Pub Date : 2024-05-09DOI: 10.3390/biomedinformatics4020066
Fatima Ghazi, A. Benkuider, F. Ayoub, Khalil Ibrahimi
Mammogram exam images are useful in identifying diseases, such as breast cancer, which is one of the deadliest cancers, affecting adult women around the world. Computational image analysis and machine learning techniques can help experts identify abnormalities in these images. In this work we present a new system to help diagnose and analyze breast mammogram images. To do this, the system a method the Selection of the Most Discriminant Attributes of the images preprocessed by BEMD “SMDA-BEMD”, this entails picking the most pertinent traits from the collection of variables that characterize the state under study. A reduction of attribute based on a transformation of the data also called an extraction of characteristics by extracting the Haralick attributes from the Co-occurrence Matrices Methods “GLCM” this reduction which consists of replacing the initial set of data by a new reduced set, constructed at from the initial set of features extracted by images decomposed using Bidimensional Empirical Multimodal Decomposition “BEMD”, for discrimination of breast mammogram images (healthy and pathology) using BEMD. This decomposition makes it possible to decompose an image into several Bidimensional Intrinsic Mode Functions “BIMFs” modes and a residue. The results obtained show that mammographic images can be represented in a relatively short space by selecting the most discriminating features based on a supervised method where they can be differentiated with high reliability between healthy mammographic images and pathologies, However, certain aspects and findings demonstrate how successful the suggested strategy is to detect the tumor. A BEMD technique is used as preprocessing on mammographic images. This suggested methodology makes it possible to obtain consistent results and establishes the discrimination threshold for mammography images (healthy and pathological), the classification rate is improved (98.6%) compared to existing cutting-edge techniques in the field. This approach is tested and validated on mammographic medical images from the Kenitra-Morocco reproductive health reference center (CRSRKM) which contains breast mammographic images of normal and pathological cases.
乳房 X 光检查图像有助于识别疾病，如乳腺癌，这是影响全球成年女性的最致命癌症之一。计算图像分析和机器学习技术可以帮助专家识别这些图像中的异常。在这项工作中，我们提出了一个帮助诊断和分析乳房 X 光图像的新系统。为此，该系统采用了一种方法，即从经过 BEMD "SMDA-BEMD "预处理的图像中选择最具鉴别力的属性，这就需要从描述所研究状态的变量集合中挑选出最相关的特征。通过共现矩阵方法（GLCM）提取 Haralick 属性，这种基于数据转换的属性还原也称为特征提取，这种还原包括将初始数据集替换为新的还原集，新还原集由使用双维经验多模态分解法（BEMD）对图像进行分解后提取的初始特征集构建而成，用于使用双维经验多模态分解法（BEMD）对乳腺 X 光图像（健康和病理）进行判别。这种分解方法可将图像分解为多个双维本征模式函数（Bidimensional Intrinsic Mode Functions "BIMFs"）模式和一个残差。研究结果表明，乳腺图像可以在相对较短的空间内通过选择最具辨别力的特征来表示，这种基于监督的方法可以在健康乳腺图像和病理图像之间进行高可靠性的区分。BEMD 技术用于乳腺 X 射线图像的预处理。与该领域现有的尖端技术相比，该方法提高了分类率（98.6%）。该方法在来自凯尼特拉-摩洛哥生殖健康参考中心（CRSRKM）的乳腺 X 射线医学影像上进行了测试和验证，其中包含正常和病理病例的乳腺 X 射线图像。
{"title":"Selection of the Discriming Feature Using the BEMD’s BIMF for Classification of Breast Cancer Mammography Image","authors":"Fatima Ghazi, A. Benkuider, F. Ayoub, Khalil Ibrahimi","doi":"10.3390/biomedinformatics4020066","DOIUrl":"https://doi.org/10.3390/biomedinformatics4020066","url":null,"abstract":"Mammogram exam images are useful in identifying diseases, such as breast cancer, which is one of the deadliest cancers, affecting adult women around the world. Computational image analysis and machine learning techniques can help experts identify abnormalities in these images. In this work we present a new system to help diagnose and analyze breast mammogram images. To do this, the system a method the Selection of the Most Discriminant Attributes of the images preprocessed by BEMD “SMDA-BEMD”, this entails picking the most pertinent traits from the collection of variables that characterize the state under study. A reduction of attribute based on a transformation of the data also called an extraction of characteristics by extracting the Haralick attributes from the Co-occurrence Matrices Methods “GLCM” this reduction which consists of replacing the initial set of data by a new reduced set, constructed at from the initial set of features extracted by images decomposed using Bidimensional Empirical Multimodal Decomposition “BEMD”, for discrimination of breast mammogram images (healthy and pathology) using BEMD. This decomposition makes it possible to decompose an image into several Bidimensional Intrinsic Mode Functions “BIMFs” modes and a residue. The results obtained show that mammographic images can be represented in a relatively short space by selecting the most discriminating features based on a supervised method where they can be differentiated with high reliability between healthy mammographic images and pathologies, However, certain aspects and findings demonstrate how successful the suggested strategy is to detect the tumor. A BEMD technique is used as preprocessing on mammographic images. This suggested methodology makes it possible to obtain consistent results and establishes the discrimination threshold for mammography images (healthy and pathological), the classification rate is improved (98.6%) compared to existing cutting-edge techniques in the field. This approach is tested and validated on mammographic medical images from the Kenitra-Morocco reproductive health reference center (CRSRKM) which contains breast mammographic images of normal and pathological cases.","PeriodicalId":72394,"journal":{"name":"BioMedInformatics","volume":null,"pages":null},"PeriodicalIF":0.0,"publicationDate":"2024-05-09","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"140994928","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Pub Date : 2024-05-08DOI: 10.3390/biomedinformatics4020065
Godfrey A. Mills, D. Dey, Mohammed Kassim, Aminu Yiwere, Kenneth Broni
Background: Rheumatic diseases are chronic diseases that affect joints, tendons, ligaments, bones, muscles, and other vital organs. Detection of rheumatic diseases is a complex process that requires careful analysis of heterogeneous content from clinical examinations, patient history, and laboratory investigations. Machine learning techniques have made it possible to integrate such techniques into the complex diagnostic process to identify inherent features that lead to disease formation, development, and progression for remedial measures. Methods: An automated diagnostic tool using a multilayer neural network computational engine is presented to detect rheumatic disorders and the type of underlying disorder for therapeutic strategies. Rheumatic disorders considered are rheumatoid arthritis, osteoarthritis, and systemic lupus erythematosus. The detection system was trained and tested using 70% and 30% respectively of labelled synthetic dataset of 100,000 records containing both single and multiple disorders. Results: The detection system was able to detect and predict underlying disorders with accuracy of 97.48%, sensitivity of 96.80%, and specificity of 97.50%. Conclusion: The good performance suggests that this solution is robust enough and can be implemented for screening patients for intervention measures. This is a much-needed solution in environments with limited specialists, as the solution promotes task-shifting from the specialist level to the primary healthcare physicians.
{"title":"Diagnostic Tool for Early Detection of Rheumatic Disorders Using Machine Learning Algorithm and Predictive Models","authors":"Godfrey A. Mills, D. Dey, Mohammed Kassim, Aminu Yiwere, Kenneth Broni","doi":"10.3390/biomedinformatics4020065","DOIUrl":"https://doi.org/10.3390/biomedinformatics4020065","url":null,"abstract":"Background: Rheumatic diseases are chronic diseases that affect joints, tendons, ligaments, bones, muscles, and other vital organs. Detection of rheumatic diseases is a complex process that requires careful analysis of heterogeneous content from clinical examinations, patient history, and laboratory investigations. Machine learning techniques have made it possible to integrate such techniques into the complex diagnostic process to identify inherent features that lead to disease formation, development, and progression for remedial measures. Methods: An automated diagnostic tool using a multilayer neural network computational engine is presented to detect rheumatic disorders and the type of underlying disorder for therapeutic strategies. Rheumatic disorders considered are rheumatoid arthritis, osteoarthritis, and systemic lupus erythematosus. The detection system was trained and tested using 70% and 30% respectively of labelled synthetic dataset of 100,000 records containing both single and multiple disorders. Results: The detection system was able to detect and predict underlying disorders with accuracy of 97.48%, sensitivity of 96.80%, and specificity of 97.50%. Conclusion: The good performance suggests that this solution is robust enough and can be implemented for screening patients for intervention measures. This is a much-needed solution in environments with limited specialists, as the solution promotes task-shifting from the specialist level to the primary healthcare physicians.","PeriodicalId":72394,"journal":{"name":"BioMedInformatics","volume":null,"pages":null},"PeriodicalIF":0.0,"publicationDate":"2024-05-08","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"140997988","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Pub Date : 2024-05-07DOI: 10.3390/biomedinformatics4020064
S. Iarlori, D. Perpetuini, M. Tritto, D. Cardone, Alessandro Tiberio, Manish Chinthakindi, C. Filippini, L. Cavanini, A. Freddi, F. Ferracuti, A. Merla, Andrea Monteriù
Background: Human-Machine Interaction (HMI) has been an important field of research in recent years, since machines will continue to be embedded in many human actvities in several contexts, such as industry and healthcare. Monitoring in an ecological mannerthe cognitive workload (CW) of users, who interact with machines, is crucial to assess their level of engagement in activities and the required effort, with the goal of preventing stressful circumstances. This study provides a comprehensive analysis of the assessment of CW using wearable sensors in HMI. Methods: this narrative review explores several techniques and procedures for collecting physiological data through wearable sensors with the possibility to integrate these multiple physiological signals, providing a multimodal monitoring of the individuals’CW. Finally, it focuses on the impact of artificial intelligence methods in the physiological signals data analysis to provide models of the CW to be exploited in HMI. Results: the review provided a comprehensive evaluation of the wearables, physiological signals, and methods of data analysis for CW evaluation in HMI. Conclusion: the literature highlighted the feasibility of employing wearable sensors to collect physiological signals for an ecological CW monitoring in HMI scenarios. However, challenges remain in standardizing these measures across different populations and contexts.
{"title":"An Overview of Approaches and Methods for the Cognitive Workload Estimation in Human–Machine Interaction Scenarios through Wearables Sensors","authors":"S. Iarlori, D. Perpetuini, M. Tritto, D. Cardone, Alessandro Tiberio, Manish Chinthakindi, C. Filippini, L. Cavanini, A. Freddi, F. Ferracuti, A. Merla, Andrea Monteriù","doi":"10.3390/biomedinformatics4020064","DOIUrl":"https://doi.org/10.3390/biomedinformatics4020064","url":null,"abstract":"Background: Human-Machine Interaction (HMI) has been an important field of research in recent years, since machines will continue to be embedded in many human actvities in several contexts, such as industry and healthcare. Monitoring in an ecological mannerthe cognitive workload (CW) of users, who interact with machines, is crucial to assess their level of engagement in activities and the required effort, with the goal of preventing stressful circumstances. This study provides a comprehensive analysis of the assessment of CW using wearable sensors in HMI. Methods: this narrative review explores several techniques and procedures for collecting physiological data through wearable sensors with the possibility to integrate these multiple physiological signals, providing a multimodal monitoring of the individuals’CW. Finally, it focuses on the impact of artificial intelligence methods in the physiological signals data analysis to provide models of the CW to be exploited in HMI. Results: the review provided a comprehensive evaluation of the wearables, physiological signals, and methods of data analysis for CW evaluation in HMI. Conclusion: the literature highlighted the feasibility of employing wearable sensors to collect physiological signals for an ecological CW monitoring in HMI scenarios. However, challenges remain in standardizing these measures across different populations and contexts.","PeriodicalId":72394,"journal":{"name":"BioMedInformatics","volume":null,"pages":null},"PeriodicalIF":0.0,"publicationDate":"2024-05-07","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"141002290","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Pub Date : 2024-04-16DOI: 10.3390/biomedinformatics4020062
Khalid Nassiri, Moulay A. Akhloufi
Recent advances in the field of large language models (LLMs) underline their high potential for applications in a variety of sectors. Their use in healthcare, in particular, holds out promising prospects for improving medical practices. As we highlight in this paper, LLMs have demonstrated remarkable capabilities in language understanding and generation that could indeed be put to good use in the medical field. We also present the main architectures of these models, such as GPT, Bloom, or LLaMA, composed of billions of parameters. We then examine recent trends in the medical datasets used to train these models. We classify them according to different criteria, such as size, source, or subject (patient records, scientific articles, etc.). We mention that LLMs could help improve patient care, accelerate medical research, and optimize the efficiency of healthcare systems such as assisted diagnosis. We also highlight several technical and ethical issues that need to be resolved before LLMs can be used extensively in the medical field. Consequently, we propose a discussion of the capabilities offered by new generations of linguistic models and their limitations when deployed in a domain such as healthcare.
{"title":"Recent Advances in Large Language Models for Healthcare","authors":"Khalid Nassiri, Moulay A. Akhloufi","doi":"10.3390/biomedinformatics4020062","DOIUrl":"https://doi.org/10.3390/biomedinformatics4020062","url":null,"abstract":"Recent advances in the field of large language models (LLMs) underline their high potential for applications in a variety of sectors. Their use in healthcare, in particular, holds out promising prospects for improving medical practices. As we highlight in this paper, LLMs have demonstrated remarkable capabilities in language understanding and generation that could indeed be put to good use in the medical field. We also present the main architectures of these models, such as GPT, Bloom, or LLaMA, composed of billions of parameters. We then examine recent trends in the medical datasets used to train these models. We classify them according to different criteria, such as size, source, or subject (patient records, scientific articles, etc.). We mention that LLMs could help improve patient care, accelerate medical research, and optimize the efficiency of healthcare systems such as assisted diagnosis. We also highlight several technical and ethical issues that need to be resolved before LLMs can be used extensively in the medical field. Consequently, we propose a discussion of the capabilities offered by new generations of linguistic models and their limitations when deployed in a domain such as healthcare.","PeriodicalId":72394,"journal":{"name":"BioMedInformatics","volume":null,"pages":null},"PeriodicalIF":0.0,"publicationDate":"2024-04-16","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"140698148","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Pub Date : 2024-04-10DOI: 10.3390/biomedinformatics4020061
Nikos Tsotsolas, Eleni Koutsouraki, Aspasia Antonakaki, S. Pizanias, Marios Kounelis, Dimitrios D. Piromalis, Dimitrios P. Kolovos, Christos Kokkotis, Themistoklis Tsatalas, George Bellis, D. Tsaopoulos, Paris Papaggelos, George Sidiropoulos, Giannis Giakas
As part of two research projects, a small gait analysis device was developed for use inside and outside the home by patients themselves. The project PARMODE aims to record accurate gait measurements in patients with Parkinson’s disease (PD) and proceed with an in-depth analysis of the gait characteristics, while the project CPWATCHER aims to assess the quality of hand movement in cerebral palsy patients. The device was mainly developed to serve the first project with additional offline processing, including machine learning algorithms that could potentially be used for the second aim. A key feature of the device is its small size (36 mm × 46 mm × 16 mm, weight: 14 g), which was designed to meet specific requirements in terms of device consumption restrictions due to the small size of the battery and the need for autonomous operation for more than ten hours. This research work describes, on the one hand, the new device with an emphasis on its functions, and on the other hand, its connection with a web platform for reading and processing data from the devices placed on patients’ feet to record the gait characteristics of patients on a continuous basis.
{"title":"Investigating the Effectiveness of an IMU Portable Gait Analysis Device: An Application for Parkinson’s Disease Management","authors":"Nikos Tsotsolas, Eleni Koutsouraki, Aspasia Antonakaki, S. Pizanias, Marios Kounelis, Dimitrios D. Piromalis, Dimitrios P. Kolovos, Christos Kokkotis, Themistoklis Tsatalas, George Bellis, D. Tsaopoulos, Paris Papaggelos, George Sidiropoulos, Giannis Giakas","doi":"10.3390/biomedinformatics4020061","DOIUrl":"https://doi.org/10.3390/biomedinformatics4020061","url":null,"abstract":"As part of two research projects, a small gait analysis device was developed for use inside and outside the home by patients themselves. The project PARMODE aims to record accurate gait measurements in patients with Parkinson’s disease (PD) and proceed with an in-depth analysis of the gait characteristics, while the project CPWATCHER aims to assess the quality of hand movement in cerebral palsy patients. The device was mainly developed to serve the first project with additional offline processing, including machine learning algorithms that could potentially be used for the second aim. A key feature of the device is its small size (36 mm × 46 mm × 16 mm, weight: 14 g), which was designed to meet specific requirements in terms of device consumption restrictions due to the small size of the battery and the need for autonomous operation for more than ten hours. This research work describes, on the one hand, the new device with an emphasis on its functions, and on the other hand, its connection with a web platform for reading and processing data from the devices placed on patients’ feet to record the gait characteristics of patients on a continuous basis.","PeriodicalId":72394,"journal":{"name":"BioMedInformatics","volume":null,"pages":null},"PeriodicalIF":0.0,"publicationDate":"2024-04-10","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"140719922","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Pub Date : 2024-04-09DOI: 10.3390/biomedinformatics4020058
Mohammad H. Ahmed, Michael Grillo, Amirtahà Taebi, Mehmet Kaya, Peshala Thibbotuwawa Gamage
Introduction: This study analyzes the efficacy of trapezius muscle electromyography (EMG) in discerning mental states, namely stress and meditation. Methods: Fifteen healthy participants were monitored to assess their physiological responses to mental stressors and meditation. Sensors were affixed to both the right and left trapezius muscles to capture EMG signals, while simultaneous electroencephalography (EEG) was conducted to validate cognitive states. Results: Our analysis of various EMG features, considering frequency ranges and sensor positioning, revealed significant changes in trapezius muscle activity during stress and meditation. Notably, low-frequency EMG features facilitated enhanced stress detection. For accurate stress identification, sensor configurations can be limited to the right trapezius muscle. Furthermore, the introduction of a novel method for determining asymmetry in EMG features suggests that applying sensors on bilateral trapezius muscles can improve the detection of mental states. Conclusion: This research presents a promising avenue for efficient cognitive state monitoring through compact and convenient sensing.
{"title":"A Comprehensive Analysis of Trapezius Muscle EMG Activity in Relation to Stress and Meditation","authors":"Mohammad H. Ahmed, Michael Grillo, Amirtahà Taebi, Mehmet Kaya, Peshala Thibbotuwawa Gamage","doi":"10.3390/biomedinformatics4020058","DOIUrl":"https://doi.org/10.3390/biomedinformatics4020058","url":null,"abstract":"Introduction: This study analyzes the efficacy of trapezius muscle electromyography (EMG) in discerning mental states, namely stress and meditation. Methods: Fifteen healthy participants were monitored to assess their physiological responses to mental stressors and meditation. Sensors were affixed to both the right and left trapezius muscles to capture EMG signals, while simultaneous electroencephalography (EEG) was conducted to validate cognitive states. Results: Our analysis of various EMG features, considering frequency ranges and sensor positioning, revealed significant changes in trapezius muscle activity during stress and meditation. Notably, low-frequency EMG features facilitated enhanced stress detection. For accurate stress identification, sensor configurations can be limited to the right trapezius muscle. Furthermore, the introduction of a novel method for determining asymmetry in EMG features suggests that applying sensors on bilateral trapezius muscles can improve the detection of mental states. Conclusion: This research presents a promising avenue for efficient cognitive state monitoring through compact and convenient sensing.","PeriodicalId":72394,"journal":{"name":"BioMedInformatics","volume":null,"pages":null},"PeriodicalIF":0.0,"publicationDate":"2024-04-09","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"140727829","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Pub Date : 2024-04-09DOI: 10.3390/biomedinformatics4020059
Aravinthan Sankar, Kunal Chaturvedi, Al-Akhir Nayan, M. H. Hesamian, Ali Braytee, Mukesh Prasad
Background: In recent years, computer-aided diagnosis for skin conditions has made significant strides, primarily driven by artificial intelligence (AI) solutions. However, despite this progress, the efficiency of AI-enabled systems remains hindered by the scarcity of high-quality and large-scale datasets, primarily due to privacy concerns. Methods: This research circumvents privacy issues associated with real-world acne datasets by creating a synthetic dataset of human faces with varying acne severity levels (mild, moderate, and severe) using Generative Adversarial Networks (GANs). Further, three object detection models—YOLOv5, YOLOv8, and Detectron2—are used to evaluate the efficacy of the augmented dataset for detecting acne. Results: Integrating StyleGAN with these models, the results demonstrate the mean average precision (mAP) scores: YOLOv5: 73.5%, YOLOv8: 73.6%, and Detectron2: 37.7%. These scores surpass the mAP achieved without GANs. Conclusions: This study underscores the effectiveness of GANs in generating synthetic facial acne images and emphasizes the importance of utilizing GANs and convolutional neural network (CNN) models for accurate acne detection.
{"title":"Utilizing Generative Adversarial Networks for Acne Dataset Generation in Dermatology","authors":"Aravinthan Sankar, Kunal Chaturvedi, Al-Akhir Nayan, M. H. Hesamian, Ali Braytee, Mukesh Prasad","doi":"10.3390/biomedinformatics4020059","DOIUrl":"https://doi.org/10.3390/biomedinformatics4020059","url":null,"abstract":"Background: In recent years, computer-aided diagnosis for skin conditions has made significant strides, primarily driven by artificial intelligence (AI) solutions. However, despite this progress, the efficiency of AI-enabled systems remains hindered by the scarcity of high-quality and large-scale datasets, primarily due to privacy concerns. Methods: This research circumvents privacy issues associated with real-world acne datasets by creating a synthetic dataset of human faces with varying acne severity levels (mild, moderate, and severe) using Generative Adversarial Networks (GANs). Further, three object detection models—YOLOv5, YOLOv8, and Detectron2—are used to evaluate the efficacy of the augmented dataset for detecting acne. Results: Integrating StyleGAN with these models, the results demonstrate the mean average precision (mAP) scores: YOLOv5: 73.5%, YOLOv8: 73.6%, and Detectron2: 37.7%. These scores surpass the mAP achieved without GANs. Conclusions: This study underscores the effectiveness of GANs in generating synthetic facial acne images and emphasizes the importance of utilizing GANs and convolutional neural network (CNN) models for accurate acne detection.","PeriodicalId":72394,"journal":{"name":"BioMedInformatics","volume":null,"pages":null},"PeriodicalIF":0.0,"publicationDate":"2024-04-09","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"140722361","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Pub Date : 2024-04-09DOI: 10.3390/biomedinformatics4020060
Jonas Bambi, Yudi Santoso, Ken Moselle, Stan Robertson, Abraham Rudnick, Ernie Chang, Alex Kuo
Background: Providing care to persons with complex problems is inherently difficult due to several factors, including the impacts of proximal determinants of health, treatment response, the natural emergence of comorbidities, and service system capacity to provide timely required services. Providing visibility into the dynamics of patients’ engagement can help to optimize care for patients with complex problems. Method: In a previous work, graph machine learning and NLP methods were used to model the products of service system dynamics as atemporal entities, using a data model that collapsed patient encounter events across time. In this paper, the order of events is put back into the data model to provide topological depictions of the dynamics that are embodied in patients’ movement across a complex healthcare system. Result: The results show that directed graphs are well suited to the task of depicting the way that the diverse components of the system are functionally coupled—or remain disconnected—by patient journeys. Conclusion: By setting the resolution on the graph topology visualization, important characteristics can be highlighted, including highly prevalent repeating sequences of service events readily interpretable by clinical subject matter experts. Moreover, this methodology provides a first step in addressing the challenge of locating potential operational problems for patients with complex issues engaging with a complex healthcare service system.
{"title":"Analyzing Patterns of Service Utilization Using Graph Topology to Understand the Dynamic of the Engagement of Patients with Complex Problems with Health Services","authors":"Jonas Bambi, Yudi Santoso, Ken Moselle, Stan Robertson, Abraham Rudnick, Ernie Chang, Alex Kuo","doi":"10.3390/biomedinformatics4020060","DOIUrl":"https://doi.org/10.3390/biomedinformatics4020060","url":null,"abstract":"Background: Providing care to persons with complex problems is inherently difficult due to several factors, including the impacts of proximal determinants of health, treatment response, the natural emergence of comorbidities, and service system capacity to provide timely required services. Providing visibility into the dynamics of patients’ engagement can help to optimize care for patients with complex problems. Method: In a previous work, graph machine learning and NLP methods were used to model the products of service system dynamics as atemporal entities, using a data model that collapsed patient encounter events across time. In this paper, the order of events is put back into the data model to provide topological depictions of the dynamics that are embodied in patients’ movement across a complex healthcare system. Result: The results show that directed graphs are well suited to the task of depicting the way that the diverse components of the system are functionally coupled—or remain disconnected—by patient journeys. Conclusion: By setting the resolution on the graph topology visualization, important characteristics can be highlighted, including highly prevalent repeating sequences of service events readily interpretable by clinical subject matter experts. Moreover, this methodology provides a first step in addressing the challenge of locating potential operational problems for patients with complex issues engaging with a complex healthcare service system.","PeriodicalId":72394,"journal":{"name":"BioMedInformatics","volume":null,"pages":null},"PeriodicalIF":0.0,"publicationDate":"2024-04-09","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"140727396","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Pub Date : 2024-04-03DOI: 10.3390/biomedinformatics4020057
Shisir Ruwali, S. Talebi, Ashen Fernando, Lakitha O. H. Wijeratne, John Waczak, Prabuddha M. H. Dewage, David J. Lary, John Sadler, T. Lary, Matthew Lary, Adam Aker
Introduction: Air pollution has numerous impacts on human health on a variety of time scales. Pollutants such as particulate matter—PM1 and PM2.5, carbon dioxide (CO2), nitrogen dioxide (NO2), and nitric oxide (NO) are exemplars of the wider human exposome. In this study, we adopted a unique approach by utilizing the responses of human autonomic systems to gauge the abundance of pollutants in inhaled air. Objective: To investigate how the human body autonomically responds to inhaled pollutants in microenvironments, including PM1, PM2.5, CO2, NO2, and NO, on small temporal and spatial scales by making use of biometric observations of the human autonomic response. To test the accuracy in predicting the concentrations of these pollutants using biological measurements of the participants. Methodology: Two experimental approaches having a similar methodology that employs a biometric suite to capture the physiological responses of cyclists were compared, and multiple sensors were used to measure the pollutants in the air surrounding them. Machine learning algorithms were used to estimate the levels of these pollutants and decipher the body’s automatic reactions to them. Results: We observed high precision in predicting PM1, PM2.5, and CO2 using a limited set of biometrics measured from the participants, as indicated with the coefficient of determination (R2) between the estimated and true values of these pollutants of 0.99, 0.96, and 0.98, respectively. Although the predictions for NO2 and NO were reliable at lower concentrations, which was observed qualitatively, the precision varied throughout the data range. Skin temperature, heart rate, and respiration rate were the common physiological responses that were the most influential in predicting the concentration of these pollutants. Conclusion: Biometric measurements can be used to estimate air quality components such as PM1, PM2.5, and CO2 with high degrees of accuracy and can also be used to decipher the effect of these pollutants on the human body using machine learning techniques. The results for NO2 and NO suggest a requirement to improve our models with more comprehensive data collection or advanced machine learning techniques to improve the results for these two pollutants.
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Pub Date : 2024-04-02DOI: 10.3390/biomedinformatics4020056
Philip Drake, Ali Algaddafi, Thomas Swift, R. Abd‐Alhameed
Magnetic Field Hyperthermia is a technique where tumours are treated through an increase in local temperature upon exposure to alternating magnetic fields (AMFs) that are mediated by magnetic nano-particles (MNPs). In an AMF, these particles heat-up and kill the cells. The relationship between an AMF and the heating-rate is complex, leading to confusion when comparing data for different MNP and AMF conditions. This work allows for the thermal-response to be monitored at multiple AMF amplitudes while keeping other parameters constant. An induction-heating coil was designed based on a Zero-Voltage-Zero-Current (ZVZC) resonant circuit. The coil operates at 93 kHz with a variable DC drive-voltage (12–30 V). NEC4 software was used to model the magnetic field distribution, and MNPs were synthesised by the coprecipitation method. The magnetic field was found to be uniform at the centre of the coil and ranged from 1 kAm−1 to 12 kAm−1, depending on the DC drive-voltage. The MNPs were found to have a specific absorption rate (SAR) of 1.37 Wg−1[Fe] and 6.13 Wg−1[Fe] at 93 kHz and 2.1 kAm−1 and 12.6 kAm−1, respectively. The measured SAR value was found to be directly proportional to the product of the frequency and field-strength (SARα f Ho). This leads to the recommendation that, when comparing data from various groups, the SAR value should be normalized following this relationship and not using the more common relationship based on the square of the field intensity (SARα f Ho2).
磁场热疗是一种通过交变磁场（AMF）提高局部温度来治疗肿瘤的技术，交变磁场由磁性纳米粒子（MNPs）介导。在交变磁场中，这些粒子升温并杀死细胞。交变磁场与加热速率之间的关系非常复杂，导致在比较不同 MNP 和交变磁场条件下的数据时出现混淆。这项研究可以在保持其他参数不变的情况下，以多种 AMF 振幅监测热反应。基于零电压-零电流（ZVZC）谐振电路设计了一个感应加热线圈。线圈工作频率为 93 kHz，直流驱动电压可变（12-30 V）。使用 NEC4 软件建立磁场分布模型，并通过共沉淀法合成 MNPs。磁场在线圈中心是均匀的，范围从 1 kAm-1 到 12 kAm-1，取决于直流驱动电压。在 93 kHz 和 2.1 kAm-1 和 12.6 kAm-1 频率下，MNPs 的比吸收率（SAR）分别为 1.37 Wg-1[Fe] 和 6.13 Wg-1[Fe]。测得的 SAR 值与频率和场强的乘积（SARα f Ho）成正比。因此，建议在比较各组数据时，应根据这一关系对 SAR 值进行归一化处理，而不是使用更常见的基于场强平方的关系（SARα f Ho2）。
{"title":"Design and Modelling of an Induction Heating Coil to Investigate the Thermal Response of Magnetic Nanoparticles for Hyperthermia Applications","authors":"Philip Drake, Ali Algaddafi, Thomas Swift, R. Abd‐Alhameed","doi":"10.3390/biomedinformatics4020056","DOIUrl":"https://doi.org/10.3390/biomedinformatics4020056","url":null,"abstract":"Magnetic Field Hyperthermia is a technique where tumours are treated through an increase in local temperature upon exposure to alternating magnetic fields (AMFs) that are mediated by magnetic nano-particles (MNPs). In an AMF, these particles heat-up and kill the cells. The relationship between an AMF and the heating-rate is complex, leading to confusion when comparing data for different MNP and AMF conditions. This work allows for the thermal-response to be monitored at multiple AMF amplitudes while keeping other parameters constant. An induction-heating coil was designed based on a Zero-Voltage-Zero-Current (ZVZC) resonant circuit. The coil operates at 93 kHz with a variable DC drive-voltage (12–30 V). NEC4 software was used to model the magnetic field distribution, and MNPs were synthesised by the coprecipitation method. The magnetic field was found to be uniform at the centre of the coil and ranged from 1 kAm−1 to 12 kAm−1, depending on the DC drive-voltage. The MNPs were found to have a specific absorption rate (SAR) of 1.37 Wg−1[Fe] and 6.13 Wg−1[Fe] at 93 kHz and 2.1 kAm−1 and 12.6 kAm−1, respectively. The measured SAR value was found to be directly proportional to the product of the frequency and field-strength (SARα f Ho). This leads to the recommendation that, when comparing data from various groups, the SAR value should be normalized following this relationship and not using the more common relationship based on the square of the field intensity (SARα f Ho2).","PeriodicalId":72394,"journal":{"name":"BioMedInformatics","volume":null,"pages":null},"PeriodicalIF":0.0,"publicationDate":"2024-04-02","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"140755102","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
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