{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,9,24]],"date-time":"2026-09-24T17:07:09Z","timestamp":1790269629481,"version":"4.1.0"},"reference-count":23,"publisher":"MDPI AG","issue":"19","license":[{"start":{"date-parts":[[2026,9,24]],"date-time":"2026-09-24T00:00:00Z","timestamp":1790208000000},"content-version":"vor","delay-in-days":0,"URL":"https:\/\/creativecommons.org\/licenses\/by\/4.0\/"}],"content-domain":{"domain":[],"crossmark-restriction":false},"short-container-title":["Diagnostics"],"abstract":"<jats:p>Background\/Objectives: Right heart catheterization (RHC) is the diagnostic gold standard for pulmonary hypertension (PH), defined as mean pulmonary arterial pressure (mPAP) greater than 20 mmHg at rest. It is invasive and carries procedural risk. Machine learning (ML) and deep learning (DL) applied to non-invasive modalities may provide a non-invasive adjunct for screening, triage, and earlier identification. Methods: Methodological quality was assessed using QUADAS-2. Primary non-comparative pooling was restricted to RHC-confirmed studies using Reitsma hierarchical bivariate random-effects models. Comparative studies were analyzed using a paired hierarchical bivariate model, with between-method differences assessed using paired Wald tests. Sensitivity, specificity, likelihood ratios, and diagnostic odds ratios were estimated; predictive values were evaluated as secondary outcomes. GRADE certainty was assessed separately for non-comparative ML\/DL, comparative ML\/DL, and traditional methods. Results: Nine studies involving multiple pulmonary hypertension subtypes met the inclusion criteria: five non-comparative (single-arm) studies including approximately 26,530 participants and four comparative (double-arm) studies including 330 paired participants per arm. In the primary synthesis restricted to RHC-confirmed single-arm studies, ML\/DL models achieved a pooled sensitivity of 0.720 (95% CI: 0.647\u20130.783) and specificity of 0.883 (95% CI: 0.796\u20130.936), with a diagnostic odds ratio of 19.4 (95% CI: 13.6\u201327.7). Estimates were similar when all five single-arm studies were included (sensitivity 0.716, 95% CI: 0.654\u20130.771; specificity 0.891, 95% CI: 0.820\u20130.937). In comparative studies analyzed with a paired hierarchical bivariate model, ML\/DL outperformed traditional methods for both sensitivity (0.838, 95% CI: 0.668\u20130.930 versus 0.707, 95% CI: 0.421\u20130.889; paired Wald p = 0.019) and specificity (0.849, 95% CI: 0.512\u20130.968 versus 0.640, 95% CI: 0.376\u20130.840; paired Wald p = 0.0076). At a 25% pre-test probability, ML\/DL was projected to yield 34 additional true positives (95% CI: \u221230 to 109) and 142 fewer false positives (95% CI: \u2212381 to 129) per 1000 patients tested, although all absolute difference intervals crossed zero. Certainty of evidence was very low across all outcomes. GRADE certainty ranged from low to very low depending on outcome and stratum. Conclusions: ML\/DL-based methods showed promising non-invasive diagnostic accuracy for PH, with significantly higher pooled sensitivity and specificity than traditional methods in paired comparisons. However, evidence certainty ranged from low to very low, and projected absolute benefits remained imprecise. These findings are hypothesis-generating; ML\/DL tools may serve as adjuncts in the diagnostic pathway but cannot replace RHC at this stage.<\/jats:p>","DOI":"10.3390\/diagnostics16193106","type":"journal-article","created":{"date-parts":[[2026,9,24]],"date-time":"2026-09-24T16:29:53Z","timestamp":1790267393000},"page":"3106","update-policy":"https:\/\/doi.org\/10.3390\/mdpi_crossmark_policy","source":"Crossref","is-referenced-by-count":0,"title":["Diagnostic Accuracy of Machine Learning-Enhanced Methods for Pulmonary Hypertension: A Systematic Review and Meta-Analysis"],"prefix":"10.3390","volume":"16","author":[{"given":"Faizan","family":"Ahmed","sequence":"first","affiliation":[{"name":"Department of Internal Medicine, Jersey Shore University Medical Center, Neptune, NJ 07753, USA"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Ayesha","family":"Zulfiqar","sequence":"additional","affiliation":[{"name":"Dow Medical College, Dow University of Health Sciences, Karachi 74200, Pakistan"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"ORCID":"https:\/\/orcid.org\/0009-0003-9964-1149","authenticated-orcid":false,"given":"Tushar Kanti","family":"Bhadra","sequence":"additional","affiliation":[{"name":"National Institute of Cardiovascular Diseases, Dhaka 1207, Bangladesh"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Noor Ul","family":"Sabah","sequence":"additional","affiliation":[{"name":"Quaid-E-Azam Medical College, University of Health Sciences, Bahawalpur 63100, Pakistan"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Taha","family":"Alam","sequence":"additional","affiliation":[{"name":"Ameer-Ud-Din Medical College, University of Health Sciences (UHS), Lahore 54000, Pakistan"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"ORCID":"https:\/\/orcid.org\/0009-0004-4013-6428","authenticated-orcid":false,"given":"Unaiza","family":"Iftikhar","sequence":"additional","affiliation":[{"name":"Foundation University Medical College, Foundation University Islamabad, Islamabad 44000, Pakistan"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Ahmad","family":"Zulaid","sequence":"additional","affiliation":[{"name":"Quaid-E-Azam Medical College, University of Health Sciences, Bahawalpur 63100, Pakistan"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"ORCID":"https:\/\/orcid.org\/0009-0004-9784-0088","authenticated-orcid":false,"given":"Hassan","family":"Farooq","sequence":"additional","affiliation":[{"name":"Ameer-Ud-Din Medical College, University of Health Sciences (UHS), Lahore 54000, Pakistan"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"ORCID":"https:\/\/orcid.org\/0009-0001-6641-5909","authenticated-orcid":false,"given":"Qais","family":"Bin Abdul Ghaffar","sequence":"additional","affiliation":[{"name":"Department of Internal Medicine, Camden Clark Medical Center, WVU Medicine, Parkersburg, WV 26101, USA"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Muhammad Faizan","family":"Tahir","sequence":"additional","affiliation":[{"name":"Shalamar Medical and Dental College, University of Health Sciences, Lahore 54840, Pakistan"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"ORCID":"https:\/\/orcid.org\/0009-0009-4346-9945","authenticated-orcid":false,"given":"Muhammad","family":"Haris","sequence":"additional","affiliation":[{"name":"Quaid-E-Azam Medical College, University of Health Sciences, Bahawalpur 63100, Pakistan"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Omar","family":"Kamel","sequence":"additional","affiliation":[{"name":"Department of Physical Therapy, Qena University, Qena 83523, Egypt"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"ORCID":"https:\/\/orcid.org\/0009-0004-7057-9975","authenticated-orcid":false,"given":"Haris Bin","family":"Tahir","sequence":"additional","affiliation":[{"name":"Department of Internal Medicine, Lahore General Hospital, Lahore 54000, Pakistan"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"ORCID":"https:\/\/orcid.org\/0000-0002-0263-9655","authenticated-orcid":false,"given":"Gabriel","family":"Benjamen","sequence":"additional","affiliation":[{"name":"School of Medicine, St. George\u2019s University, St. George\u2019s P.O. Box 7, Grenada"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"ORCID":"https:\/\/orcid.org\/0009-0003-1507-3835","authenticated-orcid":false,"given":"Ali Abbas","family":"Khaleel","sequence":"additional","affiliation":[{"name":"College of Medicine, Al-Mustansiriyah University, Baghdad 14022, Iraq"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Ali","family":"Ghani","sequence":"additional","affiliation":[{"name":"Department of Cardiology, University of Arizona, Tucson, AZ 85724, USA"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"ORCID":"https:\/\/orcid.org\/0000-0001-6836-4490","authenticated-orcid":false,"given":"Amro","family":"Taha","sequence":"additional","affiliation":[{"name":"Division of Cardiology, West Virginia University, Morgantown, WV 26506, USA"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Fawaz","family":"Alenezi","sequence":"additional","affiliation":[{"name":"Division of Cardiology, Department of Medicine, Duke University School of Medicine, Durham, NC 27710, USA"}],"role":[{"vocabulary":"crossref","role":"author"}]}],"member":"1968","published-online":{"date-parts":[[2026,9,24]]},"reference":[{"key":"ref_1","doi-asserted-by":"crossref","first-page":"1","DOI":"10.1038\/s41572-023-00486-7","article-title":"Pulmonary hypertension","volume":"10","author":"Mocumbi","year":"2024","journal-title":"Nat. Rev. Dis. Primers"},{"key":"ref_2","doi-asserted-by":"crossref","first-page":"17534666231218886","DOI":"10.1177\/17534666231218886","article-title":"The diagnostic journey of pulmonary arterial hypertension patients: Results from a multinational real-world survey","volume":"18","author":"Small","year":"2024","journal-title":"Ther. Adv. Respir. Dis."},{"key":"ref_3","doi-asserted-by":"crossref","first-page":"e024969","DOI":"10.1161\/JAHA.121.024969","article-title":"Mortality in pulmonary arterial hypertension in the modern era: The PHAR registry","volume":"11","author":"Chang","year":"2022","journal-title":"J. Am. Heart Assoc."},{"key":"ref_4","first-page":"122","article-title":"Right heart catheterization in clinical practice: A review of basic physiology and important issues relevant to interpretation","volume":"13","author":"Nugent","year":"2023","journal-title":"Am. J. Cardiovasc. Dis."},{"key":"ref_5","doi-asserted-by":"crossref","first-page":"44","DOI":"10.1038\/s41591-018-0300-7","article-title":"High-performance medicine: The convergence of human and artificial intelligence","volume":"25","author":"Topol","year":"2019","journal-title":"Nat. Med."},{"key":"ref_6","doi-asserted-by":"crossref","first-page":"260","DOI":"10.1016\/j.mayocp.2023.05.006","article-title":"Machine learning for the diagnosis of pulmonary hypertension using echocardiography","volume":"99","author":"Anand","year":"2024","journal-title":"Mayo Clin. Proc."},{"key":"ref_7","doi-asserted-by":"crossref","first-page":"2902","DOI":"10.1007\/s10278-025-01705-1","article-title":"Diagnosis of Pulmonary Hypertension by Integrating Multimodal Data with a Hybrid Graph Convolutional and Transformer Network","volume":"39","author":"Zhu","year":"2025","journal-title":"J. Imaging Inf. Med."},{"key":"ref_8","doi-asserted-by":"crossref","unstructured":"Fadilah, A., Putri, V.Y.S., Puling, I.M.D.R., and Willyanto, S.E. (2024). Assessing the precision of machine learning for diagnosing pulmonary arterial hypertension: A systematic review and meta-analysis of diagnostic accuracy studies. Front. Cardiovasc. Med., 11.","DOI":"10.3389\/fcvm.2024.1422327"},{"key":"ref_9","doi-asserted-by":"crossref","first-page":"n71","DOI":"10.1136\/bmj.n71","article-title":"The PRISMA 2020 statement: An updated guideline for reporting systematic reviews","volume":"372","author":"Page","year":"2021","journal-title":"BMJ"},{"key":"ref_10","doi-asserted-by":"crossref","first-page":"529","DOI":"10.7326\/0003-4819-155-8-201110180-00009","article-title":"QUADAS-2: A revised tool for quality assessment of diagnostic accuracy studies","volume":"155","author":"Whiting","year":"2011","journal-title":"Ann. Intern Med."},{"key":"ref_11","doi-asserted-by":"crossref","first-page":"55","DOI":"10.1002\/jrsm.1411","article-title":"Risk-of-bias VISualization (robvis): An R package and Shiny web app for visualizing risk-of-bias assessments","volume":"12","author":"McGuinness","year":"2021","journal-title":"Res. Syn. Methods"},{"key":"ref_12","doi-asserted-by":"crossref","first-page":"00229","DOI":"10.1183\/23120541.00229-2023","article-title":"Machine learning to differentiate pulmonary hypertension due to left heart disease from pulmonary arterial hypertension","volume":"9","author":"Swinnen","year":"2023","journal-title":"ERJ Open Res."},{"key":"ref_13","first-page":"1059","article-title":"A deep-learning-enabled electrocardiogram and chest X-ray for detecting pulmonary arterial hypertension","volume":"37","author":"Liu","year":"2024","journal-title":"J. Imaging Inf. Med."},{"key":"ref_14","doi-asserted-by":"crossref","first-page":"00592","DOI":"10.1183\/23120541.00592-2024","article-title":"Artificial intelligence-based echocardiography assessment to detect pulmonary hypertension","volume":"11","author":"Salehi","year":"2025","journal-title":"ERJ Open Res."},{"key":"ref_15","doi-asserted-by":"crossref","first-page":"01057","DOI":"10.1183\/23120541.01057-2024","article-title":"Detection of pulmonary hypertension in idiopathic pulmonary fibrosis using random forest models and automated measures of central computed tomography structures","volume":"11","author":"Shirahata","year":"2025","journal-title":"ERJ Open Res."},{"key":"ref_16","doi-asserted-by":"crossref","first-page":"2567","DOI":"10.1007\/s11263-024-01996-x","article-title":"Deep learning based prediction of pulmonary hypertension in newborns using echocardiograms","volume":"132","author":"Ozkan","year":"2024","journal-title":"Int. J. Comput Vis."},{"key":"ref_17","doi-asserted-by":"crossref","first-page":"586","DOI":"10.1136\/heartjnl-2023-323320","article-title":"Echocardiographic artificial intelligence for pulmonary hypertension classification","volume":"110","author":"Hirata","year":"2024","journal-title":"Heart"},{"key":"ref_18","doi-asserted-by":"crossref","unstructured":"Imai, S., Sakao, S., Nagata, J., Naito, A., Sekine, A., Sugiura, T., Shigeta, A., Nishiyama, A., Yokota, H., and Shimizu, N. (2024). Artificial intelligence model for predicting pulmonary arterial hypertension using chest X-ray images. BMC Pulm. Med., 24.","DOI":"10.1186\/s12890-024-02891-4"},{"key":"ref_19","doi-asserted-by":"crossref","first-page":"746","DOI":"10.1016\/j.csbj.2024.11.031","article-title":"A pulmonary hypertension targeted algorithm to improve referral to right heart catheterization: A machine learning approach","volume":"24","author":"Argiento","year":"2024","journal-title":"Comput Struct. Biotechnol. J."},{"key":"ref_20","doi-asserted-by":"crossref","first-page":"133457","DOI":"10.1016\/j.ijcard.2025.133457","article-title":"Machine learning-based haemodynamics quantitative assessment of pulmonary circulation using computed tomographic pulmonary angiography","volume":"437","author":"Xie","year":"2025","journal-title":"Int. J. Cardiol."},{"key":"ref_21","doi-asserted-by":"crossref","first-page":"2478","DOI":"10.1056\/NEJMoa1313600","article-title":"Cryptogenic stroke and underlying atrial fibrillation","volume":"370","author":"Sanna","year":"2014","journal-title":"N. Engl. J. Med."},{"key":"ref_22","doi-asserted-by":"crossref","first-page":"1498","DOI":"10.1016\/S0140-6736(21)01637-8","article-title":"Clinical outcomes in systematic screening for atrial fibrillation (STROKESTOP)","volume":"398","author":"Svennberg","year":"2021","journal-title":"Lancet"},{"key":"ref_23","doi-asserted-by":"crossref","first-page":"388","DOI":"10.1001\/jama.2017.19163","article-title":"Preferred Reporting Items for a Systematic Review and Meta-analysis of Diagnostic Test Accuracy Studies: The PRISMA-DTA Statement","volume":"319","author":"McInnes","year":"2018","journal-title":"JAMA"}],"container-title":["Diagnostics"],"original-title":[],"language":"en","link":[{"URL":"https:\/\/www.mdpi.com\/2075-4418\/16\/19\/3106\/pdf","content-type":"unspecified","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2026,9,24]],"date-time":"2026-09-24T16:40:41Z","timestamp":1790268041000},"score":1,"resource":{"primary":{"URL":"https:\/\/www.mdpi.com\/2075-4418\/16\/19\/3106"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2026,9,24]]},"references-count":23,"journal-issue":{"issue":"19","published-online":{"date-parts":[[2026,10]]}},"alternative-id":["diagnostics16193106"],"URL":"https:\/\/doi.org\/10.3390\/diagnostics16193106","relation":{},"ISSN":["2075-4418"],"issn-type":[{"value":"2075-4418","type":"electronic"}],"subject":[],"published":{"date-parts":[[2026,9,24]]}}}