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Elvas LB, Gomes S, Ferreira JC, Rosário LB, Brandão T. Deep learning for automatic calcium detection in echocardiography. BioData Min 2024; 17:27. [PMID: 39198921 PMCID: PMC11351547 DOI: 10.1186/s13040-024-00381-1] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 03/11/2024] [Accepted: 08/12/2024] [Indexed: 09/01/2024] Open
Abstract
Cardiovascular diseases are the main cause of death in the world and cardiovascular imaging techniques are the mainstay of noninvasive diagnosis. Aortic stenosis is a lethal cardiac disease preceded by aortic valve calcification for several years. Data-driven tools developed with Deep Learning (DL) algorithms can process and categorize medical images data, providing fast diagnoses with considered reliability, to improve healthcare effectiveness. A systematic review of DL applications on medical images for pathologic calcium detection concluded that there are established techniques in this field, using primarily CT scans, at the expense of radiation exposure. Echocardiography is an unexplored alternative to detect calcium, but still needs technological developments. In this article, a fully automated method based on Convolutional Neural Networks (CNNs) was developed to detect Aortic Calcification in Echocardiography images, consisting of two essential processes: (1) an object detector to locate aortic valve - achieving 95% of precision and 100% of recall; and (2) a classifier to identify calcium structures in the valve - which achieved 92% of precision and 100% of recall. The outcome of this work is the possibility of automation of the detection with Echocardiography of Aortic Valve Calcification, a lethal and prevalent disease.
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Affiliation(s)
- Luís B Elvas
- Department of Logistics, Molde University College, Molde, 6410, Norway.
- Instituto Universitário de Lisboa (ISCTE-IUL), ISTAR, Av. das Forças Armadas, Lisboa, 1649-026, Portugal.
- Inov Inesc Inovação-Instituto de Novas Tecnologias, Lisbon, 1000-029, Portugal.
| | - Sara Gomes
- Instituto Universitário de Lisboa (ISCTE-IUL), ISTAR, Av. das Forças Armadas, Lisboa, 1649-026, Portugal
| | - João C Ferreira
- Department of Logistics, Molde University College, Molde, 6410, Norway
- Instituto Universitário de Lisboa (ISCTE-IUL), ISTAR, Av. das Forças Armadas, Lisboa, 1649-026, Portugal
- Inov Inesc Inovação-Instituto de Novas Tecnologias, Lisbon, 1000-029, Portugal
| | - Luís Brás Rosário
- Faculty of Medicine, Lisbon University, Hospital Santa Maria/CHULN, Centro Cardiovascular da Universidade de Lisboa (CCUL@RISE), Lisbon, 1649-028, Portugal
| | - Tomás Brandão
- Instituto Universitário de Lisboa (ISCTE-IUL), ISTAR, Av. das Forças Armadas, Lisboa, 1649-026, Portugal
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Koponen M, Anwaar W, Sheikh Q, Sadiq F. Use of Artificial Intelligence in Coronary Artery Calcium Scoring. Oman Med J 2023; 38:e543. [PMID: 38053612 PMCID: PMC10694408 DOI: 10.5001/omj.2023.73] [Citation(s) in RCA: 1] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 08/31/2022] [Accepted: 11/07/2022] [Indexed: 12/07/2023] Open
Abstract
Coronary artery calcium (CAC) scoring improves traditional risk factor-based coronary heart disease (CHD) risk stratification. Here, the contribution of CAC scoring to a traditional 10-year CHD risk prediction scores and new artificial intelligence methods used to automate CAC scoring were reviewed. Research shows that traditional risk factors tend to overestimate or underestimate the actual risk of CHD, meaning that including CAC score in the risk stratification has potential to reduce over- and undertreatment. The automated CAC scoring methods are shown to be accurate and significantly more time-effective than the commonly used semi-automated method.
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Affiliation(s)
- Mia Koponen
- School of Medical Sciences, University of Aberdeen, Aberdeen, UK
| | - Waqas Anwaar
- Department of Computing, Shifa School of Computing, Shifa Tameer-e-Millat University, Islamabad, Pakistan
| | - Habib-ur-Rahman3
- School of Medical Sciences, University of Aberdeen, Aberdeen, UK
- Department of Computing, Shifa School of Computing, Shifa Tameer-e-Millat University, Islamabad, Pakistan
- Department of Cardiology, Shifa International Hospital, Islamabad, Pakistan
- Directorate of Research, Shifa Tameer-e-Millat University, Islamabad, Pakistan
| | - Qasim Sheikh
- Department of Computing, Shifa School of Computing, Shifa Tameer-e-Millat University, Islamabad, Pakistan
| | - Fouzia Sadiq
- Directorate of Research, Shifa Tameer-e-Millat University, Islamabad, Pakistan
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Khosravi B, Rouzrokh P, Faghani S, Moassefi M, Vahdati S, Mahmoudi E, Chalian H, Erickson BJ. Machine Learning and Deep Learning in Cardiothoracic Imaging: A Scoping Review. Diagnostics (Basel) 2022; 12:2512. [PMID: 36292201 PMCID: PMC9600598 DOI: 10.3390/diagnostics12102512] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 09/17/2022] [Revised: 10/14/2022] [Accepted: 10/15/2022] [Indexed: 01/17/2023] Open
Abstract
Machine-learning (ML) and deep-learning (DL) algorithms are part of a group of modeling algorithms that grasp the hidden patterns in data based on a training process, enabling them to extract complex information from the input data. In the past decade, these algorithms have been increasingly used for image processing, specifically in the medical domain. Cardiothoracic imaging is one of the early adopters of ML/DL research, and the COVID-19 pandemic resulted in more research focus on the feasibility and applications of ML/DL in cardiothoracic imaging. In this scoping review, we systematically searched available peer-reviewed medical literature on cardiothoracic imaging and quantitatively extracted key data elements in order to get a big picture of how ML/DL have been used in the rapidly evolving cardiothoracic imaging field. During this report, we provide insights on different applications of ML/DL and some nuances pertaining to this specific field of research. Finally, we provide general suggestions on how researchers can make their research more than just a proof-of-concept and move toward clinical adoption.
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Affiliation(s)
- Bardia Khosravi
- Radiology Informatics Lab (RIL), Department of Radiology, Mayo Clinic, Rochester, MN 55905, USA
- Orthopedic Surgery Artificial Intelligence Laboratory (OSAIL), Department of Orthopedic Surgery, Mayo Clinic, Rochester, MN 55905, USA
| | - Pouria Rouzrokh
- Radiology Informatics Lab (RIL), Department of Radiology, Mayo Clinic, Rochester, MN 55905, USA
- Orthopedic Surgery Artificial Intelligence Laboratory (OSAIL), Department of Orthopedic Surgery, Mayo Clinic, Rochester, MN 55905, USA
| | - Shahriar Faghani
- Radiology Informatics Lab (RIL), Department of Radiology, Mayo Clinic, Rochester, MN 55905, USA
| | - Mana Moassefi
- Radiology Informatics Lab (RIL), Department of Radiology, Mayo Clinic, Rochester, MN 55905, USA
| | - Sanaz Vahdati
- Radiology Informatics Lab (RIL), Department of Radiology, Mayo Clinic, Rochester, MN 55905, USA
| | - Elham Mahmoudi
- Radiology Informatics Lab (RIL), Department of Radiology, Mayo Clinic, Rochester, MN 55905, USA
| | - Hamid Chalian
- Department of Radiology, Cardiothoracic Imaging, University of Washington, Seattle, WA 98195, USA
| | - Bradley J. Erickson
- Radiology Informatics Lab (RIL), Department of Radiology, Mayo Clinic, Rochester, MN 55905, USA
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Okuwobi IP, Ding Z, Wan J, Ding S. Artificial intelligence model driven by transfer learning for image-based medical diagnosis. JOURNAL OF INTELLIGENT & FUZZY SYSTEMS 2022. [DOI: 10.3233/jifs-220066] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/15/2022]
Abstract
Artificial intelligent (AI) systems for clinical-decision support are an important tool in clinical routine. It has become a crucial diagnostic tool with adequate reliability and interpretability in disease diagnosis and monitoring. Undoubtedly, these models are faced with insufficient data challenges for training, which often directly determines the model’s performance. In order word, insufficient data for model training leads to inefficiency in the model built. To overcome this problem, we propose an AI-driven model by transfer learning in accurate diagnosis for medical decision support. Our approach leverages the shortage of data with a pretrained model by training the neural network with a fraction of the new dataset. For this purpose, we utilized the VGG19 network as the backbone network to support our model in integrating known features with the newly learned features for accurate diagnosis and decision making. Integrating this trained model speeds up the training phase and improve the performance of the proposed model. Experimental results show that the proposed model is effective and efficient in diagnosing different medical diseases. As such, we anticipated that this diagnosis tool will ultimately aid in facilitating early treatment of these treatable diseases, which will improve clinical out-comes.
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Affiliation(s)
- Idowu Paul Okuwobi
- School of Artificial Intelligence, Guilin University of Electronic Technology, Guilin, China
| | - Zhixiang Ding
- Department of Ophthalmology, Affiliated Hospital of Guilin Medical University, Guilin, China
| | - Jifeng Wan
- Department of Ophthalmology, Affiliated Hospital of Guilin Medical University, Guilin, China
| | - Shuxue Ding
- School of Artificial Intelligence, Guilin University of Electronic Technology, Guilin, China
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Deep Learning in Multi-Class Lung Diseases’ Classification on Chest X-ray Images. Diagnostics (Basel) 2022; 12:diagnostics12040915. [PMID: 35453963 PMCID: PMC9025806 DOI: 10.3390/diagnostics12040915] [Citation(s) in RCA: 16] [Impact Index Per Article: 8.0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 03/02/2022] [Revised: 03/28/2022] [Accepted: 04/04/2022] [Indexed: 12/04/2022] Open
Abstract
Chest X-ray radiographic (CXR) imagery enables earlier and easier lung disease diagnosis. Therefore, in this paper, we propose a deep learning method using a transfer learning technique to classify lung diseases on CXR images to improve the efficiency and accuracy of computer-aided diagnostic systems’ (CADs’) diagnostic performance. Our proposed method is a one-step, end-to-end learning, which means that raw CXR images are directly inputted into a deep learning model (EfficientNet v2-M) to extract their meaningful features in identifying disease categories. We experimented using our proposed method on three classes of normal, pneumonia, and pneumothorax of the U.S. National Institutes of Health (NIH) data set, and achieved validation performances of loss = 0.6933, accuracy = 82.15%, sensitivity = 81.40%, and specificity = 91.65%. We also experimented on the Cheonan Soonchunhyang University Hospital (SCH) data set on four classes of normal, pneumonia, pneumothorax, and tuberculosis, and achieved validation performances of loss = 0.7658, accuracy = 82.20%, sensitivity = 81.40%, and specificity = 94.48%; testing accuracy of normal, pneumonia, pneumothorax, and tuberculosis classes was 63.60%, 82.30%, 82.80%, and 89.90%, respectively.
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