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Yang C, Chen Y, Zhu L, Wang L, Lin Q. A deep learning MRI-based signature may provide risk-stratification strategies for nasopharyngeal carcinoma. Eur Arch Otorhinolaryngol 2023; 280:5039-5047. [PMID: 37358652 DOI: 10.1007/s00405-023-08084-9] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 05/18/2023] [Accepted: 06/16/2023] [Indexed: 06/27/2023]
Abstract
OBJECTIVE As the prognosis of nasopharyngeal carcinoma (NPC) is influenced by various factors, making it difficult for clinical physicians to predict the outcome, the objective of this study was to develop a deep learning-based signature for risk stratification in NPC patients. METHODS A total of 293 patients were enrolled in the study and divided into training, validation, and testing groups with a ratio of 7:1:2. MRI scans and corresponding clinical information were collected, and the 3-year disease-free survival (DFS) was chosen as the endpoint. The Res-Net18 algorithm was used to develop two deep learning (DL) models and another solely based on clinical characteristics developed by multivariate cox analysis. The performance of both models was evaluated using the area under the curve (AUC) and the concordance index (C-index). Discriminative performance was assessed using Kaplan-Meier survival analysis. RESULTS The deep learning approach identified DL prognostic models. The MRI-based DL model showed significantly better performance compared to the traditional model solely based on clinical characteristics (AUC: 0.8861 vs 0.745, p = 0.04 and C-index: 0.865 vs 0.727, p = 0.03). The survival analysis showed significant survival differences between the risk groups identified by the MRI-based model. CONCLUSION Our study highlights the potential of MRI in predicting the prognosis of NPC through DL algorithm. This approach has the potential to become a novel tool for prognosis prediction and can help physicians to develop more valid treatment strategies in the future.
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Affiliation(s)
- Chen Yang
- Department of Radiation Oncology, Xiamen Cancer Center, Xiamen Key Laboratory of Radiation Oncology, The First Affiliated Hospital of Xiamen University, School of Medicine, Xiamen University, Xiamen, 361003, Fujian, China
| | - Yuan Chen
- Department of Computer Science, Xiamen University, Xiamen, Fujian, China
| | - Luchao Zhu
- Department of Radiation Oncology, Xiamen Cancer Center, Xiamen Key Laboratory of Radiation Oncology, The First Affiliated Hospital of Xiamen University, School of Medicine, Xiamen University, Xiamen, 361003, Fujian, China
| | - Liansheng Wang
- Department of Computer Science, Xiamen University, Xiamen, Fujian, China.
| | - Qin Lin
- Department of Radiation Oncology, Xiamen Cancer Center, Xiamen Key Laboratory of Radiation Oncology, The First Affiliated Hospital of Xiamen University, School of Medicine, Xiamen University, Xiamen, 361003, Fujian, China.
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You J, Li J, Li X, Li H, Tu J, Zhang Y, Gao J, Wu J, Ye J. Risk-prediction model for incident hypertension in patients with obstructive sleep apnea based on SpO2 signals. ANNUAL INTERNATIONAL CONFERENCE OF THE IEEE ENGINEERING IN MEDICINE AND BIOLOGY SOCIETY. IEEE ENGINEERING IN MEDICINE AND BIOLOGY SOCIETY. ANNUAL INTERNATIONAL CONFERENCE 2023; 2023:1-4. [PMID: 38083398 DOI: 10.1109/embc40787.2023.10340756] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [MESH Headings] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 12/18/2023]
Abstract
This work proposes a method utilizing oxygen saturation (SpO2) for predicting incident hypertension in patients with obstructive sleep apnea (OSA). We extracted time domain features and frequency domain features from the SpO2 signal. For prediction, we employed several machine learning algorithms to establish the 3-year risk prediction model in the Chinese Sleep Health Study, including 250 subjects without baseline hypertension who underwent sleep monitoring. The proposed random forest model achieved an accuracy of 84.4%, a sensitivity of 77.0%, a specificity of 91.5% and an area under the receiver operator characteristic of 84.3% using 10-fold crossvalidation. We show that the model outperformed two sleep medicine specialists using clinical experience to predict hypertension. Furthermore, we applied the prediction results in the public Sleep Heart Health Study database and showed the subjects who were predicted to have hypertension would be at a higher risk in 4-6 years. This work shows the potential of SpO2 signal during sleep for the prediction of hypertension and could be beneficial to the early detection and timely treatment of hypertension in OSA patients.Clinical Relevance-There is no prediction model for incident hypertension in OSA patients in clinical practice. Most patients are unaware of health complexity, symptoms and risk factors before hypertension. Establishing an accurate prediction model can effectively provide early intervention for OSA patients and reduce the prevalence of hypertension.
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Khan Mamun MMR, Sherif A. Advancement in the Cuffless and Noninvasive Measurement of Blood Pressure: A Review of the Literature and Open Challenges. BIOENGINEERING (BASEL, SWITZERLAND) 2022; 10:bioengineering10010027. [PMID: 36671599 PMCID: PMC9854981 DOI: 10.3390/bioengineering10010027] [Citation(s) in RCA: 4] [Impact Index Per Article: 2.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Figures] [Subscribe] [Scholar Register] [Received: 12/10/2022] [Revised: 12/20/2022] [Accepted: 12/21/2022] [Indexed: 12/28/2022]
Abstract
Hypertension is a chronic condition that is one of the prominent reasons behind cardiovascular disease, brain stroke, and organ failure. Left unnoticed and untreated, the deterioration in a health condition could even result in mortality. If it can be detected early, with proper treatment, undesirable outcomes can be avoided. Until now, the gold standard is the invasive way of measuring blood pressure (BP) using a catheter. Additionally, the cuff-based and noninvasive methods are too cumbersome or inconvenient for frequent measurement of BP. With the advancement of sensor technology, signal processing techniques, and machine learning algorithms, researchers are trying to find the perfect relationships between biomedical signals and changes in BP. This paper is a literature review of the studies conducted on the cuffless noninvasive measurement of BP using biomedical signals. Relevant articles were selected using specific criteria, then traditional techniques for BP measurement were discussed along with a motivation for cuffless measurement use of biomedical signals and machine learning algorithms. The review focused on the progression of different noninvasive cuffless techniques rather than comparing performance among different studies. The literature survey concluded that the use of deep learning proved to be the most accurate among all the cuffless measurement techniques. On the other side, this accuracy has several disadvantages, such as lack of interpretability, computationally extensive, standard validation protocol, and lack of collaboration with health professionals. Additionally, the continuing work by researchers is progressing with a potential solution for these challenges. Finally, future research directions have been provided to encounter the challenges.
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Affiliation(s)
| | - Ahmed Sherif
- School of Computing Sciences and Computer Engineering, The University of Southern Mississippi, Hattiesburg, MS 39406, USA
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Rey-Rañal EM, Cordero A. Most Recent Trials and Advances in Hypertension. Eur Cardiol 2022; 17:e24. [PMID: 36845214 PMCID: PMC9947933 DOI: 10.15420/ecr.2022.27] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 06/12/2022] [Accepted: 07/14/2022] [Indexed: 12/14/2022] Open
Abstract
Hypertension is one of the most prevalent cardiovascular risk factors and blood pressure control remains a clinical challenge, especially for patients with established cardiovascular disease. Late-breaking clinical trials and other evidence in hypertension have evolved to assess the most accurate ways to measure blood pressure, the use of combination therapies, considerations in special populations and evaluation of new techniques. Recent evidence supports the superiority of ambulatory or 24-hour blood pressure measurements, rather than office blood pressure measurements, for the assessment of cardiovascular risk. The use of fixed-dose combinations and polypills has been demonstrated to be valid and to provide clinical benefits beyond blood pressure control. There have also been advances in new approaches such as telemedicine, devices and the use of algorithms. Clinical trials have provided valuable data on blood pressure control in primary prevention, during pregnancy and in the elderly. The role of renal denervation remains unsolved but innovative techniques using ultrasound or alcohol injections are being explored. Current evidence and results of latest trials are summarised in this review.
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Affiliation(s)
| | - Alberto Cordero
- Cardiology Department, Hospital Universitario de San JuanAlicante, Spain,Unidad de Investigación de Cardiología, Fundación para el Fomento de la Investigación Sanitaria y Biomédica de la Comunitad Valenciana (FISABIO)Valencia, Spain,Centro de Investigación Biomédica en Red de Enfermedades Cardiovasculares (CIBERCV)Madrid, Spain
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Kireev D, Sel K, Ibrahim B, Kumar N, Akbari A, Jafari R, Akinwande D. Continuous cuffless monitoring of arterial blood pressure via graphene bioimpedance tattoos. NATURE NANOTECHNOLOGY 2022; 17:864-870. [PMID: 35725927 DOI: 10.1038/s41565-022-01145-w] [Citation(s) in RCA: 55] [Impact Index Per Article: 27.5] [Reference Citation Analysis] [Abstract] [MESH Headings] [Grants] [Track Full Text] [Subscribe] [Scholar Register] [Received: 09/08/2021] [Accepted: 05/03/2022] [Indexed: 05/15/2023]
Abstract
Continuous monitoring of arterial blood pressure (BP) in non-clinical (ambulatory) settings is essential for understanding numerous health conditions, including cardiovascular diseases. Besides their importance in medical diagnosis, ambulatory BP monitoring platforms can advance disease correlation with individual behaviour, daily habits and lifestyle, potentially enabling analysis of root causes, prognosis and disease prevention. Although conventional ambulatory BP devices exist, they are uncomfortable, bulky and intrusive. Here we introduce a wearable continuous BP monitoring platform that is based on electrical bioimpedance and leverages atomically thin, self-adhesive, lightweight and unobtrusive graphene electronic tattoos as human bioelectronic interfaces. The graphene electronic tattoos are used to monitor arterial BP for >300 min, a period tenfold longer than reported in previous studies. The BP is recorded continuously and non-invasively, with an accuracy of 0.2 ± 4.5 mm Hg for diastolic pressures and 0.2 ± 5.8 mm Hg for systolic pressures, a performance equivalent to Grade A classification.
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Affiliation(s)
- Dmitry Kireev
- Department of Electrical and Computer Engineering, The University of Texas at Austin, Austin, TX, USA
- Microelectronics Research Center, The University of Texas, Austin, TX, USA
| | - Kaan Sel
- Department of Electrical and Computer Engineering, Texas A&M University, College Station, TX, USA
| | - Bassem Ibrahim
- Department of Electrical and Computer Engineering, Texas A&M University, College Station, TX, USA
| | - Neelotpala Kumar
- Department of Electrical and Computer Engineering, The University of Texas at Austin, Austin, TX, USA
| | - Ali Akbari
- Department of Biomedical Engineering, Texas A&M University, College Station, TX, USA
| | - Roozbeh Jafari
- Department of Electrical and Computer Engineering, Texas A&M University, College Station, TX, USA.
- Department of Biomedical Engineering, Texas A&M University, College Station, TX, USA.
- Department of Computer Science and Engineering, Texas A&M University, College Station, TX, USA.
| | - Deji Akinwande
- Department of Electrical and Computer Engineering, The University of Texas at Austin, Austin, TX, USA.
- Microelectronics Research Center, The University of Texas, Austin, TX, USA.
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Zhou M, Bian K, Hu F, Lai W. A New Strategy for Identification of Coal Miners With Abnormal Physical Signs Based on EN-mRMR. Front Bioeng Biotechnol 2022; 10:935481. [PMID: 35898648 PMCID: PMC9310099 DOI: 10.3389/fbioe.2022.935481] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 05/04/2022] [Accepted: 06/06/2022] [Indexed: 11/21/2022] Open
Abstract
Coal miners’ occupational health is a key part of production safety in the coal mine. Accurate identification of abnormal physical signs is the key to preventing occupational diseases and improving miners’ working environment. There are many problems when evaluating the physical health status of miners manually, such as too many sign parameters, low diagnostic efficiency, missed diagnosis, and misdiagnosis. To solve these problems, the machine learning algorithm is used to identify miners with abnormal signs. We proposed a feature screening strategy of integrating elastic net (EN) and Max-Relevance and Min-Redundancy (mRMR) to establish the model to identify abnormal signs and obtain the key physical signs. First, the raw 21 physical signs were expanded to 25 by feature construction technology. Then, the EN was used to delete redundant physical signs. Finally, the mRMR combined with the support vector classification of intelligent optimization algorithm by Gravitational Search Algorithm (GSA-SVC) is applied to further simplify the rest of 12 relatively important physical signs and obtain the optimal model with data of six physical signs. At this time, the accuracy, precision, recall, specificity, G-mean, and MCC of the test set were 97.50%, 97.78%, 97.78%, 97.14%, 0.98, and 0.95. The experimental results show that the proposed strategy improves the model performance with the smallest features and realizes the accurate identification of abnormal coal miners. The conclusion could provide reference evidence for intelligent classification and assessment of occupational health in the early stage.
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Affiliation(s)
- Mengran Zhou
- School of Electrical and Information Engineering, Anhui University of Science and Technology, Huainan, China
| | - Kai Bian
- School of Electrical and Information Engineering, Anhui University of Science and Technology, Huainan, China
| | - Feng Hu
- School of Electrical and Information Engineering, Anhui University of Science and Technology, Huainan, China
| | - Wenhao Lai
- School of Electrical and Information Engineering, Anhui University of Science and Technology, Huainan, China
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Cuffless blood pressure measuring devices: review and statement by the European Society of Hypertension Working Group on Blood Pressure Monitoring and Cardiovascular Variability. J Hypertens 2022; 40:1449-1460. [PMID: 35708294 DOI: 10.1097/hjh.0000000000003224] [Citation(s) in RCA: 59] [Impact Index Per Article: 29.5] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 12/18/2022]
Abstract
BACKGROUND Many cuffless blood pressure (BP) measuring devices are currently on the market claiming that they provide accurate BP measurements. These technologies have considerable potential to improve the awareness, treatment, and management of hypertension. However, recent guidelines by the European Society of Hypertension do not recommend cuffless devices for the diagnosis and management of hypertension. OBJECTIVE This statement by the European Society of Hypertension Working Group on BP Monitoring and Cardiovascular Variability presents the types of cuffless BP technologies, issues in their validation, and recommendations for clinical practice. STATEMENTS Cuffless BP monitors constitute a wide and heterogeneous group of novel technologies and devices with different intended uses. Cuffless BP devices have specific accuracy issues, which render the established validation protocols for cuff BP devices inadequate for their validation. In 2014, the Institute of Electrical and Electronics Engineers published a standard for the validation of cuffless BP devices, and the International Organization for Standardization is currently developing another standard. The validation of cuffless devices should address issues related to the need of individual cuff calibration, the stability of measurements post calibration, the ability to track BP changes, and the implementation of machine learning technology. Clinical field investigations may also be considered and issues regarding the clinical implementation of cuffless BP readings should be investigated. CONCLUSION Cuffless BP devices have considerable potential for changing the diagnosis and management of hypertension. However, fundamental questions regarding their accuracy, performance, and implementation need to be carefully addressed before they can be recommended for clinical use.
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A machine learning approach for hypertension detection based on photoplethysmography and clinical data. Comput Biol Med 2022; 145:105479. [DOI: 10.1016/j.compbiomed.2022.105479] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 01/12/2022] [Revised: 03/01/2022] [Accepted: 03/31/2022] [Indexed: 11/23/2022]
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Fujiwara T, McManus R, Kario K. Management of hypertension in the digital era: Perspectives and future directions. HIPERTENSION Y RIESGO VASCULAR 2022; 39:79-91. [DOI: 10.1016/j.hipert.2022.01.004] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.5] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 01/26/2022] [Accepted: 01/28/2022] [Indexed: 12/24/2022]
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10
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Kumar Y, Koul A, Singla R, Ijaz MF. Artificial intelligence in disease diagnosis: a systematic literature review, synthesizing framework and future research agenda. JOURNAL OF AMBIENT INTELLIGENCE AND HUMANIZED COMPUTING 2022; 14:8459-8486. [PMID: 35039756 PMCID: PMC8754556 DOI: 10.1007/s12652-021-03612-z] [Citation(s) in RCA: 117] [Impact Index Per Article: 58.5] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Subscribe] [Scholar Register] [Received: 12/04/2020] [Accepted: 11/18/2021] [Indexed: 05/03/2023]
Abstract
Artificial intelligence can assist providers in a variety of patient care and intelligent health systems. Artificial intelligence techniques ranging from machine learning to deep learning are prevalent in healthcare for disease diagnosis, drug discovery, and patient risk identification. Numerous medical data sources are required to perfectly diagnose diseases using artificial intelligence techniques, such as ultrasound, magnetic resonance imaging, mammography, genomics, computed tomography scan, etc. Furthermore, artificial intelligence primarily enhanced the infirmary experience and sped up preparing patients to continue their rehabilitation at home. This article covers the comprehensive survey based on artificial intelligence techniques to diagnose numerous diseases such as Alzheimer, cancer, diabetes, chronic heart disease, tuberculosis, stroke and cerebrovascular, hypertension, skin, and liver disease. We conducted an extensive survey including the used medical imaging dataset and their feature extraction and classification process for predictions. Preferred reporting items for systematic reviews and Meta-Analysis guidelines are used to select the articles published up to October 2020 on the Web of Science, Scopus, Google Scholar, PubMed, Excerpta Medical Database, and Psychology Information for early prediction of distinct kinds of diseases using artificial intelligence-based techniques. Based on the study of different articles on disease diagnosis, the results are also compared using various quality parameters such as prediction rate, accuracy, sensitivity, specificity, the area under curve precision, recall, and F1-score.
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Affiliation(s)
- Yogesh Kumar
- Department of Computer Engineering, Indus Institute of Technology and Engineering, Indus University, Ahmedabad, 382115 India
| | | | - Ruchi Singla
- Department of Research, Innovations, Sponsored Projects and Entrepreneurship, CGC Landran, Mohali, India
| | - Muhammad Fazal Ijaz
- Department of Intelligent Mechatronics Engineering, Sejong University, Seoul, 05006 South Korea
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Watanabe T, Hoshide S, Kario K. Noninvasive method to validate the variability of blood pressure during arrhythmias. Hypertens Res 2021; 45:530-532. [PMID: 34961791 DOI: 10.1038/s41440-021-00835-7] [Citation(s) in RCA: 2] [Impact Index Per Article: 0.7] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 11/12/2021] [Revised: 11/23/2021] [Accepted: 11/23/2021] [Indexed: 11/09/2022]
Affiliation(s)
- Tomonori Watanabe
- Division of Cardiovascular Medicine, Department of Internal Medicine, Jichi Medical University, Tochigi, Japan
| | - Satoshi Hoshide
- Division of Cardiovascular Medicine, Department of Internal Medicine, Jichi Medical University, Tochigi, Japan
| | - Kazuomi Kario
- Division of Cardiovascular Medicine, Department of Internal Medicine, Jichi Medical University, Tochigi, Japan.
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