Exploration of Cardiology

From: How algorithms are transforming the diagnosis of ischemic heart disease—state of the art

Table 3. AI systems and platforms in clinical practice.

System/Platform	Technology/Method	AI performance	Clinical validation	Implementation time	Regulatory status	References
AVVIGO+	Automated HD-IVUS assessment with real-time lumen/vessel/plaque morphology analysis	Accuracy: 85% (lumen boundaries), 97% (stent area)	Validated against expert manual measurements in multicenter studies	Real-time (< 1 min per pullback)	FDA cleared, CE marked	[22]
Ultreon OCT	AI-powered lumen segmentation with automated stent sizing and landing zone recommendations	High concordance with expert analysis; automated workflow guidance	Clinical studies demonstrate improved procedural efficiency and standardization	Immediate (during procedure)	FDA cleared, CE marked	[39]
PROTEUS	AI-assisted stress echocardiography interpretation with automated wall motion scoring	Non-inferior to standard practice (primary endpoint met); AUC 0.87–0.91	Randomized controlled trial; improved inter-reader consistency, especially for less-experienced operators	< 5 min additional analysis time	Under clinical investigation	[40]

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AI: artificial intelligence; AUC: area under the curve; CE: Conformité Européenne; FDA: Food and Drug Administration; HD-IVUS: high-definition intravascular ultrasound; iFR: instantaneous wave‑free ratio; OCT: optical coherence tomography.

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MT: Conceptualization, Writing—original draft, Writing—review & editing. The author read and approved the submitted version.

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References

Roth GA, Mensah GA, Johnson CO, Addolorato G, Ammirati E, Baddour LM, et al.; GBD-NHLBI-JACC Global Burden of Cardiovascular Diseases Writing Group. Global Burden of Cardiovascular Diseases and Risk Factors, 1990-2019: Update From the GBD 2019 Study. J Am Coll Cardiol. 2020;76:2982–3021. [DOI] [PubMed] [PMC]

Vrints C, Andreotti F, Koskinas KC, Rossello X, Adamo M, Ainslie J, et al.; ESC Scientific Document Group. 2024 ESC Guidelines for the management of chronic coronary syndromes. Eur Heart J. 2024;45:3415–537. [DOI] [PubMed]

Gulati M, Levy PD, Mukherjee D, Amsterdam E, Bhatt DL, Birtcher KK, et al. 2021 AHA/ACC/ASE/CHEST/SAEM/SCCT/SCMR Guideline for the Evaluation and Diagnosis of Chest Pain: A Report of the American College of Cardiology/American Heart Association Joint Committee on Clinical Practice Guidelines. Circulation. 2021;144:e368–454. [DOI] [PubMed]

Krittanawong C, Zhang H, Wang Z, Aydar M, Kitai T. Artificial Intelligence in Precision Cardiovascular Medicine. J Am Coll Cardiol. 2017;69:2657–64. [DOI] [PubMed]

Attia ZI, Friedman PA, Noseworthy PA, Lopez-Jimenez F, Ladewig DJ, Satam G, et al. Age and Sex Estimation Using Artificial Intelligence From Standard 12-Lead ECGs. Circ Arrhythm Electrophysiol. 2019;12:e007284. [DOI] [PubMed] [PMC]

Yao X, Rushlow DR, Inselman JW, McCoy RG, Thacher TD, Behnken EM, et al. Artificial intelligence-enabled electrocardiograms for identification of patients with low ejection fraction: a pragmatic, randomized clinical trial. Nat Med. 2021;27:815–9. [DOI] [PubMed]

Lin A, Manral N, McElhinney P, Killekar A, Matsumoto H, Kwiecinski J, et al. Deep learning-enabled coronary CT angiography for plaque and stenosis quantification and cardiac risk prediction: an international multicentre study. Lancet Digit Health. 2022;4:e256–65. [DOI] [PubMed] [PMC]

Commandeur F, Goeller M, Betancur J, Cadet S, Doris M, Chen X, et al. Deep Learning for Quantification of Epicardial and Thoracic Adipose Tissue From Non-Contrast CT. IEEE Trans Med Imaging. 2018;37:1835–46. [DOI] [PubMed] [PMC]

Coenen A, Kim YH, Kruk M, Tesche C, De Geer J, Kurata A, et al. Diagnostic Accuracy of a Machine-Learning Approach to Coronary Computed Tomographic Angiography-Based Fractional Flow Reserve: Result From the MACHINE Consortium. Circ Cardiovasc Imaging. 2018;11:e007217. [DOI] [PubMed]

10.

Fahmy AS, El-Rewaidy H, Nezafat M, Nakamori S, Nezafat R. Automated analysis of cardiovascular magnetic resonance myocardial native T₁ mapping images using fully convolutional neural networks. J Cardiovasc Magn Reson. 2019;21:7. [DOI] [PubMed] [PMC]

11.

Bai W, Sinclair M, Tarroni G, Oktay O, Rajchl M, Vaillant G, et al. Automated cardiovascular magnetic resonance image analysis with fully convolutional networks. J Cardiovasc Magn Reson. 2018;20:65. [DOI] [PubMed] [PMC]

12.

Bezerra HG, Costa MA, Guagliumi G, Rollins AM, Simon DI. Intracoronary optical coherence tomography: a comprehensive review clinical and research applications. JACC Cardiovasc Interv. 2009;2:1035–46. [DOI] [PubMed] [PMC]

13.

Gessert N, Lutz M, Heyder M, Latus S, Leistner DM, Abdelwahed YS, et al. Automatic Plaque Detection in IVOCT Pullbacks Using Convolutional Neural Networks. IEEE Trans Med Imaging. 2019;38:426–34. [DOI] [PubMed]

14.

Cha JJ, Son TD, Ha J, Kim JS, Hong SJ, Ahn CM, et al. Optical coherence tomography-based machine learning for predicting fractional flow reserve in intermediate coronary stenosis: a feasibility study. Sci Rep. 2020;10:20421. [DOI] [PubMed] [PMC]

15.

Niroda K, Drudi C, Byers J, Johnson J, Cozzi G, Celi LA, et al. Artificial Intelligence in Cardiology: Insights From a Multidisciplinary Perspective. J Soc Cardiovasc Angiogr Interv. 2025;4:102612. [DOI] [PubMed] [PMC]

16.

Lopez-Jimenez F, Attia Z, Arruda-Olson AM, Carter R, Chareonthaitawee P, Jouni H, et al. Artificial Intelligence in Cardiology: Present and Future. Mayo Clin Proc. 2020;95:1015–39. [DOI] [PubMed]

17.

Awasthi S, Sachdeva N, Gupta Y, Anto AG, Asfahan S, Abbou R, et al. Identification and risk stratification of coronary disease by artificial intelligence-enabled ECG. EClinicalMedicine. 2023;65:102259. [DOI] [PubMed] [PMC]

18.

Howard JP, Vasudevan N, Sarkar S, Landman S, Koehler J, Keene D. Dynamic risk stratification of worsening heart failure using a deep learning-enabled implanted ambulatory single-lead electrocardiogram. Eur Heart J Digit Health. 2024;5:435–43. [DOI] [PubMed] [PMC]

19.

Yao X, McCoy RG, Friedman PA, Shah ND, Barry BA, Behnken EM, et al. ECG AI-Guided Screening for Low Ejection Fraction (EAGLE): Rationale and design of a pragmatic cluster randomized trial. Am Heart J. 2020;219:31–6. [DOI] [PubMed]

20.

Han D, Liu J, Sun Z, Cui Y, He Y, Yang Z. Deep learning analysis in coronary computed tomographic angiography imaging for the assessment of patients with coronary artery stenosis. Comput Methods Programs Biomed. 2020;196:105651. [DOI] [PubMed]

21.

Jin X, Li Y, Yan F, Liu Y, Zhang X, Li T, et al. Automatic coronary plaque detection, classification, and stenosis grading using deep learning and radiomics on computed tomography angiography images: a multi-center multi-vendor study. Eur Radiol. 2022;32:5276–86. [DOI] [PubMed]

22.

Matsumura M, Mintz GS, Dohi T, Li W, Shang A, Fall K, et al. Accuracy of IVUS-Based Machine Learning Segmentation Assessment of Coronary Artery Dimensions and Balloon Sizing. JACC Adv. 2023;2:100564. [DOI] [PubMed] [PMC]

23.

Pinna A, Boi A, Mannelli L, Balestrieri A, Sanfilippo R, Suri J, et al. Machine Learning for Coronary Plaque Characterization: A Multimodal Review of OCT, IVUS, and CCTA. Diagnostics (Basel). 2025;15:1822. [DOI] [PubMed] [PMC]

24.

Subhan S, Malik J, Haq AU, Qadeer MS, Zaidi SMJ, Orooj F, et al. Role of Artificial Intelligence and Machine Learning in Interventional Cardiology. Curr Probl Cardiol. 2023;48:101698. [DOI] [PubMed]

25.

Moon JH, Lee DY, Cha WC, Chung MJ, Lee KS, Cho BH, et al. Automatic stenosis recognition from coronary angiography using convolutional neural networks. Comput Methods Programs Biomed. 2021;198:105819. [DOI] [PubMed]

26.

Gupta MD, Kunal S, Girish MP, Gupta A, Yadav R. Artificial intelligence in cardiology: The past, present and future. Indian Heart J. 2022;74:265–9. [DOI] [PubMed] [PMC]

27.

Kang SJ, Mintz GS, Akasaka T, Park DW, Lee JY, Kim WJ, et al. Optical coherence tomographic analysis of in-stent neoatherosclerosis after drug-eluting stent implantation. Circulation. 2011;123:2954–63. [DOI] [PubMed]

28.

Gould KL, Johnson NP, Bateman TM, Beanlands RS, Bengel FM, Bober R, et al. Anatomic versus physiologic assessment of coronary artery disease. Role of coronary flow reserve, fractional flow reserve, and positron emission tomography imaging in revascularization decision-making. J Am Coll Cardiol. 2013;62:1639–53. [DOI] [PubMed]

29.

Ziadi MC, Dekemp RA, Williams KA, Guo A, Chow BJ, Renaud JM, et al. Impaired myocardial flow reserve on rubidium-82 positron emission tomography imaging predicts adverse outcomes in patients assessed for myocardial ischemia. J Am Coll Cardiol. 2011;58:740–8. [DOI] [PubMed]

30.

Johnson NP, Kirkeeide RL, Gould KL. Is discordance of coronary flow reserve and fractional flow reserve due to methodology or clinically relevant coronary pathophysiology? JACC Cardiovasc Imaging. 2012;5:193–202. [DOI] [PubMed]

31.

Sengupta PP, Huang YM, Bansal M, Ashrafi A, Fisher M, Shameer K, et al. Cognitive Machine-Learning Algorithm for Cardiac Imaging: A Pilot Study for Differentiating Constrictive Pericarditis From Restrictive Cardiomyopathy. Circ Cardiovasc Imaging. 2016;9:e004330. [DOI] [PubMed] [PMC]

32.

Betancur J, Commandeur F, Motlagh M, Sharir T, Einstein AJ, Bokhari S, et al. Deep Learning for Prediction of Obstructive Disease From Fast Myocardial Perfusion SPECT: A Multicenter Study. JACC Cardiovasc Imaging. 2018;11:1654–63. [DOI] [PubMed] [PMC]

33.

Miller RJH, Kuronuma K, Singh A, Otaki Y, Hayes S, Chareonthaitawee P, et al. Explainable Deep Learning Improves Physician Interpretation of Myocardial Perfusion Imaging. J Nucl Med. 2022;63:1768–74. [DOI] [PubMed] [PMC]

34.

Evertz R, Lange T, Jan Backhaus S, Schulz A, Beuthner BE, Topci R, et al. ARTIFICIAL INTELLIGENCE ENABLED FULLY AUTOMATED CMR FUNCTION QUANTIFICATION FOR OPTIMIZED RISK STRATIFICATION IN PATIENTS UNDERGOING TRANSCATHETER AORTIC VALVE REPLACEMENT. J Am Coll Cardiol. 2022;79:1709. [DOI]

35.

Backhaus SJ, Staab W, Steinmetz M, Ritter CO, Lotz J, Hasenfuß G, et al. Fully automated quantification of biventricular volumes and function in cardiovascular magnetic resonance: applicability to clinical routine settings. J Cardiovasc Magn Reson. 2019;21:24. [DOI] [PubMed] [PMC]

36.

Chinnaiyan KM, Safian RD, Gallagher ML, George J, Dixon SR, Bilolikar AN, et al. Clinical Use of CT-Derived Fractional Flow Reserve in the Emergency Department. JACC Cardiovasc Imaging. 2020;13:452–61. [DOI] [PubMed]

37.

Kurata A, Fukuyama N, Hirai K, Kawaguchi N, Tanabe Y, Okayama H, et al. On-Site Computed Tomography-Derived Fractional Flow Reserve Using a Machine-Learning Algorithm - Clinical Effectiveness in a Retrospective Multicenter Cohort. Circ J. 2019;83:1563–71. [DOI] [PubMed]

38.

Ahmad T, Pencina MJ, Schulte PJ, O’Brien E, Whellan DJ, Piña IL, et al. Clinical implications of chronic heart failure phenotypes defined by cluster analysis. J Am Coll Cardiol. 2014;64:1765–74. [DOI] [PubMed] [PMC]

39.

Sibbald M, Mitchell HR, Buccola J, Pinilla-Echeverri N. Impact of Artificial Intelligence-Enhanced Optical Coherence Tomography Software on Percutaneous Coronary Intervention Decisions. J Soc Cardiovasc Angiogr Interv. 2025;4:102438. [DOI] [PubMed] [PMC]

40.

Woodward G, Bajre M, Bhattacharyya S, Breen M, Chiocchia V, Dawes H, et al. PROTEUS Study: A Prospective Randomized Controlled Trial Evaluating the Use of Artificial Intelligence in Stress Echocardiography. Am Heart J. 2023;263:123–32. [DOI] [PubMed]

41.

Vaswani A, Shazeer N, Parmar N, Uszkoreit J, Jones L, Gomez AN, et al. Attention Is All You Need. ArXiv [Preprint]. 2017 [cited 2025 Jun 26]. Available from: https://arxiv.org/abs/1706.03762

42.

Goodfellow IJ, Pouget-Abadie J, Mirza M, Xu B, Warde-Farley D, Ozair S, et al. Generative Adversarial Networks. ArXiv [Preprint]. 2014 [cited 2025 Jun 26]. Available from: https://doi.org/10.48550/arXiv.1406.2661

43.

Li T, Sahu AK, Talwalkar A, Smith V. Federated Learning: Challenges, Methods, and Future Directions. ArXiv [Preprint]. 2019 [cited 2025 Jun 26]. Available from: https://doi.org/10.48550/arXiv.1908.07873

44.

Shi W, Cao J, Zhang Q, Li Y, Xu L. Edge Computing: Vision and Challenges. IEEE Internet Things J. 2016;3:637–46. [DOI]

45.

Li X, Zhang H, Yue J, Yin L, Li W, Ding G, et al. A multi-task deep learning approach for real-time view classification and quality assessment of echocardiographic images. Sci Rep. 2024;14:20484. [DOI] [PubMed] [PMC]

46.

Henglin M, Stein G, Hushcha PV, Snoek J, Wiltschko AB, Cheng S. Machine Learning Approaches in Cardiovascular Imaging. Circ Cardiovasc Imaging. 2017;10:e005614. [DOI] [PubMed] [PMC]

47.

Liu X, Faes L, Kale AU, Wagner SK, Fu DJ, Bruynseels A, et al. A comparison of deep learning performance against health-care professionals in detecting diseases from medical imaging: a systematic review and meta-analysis. Lancet Digit Health. 2019;1:e271–97. [DOI] [PubMed]

48.

Topol EJ. High-performance medicine: the convergence of human and artificial intelligence. Nat Med. 2019;25:44–56. [DOI] [PubMed]

49.

U.S. Food and Drug Administration. Software as a Medical Device (SAMD): Clinical Evaluation - Guidance for Industry and Food and Drug Administration Staff [Internet]. Silver Spring (MD): FDA; [cited 2025 Jun 26]. Available from: https://www.fda.gov/regulatory-information/search-fda-guidance-documents/software-medical-device-samd-clinical-evaluation

50.

Muehlematter UJ, Daniore P, Vokinger KN. Approval of artificial intelligence and machine learning-based medical devices in the USA and Europe (2015-20): a comparative analysis. Lancet Digit Health. 2021;3:e195–203. [DOI] [PubMed]

51.

Rajkomar A, Dean J, Kohane I. Machine Learning in Medicine. N Engl J Med. 2019;380:1347–58. [DOI] [PubMed]

52.

Dey D, Slomka PJ, Leeson P, Comaniciu D, Shrestha S, Sengupta PP, et al. Artificial Intelligence in Cardiovascular Imaging: JACC State-of-the-Art Review. J Am Coll Cardiol. 2019;73:1317–35. [DOI]

53.

van Smeden M, Naaktgeboren CA, Reitsma JB, Moons KG, de Groot JA. Latent class models in diagnostic studies when there is no reference standard--a systematic review. Am J Epidemiol. 2014;179:423–31. [DOI] [PubMed]

54.

Narula S, Shameer K, Salem Omar AM, Dudley JT, Sengupta PP. Machine-Learning Algorithms to Automate Morphological and Functional Assessments in 2D Echocardiography. J Am Coll Cardiol. 2016;68:2287–95. [DOI] [PubMed]

55.

Åkesson J, Ostenfeld E, Carlsson M, Arheden H, Heiberg E. Deep learning can yield clinically useful right ventricular segmentations faster than fully manual analysis. Sci Rep. 2023;13:1216. [DOI] [PubMed] [PMC]

56.

Sengupta PP, Dey D, Davies RH, Duchateau N, Yanamala N. Challenges for augmenting intelligence in cardiac imaging. Lancet Digit Health. 2024;6:e739–48. [DOI] [PubMed]

57.

Ben Ali W, Pesaranghader A, Avram R, Overtchouk P, Perrin N, Laffite S, et al. Implementing Machine Learning in Interventional Cardiology: The Benefits Are Worth the Trouble. Front Cardiovasc Med. 2021;8:711401. [DOI] [PubMed] [PMC]