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Jiang M, Bian F, Zhang J, Huang T, Xia L, Chu Y, Wang Z, Jiang J. Myocardial infarction detection method based on the continuous T-wave area feature and multi-lead-fusion deep features. Physiol Meas 2024; 45:055017. [PMID: 38697203 DOI: 10.1088/1361-6579/ad46e1] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 09/22/2023] [Accepted: 05/02/2024] [Indexed: 05/04/2024]
Abstract
Objective.Myocardial infarction (MI) is one of the most threatening cardiovascular diseases. This paper aims to explore a method for using an algorithm to autonomously classify MI based on the electrocardiogram (ECG).Approach.A detection method of MI that fuses continuous T-wave area (C_TWA) feature and ECG deep features is proposed. This method consists of three main parts: (1) The onset of MI is often accompanied by changes in the shape of the T-wave in the ECG, thus the area of the T-wave displayed on different heartbeats will be quite different. The adaptive sliding window method is used to detect the start and end of the T-wave, and calculate the C_TWA on the same ECG record. Additionally, the coefficient of variation of C_TWA is defined as the C_TWA feature of the ECG. (2) The multi lead fusion convolutional neural network was implemented to extract the deep features of the ECG. (3) The C_TWA feature and deep features of the ECG were fused by soft attention, and then inputted into the multi-layer perceptron to obtain the detection result.Main results.According to the inter-patient paradigm, the proposed method reached a 97.67% accuracy, 96.59% precision, and 98.96% recall on the PTB dataset, as well as reached 93.15% accuracy, 93.20% precision, and 95.14% recall on the clinical dataset.Significance.This method accurately extracts the feature of the C_TWA, and combines the deep features of the signal, thereby improving the detection accuracy and achieving favorable results on clinical datasets.
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Affiliation(s)
- Mingfeng Jiang
- School of Computer Science and Technology (School of Artificial Intelligence), Zhejiang Sci-Tech University, Hangzhou, People's Republic of China
| | - Feibiao Bian
- School of Computer Science and Technology (School of Artificial Intelligence), Zhejiang Sci-Tech University, Hangzhou, People's Republic of China
| | - Jucheng Zhang
- Department of Clinical Engineering, School of Medicine, The Second Affiliated Hospital, Zhejiang University, Hangzhou, People's Republic of China
- Key Laboratory of Medical Molecular Imaging of Zhejiang Province, Hangzhou, People's Republic of China
| | - Tianhai Huang
- Department of Clinical Engineering, School of Medicine, The Second Affiliated Hospital, Zhejiang University, Hangzhou, People's Republic of China
| | - Ling Xia
- Key Laboratory for Biomedical Engineering of Ministry of Education, Institute of Biomedical Engineering, Zhejiang University, Hangzhou, People's Republic of China
- Research Center for Healthcare Data Science, Zhejiang Lab, Hangzhou, People's Republic of China
| | - Yonghua Chu
- Department of Clinical Engineering, School of Medicine, The Second Affiliated Hospital, Zhejiang University, Hangzhou, People's Republic of China
| | - Zhikang Wang
- Department of Clinical Engineering, School of Medicine, The Second Affiliated Hospital, Zhejiang University, Hangzhou, People's Republic of China
| | - Jun Jiang
- Department of Cardiology, School of Medicine, The Second Affiliated Hospital, Zhejiang University, Hangzhou, People's Republic of China
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Ma X, Mo C, Li Y, Chen X, Gui C. Prediction of the development of contrast‑induced nephropathy following percutaneous coronary artery intervention by machine learning. Acta Cardiol 2023; 78:912-921. [PMID: 37052397 DOI: 10.1080/00015385.2023.2198937] [Citation(s) in RCA: 2] [Impact Index Per Article: 2.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 08/03/2022] [Accepted: 03/30/2023] [Indexed: 04/14/2023]
Abstract
Contrast-induced nephropathy (CIN) is associated with increased mortality and morbidity in patients with coronary artery disease undergoing elective percutaneous coronary intervention(PCI). We developed a machine learning-based risk stratification model to predict contrast-induced nephropathy after PCI. A study retrospectively enrolling 240 patients eligible for PCI from December 2017 to May 2020 was performed. CIN was defined as a rise in serum creatinine levels ≥0.5 mg/dL or ≥25% from baseline within 72 h after surgery. Eight machine learning methods were performed based on clinical variables. Shapley Additive exPlanation values were also used to interpret the best-performing prediction models. Development of CIN was found in 37 patients(16.5%) after PCI. There were 11 significant predictors of CIN, including uric acid, peripheral vascular disease, cystatin C, creatine kinase-MB, haemoglobin, N-terminal pro-brain natriuretic peptide, age, diabetes, systemic immune-inflammatory index, total protein, and low-density lipoprotein. Regarding the efficacy of the machine learning model that accurately predicted CIN, SVM exhibited the most outstanding AUC value of 0.784. The SHAP and radar plots were used to illustrate the positive and negative effects of the 11 features attributed to the SVM. Machine learning models have the potential to identify the risk of CIN for elective PCI patients.
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Affiliation(s)
- Xiao Ma
- Department of Cardiology, The First Affiliated Hospital of Guangxi Medical University, Nanning, P. R. China
- Guangxi Key Laboratory Base of Precision Medicine in Cardiocerebrovascular Diseases Control and Prevention, Nanning, P. R. China
- Guangxi Clinical Research Center for Cardiocerebrovascular Diseases, Nanning, P. R. China
| | - Changhua Mo
- Department of Cardiology, The First Affiliated Hospital of Guangxi Medical University, Nanning, P. R. China
- Guangxi Key Laboratory Base of Precision Medicine in Cardiocerebrovascular Diseases Control and Prevention, Nanning, P. R. China
- Guangxi Clinical Research Center for Cardiocerebrovascular Diseases, Nanning, P. R. China
| | - Yujuan Li
- Department of Cardiology, The First Affiliated Hospital of Guangxi Medical University, Nanning, P. R. China
- Guangxi Key Laboratory Base of Precision Medicine in Cardiocerebrovascular Diseases Control and Prevention, Nanning, P. R. China
- Guangxi Clinical Research Center for Cardiocerebrovascular Diseases, Nanning, P. R. China
| | - Xinyuan Chen
- Department of Cardiology, The First Affiliated Hospital of Guangxi Medical University, Nanning, P. R. China
| | - Chun Gui
- Department of Cardiology, The First Affiliated Hospital of Guangxi Medical University, Nanning, P. R. China
- Guangxi Key Laboratory Base of Precision Medicine in Cardiocerebrovascular Diseases Control and Prevention, Nanning, P. R. China
- Guangxi Clinical Research Center for Cardiocerebrovascular Diseases, Nanning, P. R. China
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Karpov OE, Pitsik EN, Kurkin SA, Maksimenko VA, Gusev AV, Shusharina NN, Hramov AE. Analysis of Publication Activity and Research Trends in the Field of AI Medical Applications: Network Approach. INTERNATIONAL JOURNAL OF ENVIRONMENTAL RESEARCH AND PUBLIC HEALTH 2023; 20:5335. [PMID: 37047950 PMCID: PMC10094658 DOI: 10.3390/ijerph20075335] [Citation(s) in RCA: 1] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Figures] [Subscribe] [Scholar Register] [Received: 02/14/2023] [Revised: 03/17/2023] [Accepted: 03/22/2023] [Indexed: 06/19/2023]
Abstract
Artificial intelligence (AI) has revolutionized numerous industries, including medicine. In recent years, the integration of AI into medical practices has shown great promise in enhancing the accuracy and efficiency of diagnosing diseases, predicting patient outcomes, and personalizing treatment plans. This paper aims at the exploration of the AI-based medicine research using network approach and analysis of existing trends based on PubMed. Our findings are based on the results of PubMed search queries and analysis of the number of papers obtained by the different search queries. Our goal is to explore how are the AI-based methods used in healthcare research, which approaches and techniques are the most popular, and to discuss the potential reasoning behind the obtained results. Using analysis of the co-occurrence network constructed using VOSviewer software, we detected the main clusters of interest in AI-based healthcare research. Then, we proceeded with the thorough analysis of publication activity in various categories of medical AI research, including research on different AI-based methods applied to different types of medical data. We analyzed the results of query processing in the PubMed database over the past 5 years obtained via a specifically designed strategy for generating search queries based on the thorough selection of keywords from different categories of interest. We provide a comprehensive analysis of existing applications of AI-based methods to medical data of different modalities, including the context of various medical fields and specific diseases that carry the greatest danger to the human population.
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Affiliation(s)
- Oleg E. Karpov
- National Medical and Surgical Center Named after N. I. Pirogov, Ministry of Healthcare of the Russian Federation, 105203 Moscow, Russia
| | - Elena N. Pitsik
- Baltic Center for Neurotechnology and Artificial Intelligence, Immanuel Kant Baltic Federal University, 236041 Kaliningrad, Russia; (E.N.P.); (S.A.K.); (V.A.M.); (N.N.S.)
| | - Semen A. Kurkin
- Baltic Center for Neurotechnology and Artificial Intelligence, Immanuel Kant Baltic Federal University, 236041 Kaliningrad, Russia; (E.N.P.); (S.A.K.); (V.A.M.); (N.N.S.)
| | - Vladimir A. Maksimenko
- Baltic Center for Neurotechnology and Artificial Intelligence, Immanuel Kant Baltic Federal University, 236041 Kaliningrad, Russia; (E.N.P.); (S.A.K.); (V.A.M.); (N.N.S.)
| | - Alexander V. Gusev
- K-Skai LLC, 185031 Petrozavodsk, Russia
- Federal Research Institute for Health Organization and Informatics, 127254 Moscow, Russia
| | - Natali N. Shusharina
- Baltic Center for Neurotechnology and Artificial Intelligence, Immanuel Kant Baltic Federal University, 236041 Kaliningrad, Russia; (E.N.P.); (S.A.K.); (V.A.M.); (N.N.S.)
| | - Alexander E. Hramov
- Baltic Center for Neurotechnology and Artificial Intelligence, Immanuel Kant Baltic Federal University, 236041 Kaliningrad, Russia; (E.N.P.); (S.A.K.); (V.A.M.); (N.N.S.)
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Identification of Clinical Features Associated with Mortality in COVID-19 Patients. OPERATIONS RESEARCH FORUM 2023. [PMCID: PMC9984757 DOI: 10.1007/s43069-022-00191-3] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 03/06/2023]
Abstract
AbstractUnderstanding clinical features and risk factors associated with COVID-19 mortality is needed to early identify critically ill patients, initiate treatments and prevent mortality. A retrospective study on COVID-19 patients referred to a tertiary hospital in Iran between March and November 2020 was conducted. COVID-19-related mortality and its association with clinical features including headache, chest pain, symptoms on computerized tomography (CT), hospitalization, time to infection, history of neurological disorders, having a single or multiple risk factors, fever, myalgia, dizziness, seizure, abdominal pain, nausea, vomiting, diarrhoea and anorexia were investigated. Based on the investigation outcome, decision tree and dimension reduction algorithms were used to identify the aforementioned risk factors. Of the 3008 patients (mean age 59.3 ± 18.7 years, 44% women) with COVID-19, 373 died. There was a significant association between COVID-19 mortality and old age, headache, chest pain, low respiratory rate, oxygen saturation < 93%, need for a mechanical ventilator, having symptoms on CT, hospitalization, time to infection, neurological disorders, cardiovascular diseases and having a risk factor or multiple risk factors. In contrast, there was no significant association between mortality and gender, fever, myalgia, dizziness, seizure, abdominal pain, nausea, vomiting, diarrhoea and anorexia. Our results might help identify early symptoms related to COVID-19 and better manage patients according to the extracted decision tree. The proposed ML models identified a number of clinical features and risk factors associated with mortality in COVID-19 patients. These models if implemented in a clinical setting might help to early identify patients needing medical attention and care. However, more studies are needed to confirm these findings.
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Breast Cancer Dataset, Classification and Detection Using Deep Learning. Healthcare (Basel) 2022; 10:healthcare10122395. [PMID: 36553919 PMCID: PMC9778593 DOI: 10.3390/healthcare10122395] [Citation(s) in RCA: 3] [Impact Index Per Article: 1.5] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 11/13/2022] [Revised: 11/24/2022] [Accepted: 11/25/2022] [Indexed: 12/05/2022] Open
Abstract
Incorporating scientific research into clinical practice via clinical informatics, which includes genomics, proteomics, bioinformatics, and biostatistics, improves patients' treatment. Computational pathology is a growing subspecialty with the potential to integrate whole slide images, multi-omics data, and health informatics. Pathology and laboratory medicine are critical to diagnosing cancer. This work will review existing computational and digital pathology methods for breast cancer diagnosis with a special focus on deep learning. The paper starts by reviewing public datasets related to breast cancer diagnosis. Additionally, existing deep learning methods for breast cancer diagnosis are reviewed. The publicly available code repositories are introduced as well. The paper is closed by highlighting challenges and future works for deep learning-based diagnosis.
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Sun Q, Liang C, Chen T, Ji B, Liu R, Wang L, Tang M, Chen Y, Wang C. Early detection of myocardial ischemia in 12-lead ECG using deterministic learning and ensemble learning. COMPUTER METHODS AND PROGRAMS IN BIOMEDICINE 2022; 226:107124. [PMID: 36156437 DOI: 10.1016/j.cmpb.2022.107124] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Subscribe] [Scholar Register] [Received: 11/25/2021] [Revised: 08/18/2022] [Accepted: 09/09/2022] [Indexed: 06/16/2023]
Abstract
BACKGROUND AND OBJECTIVE Early detection of myocardial ischemia is a necessary but difficult problem in cardiovascular diseases. Approaches that exclusively rely on classical ST and T wave changes on the standard 12-lead electrocardiogram (ECG) lack sufficient accuracy in detecting myocardial ischemia. This study aims to construct generalizable models for the detection of myocardial ischemia in patients with subtle ECG waveform changes (namely non-diagnostic ECG) using ensemble learning to integrate ECG dynamic features acquired via deterministic learning. METHODS First, cardiodynamicsgram (CDG), a noninvasive spatiotemporal electrocardiographic method, is generated through dynamic modeling of ECG signals using the deterministic learning algorithm. Then, the spectral fitting exponent, Lyapunov exponent, and Lempel-Ziv complexity are extracted from CDG. Subsequently, the bagging-based heterogeneous ensemble algorithm is applied on CDG features to generate diverse base classifiers and aggregate them with weighted voting to obtain an ensemble model for myocardial ischemia detection. Finally, we train and test the proposed heterogeneous ensemble model on a real-world clinical dataset. This dataset consists of 499 non-diagnostic 12-lead ECG records from 499 patients collected from three independent medical centers, including 383 patients with myocardial ischemia and 116 patients without ischemia. RESULTS With 10-times 5-fold cross-validation technology, our proposed method achieves an average accuracy of 89.10%, sensitivity of 91.72%, and specificity of 82.69% using the heterogeneous ensemble algorithm on the real-world clinical dataset. On three independent medical centers, our ensemble model also achieves accuracy performance over 82% for patients with non-diagnostic ECG. Furthermore, our ensemble model trained with real-world clinical data yields promising results of 91.11% accuracy, 90.49% sensitivity, and 92.88% specificity on the external test set of the public PTB dataset. CONCLUSION The experimental results demonstrate that the proposed model combining ensemble learning and deterministic learning presents excellent diagnostic accuracy and generalization in clinical practice, and could be implemented as a complement to the standard ECG in the clinical diagnosis of myocardial ischemia.
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Affiliation(s)
- Qinghua Sun
- Center for Intelligent Medical Engineering, School of Control Science and Engineering, Shandong University, Jinan, China
| | - Chunmiao Liang
- Center for Intelligent Medical Engineering, School of Control Science and Engineering, Shandong University, Jinan, China
| | - Tianrui Chen
- Center for Intelligent Medical Engineering, School of Control Science and Engineering, Shandong University, Jinan, China
| | - Bing Ji
- Center for Intelligent Medical Engineering, School of Control Science and Engineering, Shandong University, Jinan, China
| | - Rugang Liu
- Department of Emergency, Qilu Hospital of Shandong University, Jinan, China
| | - Lei Wang
- Department of Cardiology, Shihezi People's Hospital, Shihezi, China
| | - Min Tang
- State Key Laboratory of Cardiovascular Disease, Fuwai Hospital, National Center for Cardiovascular Diseases, Chinese Academy of Medical Sciences and Peking Union Medical College, Beijing, China
| | - Yuguo Chen
- Department of Emergency, Qilu Hospital of Shandong University, Jinan, China
| | - Cong Wang
- Center for Intelligent Medical Engineering, School of Control Science and Engineering, Shandong University, Jinan, China.
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