ArticleJournal of digital imaging2023
Myocardial Perfusion SPECT Imaging Radiomic Features and Machine Learning Algorithms for Cardiac Contractile Pattern Recognition.
Article in Journal of digital imaging, 2023. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 18 papers.
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Who cites it
18 citing papers in PubMed, 37 citations in OpenAlex.
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- Radiomics early assessment of post chemotherapy cardiotoxicity in cancer patients using 2D echocardiography imaging an interpretable machine learning study.Scientific reports · 2025Article
- Viability Test in Prediction of Response to Cardiac Resynchronization Therapy.Journal of clinical medicine · 2025Article
- AI in SPECT Imaging: Opportunities and Challenges.Seminars in nuclear medicine · 2025Review
- ELTIRADS framework for thyroid nodule classification integrating elastography, TIRADS, and radiomics with interpretable machine learning.Scientific reports · 2025Article
- Myocardial perfusion SPECT radiomic features reproducibility assessment: Impact of image reconstruction and harmonization.Medical physics · 2025Article
- Machine Learning and Deep Learning Applications in Magnetic Particle Imaging.Journal of magnetic resonance imaging : JMRI · 2025Review
- Radiomics Nomogram Derived from Gated Myocardial Perfusion SPECT for Identifying Ischemic Cardiomyopathy.Journal of imaging informatics in medicine · 2024Article
- Deep learning automatically distinguishes myocarditis patients from normal subjects based on MRI.The international journal of cardiovascular imaging · 2024Article
- A Novel Approach to Identifying Hibernating Myocardium Using Radiomics-Based Machine Learning.Cureus · 2024Article
- Prediction of Ablation Rate for High-Intensity Focused Ultrasound Therapy of Adenomyosis in MR Images Based on Multi-model Fusion.Journal of imaging informatics in medicine · 2024Article
- Comparing various AI approaches to traditional quantitative assessment of the myocardial perfusion in [Scientific reports · 2024Article
- Left Ventricular Myocardial Dysfunction Evaluation in Thalassemia Patients Using Echocardiographic Radiomic Features and Machine Learning Algorithms.Journal of digital imaging · 2023Article
- Comparison of Machine Learning Algorithms Using Manual/Automated Features on 12-Lead Signal Electrocardiogram Classification: A Large Cohort Study on Students Aged Between 6 to 18 Years Old.Cardiovascular engineering and technology · 2023Article
- Artificial Intelligence Empowered Nuclear Medicine and Molecular Imaging in Cardiology: A State-of-the-Art Review.Phenomics (Cham, Switzerland) · 2023Review
- Time-to-event overall survival prediction in glioblastoma multiforme patients using magnetic resonance imaging radiomics.La Radiologia medica · 2023Article
- Machine learning-based diagnosis and risk classification of coronary artery disease using myocardial perfusion imaging SPECT: A radiomics study.Scientific reports · 2023Article
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Authors and funding
12 authors at 7 institutions in 6 countries.
Funding
No grant is acknowledged in the PubMed record.
Abstract
A U-shaped contraction pattern was shown to be associated with a better Cardiac resynchronization therapy (CRT) response. The main goal of this study is to automatically recognize left ventricular contractile patterns using machine learning algorithms trained on conventional quantitative features (ConQuaFea) and radiomic features extracted from Gated single-photon emission computed tomography myocardial perfusion imaging (GSPECT MPI). Among 98 patients with standard resting GSPECT MPI included in this study, 29 received CRT therapy and 69 did not (also had CRT inclusion criteria but did not receive treatment yet at the time of data collection, or refused treatment). A total of 69 non-CRT patients were employed for training, and the 29 were employed for testing. The models were built utilizing features from three distinct feature sets (ConQuaFea, radiomics, and ConQuaFea + radiomics (combined)), which were chosen using Recursive feature elimination (RFE) feature selection (FS), and then trained using seven different machine learning (ML) classifiers. In addition, CRT outcome prediction was assessed by different treatment inclusion criteria as the study's final phase. The MLP classifier had the highest performance among ConQuaFea models (AUC, SEN, SPE = 0.80, 0.85, 0.76). RF achieved the best performance in terms of AUC, SEN, and SPE with values of 0.65, 0.62, and 0.68, respectively, among radiomic models. GB and RF approaches achieved the best AUC, SEN, and SPE values of 0.78, 0.92, and 0.63 and 0.74, 0.93, and 0.56, respectively, among the combined models. A promising outcome was obtained when using radiomic and ConQuaFea from GSPECT MPI to detect left ventricular contractile patterns by machine learning.
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