolute Percentage Errors (MAPE) of 8.006 ± 8.839% (Width), 6.725 ± 4.595% (Height), 9.952 ± 6.733% (Depth), and 10.429 ± 7.409% (Volume). These results suggest the feasibility of this non-invasive approach for clinical use. Conclusions: This method aligns with the growing focus on telemedicine, presenting significant reductions in healthcare costs and radiation exposure from CT scans. It marks a substantial advancement in otolaryngology and maxillofacial surgery, showcasing the integration of smartphone technology in medical diagnostics and opening avenues for innovative, patient-friendly, and cost-effective healthcare solutions.
{"title":"Estimating Maxillary Sinus Volume Using Smartphone Camera","authors":"Christoforos Meliadis;Emily Feng;Ezekiel Johnson;Wendy Zhu;Paramesh Gopi;Vivek Mohan;Peter H. Hwang;Jacob Johnson;Bryant Y. Lin","doi":"10.1109/OJEMB.2024.3516320","DOIUrl":"https://doi.org/10.1109/OJEMB.2024.3516320","url":null,"abstract":"<italic>Goal:</i>\u0000 This study aims to introduce a novel method for estimating maxillary sinus volume using smartphone technology, providing an accessible alternative to traditional imaging techniques. \u0000<italic>Methods:</i>\u0000 We recruited 40 participants to conduct a comparative analysis between Computed Tomography (CT) and face scans obtained using an Apple iPhone. Utilizing Apple's ARKit for 3D facial mesh modeling, we estimated sinus dimensions based on established craniofacial landmarks and calculated the volume through a geometric approximation of the maxillary sinus. \u0000<italic>Results:</i>\u0000 We demonstrated a high degree of agreement between CT and face scans, with Mean Absolute Percentage Errors (MAPE) of 8.006 ± 8.839% (Width), 6.725 ± 4.595% (Height), 9.952 ± 6.733% (Depth), and 10.429 ± 7.409% (Volume). These results suggest the feasibility of this non-invasive approach for clinical use. \u0000<italic>Conclusions:</i>\u0000 This method aligns with the growing focus on telemedicine, presenting significant reductions in healthcare costs and radiation exposure from CT scans. It marks a substantial advancement in otolaryngology and maxillofacial surgery, showcasing the integration of smartphone technology in medical diagnostics and opening avenues for innovative, patient-friendly, and cost-effective healthcare solutions.","PeriodicalId":33825,"journal":{"name":"IEEE Open Journal of Engineering in Medicine and Biology","volume":"6 ","pages":"256-260"},"PeriodicalIF":2.7,"publicationDate":"2024-12-12","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://ieeexplore.ieee.org/stamp/stamp.jsp?tp=&arnumber=10795754","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142938158","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Pub Date : 2024-12-11DOI: 10.1109/OJEMB.2024.3515473
G. Luzzani;I. Buraioli;G. Guglieri;D. Demarchi
Objective: The growth of autonomous systems interacting with humans leads to assessing operators' stress and mental workload (MWL), especially in safety-critical situations. Therefore, a system providing information about the psychophysiological workers' condition is fundamental and still missing. This paper aims to study the statistical relationship between the variation of Photoplethysmogram signal (PPG), Electrodermal Activity (EDA), and skin temperature with respect to stress and MWL levels, assessed through an ad-hoc developed subjective questionnaire. Results: 43 features were calculated from these signals during the execution of two cognitive tests and processed through a statistical analysis based on Kruskal-Wallis and Mann-Whitney U tests. This analysis proved that about 50% of them offered statistical evidence in differentiating relaxed and altered emotional conditions. Moreover, fifteen features were found to provide sufficient information to detect at the same time stress and MWL. Conclusions: These results demonstrate the feasibility of this approach and push to continue this research about the relationship between physiological signals and the variation of stress and MWL by enhancing the population and considering more biosignals.
{"title":"EDA, PPG and Skin Temperature as Predictive Signals for Mental Failure by a Statistical Analysis on Stress and Mental Workload","authors":"G. Luzzani;I. Buraioli;G. Guglieri;D. Demarchi","doi":"10.1109/OJEMB.2024.3515473","DOIUrl":"https://doi.org/10.1109/OJEMB.2024.3515473","url":null,"abstract":"<italic>Objective:</i>\u0000 The growth of autonomous systems interacting with humans leads to assessing operators' stress and mental workload (MWL), especially in safety-critical situations. Therefore, a system providing information about the psychophysiological workers' condition is fundamental and still missing. This paper aims to study the statistical relationship between the variation of Photoplethysmogram signal (PPG), Electrodermal Activity (EDA), and skin temperature with respect to stress and MWL levels, assessed through an ad-hoc developed subjective questionnaire. \u0000<italic>Results:</i>\u0000 43 features were calculated from these signals during the execution of two cognitive tests and processed through a statistical analysis based on Kruskal-Wallis and Mann-Whitney U tests. This analysis proved that about 50% of them offered statistical evidence in differentiating relaxed and altered emotional conditions. Moreover, fifteen features were found to provide sufficient information to detect at the same time stress and MWL. \u0000<italic>Conclusions:</i>\u0000 These results demonstrate the feasibility of this approach and push to continue this research about the relationship between physiological signals and the variation of stress and MWL by enhancing the population and considering more biosignals.","PeriodicalId":33825,"journal":{"name":"IEEE Open Journal of Engineering in Medicine and Biology","volume":"6 ","pages":"248-255"},"PeriodicalIF":2.7,"publicationDate":"2024-12-11","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://ieeexplore.ieee.org/stamp/stamp.jsp?tp=&arnumber=10791858","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142905895","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
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