Bohr A, Memarzadeh K. Artificial Intelligence in Healthcare. Academic; 2020.

Aung YYM, Wong DCS, Ting DSW. The promise of artificial intelligence: a review of the opportunities and challenges of artificial intelligence in healthcare. Br Med Bull. 2021;139:4–15. https://doi.org/10.1093/bmb/ldab016.

Article  PubMed  Google Scholar 

Alowais SA, Alghamdi SS, Alsuhebany N, Alqahtani T, Alshaya AI, Almohareb SN, et al. Revolutionizing healthcare: the role of artificial intelligence in clinical practice. BMC Med Educ. 2023;23:689. https://doi.org/10.1186/s12909-023-04698-z.

Article  PubMed  PubMed Central  Google Scholar 

Humbert M, Kovacs G, Hoeper MM, Badagliacca R, Berger RMF, Brida M, et al. 2022 ESC/ERS Guidelines for the diagnosis and treatment of pulmonary hypertension: Developed by the task force for the diagnosis and treatment of pulmonary hypertension of the European Society of Cardiology (ESC) and the European Respiratory Society (ERS). Endorsed by the International Society for Heart and Lung Transplantation (ISHLT) and the European Reference Network on rare respiratory diseases (ERN-LUNG). Eur Heart J. 2022;43:3618–731. https://doi.org/10.1093/eurheartj/ehac237.

Article  CAS  PubMed  Google Scholar 

Simonneau G, Montani D, Celermajer DS, Denton CP, Gatzoulis MA, Krowka M, et al. Haemodynamic definitions and updated clinical classification of pulmonary hypertension. Eur Respir J [Internet] Eur Respiratory Soc. 2019. https://doi.org/10.1183/13993003.01913-2018. [cited 2025 Feb 27];53.

Article  Google Scholar 

Kovacs G, Bartolome S, Denton CP, Gatzoulis MA, Gu S, Khanna D, et al. Definition, classification and diagnosis of pulmonary hypertension. Eur Respir J [Internet] Eur Respiratory Soc. 2024. https://doi.org/10.1183/13993003.01324-2024. [cited 2024 Oct 27].

Article  Google Scholar 

Gibbs JSR. Making a diagnosis in PAH. Eur Respir Rev Eur Respiratory Soc. 2007;16:8–12. https://doi.org/10.1183/09059180.00010203.

Article  Google Scholar 

DuBrock HM, Germack HD, Gauthier-Loiselle M, Linder J, Satija A, Manceur AM, et al. Economic Burden of Delayed Diagnosis in Patients with Pulmonary Arterial Hypertension (PAH). PharmacoEconomics Open. 2023;8:133–46. https://doi.org/10.1007/s41669-023-00453-8.

Article  PubMed  PubMed Central  Google Scholar 

Becerra-Muñoz VM, Gómez Sáenz JT, Escribano Subías P. The importance of data in Pulmonary Arterial Hypertension: from international registries to Machine Learning. Med Clin (Barc). 2024;162:591–8. https://doi.org/10.1016/j.medcli.2023.12.010.

Article  PubMed  Google Scholar 

Ong M, Klann JG, Lin KJ, Maron BA, Murphy SN, Natter MD et al. Claims-based algorithms for identifying patients with pulmonary hypertension: A comparison of decision rules and machine‐learning approaches. J Am Heart Assoc. Wiley; 2020;9:e016648. https://doi.org/10.1161/JAHA.120.016648

Schuler KP, Hemnes AR, Annis J, Farber-Eger E, Lowery BD, Halliday SJ, et al. An algorithm to identify cases of pulmonary arterial hypertension from the electronic medical record. Respir Res. 2022;23:138. https://doi.org/10.1186/s12931-022-02055-0.

Article  PubMed  PubMed Central  Google Scholar 

Hyde B, Paoli CJ, Panjabi S, Bettencourt KC, Bell Lynum KS, Selej M. A claims-based, machine‐learning algorithm to identify patients with pulmonary arterial hypertension. Pulm Circ. 2023;13:e12237. https://doi.org/10.1002/pul2.12237.

Article  CAS  PubMed  PubMed Central  Google Scholar 

Kogan E, Didden E-M, Lee E, Nnewihe A, Stamatiadis D, Mataraso S, et al. A machine learning approach to identifying patients with pulmonary hypertension using real-world electronic health records. Int J Cardiol. 2023;374:95–9. https://doi.org/10.1016/j.ijcard.2022.12.016.

Article  PubMed  Google Scholar 

Kiely DG, Doyle O, Drage E, Jenner H, Salvatelli V, Daniels FA, et al. Utilising artificial intelligence to determine patients at risk of a rare disease: idiopathic pulmonary arterial hypertension. Pulm Circ. 2019;9:2045894019890549. https://doi.org/10.1177/2045894019890549.

Article  PubMed  PubMed Central  Google Scholar 

Bergemann R, Allsopp J, Jenner H, Daniels FA, Drage E, Samyshkin Y et al. High levels of healthcare utilization prior to diagnosis in idiopathic pulmonary arterial hypertension support the feasibility of an early diagnosis algorithm: the SPHInX project. [cited 2025 Jan 2]; https://doi.org/10.1177/2045894018798613

Gauthier-Loiselle M, Tsang Y, Lefebvre P, Agron P, Royer J, Bell Lynum KS, et al. Development and evaluation of a predictive algorithm for unsatisfactory response among patients with pulmonary arterial hypertension using health insurance claims data. Curr Med Res Opin Taylor Francis. 2022;38:1019–30. https://doi.org/10.1080/03007995.2022.2049162.

Article  Google Scholar 

Kusunose K, Hirata Y, Tsuji T, Kotoku J, Sata M. Deep learning to predict elevated pulmonary artery pressure in patients with suspected pulmonary hypertension using standard chest X ray. Sci Rep Nat Publishing Group. 2020;10:19311. https://doi.org/10.1038/s41598-020-76359-w.

Article  CAS  Google Scholar 

Imai S, Sakao S, Nagata J, Naito A, Sekine A, Sugiura T, et al. Artificial intelligence-based model for predicting pulmonary arterial hypertension on chest x-ray images. BMC Pulm Med. 2024;24:101. https://doi.org/10.1186/s12890-024-02891-4.

Article  PubMed  PubMed Central  Google Scholar 

Han P-L, Jiang L, Cheng J-L, Shi K, Huang S, Jiang Y, et al. Artificial intelligence-assisted diagnosis of congenital heart disease and associated pulmonary arterial hypertension from chest radiographs: A multi-reader multi-case study. Eur J Radiol. 2024;171:111277. https://doi.org/10.1016/j.ejrad.2023.111277.

Article  PubMed  Google Scholar 

Abdulaal L, Maiter A, Salehi M, Sharkey M, Alnasser T, Garg P, et al. A systematic review of artificial intelligence tools for chronic pulmonary embolism on CT pulmonary angiography. Front Radiol [Internet] Front. 2024. https://doi.org/10.3389/fradi.2024.1335349. [cited 2024 Sept 10];4.

Article  Google Scholar 

Vainio T, Mäkelä T, Savolainen S, Kangasniemi M. Performance of a 3D convolutional neural network in the detection of hypoperfusion at CT pulmonary angiography in patients with chronic pulmonary embolism: a feasibility study. Eur Radiol Exp. 2021;5:45. https://doi.org/10.1186/s41747-021-00235-z.

Article  PubMed  PubMed Central  Google Scholar 

Khan M, Shah PM, Khan IA, Islam S, ul, Ahmad Z, Khan F, et al. IoMT-Enabled Computer-Aided Diagnosis of Pulmonary Embolism from Computed Tomography Scans Using Deep Learning. Sens Multidisciplinary Digit Publishing Inst. 2023;23:1471. https://doi.org/10.3390/s23031471.

Article  Google Scholar 

Ma X, Ferguson EC, Jiang X, Savitz SI, Shams S. A multitask deep learning approach for pulmonary embolism detection and identification. Sci Rep Nat Publishing Group. 2022;12:13087. https://doi.org/10.1038/s41598-022-16976-9.

Article  CAS  Google Scholar 

Bird E, Hasenstab K, Kim N, Madani M, Malhotra A, Hahn L, et al. Mapping the spatial extent of hypoperfusion in chronic thromboembolic pulmonary hypertension using multienergy CT. Radiol Cardiothorac Imaging [Internet]. Radiological Society of North America; 2023. [cited 2024 Nov 3]. https://doi.org/10.1148/ryct.220221.

Vainio T, Mäkelä T, Arkko A, Savolainen S, Kangasniemi M. Leveraging open dataset and transfer learning for accurate recognition of chronic pulmonary embolism from CT angiogram maximum intensity projection images. Eur Radiol Exp. 2023;7:33. https://doi.org/10.1186/s41747-023-00346-9.

Article  PubMed  PubMed Central  Google Scholar 

Isensee F, Jaeger PF, Kohl SAA, Petersen J, Maier-Hein KH. nnU-Net: a self-configuring method for deep learning-based biomedical image segmentation. Nat Methods Nat Publishing Group. 2021;18:203–11. https://doi.org/10.1038/s41592-020-01008-z.

Article  CAS  Google Scholar 

Dwivedi K, Sharkey M, Alabed S, Langlotz CP, Swift AJ, Bluethgen C. External validation, radiological evaluation, and development of deep learning automatic lung segmentation in contrast-enhanced chest CT. Eur Radiol. 2024;34:2727–37. https://doi.org/10.1007/s00330-023-10235-9.

Article  PubMed  Google Scholar 

Dwivedi K, Sharkey M, Delaney L, Alabed S, Rajaram S, Hill C, et al. Improving Prognostication in Pulmonary Hypertension Using AI-quantified Fibrosis and Radiologic Severity Scoring at Baseline CT. Radiol Radiological Soc North Am. 2024;310:e231718. https://doi.org/10.1148/radiol.231718.

Article  Google Scholar 

Charters PFP, Rossdale J, Brown W, Burnett TA, Komber HMEI, Thompson C, et al. Diagnostic accuracy of an automated artificial intelligence derived right ventricular to left ventricular diameter ratio tool on CT pulmonary angiography to predict pulmonary hypertension at right heart catheterisation. Clin Radiol. 2022;77:e500–8. https://doi.org/10.1016/j.crad.2022.03.009.

Article  CAS  PubMed  Google Scholar 

Ende-Verhaar YM, Cannegieter SC, Noordegraaf AV, Delcroix M, Pruszczyk P, Mairuhu ATA, et al. Incidence of chronic thromboembolic pulmonary hypertension after acute pulmonary embolism: a contemporary view of the published literature. Eur Respir J [Internet] Eur Respiratory Soc. 2017;49. https://doi.org/10.1183/13993003.01792-2016. [cited 2025 Feb 6];.

Jimenez-Del-Toro O, Dicente Cid Y, Platon A, Hachulla A-L, Lador F, Poletti P-A, et al. A lung graph model for the radiological assessment of chronic thromboembolic pulmonary hypertension in CT. Comput Biol Med. 2020;125:103962. https://doi.org/10.1016/j.compbiomed.2020.103962.

Article  CAS  PubMed  Google Scholar 

Gawlitza J, Endres S, Fries P, Graf M, Wilkens H, Stroeder J, et al. Machine learning assisted feature identification and prediction of hemodynamic endpoints using computed tomography in patients with CTEPH. Int J Cardiovasc Imaging. 2024;40:569–77. https://doi.org/10.1007/s10554-023-03026-2.

Article  PubMed  Google Scholar 

Matsunaga T, Kono A, Nishio M, Yoshii T, Matsuo H, Takahashi M, et al. Development and web deployment of prediction model for pulmonary arterial pressure in chronic thromboembolic pulmonary hypertension using machine learning. PLoS ONE. 2024;19:e0300716. https://doi.org/10.1371/journal.pone.0300716.

Article  CAS