Pub Date : 2024-12-09DOI: 10.1109/OJEMB.2024.3512932
Chih-En Kuo;Jun-Zhou Li;Jenn-Jhy Tseng;Feng-Chu Lo;Ming-Jer Chen;Chien-Hsing Lu
Goal: Chromosomes are intracellular aggregates that carry genetic information. An abnormal number or structure of chromosomes causes chromosomal disorders. Thus, chromosome screening is crucial for prenatal care; however, manual analysis of chromosomes is time consuming. With the increasing popularity of prenatal diagnosis, human labor resources are overstretched. Therefore, an automatic approach for chromosome detection and recognition is necessary. Methods: In the present study, we proposed a deep learning–based system for the automatic chromosome detection and recognition of metaphase cell images. We used a large database that included 5,000 metaphase cell images consisting of a total of 229,852 chromosomes. The proposed system was then developed and evaluated. The system, called ChromosomesNet, which combines the advantages of one-stage and two-stage models. The model uses original images as inputs without requiring preprocessing; it is thus applicable for clinical settings. To verify the clinical applicability of our system, we included 3,827 simple images and 1,173 difficult images, as identified by physicians, in our database. Results: We used COCOAPI's mAP50 evaluation method, which has average performance and a high accuracy of 99.60%. Moreover, the recall and F1 score of our proposed method were 99.9% and 99.49%, respectively. We also compared our method with five well-known object detection methods, including Faster-RCNN, YOLOv7, Retinanet, Swin transformer, and Centernet++. The results indicated that ChromosomesNet had the highest accuracy, recall, and F1 score. Unlike previous studies that have reported simple chromosome images as identification results, we obtained a 99.5% accuracy in the detection of difficult images. Conclusions: The volume of data we tested, even including difficult images, was much larger than those in the literature. The results indicated that our proposed method is sufficiently stable, robustness, and practical for clinical use. Future studies are warranted to confirm the clinical applicability of our proposed method by using data from other hospitals for cross-hospital validation.
{"title":"ChromosomeNet: Deep Learning-Based Automated Chromosome Detection in Metaphase Cell Images","authors":"Chih-En Kuo;Jun-Zhou Li;Jenn-Jhy Tseng;Feng-Chu Lo;Ming-Jer Chen;Chien-Hsing Lu","doi":"10.1109/OJEMB.2024.3512932","DOIUrl":"https://doi.org/10.1109/OJEMB.2024.3512932","url":null,"abstract":"<italic>Goal:</i>\u0000 Chromosomes are intracellular aggregates that carry genetic information. An abnormal number or structure of chromosomes causes chromosomal disorders. Thus, chromosome screening is crucial for prenatal care; however, manual analysis of chromosomes is time consuming. With the increasing popularity of prenatal diagnosis, human labor resources are overstretched. Therefore, an automatic approach for chromosome detection and recognition is necessary. \u0000<italic>Methods:</i>\u0000 In the present study, we proposed a deep learning–based system for the automatic chromosome detection and recognition of metaphase cell images. We used a large database that included 5,000 metaphase cell images consisting of a total of 229,852 chromosomes. The proposed system was then developed and evaluated. The system, called ChromosomesNet, which combines the advantages of one-stage and two-stage models. The model uses original images as inputs without requiring preprocessing; it is thus applicable for clinical settings. To verify the clinical applicability of our system, we included 3,827 simple images and 1,173 difficult images, as identified by physicians, in our database. \u0000<italic>Results:</i>\u0000 We used COCOAPI's mAP50 evaluation method, which has average performance and a high accuracy of 99.60%. Moreover, the recall and F1 score of our proposed method were 99.9% and 99.49%, respectively. We also compared our method with five well-known object detection methods, including Faster-RCNN, YOLOv7, Retinanet, Swin transformer, and Centernet++. The results indicated that ChromosomesNet had the highest accuracy, recall, and F1 score. Unlike previous studies that have reported simple chromosome images as identification results, we obtained a 99.5% accuracy in the detection of difficult images. \u0000<italic>Conclusions:</i>\u0000 The volume of data we tested, even including difficult images, was much larger than those in the literature. The results indicated that our proposed method is sufficiently stable, robustness, and practical for clinical use. Future studies are warranted to confirm the clinical applicability of our proposed method by using data from other hospitals for cross-hospital validation.","PeriodicalId":33825,"journal":{"name":"IEEE Open Journal of Engineering in Medicine and Biology","volume":"6 ","pages":"227-236"},"PeriodicalIF":2.7,"publicationDate":"2024-12-09","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://ieeexplore.ieee.org/stamp/stamp.jsp?tp=&arnumber=10786357","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142858871","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}
The National Institutes of Health's Rapid Acceleration of Diagnostics (RADx) program answered the call to accelerate the development of point-of-care (POC) and over-the-counter (OTC) COVID-19 tests. The widespread availability and access to self-tests has increased the public's familiarity and acceptance of at-home diagnostics. This paper examines the current state of OTC diagnostic testing, discusses potential applications of OTC testing, and highlights the implications of widespread OTC testing for clinical medicine.
{"title":"The Shift to Over-the-Counter Diagnostic Testing After RADx: Clinical, Regulatory, and Societal Implications","authors":"Maren Downing;John Broach;Wilbur Lam;Yukari C. Manabe;Greg Martin;David McManus;Robert Murphy;Apurv Soni;Steven Schachter","doi":"10.1109/OJEMB.2024.3512189","DOIUrl":"https://doi.org/10.1109/OJEMB.2024.3512189","url":null,"abstract":"The National Institutes of Health's Rapid Acceleration of Diagnostics (RADx) program answered the call to accelerate the development of point-of-care (POC) and over-the-counter (OTC) COVID-19 tests. The widespread availability and access to self-tests has increased the public's familiarity and acceptance of at-home diagnostics. This paper examines the current state of OTC diagnostic testing, discusses potential applications of OTC testing, and highlights the implications of widespread OTC testing for clinical medicine.","PeriodicalId":33825,"journal":{"name":"IEEE Open Journal of Engineering in Medicine and Biology","volume":"6 ","pages":"237-240"},"PeriodicalIF":2.7,"publicationDate":"2024-12-09","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://ieeexplore.ieee.org/stamp/stamp.jsp?tp=&arnumber=10783436","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142858872","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 : 2024-11-29DOI: 10.1109/OJEMB.2024.3509379
Marion Taconné;Valentina D.A. Corino;Luca Mainardi
Goal: Despite the high incidence of left ventricular hypertrophy (LVH), clinical LVH-electrocardiography (ECG) criteria remain unsatisfactory due to low sensitivity. We propose an automatic LVH detection method based on ECG-extracted features and machine learning. Methods: ECG features were automatically extracted from two publicly available databases: PTB-XL with 2181 LVH and 9001 controls, and Georgia with 1012 LVH and 1387 controls. After preprocessing and feature extraction, the most relevant features from PTB-XL were selected to train three models: logistic regression, random forest (RF), and support vector machine (SVM). These classifiers, trained with selected features and a reduced set of five features, were evaluated on the Georgia database and compared with clinical LVH-ECG criteria. Results: RF and SVM models showed accuracies above 90% and increased sensitivity to above 86%, compared to clinical criteria achieving 38% at maximum. Conclusions: Automatic ECG-based LVH detection using machine learning outperforms conventional diagnostic criteria, benefiting clinical practice.
{"title":"An ECG-Based Model for Left Ventricular Hypertrophy Detection: A Machine Learning Approach","authors":"Marion Taconné;Valentina D.A. Corino;Luca Mainardi","doi":"10.1109/OJEMB.2024.3509379","DOIUrl":"https://doi.org/10.1109/OJEMB.2024.3509379","url":null,"abstract":"<italic>Goal:</i>\u0000 Despite the high incidence of left ventricular hypertrophy (LVH), clinical LVH-electrocardiography (ECG) criteria remain unsatisfactory due to low sensitivity. We propose an automatic LVH detection method based on ECG-extracted features and machine learning. \u0000<italic>Methods:</i>\u0000 ECG features were automatically extracted from two publicly available databases: PTB-XL with 2181 LVH and 9001 controls, and Georgia with 1012 LVH and 1387 controls. After preprocessing and feature extraction, the most relevant features from PTB-XL were selected to train three models: logistic regression, random forest (RF), and support vector machine (SVM). These classifiers, trained with selected features and a reduced set of five features, were evaluated on the Georgia database and compared with clinical LVH-ECG criteria. \u0000<italic>Results:</i>\u0000 RF and SVM models showed accuracies above 90% and increased sensitivity to above 86%, compared to clinical criteria achieving 38% at maximum. \u0000<italic>Conclusions:</i>\u0000 Automatic ECG-based LVH detection using machine learning outperforms conventional diagnostic criteria, benefiting clinical practice.","PeriodicalId":33825,"journal":{"name":"IEEE Open Journal of Engineering in Medicine and Biology","volume":"6 ","pages":"219-226"},"PeriodicalIF":2.7,"publicationDate":"2024-11-29","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://ieeexplore.ieee.org/stamp/stamp.jsp?tp=&arnumber=10772010","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142844376","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 : 2024-11-28DOI: 10.1109/OJEMB.2024.3508477
Nicolò La Porta;Stefano Scafa;Michela Papandrea;Filippo Molinari;Alessandro Puiatti
Goal: The gold standard for detecting the presence of apneic events is a time and effort-consuming manual evaluation of type I polysomnographic recordings by experts, often not error-free. Such acquisition protocol requires dedicated facilities resulting in high costs and long waiting lists. The usage of artificial intelligence models assists the clinician's evaluation overcoming the aforementioned limitations and increasing healthcare quality. Methods: The present work proposes a machine learning-based approach for automatically recognizing apneic events in subjects affected by sleep apnea-hypopnea syndrome. It embraces a vast and diverse pool of subjects, the Wisconsin Sleep Cohort (WSC) database. Results: An overall accuracy of 87.2 $pm$ 1.8% is reached for the event detection task, significantly higher than other works in literature performed over the same dataset. The distinction between different types of apnea was also studied, obtaining an overall accuracy of 62.9 $pm$ 4.1%. Conclusions: The proposed approach for sleep apnea events recognition, validated over a wide pool of subjects, enlarges the landscape of possibilities for sleep apnea events recognition, identifying a subset of signals that improves State-of-the-art performance and guarantees simple interpretation.
{"title":"Sleep Apnea Events Recognition Based on Polysomnographic Recordings: A Large-Scale Multi-Channel Machine Learning approach","authors":"Nicolò La Porta;Stefano Scafa;Michela Papandrea;Filippo Molinari;Alessandro Puiatti","doi":"10.1109/OJEMB.2024.3508477","DOIUrl":"https://doi.org/10.1109/OJEMB.2024.3508477","url":null,"abstract":"<italic>Goal:</i>\u0000 The gold standard for detecting the presence of apneic events is a time and effort-consuming manual evaluation of type I polysomnographic recordings by experts, often not error-free. Such acquisition protocol requires dedicated facilities resulting in high costs and long waiting lists. The usage of artificial intelligence models assists the clinician's evaluation overcoming the aforementioned limitations and increasing healthcare quality. \u0000<italic>Methods:</i>\u0000 The present work proposes a machine learning-based approach for automatically recognizing apneic events in subjects affected by sleep apnea-hypopnea syndrome. It embraces a vast and diverse pool of subjects, the Wisconsin Sleep Cohort (WSC) database. \u0000<italic>Results:</i>\u0000 An overall accuracy of 87.2\u0000<inline-formula><tex-math>$pm$</tex-math></inline-formula>\u00001.8% is reached for the event detection task, significantly higher than other works in literature performed over the same dataset. The distinction between different types of apnea was also studied, obtaining an overall accuracy of 62.9\u0000<inline-formula><tex-math>$pm$</tex-math></inline-formula>\u00004.1%. \u0000<italic>Conclusions:</i>\u0000 The proposed approach for sleep apnea events recognition, validated over a wide pool of subjects, enlarges the landscape of possibilities for sleep apnea events recognition, identifying a subset of signals that improves State-of-the-art performance and guarantees simple interpretation.","PeriodicalId":33825,"journal":{"name":"IEEE Open Journal of Engineering in Medicine and Biology","volume":"6 ","pages":"202-211"},"PeriodicalIF":2.7,"publicationDate":"2024-11-28","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://ieeexplore.ieee.org/stamp/stamp.jsp?tp=&arnumber=10770579","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142821283","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 : 2024-11-28DOI: 10.1109/OJEMB.2024.3508472
Muhammed Halil Akpinar;Abdulkadir Sengur;Massimo Salvi;Silvia Seoni;Oliver Faust;Hasan Mir;Filippo Molinari;U. Rajendra Acharya
Generative Adversarial Networks (GANs) have emerged as a powerful tool in artificial intelligence, particularly for unsupervised learning. This systematic review analyzes GAN applications in healthcare, focusing on image and signal-based studies across various clinical domains. Following Preferred Reporting Items for Systematic reviews and Meta-Analyses (PRISMA) guidelines, we reviewed 72 relevant journal articles. Our findings reveal that magnetic resonance imaging (MRI) and electrocardiogram (ECG) signal acquisition techniques were most utilized, with brain studies (22%), cardiology (18%), cancer (15%), ophthalmology (12%), and lung studies (10%) being the most researched areas. We discuss key GAN architectures, including cGAN (31%) and CycleGAN (18%), along with datasets, evaluation metrics, and performance outcomes. The review highlights promising data augmentation, anonymization, and multi-task learning results. We identify current limitations, such as the lack of standardized metrics and direct comparisons, and propose future directions, including the development of no-reference metrics, immersive simulation scenarios, and enhanced interpretability.
{"title":"Synthetic Data Generation via Generative Adversarial Networks in Healthcare: A Systematic Review of Image- and Signal-Based Studies","authors":"Muhammed Halil Akpinar;Abdulkadir Sengur;Massimo Salvi;Silvia Seoni;Oliver Faust;Hasan Mir;Filippo Molinari;U. Rajendra Acharya","doi":"10.1109/OJEMB.2024.3508472","DOIUrl":"https://doi.org/10.1109/OJEMB.2024.3508472","url":null,"abstract":"Generative Adversarial Networks (GANs) have emerged as a powerful tool in artificial intelligence, particularly for unsupervised learning. This systematic review analyzes GAN applications in healthcare, focusing on image and signal-based studies across various clinical domains. Following Preferred Reporting Items for Systematic reviews and Meta-Analyses (PRISMA) guidelines, we reviewed 72 relevant journal articles. Our findings reveal that magnetic resonance imaging (MRI) and electrocardiogram (ECG) signal acquisition techniques were most utilized, with brain studies (22%), cardiology (18%), cancer (15%), ophthalmology (12%), and lung studies (10%) being the most researched areas. We discuss key GAN architectures, including cGAN (31%) and CycleGAN (18%), along with datasets, evaluation metrics, and performance outcomes. The review highlights promising data augmentation, anonymization, and multi-task learning results. We identify current limitations, such as the lack of standardized metrics and direct comparisons, and propose future directions, including the development of no-reference metrics, immersive simulation scenarios, and enhanced interpretability.","PeriodicalId":33825,"journal":{"name":"IEEE Open Journal of Engineering in Medicine and Biology","volume":"6 ","pages":"183-192"},"PeriodicalIF":2.7,"publicationDate":"2024-11-28","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://ieeexplore.ieee.org/stamp/stamp.jsp?tp=&arnumber=10770591","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142810706","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 : 2024-11-26DOI: 10.1109/OJEMB.2024.3506914
Muneeb Ullah;Xiaodong Yang;Zhiya Zhang;Tong Wu;Nan Zhao;Lei Guan;Malik Muhammad Arslan;Akram Alomainy;Hafiza Maryum Ishfaq;Qammer H. Abbasi
Objective: Contactless detection and classification of abnormal respiratory patterns is challenging, especially in multi-person scenarios. While Software-Defined Radio (SDR) systems have shown promise in capturing subtle respiratory movements, the presence of multiple people introduces interference and complexity, making it difficult to distinguish individual breathing patterns, particularly when subjects are close together or have similar respiratory conditions. Results: This paper presents a contactless, non-invasive system for monitoring and classifying abnormal breathing patterns in both single and multi-person scenarios using orthogonal frequency division multiplexing (OFDM) signals and deep learning techniques. The system automatically detects various respiratory patterns, such as whooping cough, Acute Cough, eupnea, Bradypnea, tachypnea, Biot's, sighing, Cheyne-Stokes, Kussmaul, CSA, and OSA. Using SDR technology, the system leverages OFDM signals to detect subtle respiratory movements, allowing real-time classification in different environments. A hybrid deep learning model, VGG16-GRU, combining convolutional neural networks (CNNs) and gated recurrent units (GRUs), was developed to capture both spatial and temporal features of continuous respiratory data. The model successfully classified 11 distinct breathing patterns with high accuracy, achieving an overall accuracy of 99.07%, precision of 99.08%, recall of 99.09%, and an F1-score of 99.07%. The dataset, collected in an office environment, includes complex scenarios with multiple subjects, demonstrating the system's effectiveness in distinguishing individual breathing patterns, even in multi-person settings. Conclusions: This research advances contactless respiratory monitoring by offering a reliable, scalable solution for real-time detection and classification of respiratory conditions. It has significant implications for the development of automated diagnostic tools for respiratory disorders, offering potential benefits for clinical and healthcare applications. Future work will expand the dataset and refine the models to improve performance across diverse respiratory patterns and real-world data from a respiratory unit.
{"title":"Contactless Detection of Abnormal Breathing Using Orthogonal Frequency Division Multiplexing Signals and Deep Learning in Multi-Person Scenarios","authors":"Muneeb Ullah;Xiaodong Yang;Zhiya Zhang;Tong Wu;Nan Zhao;Lei Guan;Malik Muhammad Arslan;Akram Alomainy;Hafiza Maryum Ishfaq;Qammer H. Abbasi","doi":"10.1109/OJEMB.2024.3506914","DOIUrl":"https://doi.org/10.1109/OJEMB.2024.3506914","url":null,"abstract":"<italic>Objective:</i>\u0000 Contactless detection and classification of abnormal respiratory patterns is challenging, especially in multi-person scenarios. While Software-Defined Radio (SDR) systems have shown promise in capturing subtle respiratory movements, the presence of multiple people introduces interference and complexity, making it difficult to distinguish individual breathing patterns, particularly when subjects are close together or have similar respiratory conditions. \u0000<italic>Results:</i>\u0000 This paper presents a contactless, non-invasive system for monitoring and classifying abnormal breathing patterns in both single and multi-person scenarios using orthogonal frequency division multiplexing (OFDM) signals and deep learning techniques. The system automatically detects various respiratory patterns, such as whooping cough, Acute Cough, eupnea, Bradypnea, tachypnea, Biot's, sighing, Cheyne-Stokes, Kussmaul, CSA, and OSA. Using SDR technology, the system leverages OFDM signals to detect subtle respiratory movements, allowing real-time classification in different environments. A hybrid deep learning model, VGG16-GRU, combining convolutional neural networks (CNNs) and gated recurrent units (GRUs), was developed to capture both spatial and temporal features of continuous respiratory data. The model successfully classified 11 distinct breathing patterns with high accuracy, achieving an overall accuracy of 99.07%, precision of 99.08%, recall of 99.09%, and an F1-score of 99.07%. The dataset, collected in an office environment, includes complex scenarios with multiple subjects, demonstrating the system's effectiveness in distinguishing individual breathing patterns, even in multi-person settings. \u0000<italic>Conclusions:</i>\u0000 This research advances contactless respiratory monitoring by offering a reliable, scalable solution for real-time detection and classification of respiratory conditions. It has significant implications for the development of automated diagnostic tools for respiratory disorders, offering potential benefits for clinical and healthcare applications. Future work will expand the dataset and refine the models to improve performance across diverse respiratory patterns and real-world data from a respiratory unit.","PeriodicalId":33825,"journal":{"name":"IEEE Open Journal of Engineering in Medicine and Biology","volume":"6 ","pages":"241-247"},"PeriodicalIF":2.7,"publicationDate":"2024-11-26","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://ieeexplore.ieee.org/stamp/stamp.jsp?tp=&arnumber=10768903","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142905894","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 : 2024-11-20DOI: 10.1109/OJEMB.2024.3503494
Rory Bennett;Tristan Barrett;Vincent J. Gnanapragasam;Zion Tse
Surface-based ultrasound (SUS) systems have undergone substantial improvement over the years in image quality, ease-of-use, and reduction in size. Their ability to image organs non-invasively makes them a prime technology for the diagnosis and monitoring of various diseases and conditions. An example is the screening/risk- stratification of prostate cancer (PCa) using prostate-specific antigen density (PSAD). Current literature predominantly focuses on prostate volume (PV) estimation techniques that make use of magnetic resonance imaging (MRI) or transrectal ultrasound (TRUS) imaging, while SUS techniques are largely overlooked. If a reliable SUS PCa screening method can be introduced, patients may be able to forgo unnecessary MRI or TRUS scans. Such a screening procedure could be introduced into standard primary care settings with point-of-care ultrasound systems available at a fraction of the cost of their larger hospital counterparts. This review analyses whether literature suggests it is possible to use SUS-derived PV in the calculation of PSAD.
{"title":"Surface-Based Ultrasound Scans for the Screening of Prostate Cancer","authors":"Rory Bennett;Tristan Barrett;Vincent J. Gnanapragasam;Zion Tse","doi":"10.1109/OJEMB.2024.3503494","DOIUrl":"https://doi.org/10.1109/OJEMB.2024.3503494","url":null,"abstract":"Surface-based ultrasound (SUS) systems have undergone substantial improvement over the years in image quality, ease-of-use, and reduction in size. Their ability to image organs non-invasively makes them a prime technology for the diagnosis and monitoring of various diseases and conditions. An example is the screening/risk- stratification of prostate cancer (PCa) using prostate-specific antigen density (PSAD). Current literature predominantly focuses on prostate volume (PV) estimation techniques that make use of magnetic resonance imaging (MRI) or transrectal ultrasound (TRUS) imaging, while SUS techniques are largely overlooked. If a reliable SUS PCa screening method can be introduced, patients may be able to forgo unnecessary MRI or TRUS scans. Such a screening procedure could be introduced into standard primary care settings with point-of-care ultrasound systems available at a fraction of the cost of their larger hospital counterparts. This review analyses whether literature suggests it is possible to use SUS-derived PV in the calculation of PSAD.","PeriodicalId":33825,"journal":{"name":"IEEE Open Journal of Engineering in Medicine and Biology","volume":"6 ","pages":"212-218"},"PeriodicalIF":2.7,"publicationDate":"2024-11-20","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://ieeexplore.ieee.org/stamp/stamp.jsp?tp=&arnumber=10758798","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142844377","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 : 2024-11-20DOI: 10.1109/OJEMB.2024.3503499
Mario Francisco Munoz;Hoang Vu Huy;Thanh-Dung Le;Philippe Jouvet;Rita Noumeir
Remote patient monitoring has emerged as a prominent non-invasive method, using digital technologies and computer vision (CV) to replace traditional invasive monitoring. While neonatal and pediatric departments embrace this approach, Pediatric Intensive Care Units (PICUs) face the challenge of occlusions hindering accurate image analysis and interpretation. Goal: In this study, we propose a hybrid approach to effectively segment common occlusions encountered in remote monitoring applications within PICUs. Our approach centers on creating a deep-learning pipeline for limited training data scenarios. Methods: First, a combination of the well-established Google DeepLabV3+ segmentation model with the transformer-based Segment Anything Model (SAM) is devised for occlusion segmentation mask proposal and refinement. We then train and validate this pipeline using a small dataset acquired from real-world PICU settings with a Microsoft Kinect camera, achieving an Intersection-over-Union (IoU) metric of 85%. Results: Both quantitative and qualitative analyses underscore the effectiveness of our proposed method. The proposed framework yields an overall classification performance with 92.5% accuracy, 93.8% recall, 90.3% precision, and 92.0% F1-score. Consequently, the proposed method consistently improves the predictions across all metrics, with an average of 2.75% gain in performance compared to the baseline CNN-based framework. Conclusions: Our proposed hybrid approach significantly enhances the segmentation of occlusions in remote patient monitoring within PICU settings. This advancement contributes to improving the quality of care for pediatric patients, addressing a critical need in clinical practice by ensuring more accurate and reliable remote monitoring.
{"title":"Hybrid Deep Learning-Based Enhanced Occlusion Segmentation in PICU Patient Monitoring","authors":"Mario Francisco Munoz;Hoang Vu Huy;Thanh-Dung Le;Philippe Jouvet;Rita Noumeir","doi":"10.1109/OJEMB.2024.3503499","DOIUrl":"https://doi.org/10.1109/OJEMB.2024.3503499","url":null,"abstract":"Remote patient monitoring has emerged as a prominent non-invasive method, using digital technologies and computer vision (CV) to replace traditional invasive monitoring. While neonatal and pediatric departments embrace this approach, Pediatric Intensive Care Units (PICUs) face the challenge of occlusions hindering accurate image analysis and interpretation. \u0000<italic>Goal:</i>\u0000 In this study, we propose a hybrid approach to effectively segment common occlusions encountered in remote monitoring applications within PICUs. Our approach centers on creating a deep-learning pipeline for limited training data scenarios. \u0000<italic>Methods:</i>\u0000 First, a combination of the well-established Google DeepLabV3+ segmentation model with the transformer-based Segment Anything Model (SAM) is devised for occlusion segmentation mask proposal and refinement. We then train and validate this pipeline using a small dataset acquired from real-world PICU settings with a Microsoft Kinect camera, achieving an Intersection-over-Union (IoU) metric of 85%. \u0000<italic>Results:</i>\u0000 Both quantitative and qualitative analyses underscore the effectiveness of our proposed method. The proposed framework yields an overall classification performance with 92.5% accuracy, 93.8% recall, 90.3% precision, and 92.0% F1-score. Consequently, the proposed method consistently improves the predictions across all metrics, with an average of 2.75% gain in performance compared to the baseline CNN-based framework. \u0000<italic>Conclusions:</i>\u0000 Our proposed hybrid approach significantly enhances the segmentation of occlusions in remote patient monitoring within PICU settings. This advancement contributes to improving the quality of care for pediatric patients, addressing a critical need in clinical practice by ensuring more accurate and reliable remote monitoring.","PeriodicalId":33825,"journal":{"name":"IEEE Open Journal of Engineering in Medicine and Biology","volume":"6 ","pages":"176-182"},"PeriodicalIF":2.7,"publicationDate":"2024-11-20","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://ieeexplore.ieee.org/stamp/stamp.jsp?tp=&arnumber=10758753","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142810705","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}
Presents corrections to the paper, Gastric Section Correlation Network for Gastric Precancerous Lesion Diagnosis.
介绍对论文《用于胃癌前病变诊断的胃部切片相关网络》的更正。
{"title":"Corrections to “Gastric Section Correlation Network for Gastric Precancerous Lesion Diagnosis”","authors":"Jyun-Yao Jhang;Yu-Ching Tsai;Tzu-Chun Hsu;Chun-Rong Huang;Hsiu-Chi Cheng;Bor-Shyang Sheu","doi":"10.1109/OJEMB.2024.3452970","DOIUrl":"https://doi.org/10.1109/OJEMB.2024.3452970","url":null,"abstract":"Presents corrections to the paper, Gastric Section Correlation Network for Gastric Precancerous Lesion Diagnosis.","PeriodicalId":33825,"journal":{"name":"IEEE Open Journal of Engineering in Medicine and Biology","volume":"6 ","pages":"68-68"},"PeriodicalIF":2.7,"publicationDate":"2024-11-11","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://ieeexplore.ieee.org/stamp/stamp.jsp?tp=&arnumber=10750350","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142600203","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 : 2024-11-08DOI: 10.1109/OJEMB.2024.3387893
{"title":"IEEE Open Journal of Engineering in Medicine and Biology Author Instructions","authors":"","doi":"10.1109/OJEMB.2024.3387893","DOIUrl":"https://doi.org/10.1109/OJEMB.2024.3387893","url":null,"abstract":"","PeriodicalId":33825,"journal":{"name":"IEEE Open Journal of Engineering in Medicine and Biology","volume":"5 ","pages":"C3-C3"},"PeriodicalIF":2.7,"publicationDate":"2024-11-08","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://ieeexplore.ieee.org/stamp/stamp.jsp?tp=&arnumber=10747777","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142595891","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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