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Dai R, Sun M, Lu M, Deng L. Interpretable machine learning models based on shear-wave elastography radiomics for predicting cardiovascular disease in diabetic kidney disease patients. J Diabetes Investig 2024. [PMID: 39171653 DOI: 10.1111/jdi.14294] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 05/09/2024] [Revised: 07/24/2024] [Accepted: 08/02/2024] [Indexed: 08/23/2024] Open
Abstract
BACKGROUND The risk of cardiovascular complications is significantly elevated in patients with diabetic kidney disease (DKD). Recognizing the link between the progression of DKD and an increased risk of cardiovascular disease (CVD), it is crucial to focus on the early prediction and management of CVD risk factors among these patients to potentially enhance their health outcomes. OBJECTIVE This study sought to bridge the existing gap by developing and validating machine learning (ML) models that utilize clinical data and shear wave elastography (SWE) radiomics features to identify patients at risk of CVD, ultimately aiming to improve the management of DKD. MATERIALS AND METHODS This study conducted a retrospective analysis of 586 patients with DKD, dividing them into training and external validation cohorts. We categorized patients based on the presence or absence of CVD. Utilizing SWE imaging, we extracted and standardized radiomics features to develop multiple ML models. These models underwent internal validation using radiomics features alone, clinical data, or a combination thereof. The optimal model was then identified, and its feature importance was assessed through the Shapley Additive Explanations (SHAP) method, before proceeding to external validation. RESULTS Among the 586 patients analyzed, 30.7% (180/586) were identified as at risk for CVD. The study pinpointed six significant radiomics features related to CVD, alongside six critical pieces of clinical data. The Support Vector Machine (SVM) model outperformed others in both internal and external validations. Further, SHAP analysis highlighted five principal determinants of CVD risk, comprising three clinical indicators and two SWE radiomics features. CONCLUSIONS This study highlights the effectiveness of an SVM model that combines clinical and radiomics features in predicting CVD risk among DKD patients. It enables early prediction of CVD in this patient group, thereby supporting the implementation of timely and suitable interventions.
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Affiliation(s)
- Ruihong Dai
- Department of Ultrasound, Meng Cheng County Hospital of Chinese Medicine, Bozhou City, Anhui Province, China
| | - Miaomiao Sun
- Department of Ultrasound, Meng Cheng County Hospital of Chinese Medicine, Bozhou City, Anhui Province, China
| | - Mei Lu
- Department of Ultrasound, Meng Cheng County Hospital of Chinese Medicine, Bozhou City, Anhui Province, China
| | - Lanhua Deng
- Department of Ultrasound, Meng Cheng County Hospital of Chinese Medicine, Bozhou City, Anhui Province, China
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Hathaway QA, Abdeen Y, Conte J, Hass R, Santer MJ, Alyami B, Avalon JC, Patel B. Prediction of heart failure and all-cause mortality using cardiac ultrasomics in patients with breast cancer. THE INTERNATIONAL JOURNAL OF CARDIOVASCULAR IMAGING 2024; 40:1305-1317. [PMID: 38625628 DOI: 10.1007/s10554-024-03101-2] [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: 12/18/2023] [Accepted: 04/02/2024] [Indexed: 04/17/2024]
Abstract
Breast cancer chemotherapy/immunotherapy can be associated with treatment-limiting cardiotoxicity. Radiomics techniques applied to ultrasound, known as ultrasomics, can be used in cardio-oncology to leverage echocardiography for added prognostic value. To utilize ultrasomics features collected prior to antineoplastic therapy to enhance prediction of mortality and heart failure (HF) in patients with breast cancer. Patients were retrospectively recruited in a study at the West Virginia University Cancer Institute. The final inclusion criteria were met by a total of 134 patients identified for the study. Patients were imaged using echocardiography in the parasternal long axis prior to receiving chemotherapy. All-cause mortality and HF, developed during treatment, were the primary outcomes. 269 features were assessed, grouped into four major classes: demographics (n = 21), heart function (n = 7), antineoplastic medication (n = 17), and ultrasomics (n = 224). Data was split into an internal training (60%, n = 81) and testing (40%, n = 53) set. Ultrasomics features augmented classification of mortality (area under the curve (AUC) 0.89 vs. 0.65, P = 0.003), when compared to demographic variables. When developing a risk prediction score for each feature category, ultrasomics features were significantly associated with both mortality (P = 0.031, log-rank test) and HF (P = 0.002, log-rank test). Further, only ultrasomics features provided significant improvement over demographic variables when predicting mortality (C-Index: 0.78 vs. 0.65, P = 0.044) and HF (C-Index: 0.77 vs. 0.60, P = 0.017), respectively. With further investigation, a clinical decision support tool could be developed utilizing routinely obtained patient data alongside ultrasomics variables to augment treatment regimens.
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Affiliation(s)
- Quincy A Hathaway
- Department of Medical Education, West Virginia University, Morgantown, WV, USA
- Heart and Vascular Institute, West Virginia University, Morgantown, WV, USA
| | - Yahya Abdeen
- Heart and Vascular Institute, West Virginia University, Morgantown, WV, USA
| | - Justin Conte
- Heart and Vascular Institute, West Virginia University, Morgantown, WV, USA
| | - Rotem Hass
- Heart and Vascular Institute, West Virginia University, Morgantown, WV, USA
| | - Matthew J Santer
- Heart and Vascular Institute, West Virginia University, Morgantown, WV, USA
| | - Bandar Alyami
- Heart and Vascular Institute, West Virginia University, Morgantown, WV, USA
| | - Juan Carlo Avalon
- Heart and Vascular Institute, West Virginia University, Morgantown, WV, USA
| | - Brijesh Patel
- Heart and Vascular Institute, West Virginia University, Morgantown, WV, USA.
- Department of Cardiovascular and Thoracic Surgery, West Virginia University School of Medicine, 1 Medical Center Drive, Morgantown, WV, 26505, USA.
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Sahashi Y, Vukadinovic M, Duffy G, Li D, Cheng S, Berman DS, Ouyang D, Kwan AC. Using Deep learning to Predict Cardiovascular Magnetic Resonance Findings from Echocardiography Videos. MEDRXIV : THE PREPRINT SERVER FOR HEALTH SCIENCES 2024:2024.04.16.24305936. [PMID: 38699330 PMCID: PMC11065018 DOI: 10.1101/2024.04.16.24305936] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 05/05/2024]
Abstract
Background Echocardiography is the most common modality for assessing cardiac structure and function. While cardiac magnetic resonance (CMR) imaging is less accessible, CMR can provide unique tissue characterization including late gadolinium enhancement (LGE), T1 and T2 mapping, and extracellular volume (ECV) which are associated with tissue fibrosis, infiltration, and inflammation. While deep learning has been shown to uncover findings not recognized by clinicians, it is unknown whether CMR-based tissue characteristics can be derived from echocardiography videos using deep learning. We hypothesized that deep learning applied to echocardiography could predict CMR-based measurements. Methods In a retrospective single-center study, adult patients with CMRs and echocardiography studies within 30 days were included. A video-based convolutional neural network was trained on echocardiography videos to predict CMR-derived labels including wall motion abnormality (WMA) presence, LGE presence, and abnormal T1, T2 or ECV across echocardiography views. The model performance was evaluated in a held-out test dataset not used for training. Results The study population included 1,453 adult patients (mean age 56±18 years, 42% female) with 2,556 paired echocardiography studies occurring on average 2 days after CMR (interquartile range 2 days prior to 6 days after). The model had high predictive capability for presence of WMA (AUC 0.873 [95%CI 0.816-0.922]), however, the model was unable to reliably detect the presence of LGE (AUC 0.699 [0.613-0.780]), native T1 (AUC 0.614 [0.500-0.715]), T2 0.553 [0.420-0.692], or ECV 0.564 [0.455-0.691]). Conclusions Deep learning applied to echocardiography accurately identified CMR-based WMA, but was unable to predict tissue characteristics, suggesting that signal for these tissue characteristics may not be present within ultrasound videos, and that the use of CMR for tissue characterization remains essential within cardiology. Clinical Perspective Tissue characterization of the heart muscle is useful for clinical diagnosis and prognosis by identifying myocardial fibrosis, inflammation, and infiltration, and can be measured using cardiac MRI. While echocardiography is highly accessible and provides excellent functional information, its ability to provide tissue characterization information is limited at this time. Our study using a deep learning approach to predict cardiac MRI-based tissue characteristics from echocardiography showed limited ability to do so, suggesting that alternative approaches, including non-deep learning methods should be considered in future research.
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Chen J, Wen Z, Yang X, Jia J, Zhang X, Pian L, Zhao P. Ultrasound-Based Radiomics for the Classification of Henoch-Schönlein Purpura Nephritis in Children. ULTRASONIC IMAGING 2024; 46:110-120. [PMID: 38140769 DOI: 10.1177/01617346231220000] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 12/24/2023]
Abstract
Henoch-Schönlein purpura nephritis (HSPN) is one of the most common kidney diseases in children. The current diagnosis and classification of HSPN depend on pathological biopsy, which is seriously limited by its invasive and high-risk nature. The aim of the study was to explore the potential of radiomics model for evaluating the histopathological classification of HSPN based on the ultrasound (US) images. A total of 440 patients with Henoch-Schönlein purpura nephritis proved by biopsy were analyzed retrospectively. They were grouped according to two histopathological categories: those without glomerular crescent formation (ISKDC grades I-II) and those with glomerular crescent formation (ISKDC grades III-V). The patients were randomly assigned to either a training cohort (n = 308) or a validation cohort (n = 132) with a ratio of 7:3. The sonologist manually drew the regions of interest (ROI) on the ultrasound images of the right kidney including the cortex and medulla. Then, the ultrasound radiomics features were extracted using the Pyradiomics package. The dimensions of radiomics features were reduced by Spearman correlation coefficients and least absolute shrinkage and selection operator (LASSO) method. Finally, three radiomics models using k-nearest neighbor (KNN), logistic regression (LR), and support vector machine (SVM) were established, respectively. The predictive performance of such classifiers was assessed with receiver operating characteristic (ROC) curve. 105 radiomics features were extracted from derived US images of each patient and 14 features were ultimately selected for the machine learning analysis. Three machine learning models including k-nearest neighbor (KNN), logistic regression (LR), and support vector machine (SVM) were established for HSPN classification. Of the three classifiers, the SVM classifier performed the best in the validation cohort [area under the curve (AUC) =0.870 (95% CI, 0.795-0.944), sensitivity = 0.706, specificity = 0.950]. The US-based radiomics had good predictive value for HSPN classification, which can be served as a noninvasive tool to evaluate the severity of renal pathology and crescentic formation in children with HSPN.
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Affiliation(s)
- Jie Chen
- Department of Ultrasound Medical, The First Affiliated Hospital of Guangzhou University of Chinese Medicine, Guangzhou, China
- Department of Ultrasound Medical, The First Affiliated Hospital of Henan University of Chinese Medicine, Zhengzhou, China
| | - Zeying Wen
- Department of Radiology, The First Affiliated Hospital of Henan University of Chinese Medicine, Zhengzhou, China
| | - Xiaoqing Yang
- Department of Pathology, The First Affiliated Hospital of Henan University of Chinese Medicine, Zhengzhou, China
| | - Jie Jia
- Department of Ultrasound Medical, The First Affiliated Hospital of Guangzhou University of Chinese Medicine, Guangzhou, China
| | - Xiaodong Zhang
- Department of Ultrasound Medical, The First Affiliated Hospital of Henan University of Chinese Medicine, Zhengzhou, China
| | - Linping Pian
- Department of Ultrasound Medical, The First Affiliated Hospital of Henan University of Chinese Medicine, Zhengzhou, China
| | - Ping Zhao
- Department of Ultrasound Medical, The First Affiliated Hospital of Guangzhou University of Chinese Medicine, Guangzhou, China
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Sengupta PP, Chandrashekhar Y. Advancing Myocardial Tissue Analysis Using Echocardiography. JACC Cardiovasc Imaging 2024; 17:228-231. [PMID: 38325962 DOI: 10.1016/j.jcmg.2024.01.002] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [MESH Headings] [Track Full Text] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Indexed: 02/09/2024]
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Meng F, Wu Q, Zhang W, Hou S. Shear-Wave Elastography-Based Radiomics Nomogram for the Prediction of Cardiovascular Disease in Patients with Diabetic Kidney Disease. Diabetes Metab Syndr Obes 2023; 16:2705-2716. [PMID: 37701720 PMCID: PMC10494864 DOI: 10.2147/dmso.s422364] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 05/23/2023] [Accepted: 08/29/2023] [Indexed: 09/14/2023] Open
Abstract
Background Diabetic kidney disease (DKD) patients have a high risk of suffering from cardiovascular disease (CVD), placing a heavy cost on the public health system. In this study, we intended to develop and validate a shear-wave elastography (SWE)-based radiomics nomogram for predicting the development of CVD in DKD patients. This approach allows extensive use of the valuable information contained in ultrasound images, thus helping clinicians to identify CVD in DKD patients. Methods Totally 337 and 145 patients constituted the training and validation cohorts, respectively. The radiomics features of the segmented kidney in ultrasound images were extracted and selected to generate the rad-score of each patient. These rad-score, as well as the predictors of risk of CVD occurrence from the clinical characteristics, were included in the multivariate analysis to develop a nomogram. It was further assessed in the training and validation cohorts. Results Patients with CVD accounted for 30.9% (104/337) in the training cohort and 31.0% (45/145) in the validation cohort. The rad-score was calculated for each patient using 6 features extracted from the ultrasound images. The radiomics nomogram was built with the rad-score, age, systolic blood pressure (SBP), low-density lipoprotein cholesterol (LDL-C). It was superior to the clinical nomogram developed without the rad-score and demonstrated promising discrimination, calibration, and clinical utility in both training and validation cohorts. Conclusion We developed and validated an SWE-based radiomics nomogram to predict CVD risk in patients with DKD. The model was demonstrated to have a promising prediction performance, showing its potential to identify CVD in DKD patients and assist decision-making for appropriate early intervention.
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Affiliation(s)
- Fei Meng
- Department of Ultrasound, Xuan Cheng City Central Hospital, Xuancheng, Anhui, People’s Republic of China
| | - Qin Wu
- Department of Ultrasound, Xuan Cheng City Central Hospital, Xuancheng, Anhui, People’s Republic of China
| | - Wei Zhang
- Department of Ultrasound, Xuan Cheng City Central Hospital, Xuancheng, Anhui, People’s Republic of China
| | - Shirong Hou
- Department of Ultrasound, Xuan Cheng City Central Hospital, Xuancheng, Anhui, People’s Republic of China
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Lee KCS, Breznen B, Ukhova A, Martin SS, Koehler F. Virtual healthcare solutions in heart failure: a literature review. Front Cardiovasc Med 2023; 10:1231000. [PMID: 37745104 PMCID: PMC10513031 DOI: 10.3389/fcvm.2023.1231000] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 05/29/2023] [Accepted: 08/29/2023] [Indexed: 09/26/2023] Open
Abstract
The widespread adoption of mobile technologies offers an opportunity for a new approach to post-discharge care for patients with heart failure (HF). By enabling non-invasive remote monitoring and two-way, real-time communication between the clinic and home-based patients, as well as a host of other capabilities, mobile technologies have a potential to significantly improve remote patient care. This literature review summarizes clinical evidence related to virtual healthcare (VHC), defined as a care team + connected devices + a digital solution in post-release care of patients with HF. Searches were conducted on Embase (06/12/2020). A total of 171 studies were included for data extraction and evidence synthesis: 96 studies related to VHC efficacy, and 75 studies related to AI in HF. In addition, 15 publications were included from the search on studies scaling up VHC solutions in HF within the real-world setting. The most successful VHC interventions, as measured by the number of reported significant results, were those targeting reduction in rehospitalization rates. In terms of relative success rate, the two most effective interventions targeted patient self-care and all-cause hospital visits in their primary endpoint. Among the three categories of VHC identified in this review (telemonitoring, remote patient management, and patient self-empowerment) the integrated approach in remote patient management solutions performs the best in decreasing HF patients' re-admission rates and overall hospital visits. Given the increased amount of data generated by VHC technologies, artificial intelligence (AI) is being investigated as a tool to aid decision making in the context of primary diagnostics, identifying disease phenotypes, and predicting treatment outcomes. Currently, most AI algorithms are developed using data gathered in clinic and only a few studies deploy AI in the context of VHC. Most successes have been reported in predicting HF outcomes. Since the field of VHC in HF is relatively new and still in flux, this is not a typical systematic review capturing all published studies within this domain. Although the standard methodology for this type of reviews was followed, the nature of this review is qualitative. The main objective was to summarize the most promising results and identify potential research directions.
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Affiliation(s)
| | - Boris Breznen
- Evidence Synthesis, Evidinno Outcomes Research Inc., Vancouver, BC, Canada
| | | | - Seth Shay Martin
- Ciccarone Center for the Prevention of Cardiovascular Disease, Johns Hopkins University School of Medicine, Baltimore, MD, United States
| | - Friedrich Koehler
- Deutsches Herzzentrum der Charité (DHZC), Centre for Cardiovascular Telemedicine, Campus Charité Mitte, Berlin, Germany
- Division of Cardiology and Angiology, Charité – Universitätsmedizin Berlin, Corporate Member of Freie Universität Berlin and Humboldt-Universität zu Berlin, Berlin, Germany
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Lin K, Sarnari R, Carr JC, Markl M. Cine MRI-Derived Radiomics Features of the Cardiac Blood Pool: Periodicity, Specificity, and Reproducibility. J Magn Reson Imaging 2023; 58:807-814. [PMID: 36533630 PMCID: PMC10277313 DOI: 10.1002/jmri.28572] [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: 10/10/2022] [Revised: 12/05/2022] [Accepted: 12/06/2022] [Indexed: 12/23/2022] Open
Abstract
BACKGROUND Although radiomics features of the left ventricular wall have been used to assess cardiac diseases, radiomics features of the cardiac blood pool have been relatively ignored. PURPOSE To test the hypothesis that cine MRI-derived radiomics features of the cardiac blood pool are associated with cardiac function and motion. STUDY TYPE Retrospective. POPULATION A total of 26 healthy volunteers (51.2 ± 15.6 years; 17 males). FIELD STRENGTH/SEQUENCE A 1.5 T/balanced steady-state free precession (bSSFP). ASSESSMENT The radiomics features (107 features in seven classes) of the blood pool of the left/right ventricle/atrium (LV/RV/LA/RA) were extracted on four-chamber cine images (25 phases). Conventional cardiac function parameters (volumes, ejection fraction [EF] and longitudinal strain) were assessed in each cardiac chamber. Intraobserver- and interobserver agreements of radiomics features of all chambers acquired at all phases were assessed, as well as scan-rescan agreement in a subset of 13 volunteers. STATISTICAL TESTS Pearson correlation coefficients (r) were used to assess the associations between peak values of radiomics features and end-diastolic (or maximal) volume, end-systolic (or minimal) volume, EF, and longitudinal strain of corresponding chambers. Good intraobserver, interobserver, and scan-rescan agreements for radiomics features acquired were defined as intraclass correlation coefficient (ICC) > 0.7 or coefficient of variation (CoV) < 20%. RESULTS Most radiomics features of the blood pool varied periodically throughout the cardiac cycle. Peak values of chamber-specific blood pool radiomics features were correlated with traditional cardiac function and motion indices of corresponding chambers (r: 0.4-0.87). Ninety-three (87%), 86 (80%), and 73 (68%) radiomics features demonstrated good intraobserver, interobserver, and scan-rescan reproducibility, respectively. CONCLUSION Cine MRI-derived radiomics features within LV/RV/LA/RA are associated with traditional cardiac function and motion indices of corresponding chambers and may have the potential to become novel quantitative imaging biomarkers in cardiovascular medicine. EVIDENCE LEVEL 3. TECHNICAL EFFICACY 1.
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Affiliation(s)
- Kai Lin
- Department of Radiology, Northwestern University, Chicago, Illinois, USA
| | - Roberto Sarnari
- Department of Radiology, Northwestern University, Chicago, Illinois, USA
| | - James C Carr
- Department of Radiology, Northwestern University, Chicago, Illinois, USA
| | - Michael Markl
- Department of Radiology, Northwestern University, Chicago, Illinois, USA
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Singh Y, Atalla S, Mansoor W, Paul R, Deepa D. To predict the left ventricular endocardial scar tissue pattern using Radon descriptor-based machine learning. BMC Res Notes 2023; 16:185. [PMID: 37620937 PMCID: PMC10464130 DOI: 10.1186/s13104-023-06466-0] [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: 09/16/2022] [Accepted: 08/21/2023] [Indexed: 08/26/2023] Open
Abstract
OBJECTIVE Scar tissue is an identified cause for the development of malignant ventricular arrhythmias in patients of myocardial infarction, which ultimately leads to cardiac death, a fatal outcome. We aim to evaluate the left ventricular endocardial Scar tissue pattern using Radon descriptor-based machine learning. We performed automated Left ventricle (LV) segmentation to find the LV endocardial wall, performed morphological operations, and marked the region of the scar tissue on the endocardial wall of LV. Motivated by a Radon descriptor-based machine learning approach; the patches of 17 patients from Computer tomography (CT) images of the heart were used and categorized into "endocardial Scar tissue" and "normal tissue" groups. The ten feature vectors are extracted from patches using Radon descriptors and fed into a traditional machine learning model. RESULTS The decision tree has shown the best performance with 98.07% accuracy. This study is the first attempt to provide a Radon transform-based machine learning method to distinguish patterns between "endocardial Scar tissue" and "normal tissue" groups. Our proposed research method could be potentially used in advanced interventions.
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Affiliation(s)
- Yashbir Singh
- Biomedical Engineering, Chung Yuan Christian University, Zhongli, Taiwan.
- Department of Radiology, Mayo clinic, Rochester, MN, USA.
| | - Shadi Atalla
- Engineering & Information Technology, University of Dubai, Dubai, United Arab Emirates.
| | - Wathiq Mansoor
- Engineering & Information Technology, University of Dubai, Dubai, United Arab Emirates
| | - Rahul Paul
- Department of Radiation Oncology, Massachusetts General Hospital, Boston, USA
| | - Deepa Deepa
- Biomedical Engineering, Chung Yuan Christian University, Zhongli, Taiwan
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Singh Y, Farrelly CM, Hathaway QA, Leiner T, Jagtap J, Carlsson GE, Erickson BJ. Topological data analysis in medical imaging: current state of the art. Insights Imaging 2023; 14:58. [PMID: 37005938 PMCID: PMC10067000 DOI: 10.1186/s13244-023-01413-w] [Citation(s) in RCA: 9] [Impact Index Per Article: 9.0] [Reference Citation Analysis] [Abstract] [Key Words] [Journal Information] [Subscribe] [Scholar Register] [Received: 01/11/2023] [Accepted: 03/22/2023] [Indexed: 04/04/2023] Open
Abstract
Machine learning, and especially deep learning, is rapidly gaining acceptance and clinical usage in a wide range of image analysis applications and is regarded as providing high performance in detecting anatomical structures and identification and classification of patterns of disease in medical images. However, there are many roadblocks to the widespread implementation of machine learning in clinical image analysis, including differences in data capture leading to different measurements, high dimensionality of imaging and other medical data, and the black-box nature of machine learning, with a lack of insight into relevant features. Techniques such as radiomics have been used in traditional machine learning approaches to model the mathematical relationships between adjacent pixels in an image and provide an explainable framework for clinicians and researchers. Newer paradigms, such as topological data analysis (TDA), have recently been adopted to design and develop innovative image analysis schemes that go beyond the abilities of pixel-to-pixel comparisons. TDA can automatically construct filtrations of topological shapes of image texture through a technique known as persistent homology (PH); these features can then be fed into machine learning models that provide explainable outputs and can distinguish different image classes in a computationally more efficient way, when compared to other currently used methods. The aim of this review is to introduce PH and its variants and to review TDA's recent successes in medical imaging studies.
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Zhang X, Liang T, Su C, Qin S, Li J, Zeng D, Cai Y, Huang T, Wu J. Deep learn-based computer-assisted transthoracic echocardiography: approach to the diagnosis of cardiac amyloidosis. Int J Cardiovasc Imaging 2023; 39:955-965. [PMID: 36763207 PMCID: PMC10159959 DOI: 10.1007/s10554-023-02806-0] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 07/21/2022] [Accepted: 01/30/2023] [Indexed: 02/11/2023]
Abstract
Myocardial amyloidosis (CA) differs from other etiological pathologies of left ventricular hypertrophy in that transthoracic echocardiography is challenging to assess the texture features based on human visual observation. There are few studies on myocardial texture based on echocardiography. Therefore, this paper proposes an adaptive machine learning method based on ultrasonic image texture features to identify CA. In this retrospective study, a total of 289 participants (50 cases of myocardial amyloidosis; Hypertrophic cardiomyopathy: 70 cases; Uremic cardiomyopathy: 92 cases; Hypertensive heart disease: 77 cases). We extracted the myocardial ultrasonic imaging features of these patients and screened the features, and four models of random forest (RF), support vector machine (SVM), logistic regression (LR) and gradient decision-making lifting tree (GBDT) were established to distinguish myocardial amyloidosis from other diseases. Finally, the diagnostic efficiency of the model was evaluated and compared with the traditional ultrasonic diagnostic methods. In the overall population, the four machine learning models we established could effectively distinguish CA from nonCA diseases, AUC (RF 0.77, SVM 0.81, LR 0.81, GBDT 0.71). The LR model had the best diagnostic efficiency with recall, F1-score, sensitivity and specificity of 0.21, 0.34, 0.21 and 1.0, respectively. Slightly better than the traditional ultrasonic diagnosis model. In further subgroup analysis, the myocardial amyloidosis group was compared one-by-one with the patients with hypertrophic cardiomyopathy, uremic cardiomyopathy, and hypertensive heart disease groups, and the same method was used for feature extraction and data modeling. The diagnostic efficiency of the model was further improved. Notably, in identifying of the CA group and HHD group, AUC values reached more than 0.92, accuracy reached more than 0.87, sensitivity equal to or greater than 0.81, specificity 0.91, and F1 score higher than 0.84. This novel method based on echocardiography combined with machine learning may have the potential to be used in the diagnosis of CA.
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Affiliation(s)
- Xiaofeng Zhang
- Department of Medical Ultrasonics, First Affiliated Hospital of Guangxi Medical University, No.6 Shuangyong Road, Qingxiu District, Nanning, 530021 People’s Republic of China
| | - Tianyi Liang
- Department of Medical Ultrasonics, First Affiliated Hospital of Guangxi Medical University, No.6 Shuangyong Road, Qingxiu District, Nanning, 530021 People’s Republic of China
| | - Chunxiao Su
- Department of Medical Ultrasonics, First Affiliated Hospital of Guangxi Medical University, No.6 Shuangyong Road, Qingxiu District, Nanning, 530021 People’s Republic of China
| | - Shiyun Qin
- Department of Medical Ultrasonics, First Affiliated Hospital of Guangxi Medical University, No.6 Shuangyong Road, Qingxiu District, Nanning, 530021 People’s Republic of China
| | - Jingtao Li
- Department of Medical Ultrasonics, First Affiliated Hospital of Guangxi Medical University, No.6 Shuangyong Road, Qingxiu District, Nanning, 530021 People’s Republic of China
| | - Decai Zeng
- Department of Medical Ultrasonics, First Affiliated Hospital of Guangxi Medical University, No.6 Shuangyong Road, Qingxiu District, Nanning, 530021 People’s Republic of China
| | - Yongzhi Cai
- Department of Medical Ultrasonics, First Affiliated Hospital of Guangxi Medical University, No.6 Shuangyong Road, Qingxiu District, Nanning, 530021 People’s Republic of China
| | - Tongtong Huang
- Department of Medical Ultrasonics, First Affiliated Hospital of Guangxi Medical University, No.6 Shuangyong Road, Qingxiu District, Nanning, 530021 People’s Republic of China
| | - Ji Wu
- Department of Medical Ultrasonics, First Affiliated Hospital of Guangxi Medical University, No.6 Shuangyong Road, Qingxiu District, Nanning, 530021 People’s Republic of China
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McCague C, Ramlee S, Reinius M, Selby I, Hulse D, Piyatissa P, Bura V, Crispin-Ortuzar M, Sala E, Woitek R. Introduction to radiomics for a clinical audience. Clin Radiol 2023; 78:83-98. [PMID: 36639175 DOI: 10.1016/j.crad.2022.08.149] [Citation(s) in RCA: 30] [Impact Index Per Article: 30.0] [Reference Citation Analysis] [Abstract] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 08/02/2022] [Accepted: 08/31/2022] [Indexed: 01/12/2023]
Abstract
Radiomics is a rapidly developing field of research focused on the extraction of quantitative features from medical images, thus converting these digital images into minable, high-dimensional data, which offer unique biological information that can enhance our understanding of disease processes and provide clinical decision support. To date, most radiomics research has been focused on oncological applications; however, it is increasingly being used in a raft of other diseases. This review gives an overview of radiomics for a clinical audience, including the radiomics pipeline and the common pitfalls associated with each stage. Key studies in oncology are presented with a focus on both those that use radiomics analysis alone and those that integrate its use with other multimodal data streams. Importantly, clinical applications outside oncology are also presented. Finally, we conclude by offering a vision for radiomics research in the future, including how it might impact our practice as radiologists.
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Affiliation(s)
- C McCague
- Department of Radiology, University of Cambridge, Cambridge, UK; Cancer Research UK Cambridge Centre, University of Cambridge, Cambridge, UK; Cancer Research UK Cambridge Institute, University of Cambridge, Cambridge, UK; Cambridge University Hospitals NHS Foundation Trust, Cambridge, UK.
| | - S Ramlee
- Department of Radiology, University of Cambridge, Cambridge, UK
| | - M Reinius
- Cancer Research UK Cambridge Centre, University of Cambridge, Cambridge, UK; Cancer Research UK Cambridge Institute, University of Cambridge, Cambridge, UK; Cambridge University Hospitals NHS Foundation Trust, Cambridge, UK
| | - I Selby
- Department of Radiology, University of Cambridge, Cambridge, UK; Cambridge University Hospitals NHS Foundation Trust, Cambridge, UK
| | - D Hulse
- Department of Radiology, University of Cambridge, Cambridge, UK; Cambridge University Hospitals NHS Foundation Trust, Cambridge, UK
| | - P Piyatissa
- Department of Radiology, University of Cambridge, Cambridge, UK
| | - V Bura
- Department of Radiology, University of Cambridge, Cambridge, UK; Cambridge University Hospitals NHS Foundation Trust, Cambridge, UK; Department of Radiology and Medical Imaging, County Clinical Emergency Hospital, Cluj-Napoca, Romania
| | - M Crispin-Ortuzar
- Cancer Research UK Cambridge Centre, University of Cambridge, Cambridge, UK; Cancer Research UK Cambridge Institute, University of Cambridge, Cambridge, UK; Department of Oncology, University of Cambridge, Cambridge, UK
| | - E Sala
- Department of Radiology, University of Cambridge, Cambridge, UK; Cancer Research UK Cambridge Centre, University of Cambridge, Cambridge, UK; Cambridge University Hospitals NHS Foundation Trust, Cambridge, UK
| | - R Woitek
- Department of Radiology, University of Cambridge, Cambridge, UK; Cancer Research UK Cambridge Centre, University of Cambridge, Cambridge, UK; Cambridge University Hospitals NHS Foundation Trust, Cambridge, UK; Research Centre for Medical Image Analysis and Artificial Intelligence (MIAAI), Department of Medicine, Faculty of Medicine and Dentistry, Danube Private University, Krems, Austria
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13
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Marwick TH. Assessment of Myocardial Texture. J Am Coll Cardiol 2022; 80:2202-2204. [DOI: 10.1016/j.jacc.2022.10.003] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 09/26/2022] [Accepted: 10/03/2022] [Indexed: 11/29/2022]
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14
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Ultrasonic Texture Features for Assessing Cardiac Remodeling and Dysfunction. J Am Coll Cardiol 2022; 80:2187-2201. [DOI: 10.1016/j.jacc.2022.09.036] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 05/11/2022] [Revised: 09/08/2022] [Accepted: 09/20/2022] [Indexed: 11/29/2022]
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15
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Zhu M, Tang L, Yang W, Xu Y, Che X, Zhou Y, Shao X, Zhou W, Zhang M, Li G, Zheng M, Wang Q, Li H, Mou S. Predicting Progression of Kidney Injury Based on Elastography Ultrasound and Radiomics Signatures. Diagnostics (Basel) 2022; 12:diagnostics12112678. [PMID: 36359519 PMCID: PMC9689562 DOI: 10.3390/diagnostics12112678] [Citation(s) in RCA: 2] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 09/03/2022] [Revised: 10/30/2022] [Accepted: 10/31/2022] [Indexed: 11/06/2022] Open
Abstract
Background: Shear wave elastography ultrasound (SWE) is an emerging non-invasive candidate for assessing kidney stiffness. However, its prognostic value regarding kidney injury is unclear. Methods: A prospective cohort was created from kidney biopsy patients in our hospital from May 2019 to June 2020. The primary outcome was the initiation of renal replacement therapy or death, while the secondary outcome was eGFR < 60 mL/min/1.73 m2. Ultrasound, biochemical, and biopsy examinations were performed on the same day. Radiomics signatures were extracted from the SWE images. Results: In total, 187 patients were included and followed up for 24.57 ± 5.52 months. The median SWE value of the left kidney cortex (L_C_median) is an independent risk factor for kidney prognosis for stage 3 or over (HR 0.890 (0.796−0.994), p < 0.05). The inclusion of 9 out of 2511 extracted radiomics signatures improved the prognostic performance of the Cox regression models containing the SWE and the traditional index (chi-square test, p < 0.001). The traditional Cox regression model had a c-index of 0.9051 (0.8460−0.9196), which was no worse than the machine learning models, Support Vector Machine (SVM), SurvivalTree, Random survival forest (RSF), Coxboost, and Deepsurv. Conclusions: SWE can predict kidney injury progression with an improved performance by radiomics and Cox regression modeling.
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Affiliation(s)
- Minyan Zhu
- Molecular Cell Laboratory for Kidney Disease, Department of Nephrology, Shanghai Peritoneal Dialysis Research Center, Uremia Diagnosis and Treatment Center, Renji Hospital, School of Medicine, Shanghai Jiao Tong University, Shanghai 200127, China
| | - Lumin Tang
- Molecular Cell Laboratory for Kidney Disease, Department of Nephrology, Shanghai Peritoneal Dialysis Research Center, Uremia Diagnosis and Treatment Center, Renji Hospital, School of Medicine, Shanghai Jiao Tong University, Shanghai 200127, China
| | - Wenqi Yang
- School of Medicine, Department of Ultrasound, Renji Hospital, Shanghai Jiao Tong University, Shanghai 200127, China
| | - Yao Xu
- Molecular Cell Laboratory for Kidney Disease, Department of Nephrology, Shanghai Peritoneal Dialysis Research Center, Uremia Diagnosis and Treatment Center, Renji Hospital, School of Medicine, Shanghai Jiao Tong University, Shanghai 200127, China
| | - Xiajing Che
- Molecular Cell Laboratory for Kidney Disease, Department of Nephrology, Shanghai Peritoneal Dialysis Research Center, Uremia Diagnosis and Treatment Center, Renji Hospital, School of Medicine, Shanghai Jiao Tong University, Shanghai 200127, China
| | - Yin Zhou
- Molecular Cell Laboratory for Kidney Disease, Department of Nephrology, Shanghai Peritoneal Dialysis Research Center, Uremia Diagnosis and Treatment Center, Renji Hospital, School of Medicine, Shanghai Jiao Tong University, Shanghai 200127, China
| | - Xinghua Shao
- Molecular Cell Laboratory for Kidney Disease, Department of Nephrology, Shanghai Peritoneal Dialysis Research Center, Uremia Diagnosis and Treatment Center, Renji Hospital, School of Medicine, Shanghai Jiao Tong University, Shanghai 200127, China
| | - Wenyan Zhou
- Molecular Cell Laboratory for Kidney Disease, Department of Nephrology, Shanghai Peritoneal Dialysis Research Center, Uremia Diagnosis and Treatment Center, Renji Hospital, School of Medicine, Shanghai Jiao Tong University, Shanghai 200127, China
| | - Minfang Zhang
- Molecular Cell Laboratory for Kidney Disease, Department of Nephrology, Shanghai Peritoneal Dialysis Research Center, Uremia Diagnosis and Treatment Center, Renji Hospital, School of Medicine, Shanghai Jiao Tong University, Shanghai 200127, China
| | - Guanghan Li
- China-Japan Friendship Hospital, Department of Ultrasound, Beijing 100029, China
| | - Min Zheng
- China-Japan Friendship Hospital, Department of Ultrasound, Beijing 100029, China
| | - Qin Wang
- Molecular Cell Laboratory for Kidney Disease, Department of Nephrology, Shanghai Peritoneal Dialysis Research Center, Uremia Diagnosis and Treatment Center, Renji Hospital, School of Medicine, Shanghai Jiao Tong University, Shanghai 200127, China
| | - Hongli Li
- School of Medicine, Department of Ultrasound, Renji Hospital, Shanghai Jiao Tong University, Shanghai 200127, China
- Correspondence: (H.L.); or (S.M.)
| | - Shan Mou
- Molecular Cell Laboratory for Kidney Disease, Department of Nephrology, Shanghai Peritoneal Dialysis Research Center, Uremia Diagnosis and Treatment Center, Renji Hospital, School of Medicine, Shanghai Jiao Tong University, Shanghai 200127, China
- Correspondence: (H.L.); or (S.M.)
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16
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Xu Z, Yu F, Zhang B, Zhang Q. Intelligent diagnosis of left ventricular hypertrophy using transthoracic echocardiography videos. COMPUTER METHODS AND PROGRAMS IN BIOMEDICINE 2022; 226:107182. [PMID: 36257197 DOI: 10.1016/j.cmpb.2022.107182] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.5] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Subscribe] [Scholar Register] [Received: 05/05/2022] [Revised: 09/14/2022] [Accepted: 10/08/2022] [Indexed: 06/16/2023]
Abstract
PURPOSE Left ventricular hypertrophy (LVH) is an independent risk factor for cardiovascular events and mortality. Pathological LVH can be caused by various diseases. In this study, we explored the possibility of using time and frequency domain analysis of myocardial radiomics features for patients with LVH in differentiating hypertrophic cardiomyopathy (HCM), hypertensive heart disease (HHD) and uremic cardiomyopathy (UCM) based on transthoracic echocardiography (TTE). This was the first study to explore TTE myocardial time and frequency domain analyses for multiple LVH etiology differentiation. MATERIALS AND METHODS We proposed an artificially intelligent diagnosis system based on radiomics techniques for differentiating HCM, HHD and UCM on TTE videos of the apical four-chamber view, which mainly included interventricular septum (IVS) segmentation, feature extraction and classification. We used two independent cohorts, one with 150 patients, including 50 HHD, 50 HCM and 50 UCM, for segmentation training and testing, and another with 149 patients (namely the main cohort), including 50 HHD, 46 HCM and 53 UCM, for classification training and testing after segmentation and feature extraction. Firstly, the U-Net, Residual U-Net (ResUNet) and nnU-Net were trained and tested to segment the IVS on TTE still images in the first cohort. Then the trained model with the best segmentation performance was further used for IVS prediction of ordered TTE images in video sequences in the main cohort. The post-processing was used to eliminate the noisy debris by selecting the maximum connected region and smoothing the edges of the predicted IVS region. Secondly, static radiomics features were extracted from the IVS of ordered TTE images in each video sequence, and subsequently the time and frequency domain features were further extracted from each time series of a static radiomics feature in the video sequence. Finally, the point-wise gated Boltzmann machine (PGBM) was used to learn and fuse the time and frequency domain features, and the support vector machine was used to classify the learned features for LVH diagnosis. The classification was performed with five-fold cross validation. RESULTS The ResUNet showed the best segmentation performance, with Dice coefficient, sensitivity, specificity and accuracy of 0.817, 76.3%, 99.6% and 98.6%, respectively. With post-processing, the Dice coefficient, sensitivity, specificity and accuracy of the ResUNet were further improved to 0.839, 77.0%, 99.8%, and 98.8%, respectively. The classification areas under the receiver operating characteristic curves (AUCs) were 0.838 ± 0.049 for HHD vs. HCM, 0.868 ± 0.042 for HCM vs. UCM and 0.701 ± 0.140 for HHD vs. UCM. CONCLUSION In this work, we proposed an intelligent identification system for LVH etiology classification based on routine TTE video images with good diagnostic performance. This deep learning method is feasible in automatic TTE images interpretation and expected to assist clinicians in detecting the primary cause of LVH.
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Affiliation(s)
- Zhou Xu
- The SMART (Smart Medicine and AI-Based Radiology Technology) Lab, Shanghai Institute for Advanced Communication and Data Science, Shanghai University, Shanghai, China; School of Communication and Information Engineering, Shanghai University, Shanghai, China
| | - Fei Yu
- Department of Ultrasound in Medicine, Shanghai East Hospital, Tongji University School of Medicine, Shanghai, China; Department of Ultrasound in Medicine, Ningbo First Hospital, Ningbo, China
| | - Bo Zhang
- Department of Ultrasound in Medicine, Shanghai East Hospital, Tongji University School of Medicine, Shanghai, China.
| | - Qi Zhang
- The SMART (Smart Medicine and AI-Based Radiology Technology) Lab, Shanghai Institute for Advanced Communication and Data Science, Shanghai University, Shanghai, China; School of Communication and Information Engineering, Shanghai University, Shanghai, China.
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17
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Kwan A, Demosthenes E, Salto G, Ouyang D, Nguyen T, Nwabuo CC, Luong E, Hoang A, Osypiuk E, Stantchev P, Kim EH, Hiremath P, Li D, Vasan R, Xanthakis V, Cheng S. Cardiac microstructural alterations measured by echocardiography identify sex-specific risk for heart failure. Heart 2022; 108:1800-1806. [PMID: 35680379 PMCID: PMC9626911 DOI: 10.1136/heartjnl-2022-320876] [Citation(s) in RCA: 7] [Impact Index Per Article: 3.5] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 01/26/2022] [Accepted: 05/16/2022] [Indexed: 12/20/2022] Open
Abstract
OBJECTIVE Established preclinical imaging assessments of heart failure (HF) risk are based on macrostructural cardiac remodelling. Given that microstructural alterations may also influence HF risk, particularly in women, we examined associations between microstructural alterations and incident HF. METHODS We studied N=2511 adult participants (mean age 65.7±8.8 years, 56% women) of the Framingham Offspring Study who were free of cardiovascular disease at baseline. We employed texture analysis of echocardiography to quantify microstructural alteration, based on the high spectrum signal intensity coefficient (HS-SIC). We examined its relations to incident HF in sex-pooled and sex-specific Cox models accounting for traditional HF risk factors and macrostructural alterations. RESULTS We observed 94 new HF events over 7.4±1.7 years. Individuals with higher HS-SIC had increased risk for incident HF (HR 1.67 per 1-SD in HS-SIC, 95% CI 1.31 to 2.13; p<0.0001). Adjusting for age and antihypertensive medication use, this association was significant in women (p=0.02) but not men (p=0.78). Adjusting for traditional risk factors (including body mass index, total/high-density lipoprotein cholesterol, blood pressure traits, diabetes and smoking) attenuated the association in women (HR 1.30, p=0.07), with mediation of HF risk by the HS-SIC seen for a majority of these risk factors. However, the HS-SIC association with HF in women remained significant after adjusting for relative wall thickness (representing macrostructure alteration) in addition to these risk factors (HR 1.47, p=0.02). CONCLUSIONS Cardiac microstructural alterations are associated with elevated risk for HF, particularly in women. Microstructural alteration may identify sex-specific pathways by which individuals progress from risk factors to clinical HF.
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Affiliation(s)
- Alan Kwan
- Department of Cardiology, Smidt Heart Institute and Biomedical Imaging Research Institute, Cedars-Sinai Medical Center, Los Angeles, California, USA
| | | | - Gerran Salto
- Department of Cardiology, Smidt Heart Institute and Biomedical Imaging Research Institute, Cedars-Sinai Medical Center, Los Angeles, California, USA
- Framingham Heart Study, Framingham, Massachusetts, USA
| | - David Ouyang
- Department of Cardiology, Smidt Heart Institute and Biomedical Imaging Research Institute, Cedars-Sinai Medical Center, Los Angeles, California, USA
| | - Trevor Nguyen
- Department of Cardiology, Smidt Heart Institute and Biomedical Imaging Research Institute, Cedars-Sinai Medical Center, Los Angeles, California, USA
| | - Chike C Nwabuo
- Division of Cardiology, Department of Medicine, Johns Hopkins University, Baltimore, Maryland, USA
- Ronin Institute, Montclair, New Jersey, USA
| | - Eric Luong
- Department of Cardiology, Smidt Heart Institute and Biomedical Imaging Research Institute, Cedars-Sinai Medical Center, Los Angeles, California, USA
| | - Amy Hoang
- Department of Cardiology, Smidt Heart Institute and Biomedical Imaging Research Institute, Cedars-Sinai Medical Center, Los Angeles, California, USA
| | - Ewa Osypiuk
- Framingham Heart Study, Framingham, Massachusetts, USA
| | | | - Elizabeth H Kim
- Department of Cardiology, Smidt Heart Institute and Biomedical Imaging Research Institute, Cedars-Sinai Medical Center, Los Angeles, California, USA
| | - Pranoti Hiremath
- Division of Cardiology, Department of Medicine, Johns Hopkins University, Baltimore, Maryland, USA
| | - Debiao Li
- Department of Cardiology, Smidt Heart Institute and Biomedical Imaging Research Institute, Cedars-Sinai Medical Center, Los Angeles, California, USA
| | - Ramachandran Vasan
- Framingham Heart Study, Framingham, Massachusetts, USA
- Departments of Medicine, Biostatistics, and Epidemiology, Boston University Schools of Medicine and Public Health, Boston, Massachusetts, USA
| | - Vanessa Xanthakis
- Framingham Heart Study, Framingham, Massachusetts, USA
- Departments of Medicine, Biostatistics, and Epidemiology, Boston University Schools of Medicine and Public Health, Boston, Massachusetts, USA
| | - Susan Cheng
- Department of Cardiology, Smidt Heart Institute and Biomedical Imaging Research Institute, Cedars-Sinai Medical Center, Los Angeles, California, USA
- Framingham Heart Study, Framingham, Massachusetts, USA
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18
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Belzile D, Sénéchal M. Microstructure: a potential path to elucidate sex-specific pathological mechanisms in heart failure? Heart 2022; 108:1756-1758. [PMID: 35835542 DOI: 10.1136/heartjnl-2022-321250] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Key Words] [Track Full Text] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Indexed: 11/04/2022] Open
Affiliation(s)
- David Belzile
- Cardiology, Institut Universitaire de Cardiologie et de Pneumologie de Québec-Université Laval, Quebec City, Quebec, Canada
| | - Mario Sénéchal
- Cardiology, Institut Universitaire de Cardiologie et de Pneumologie de Québec-Université Laval, Quebec City, Quebec, Canada
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Gu Y, Zhang S, Chen X, Li Y, Liu Y. LongShengZhi alleviated cardiac remodeling via upregulation microRNA-150-5p with matrix metalloproteinase 14 as the target. JOURNAL OF ETHNOPHARMACOLOGY 2022; 291:115156. [PMID: 35245628 DOI: 10.1016/j.jep.2022.115156] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.5] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Subscribe] [Scholar Register] [Received: 11/03/2021] [Revised: 02/22/2022] [Accepted: 02/25/2022] [Indexed: 06/14/2023]
Abstract
ETHNOPHARMACOLOGICAL RELEVANCE LongShengZhi capsule (LSZ), a traditional Chinese medicine, is used for treatment of patients with vascular diseases. LSZ reduced doxorubicin-induced heart failure by reducing production of reactive oxygen species and inhibiting inflammation and apoptosis. AIM OF THE STUDY This study was to explore whether LSZ could alleviate cardiac remodeling via upregulation of microRNA (miR)-150-5p and the downstream target. Cardiac remodeling was induced by Ang II in vivo and in vitro. RESULTS LSZ attenuated Ang II-induced cardiac hypertrophy and fibrosis in rats, and in primary cardiomyocytes (CMs) and primary cardiac fibroblasts (CFs). MiR-150-5p was downregulated in Ang II-induced rat heart, CMs and CFs, and these decreases were reserved by LSZ. In vivo overexpression of miR-150-5p by transfection of miR-150-5p agomiR protected Ang II-induced cardiac hypertrophy and fibrosis in rats. Meanwhile, its overexpression also reversed Ang II-induced upregulation of atrial natriuretic peptide (ANP), brain natriuretic peptide (BNP) and β-myosin heavy chain (β-MHC) in rat hearts and primary CMs, as well as upregulation of collagen I, collagen III and transforming growth factor-β (TGF-β) in rat hearts and primary CFs. Matrix metalloproteinase 14 (MMP14) was validated as the target gene of miR-150-5p, which was overexpressed in Ang II-induced rat heart, rat primary CMs and primary CFs. Notably, overexpression of MMP14 induced cardiac remodeling, and reversed the protective role of miR-150-5p in downregulating Ang II-induced upregulation of hypertrophy and fibrosis markers in vitro. CONCLUSION Collectively, LSZ protects Ang II-induced cardiac dysfunction and remodeling via upregulation of miR-150-5p to target MMP14. Administration of LSZ, upregulation of miR-150-5p or targeting of MMP14 may be strategies for cardiac remodeling therapy.
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Affiliation(s)
- Yang Gu
- Department of Cardiology, The Affiliated Huaian No.1 People's Hospital of Nanjing Medical University, Huai'an, China
| | - Shimeng Zhang
- Department of Cardiology, The First Affiliated Hospital of Nanjing Medical University, Nanjing, China
| | - Xun Chen
- Department of Cardiology, The First Affiliated Hospital of Nanjing Medical University, Nanjing, China
| | - Yong Li
- Department of Cardiology, The First Affiliated Hospital of Nanjing Medical University, Nanjing, China.
| | - Yun Liu
- Department of Intensive Care Medicine, The First Affiliated Hospital of Nanjing Medical University, Nanjing, China.
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Abstract
Machine learning (ML) methods are pervading an increasing number of fields of application because of their capacity to effectively solve a wide variety of challenging problems. The employment of ML techniques in ultrasound imaging applications started several years ago but the scientific interest in this issue has increased exponentially in the last few years. The present work reviews the most recent (2019 onwards) implementations of machine learning techniques for two of the most popular ultrasound imaging fields, medical diagnostics and non-destructive evaluation. The former, which covers the major part of the review, was analyzed by classifying studies according to the human organ investigated and the methodology (e.g., detection, segmentation, and/or classification) adopted, while for the latter, some solutions to the detection/classification of material defects or particular patterns are reported. Finally, the main merits of machine learning that emerged from the study analysis are summarized and discussed.
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21
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Kagiyama N, Tokodi M, Sengupta PP. Machine Learning in Cardiovascular Imaging. Heart Fail Clin 2022; 18:245-258. [DOI: 10.1016/j.hfc.2021.11.003] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Indexed: 11/30/2022]
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22
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Radiomics in Cardiovascular Disease Imaging: from Pixels to the Heart of the Problem. CURRENT CARDIOVASCULAR IMAGING REPORTS 2022. [DOI: 10.1007/s12410-022-09563-z] [Citation(s) in RCA: 3] [Impact Index Per Article: 1.5] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 02/07/2023]
Abstract
Abstract
Purpose of Review
This review of the literature aims to present potential applications of radiomics in cardiovascular radiology and, in particular, in cardiac imaging.
Recent Findings
Radiomics and machine learning represent a technological innovation which may be used to extract and analyze quantitative features from medical images. They aid in detecting hidden pattern in medical data, possibly leading to new insights in pathophysiology of different medical conditions. In the recent literature, radiomics and machine learning have been investigated for numerous potential applications in cardiovascular imaging. They have been proposed to improve image acquisition and reconstruction, for anatomical structure automated segmentation or automated characterization of cardiologic diseases.
Summary
The number of applications for radiomics and machine learning is continuing to rise, even though methodological and implementation issues still limit their use in daily practice. In the long term, they may have a positive impact in patient management.
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Echocardiographic Advances in Dilated Cardiomyopathy. J Clin Med 2021; 10:jcm10235518. [PMID: 34884220 PMCID: PMC8658091 DOI: 10.3390/jcm10235518] [Citation(s) in RCA: 6] [Impact Index Per Article: 2.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 10/13/2021] [Revised: 11/05/2021] [Accepted: 11/23/2021] [Indexed: 12/29/2022] Open
Abstract
Although the overall survival of patients with dilated cardiomyopathy (DCM) has improved significantly in the last decades, a non-negligible proportion of DCM patients still shows an unfavorable prognosis. DCM patients not only need imaging techniques that are effective in diagnosis, but also suitable for long-term follow-up with frequent re-evaluations. The exponential growth of echocardiography’s technology and performance in recent years has resulted in improved diagnostic accuracy, stratification, management and follow-up of patients with DCM. This review summarizes some new developments in echocardiography and their promising applications in DCM. Although nowadays cardiac magnetic resonance (CMR) remains the gold standard technique in DCM, the echocardiographic advances and novelties proposed in the manuscript, if properly integrated into clinical practice, could bring echocardiography closer to CMR in terms of accuracy and may certify ultrasound as the technique of choice in the follow-up of DCM patients. The application in DCM patients of novel echocardiographic techniques represents an interesting emergent research area for scholars in the near future.
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de Siqueira VS, Borges MM, Furtado RG, Dourado CN, da Costa RM. Artificial intelligence applied to support medical decisions for the automatic analysis of echocardiogram images: A systematic review. Artif Intell Med 2021; 120:102165. [PMID: 34629153 DOI: 10.1016/j.artmed.2021.102165] [Citation(s) in RCA: 4] [Impact Index Per Article: 1.3] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 04/20/2021] [Revised: 08/07/2021] [Accepted: 08/31/2021] [Indexed: 12/16/2022]
Abstract
The echocardiogram is a test that is widely used in Heart Disease Diagnoses. However, its analysis is largely dependent on the physician's experience. In this regard, artificial intelligence has become an essential technology to assist physicians. This study is a Systematic Literature Review (SLR) of primary state-of-the-art studies that used Artificial Intelligence (AI) techniques to automate echocardiogram analyses. Searches on the leading scientific article indexing platforms using a search string returned approximately 1400 articles. After applying the inclusion and exclusion criteria, 118 articles were selected to compose the detailed SLR. This SLR presents a thorough investigation of AI applied to support medical decisions for the main types of echocardiogram (Transthoracic, Transesophageal, Doppler, Stress, and Fetal). The article's data extraction indicated that the primary research interest of the studies comprised four groups: 1) Improvement of image quality; 2) identification of the cardiac window vision plane; 3) quantification and analysis of cardiac functions, and; 4) detection and classification of cardiac diseases. The articles were categorized and grouped to show the main contributions of the literature to each type of ECHO. The results indicate that the Deep Learning (DL) methods presented the best results for the detection and segmentation of the heart walls, right and left atrium and ventricles, and classification of heart diseases using images/videos obtained by echocardiography. The models that used Convolutional Neural Network (CNN) and its variations showed the best results for all groups. The evidence produced by the results presented in the tabulation of the studies indicates that the DL contributed significantly to advances in echocardiogram automated analysis processes. Although several solutions were presented regarding the automated analysis of ECHO, this area of research still has great potential for further studies to improve the accuracy of results already known in the literature.
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Affiliation(s)
- Vilson Soares de Siqueira
- Federal Institute of Tocantins, Av. Bernado Sayão, S/N, Santa Maria, Colinas do Tocantins, TO, Brazil; Federal University of Goias, Alameda Palmeiras, Quadra D, Câmpus Samambaia, Goiânia, GO, Brazil.
| | - Moisés Marcos Borges
- Diagnostic Imaging Center - CDI, Av. Portugal, 1155, St. Marista, Goiânia, GO, Brazil
| | - Rogério Gomes Furtado
- Diagnostic Imaging Center - CDI, Av. Portugal, 1155, St. Marista, Goiânia, GO, Brazil
| | - Colandy Nunes Dourado
- Diagnostic Imaging Center - CDI, Av. Portugal, 1155, St. Marista, Goiânia, GO, Brazil. http://www.cdigoias.com.br
| | - Ronaldo Martins da Costa
- Federal University of Goias, Alameda Palmeiras, Quadra D, Câmpus Samambaia, Goiânia, GO, Brazil.
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Fouad M, El Ghany MAA, Huebner M, Schmitz G. A Deep Learning Signal-Based Approach to Fast Harmonic Imaging. 2021 IEEE INTERNATIONAL ULTRASONICS SYMPOSIUM (IUS) 2021. [DOI: 10.1109/ius52206.2021.9593348] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 09/01/2023]
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26
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Zheng J, Yu H, Batur J, Shi Z, Tuerxun A, Abulajiang A, Lu S, Kong J, Huang L, Wu S, Wu Z, Qiu Y, Lin T, Zou X. A multicenter study to develop a non-invasive radiomic model to identify urinary infection stone in vivo using machine-learning. Kidney Int 2021; 100:870-880. [PMID: 34129883 DOI: 10.1016/j.kint.2021.05.031] [Citation(s) in RCA: 22] [Impact Index Per Article: 7.3] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 01/12/2021] [Revised: 04/15/2021] [Accepted: 05/14/2021] [Indexed: 02/06/2023]
Abstract
Urolithiasis is a common urological disease, and treatment strategy options vary between different stone types. However, accurate detection of stone composition can only be performed in vitro. The management of infection stones is particularly challenging with yet no effective approach to pre-operatively identify infection stones from non-infection stones. Therefore, we aimed to develop a radiomic model for preoperatively identifying infection stones with multicenter validation. In total, 1198 eligible patients with urolithiasis from three centers were divided into a training set, an internal validation set, and two external validation sets. Stone composition was determined by Fourier transform infrared spectroscopy. A total of 1316 radiomic features were extracted from the pre-treatment Computer Tomography images of each patient. Using the least absolute shrinkage and selection operator algorithm, we identified a radiomic signature that achieved favorable discrimination in the training set, which was confirmed in the validation sets. Moreover, we then developed a radiomic model incorporating the radiomic signature, urease-producing bacteria in urine, and urine pH based on multivariate logistic regression analysis. The nomogram showed favorable calibration and discrimination in the training and three validation sets (area under the curve [95% confidence interval], 0.898 [0.840-0.956], 0.832 [0.742-0.923], 0.825 [0.783-0.866], and 0.812 [0.710-0.914], respectively). Decision curve analysis demonstrated the clinical utility of the radiomic model. Thus, our proposed radiomic model can serve as a non-invasive tool to identify urinary infection stones in vivo, which may optimize disease management in urolithiasis and improve patient prognosis.
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Affiliation(s)
- Junjiong Zheng
- Department of Urology, Sun Yat-Sen Memorial Hospital, Sun Yat-Sen University, Guangzhou, People's Republic of China; Guangdong Provincial Key Laboratory of Malignant Tumor Epigenetics and Gene Regulation, Sun Yat-Sen Memorial Hospital, Sun Yat-Sen University, Guangzhou, People's Republic of China
| | - Hao Yu
- Department of Urology, Sun Yat-Sen Memorial Hospital, Sun Yat-Sen University, Guangzhou, People's Republic of China; Guangdong Provincial Key Laboratory of Malignant Tumor Epigenetics and Gene Regulation, Sun Yat-Sen Memorial Hospital, Sun Yat-Sen University, Guangzhou, People's Republic of China
| | - Jesur Batur
- Department of Urology, the First People's Hospital of Kashi Prefecture, Affiliated Kashi Hospital of Sun Yat-Sen University, Kashi, People's Republic of China
| | - Zhenfeng Shi
- Department of Urology, the People's Hospital of Xinjiang Uyghur Autonomous Region, Xinjiang, People's Republic of China
| | - Aierken Tuerxun
- Department of Urology, the First People's Hospital of Kashi Prefecture, Affiliated Kashi Hospital of Sun Yat-Sen University, Kashi, People's Republic of China
| | - Abudukeyoumu Abulajiang
- Department of Information Technology, the First People's Hospital of Kashi Prefecture, Affiliated Kashi Hospital of Sun Yat-Sen University, Kashi, People's Republic of China
| | - Sihong Lu
- Department of Urology, Sun Yat-Sen Memorial Hospital, Sun Yat-Sen University, Guangzhou, People's Republic of China; Guangdong Provincial Key Laboratory of Malignant Tumor Epigenetics and Gene Regulation, Sun Yat-Sen Memorial Hospital, Sun Yat-Sen University, Guangzhou, People's Republic of China
| | - Jianqiu Kong
- Department of Urology, Sun Yat-Sen Memorial Hospital, Sun Yat-Sen University, Guangzhou, People's Republic of China; Guangdong Provincial Key Laboratory of Malignant Tumor Epigenetics and Gene Regulation, Sun Yat-Sen Memorial Hospital, Sun Yat-Sen University, Guangzhou, People's Republic of China
| | - Lifang Huang
- Department of Urology, Sun Yat-Sen Memorial Hospital, Sun Yat-Sen University, Guangzhou, People's Republic of China; Guangdong Provincial Key Laboratory of Malignant Tumor Epigenetics and Gene Regulation, Sun Yat-Sen Memorial Hospital, Sun Yat-Sen University, Guangzhou, People's Republic of China
| | - Shaoxu Wu
- Department of Urology, Sun Yat-Sen Memorial Hospital, Sun Yat-Sen University, Guangzhou, People's Republic of China; Guangdong Provincial Key Laboratory of Malignant Tumor Epigenetics and Gene Regulation, Sun Yat-Sen Memorial Hospital, Sun Yat-Sen University, Guangzhou, People's Republic of China
| | - Zhuo Wu
- Department of Radiology, Sun Yat-Sen Memorial Hospital, Sun Yat-Sen University, Guangzhou, People's Republic of China
| | - Ya Qiu
- Department of Radiology, the First People's Hospital of Kashi Prefecture, Affiliated Kashi Hospital of Sun Yat-Sen University, Kashi, People's Republic of China
| | - Tianxin Lin
- Department of Urology, Sun Yat-Sen Memorial Hospital, Sun Yat-Sen University, Guangzhou, People's Republic of China; Guangdong Provincial Key Laboratory of Malignant Tumor Epigenetics and Gene Regulation, Sun Yat-Sen Memorial Hospital, Sun Yat-Sen University, Guangzhou, People's Republic of China; State Key Laboratory of Oncology in South China, Guangdong Provincial Clinical Research Center for Urological Diseases, Guangdong, People's Republic of China.
| | - Xiaoguang Zou
- Department of Pharmacy, the First People's Hospital of Kashi Prefecture, Affiliated Kashi Hospital of Sun Yat-Sen University, Kashi, People's Republic of China.
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27
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Patel B, Bianco C, Sengupta PP. Early Tracking of Radiation-Induced Cardiotoxicity: Paradigm, Pragmatism, and Possibilities. JACC CardioOncol 2021; 3:290-293. [PMID: 34396336 PMCID: PMC8352235 DOI: 10.1016/j.jaccao.2021.04.007] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/25/2022] Open
Affiliation(s)
- Brijesh Patel
- Heart and Vascular Institute, West Virginia University, Morgantown, West Virginia, USA
| | - Christopher Bianco
- Heart and Vascular Institute, West Virginia University, Morgantown, West Virginia, USA
| | - Partho P. Sengupta
- Heart and Vascular Institute, West Virginia University, Morgantown, West Virginia, USA
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28
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Yoon YE, Kim S, Chang HJ. Artificial Intelligence and Echocardiography. J Cardiovasc Imaging 2021; 29:193-204. [PMID: 34080347 PMCID: PMC8318807 DOI: 10.4250/jcvi.2021.0039] [Citation(s) in RCA: 7] [Impact Index Per Article: 2.3] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 02/25/2021] [Revised: 03/30/2021] [Accepted: 04/05/2021] [Indexed: 12/13/2022] Open
Abstract
Artificial intelligence (AI) is evolving in the field of diagnostic medical imaging, including echocardiography. Although the dynamic nature of echocardiography presents challenges beyond those of static images from X-ray, computed tomography, magnetic resonance, and radioisotope imaging, AI has influenced all steps of echocardiography, from image acquisition to automatic measurement and interpretation. Considering that echocardiography often is affected by inter-observer variability and shows a strong dependence on the level of experience, AI could be extremely advantageous in minimizing observer variation and providing reproducible measures, enabling accurate diagnosis. Currently, most reported AI applications in echocardiographic measurement have focused on improved image acquisition and automation of repetitive and tedious tasks; however, the role of AI applications should not be limited to conventional processes. Rather, AI could provide clinically important insights from subtle and non-specific data, such as changes in myocardial texture in patients with myocardial disease. Recent initiatives to develop large echocardiographic databases can facilitate development of AI applications. The ultimate goal of applying AI to echocardiography is automation of the entire process of echocardiogram analysis. Once automatic analysis becomes reliable, workflows in clinical echocardiographic will change radically. The human expert will remain the master controlling the overall diagnostic process, will not be replaced by AI, and will obtain significant support from AI systems to guide acquisition, perform measurements, and integrate and compare data on request.
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Affiliation(s)
- Yeonyee E Yoon
- Cardiovascular Center, Seoul National University Bundang Hospital, Seongnam, Korea.,Department of Internal Medicine, Cardiovascular Center, Seoul National University Hospital, Seoul National University College of Medicine, Seoul, Korea
| | - Sekeun Kim
- Graduate School of Biomedical Engineering, Yonsei University College of Medicine, Seoul, Korea.,Ontact Health Co., Ltd., Seoul, Korea
| | - Hyuk Jae Chang
- CONNECT-AI Research Center, Yonsei University Health System, Yonsei University College of Medicine, Seoul, Korea.,Division of Cardiology, Severance Cardiovascular Hospital, Yonsei University Health System, Yonsei University College of Medicine, Seoul, Korea.
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29
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Mancio J, Pashakhanloo F, El-Rewaidy H, Jang J, Joshi G, Csecs I, Ngo L, Rowin E, Manning W, Maron M, Nezafat R. Machine learning phenotyping of scarred myocardium from cine in hypertrophic cardiomyopathy. Eur Heart J Cardiovasc Imaging 2021; 23:532-542. [PMID: 33779725 DOI: 10.1093/ehjci/jeab056] [Citation(s) in RCA: 13] [Impact Index Per Article: 4.3] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 12/07/2020] [Indexed: 12/12/2022] Open
Abstract
AIMS Cardiovascular magnetic resonance (CMR) with late-gadolinium enhancement (LGE) is increasingly being used in hypertrophic cardiomyopathy (HCM) for diagnosis, risk stratification, and monitoring. However, recent data demonstrating brain gadolinium deposits have raised safety concerns. We developed and validated a machine-learning (ML) method that incorporates features extracted from cine to identify HCM patients without fibrosis in whom gadolinium can be avoided. METHODS AND RESULTS An XGBoost ML model was developed using regional wall thickness and thickening, and radiomic features of myocardial signal intensity, texture, size, and shape from cine. A CMR dataset containing 1099 HCM patients collected using 1.5T CMR scanners from different vendors and centres was used for model development (n=882) and validation (n=217). Among the 2613 radiomic features, we identified 7 features that provided best discrimination between +LGE and -LGE using 10-fold stratified cross-validation in the development cohort. Subsequently, an XGBoost model was developed using these radiomic features, regional wall thickness and thickening. In the independent validation cohort, the ML model yielded an area under the curve of 0.83 (95% CI: 0.77-0.89), sensitivity of 91%, specificity of 62%, F1-score of 77%, true negatives rate (TNR) of 34%, and negative predictive value (NPV) of 89%. Optimization for sensitivity provided sensitivity of 96%, F2-score of 83%, TNR of 19% and NPV of 91%; false negatives halved from 4% to 2%. CONCLUSION An ML model incorporating novel radiomic markers of myocardium from cine can rule-out myocardial fibrosis in one-third of HCM patients referred for CMR reducing unnecessary gadolinium administration.
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Affiliation(s)
- Jennifer Mancio
- Department of Medicine, Beth Israel Deaconess Medical Centre and Harvard Medical School, 330 Brookline Avenue, Boston, MA 02215, USA
| | - Farhad Pashakhanloo
- Department of Medicine, Beth Israel Deaconess Medical Centre and Harvard Medical School, 330 Brookline Avenue, Boston, MA 02215, USA
| | - Hossam El-Rewaidy
- Department of Medicine, Beth Israel Deaconess Medical Centre and Harvard Medical School, 330 Brookline Avenue, Boston, MA 02215, USA.,Department of Computer Science, Technical University of Munich, Arcisstraße 21, 80333 Munich, Germany
| | - Jihye Jang
- Department of Medicine, Beth Israel Deaconess Medical Centre and Harvard Medical School, 330 Brookline Avenue, Boston, MA 02215, USA.,Department of Computer Science, Technical University of Munich, Arcisstraße 21, 80333 Munich, Germany
| | - Gargi Joshi
- Department of Medicine, Beth Israel Deaconess Medical Centre and Harvard Medical School, 330 Brookline Avenue, Boston, MA 02215, USA
| | - Ibolya Csecs
- Department of Medicine, Beth Israel Deaconess Medical Centre and Harvard Medical School, 330 Brookline Avenue, Boston, MA 02215, USA
| | - Long Ngo
- Department of Medicine, Beth Israel Deaconess Medical Centre and Harvard Medical School, 330 Brookline Avenue, Boston, MA 02215, USA.,Department of Biostatistics, Harvard T.H. Chan School of Public Health, 677 Huntington Ave, Boston, MA 02115, USA
| | - Ethan Rowin
- HCM Institute, Division of Cardiology, Tufts Medical Centre, 860 Washington St Building, 6th Floor, Boston, MA 02111, USA
| | - Warren Manning
- Department of Medicine, Beth Israel Deaconess Medical Centre and Harvard Medical School, 330 Brookline Avenue, Boston, MA 02215, USA.,Department of Radiology, Beth Israel Deaconess Medical Centre and Harvard Medical School, 330 Brookline Avenue, Boston, MA 02215, USA
| | - Martin Maron
- HCM Institute, Division of Cardiology, Tufts Medical Centre, 860 Washington St Building, 6th Floor, Boston, MA 02111, USA
| | - Reza Nezafat
- Department of Medicine, Beth Israel Deaconess Medical Centre and Harvard Medical School, 330 Brookline Avenue, Boston, MA 02215, USA
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30
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Yu F, Huang H, Yu Q, Ma Y, Zhang Q, Zhang B. Artificial intelligence-based myocardial texture analysis in etiological differentiation of left ventricular hypertrophy. ANNALS OF TRANSLATIONAL MEDICINE 2021; 9:108. [PMID: 33569410 PMCID: PMC7867873 DOI: 10.21037/atm-20-4891] [Citation(s) in RCA: 9] [Impact Index Per Article: 3.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Subscribe] [Scholar Register] [Indexed: 01/11/2023]
Abstract
Background Transthoracic echocardiography (TTE) is widely used in clinics to evaluate left ventricular hypertrophy (LVH). However, TTE is usually insufficient for the etiological diagnoses when morphological and functional features are nonspecific. With the booming of computer science and artificial intelligence (AI), previous literature has reported the application of radiomics based on cardiac magnetic resonance imaging, cardiac computed tomography and TTE in diagnosing several myocardial abnormalities, such as myocardial infarction, myocarditis, cardiac amyloidosis, and hypertrophic cardiomyopathy (HCM). In this study, we explored the possibility of using myocardial texture features in differentiating HCM, hypertensive heart disease (HHD) and uremic cardiomyopathy (UCM) based on echocardiography. To our knowledge, this was the first study to explore TTE myocardial texture analysis for multiple LVH etiology differentiation. Methods TTE images were reviewed retrospectively from January 2018 to collect 50 cases for each group of HHD, HCM and UCM. The apical four chamber view was retrieved. Seventeen first-order statistics and 60 gray level co-occurrence matrix (GLCM) features were extracted for statistics and classification test by support vector machine (SVM). Results Of all the parameters, entropy of brightness (EtBrt), standard deviation (Std), coefficient of variation (CoV), skewness (Skew), contrast7 (Cont7) and homogeneity5 (Hm5) were found statistically significant among the three groups (all P<0.05) and with acceptable reproducibility (intraobserver and interobserver ICC >0.50). As a result, HCM showed the most homogeneous myocardial texture, and was significantly different from HHD and UCM (all six features: P≤0.005). HHD appeared slightly more homogeneous than UCM, as only EtBrt and CoV were significant (P=0.011 and P=0.008). According to higher areas under the receiver operating characteristic curve (AUC) (>0.50), EtBrt, Std, and CoV were selected for test of classification as a combination of features. The AUC derived from SVM model was slightly improved compared with those of EtBrt, Std and CoV individually. Conclusions AI-based myocardial texture analysis using ultrasonic images may be a potential approach to aiding LVH etiology differentiation.
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Affiliation(s)
- Fei Yu
- Department of Ultrasound in Medicine, Shanghai East Hospital, Tongji University School of Medicine, Shanghai, China
| | - Haibo Huang
- Shanghai Institute of Advanced Communication and Data Science, The SMART (Smart Medicine and AI-based Radiology Technology) Lab, Shanghai University, Shanghai, China.,Institute of Biomedical Engineering, School of Communication and Information Engineering, Shanghai University, Shanghai, China
| | - Qihui Yu
- Shanghai Institute of Advanced Communication and Data Science, The SMART (Smart Medicine and AI-based Radiology Technology) Lab, Shanghai University, Shanghai, China.,Institute of Biomedical Engineering, School of Communication and Information Engineering, Shanghai University, Shanghai, China
| | - Yuqing Ma
- Department of Ultrasound in Medicine, Shanghai East Hospital, Tongji University School of Medicine, Shanghai, China
| | - Qi Zhang
- Shanghai Institute of Advanced Communication and Data Science, The SMART (Smart Medicine and AI-based Radiology Technology) Lab, Shanghai University, Shanghai, China.,Institute of Biomedical Engineering, School of Communication and Information Engineering, Shanghai University, Shanghai, China.,Shanghai Key Laboratory of Artificial Intelligence for Medical Image and Knowledge Graph, Shanghai, China
| | - Bo Zhang
- Department of Ultrasound in Medicine, Shanghai East Hospital, Tongji University School of Medicine, Shanghai, China
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31
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Seetharam K, Brito D, Farjo PD, Sengupta PP. The Role of Artificial Intelligence in Cardiovascular Imaging: State of the Art Review. Front Cardiovasc Med 2020; 7:618849. [PMID: 33426010 PMCID: PMC7786371 DOI: 10.3389/fcvm.2020.618849] [Citation(s) in RCA: 17] [Impact Index Per Article: 4.3] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 10/18/2020] [Accepted: 12/08/2020] [Indexed: 12/15/2022] Open
Abstract
In this current digital landscape, artificial intelligence (AI) has established itself as a powerful tool in the commercial industry and is an evolving technology in healthcare. Cutting-edge imaging modalities outputting multi-dimensional data are becoming increasingly complex. In this era of data explosion, the field of cardiovascular imaging is undergoing a paradigm shift toward machine learning (ML) driven platforms. These diverse algorithms can seamlessly analyze information and automate a range of tasks. In this review article, we explore the role of ML in the field of cardiovascular imaging.
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Affiliation(s)
- Karthik Seetharam
- Department of Cardiology, West Virginia University Medicine Heart & Vascular Institute, Morgantown, WV, United States
| | - Daniel Brito
- Department of Cardiology, West Virginia University Medicine Heart & Vascular Institute, Morgantown, WV, United States
| | - Peter D Farjo
- Department of Cardiology, West Virginia University Medicine Heart & Vascular Institute, Morgantown, WV, United States
| | - Partho P Sengupta
- Department of Cardiology, West Virginia University Medicine Heart & Vascular Institute, Morgantown, WV, United States
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33
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Hua C, Liu J, Hua X, Wang X. Synergistic Fabrication of Dose-Response Chitosan/Dextran/β-Glycerophosphate Injectable Hydrogel as Cell Delivery Carrier for Cardiac Healing After Acute Myocardial Infarction. Dose Response 2020; 18:1559325820941323. [PMID: 32922226 PMCID: PMC7457666 DOI: 10.1177/1559325820941323] [Citation(s) in RCA: 4] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 04/22/2020] [Revised: 05/26/2020] [Accepted: 06/01/2020] [Indexed: 12/14/2022] Open
Abstract
The human mesenchymal stem cells (hMSCs) therapy offering an encouraging the new methods to establish the conveying on the chitosan (C)/dextran (D)/β-glycerophosphate (β-GP) loaded with hMSCs to enhance the acute myocardial infarctions. The synthesized hMSCs-CD@β-GP system displayed the ratio of determination modules, size of the pore, absorbency, and the swellings ratio in the assortment of the 65 ka, 149 ± 39.8 µm, 92.2%, 42 ± 1.38, and 29 ± 1.9, respectively. The fabricated hMSCs-CD@β-GP was highly stable and physicochemical investigated and confirmed the suitability of the materials for cardiac regeneration applications. The in vitro examinations of the injectable hydrogels with hMSCs-CD@β-GP have recognized that the improved survival rate of the cells, increased the pro-inflammatory expressions factors, pro-angiogenic factors analysis confirmed the promising results of the ejection of fractions, fibrosis area, vessel density with decreased infractions size, with suggesting that the remarkable improvement of the heart regenerative function after myocardial infarctions. The new synergistic approach of the injectable hydrogels with hMSCs could able appropriate for the effective treatment of cardiac therapies after acute myocardial infarctions.
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Affiliation(s)
- Chongjun Hua
- Department of Cardiology, Jinhua Central Hospital, Jinhua, China
| | - Jing Liu
- Department of Cardiology, Yantai Affiliated Hospital of Binzhou Medical University, Yantai, China
| | - Xiuhong Hua
- Department of Pharmacy, Jinhua Fifth Hospital, Jinhua, China
| | - Xinyu Wang
- Department of Ultrasonography, Xiamen Cardiovascular Hospital Xiamen University, Xiamen, China
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