A Comprehensive Review of Artificial Intelligence (AI) Applications in Pulmonary Hypertension (PH)
<i>Background:</i> Pulmonary hypertension (PH) is a complex condition associated with significant morbidity and mortality. Traditional diagnostic and management approaches for PH often face limitations, leading to delays in diagnosis and potentially suboptimal treatment outcomes. Artific...
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2025-01-01
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| author | Sogol Attaripour Esfahani Nima Baba Ali Juan M. Farina Isabel G. Scalia Milagros Pereyra Mohammed Tiseer Abbas Niloofar Javadi Nadera N. Bismee Fatmaelzahraa E. Abdelfattah Kamal Awad Omar H. Ibrahim Hesham Sheashaa Timothy Barry Robert L. Scott Chadi Ayoub Reza Arsanjani |
| author_facet | Sogol Attaripour Esfahani Nima Baba Ali Juan M. Farina Isabel G. Scalia Milagros Pereyra Mohammed Tiseer Abbas Niloofar Javadi Nadera N. Bismee Fatmaelzahraa E. Abdelfattah Kamal Awad Omar H. Ibrahim Hesham Sheashaa Timothy Barry Robert L. Scott Chadi Ayoub Reza Arsanjani |
| author_sort | Sogol Attaripour Esfahani |
| collection | DOAJ |
| description | <i>Background:</i> Pulmonary hypertension (PH) is a complex condition associated with significant morbidity and mortality. Traditional diagnostic and management approaches for PH often face limitations, leading to delays in diagnosis and potentially suboptimal treatment outcomes. Artificial intelligence (AI), encompassing machine learning (ML) and deep learning (DL) offers a transformative approach to PH care. <i>Materials and Methods:</i> We systematically searched PubMed, Scopus, and Web of Science for original studies on AI applications in PH, using predefined keywords. Out of more than 500 initial articles, 45 relevant studies were selected. Risk of bias was evaluated using PROBAST (Prediction model Risk of Bias Assessment Tool). <i>Results:</i> This review examines the potential applications of AI in PH, focusing on its role in enhancing diagnosis, disease classification, and prognostication. We discuss how AI-powered analysis of medical data can improve the accuracy and efficiency of detecting PH. Furthermore, we explore the potential of AI in risk stratification, leading to treatment optimization for PH. <i>Conclusions:</i> While acknowledging the existing challenges and limitations and the need for continued exploration and refinement of AI-driven tools, this review highlights the significant promise of AI in revolutionizing PH management to improve patient outcomes. |
| format | Article |
| id | doaj-art-1066e8fbe0bf4e94a17a7229250fc009 |
| institution | Kabale University |
| issn | 1010-660X 1648-9144 |
| language | English |
| publishDate | 2025-01-01 |
| publisher | MDPI AG |
| record_format | Article |
| series | Medicina |
| spelling | doaj-art-1066e8fbe0bf4e94a17a7229250fc0092025-01-24T13:40:31ZengMDPI AGMedicina1010-660X1648-91442025-01-016118510.3390/medicina61010085A Comprehensive Review of Artificial Intelligence (AI) Applications in Pulmonary Hypertension (PH)Sogol Attaripour Esfahani0Nima Baba Ali1Juan M. Farina2Isabel G. Scalia3Milagros Pereyra4Mohammed Tiseer Abbas5Niloofar Javadi6Nadera N. Bismee7Fatmaelzahraa E. Abdelfattah8Kamal Awad9Omar H. Ibrahim10Hesham Sheashaa11Timothy Barry12Robert L. Scott13Chadi Ayoub14Reza Arsanjani15Department of Cardiovascular Medicine, Mayo Clinic, Phoenix, AZ 85054, USADepartment of Cardiovascular Medicine, Mayo Clinic, Phoenix, AZ 85054, USADepartment of Cardiovascular Medicine, Mayo Clinic, Phoenix, AZ 85054, USADepartment of Cardiovascular Medicine, Mayo Clinic, Phoenix, AZ 85054, USADepartment of Cardiovascular Medicine, Mayo Clinic, Phoenix, AZ 85054, USADepartment of Cardiovascular Medicine, Mayo Clinic, Phoenix, AZ 85054, USADepartment of Cardiovascular Medicine, Mayo Clinic, Phoenix, AZ 85054, USADepartment of Cardiovascular Medicine, Mayo Clinic, Phoenix, AZ 85054, USADepartment of Cardiovascular Medicine, Mayo Clinic, Phoenix, AZ 85054, USADepartment of Cardiovascular Medicine, Mayo Clinic, Phoenix, AZ 85054, USADepartment of Cardiovascular Medicine, Mayo Clinic, Phoenix, AZ 85054, USADepartment of Cardiovascular Medicine, Mayo Clinic, Phoenix, AZ 85054, USADepartment of Cardiovascular Medicine, Mayo Clinic, Phoenix, AZ 85054, USADepartment of Cardiovascular Medicine, Mayo Clinic, Phoenix, AZ 85054, USADepartment of Cardiovascular Medicine, Mayo Clinic, Phoenix, AZ 85054, USADepartment of Cardiovascular Medicine, Mayo Clinic, Phoenix, AZ 85054, USA<i>Background:</i> Pulmonary hypertension (PH) is a complex condition associated with significant morbidity and mortality. Traditional diagnostic and management approaches for PH often face limitations, leading to delays in diagnosis and potentially suboptimal treatment outcomes. Artificial intelligence (AI), encompassing machine learning (ML) and deep learning (DL) offers a transformative approach to PH care. <i>Materials and Methods:</i> We systematically searched PubMed, Scopus, and Web of Science for original studies on AI applications in PH, using predefined keywords. Out of more than 500 initial articles, 45 relevant studies were selected. Risk of bias was evaluated using PROBAST (Prediction model Risk of Bias Assessment Tool). <i>Results:</i> This review examines the potential applications of AI in PH, focusing on its role in enhancing diagnosis, disease classification, and prognostication. We discuss how AI-powered analysis of medical data can improve the accuracy and efficiency of detecting PH. Furthermore, we explore the potential of AI in risk stratification, leading to treatment optimization for PH. <i>Conclusions:</i> While acknowledging the existing challenges and limitations and the need for continued exploration and refinement of AI-driven tools, this review highlights the significant promise of AI in revolutionizing PH management to improve patient outcomes.https://www.mdpi.com/1648-9144/61/1/85artificial intelligencepulmonary hypertensionmachine learningdeep learningechocardiographycomputed tomography |
| spellingShingle | Sogol Attaripour Esfahani Nima Baba Ali Juan M. Farina Isabel G. Scalia Milagros Pereyra Mohammed Tiseer Abbas Niloofar Javadi Nadera N. Bismee Fatmaelzahraa E. Abdelfattah Kamal Awad Omar H. Ibrahim Hesham Sheashaa Timothy Barry Robert L. Scott Chadi Ayoub Reza Arsanjani A Comprehensive Review of Artificial Intelligence (AI) Applications in Pulmonary Hypertension (PH) Medicina artificial intelligence pulmonary hypertension machine learning deep learning echocardiography computed tomography |
| title | A Comprehensive Review of Artificial Intelligence (AI) Applications in Pulmonary Hypertension (PH) |
| title_full | A Comprehensive Review of Artificial Intelligence (AI) Applications in Pulmonary Hypertension (PH) |
| title_fullStr | A Comprehensive Review of Artificial Intelligence (AI) Applications in Pulmonary Hypertension (PH) |
| title_full_unstemmed | A Comprehensive Review of Artificial Intelligence (AI) Applications in Pulmonary Hypertension (PH) |
| title_short | A Comprehensive Review of Artificial Intelligence (AI) Applications in Pulmonary Hypertension (PH) |
| title_sort | comprehensive review of artificial intelligence ai applications in pulmonary hypertension ph |
| topic | artificial intelligence pulmonary hypertension machine learning deep learning echocardiography computed tomography |
| url | https://www.mdpi.com/1648-9144/61/1/85 |
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