Reviews
Vol. 2 No. 4 (2023)
Artificial intelligence and machine learning in hemostasis and thrombosis
Publisher's note
All claims expressed in this article are solely those of the authors and do not necessarily represent those of their affiliated organizations, or those of the publisher, the editors and the reviewers. Any product that may be evaluated in this article or claim that may be made by its manufacturer is not guaranteed or endorsed by the publisher.
All claims expressed in this article are solely those of the authors and do not necessarily represent those of their affiliated organizations, or those of the publisher, the editors and the reviewers. Any product that may be evaluated in this article or claim that may be made by its manufacturer is not guaranteed or endorsed by the publisher.
Received: 21 December 2023
Accepted: 28 December 2023
Accepted: 28 December 2023
2568
Views
529
Downloads
Authors
Artificial intelligence (AI) is rapidly becoming more important in our daily lives, and it’s beginning to be used in life sciences and in healthcare. AI and machine learning (ML) models are just starting to be applied in the field of hemostasis and thrombosis, but there are already many examples of how they can be useful in basic research/pathophysiology, laboratory diagnostics, and clinical settings. This review wants to shortly explain how AI works, what have been its uses in hemostasis and thrombosis so far and what are possible future developments. Besides the great potential advantages of a correct application of AI to the field of hemostasis and thrombosis, possible risks of inaccurate or deliberately mischievous use of it must be carefully considered. A close monitoring of AI employment in healthcare and research will have to be applied over the next years, but it is expected that the appropriate employment of this new revolutionary technology will bring great advances to the medical field, including to the hemostasis and thrombosis area. The current review, addressed to non-experts in the field, aims to go through the applications of AI in the field of hemostasis and thrombosis that have been explored so far and to examine its advantages, drawbacks and future perspectives.
Downloads
Download data is not yet available.
Citations
Rashidi HH, Bowkers KA, Reyes Gil M. Machine learning in the coagulation and hemostastis arena: an overview and evaluation of methods, review of literature, and future directions. J Thromb Haemost 2023;21:728-43. DOI: https://doi.org/10.1016/j.jtha.2022.12.019
Yala A, Mikhael PG, Lehman C, et al. Optimizing risk-based breast cancer screening policies with reinforcement learning. Nat Med 2022;28:136-43. DOI: https://doi.org/10.1038/s41591-021-01599-w
Peréz-Sanchéz L, Patiño-Trives AM, Aguirre-Zamorano MA, et al. Characterization of antiphospholipid syndrome atherothrombotic risk by unsupervised integrated transcriptomic analyses. Arterioscler Thromb Vasc Biol 2021;41:865-77. DOI: https://doi.org/10.1161/ATVBAHA.120.315346
Kempster C, Butler G, Kuznecova E, et al. Fully automated platelet differential contrast image analysis via deep learning. Sci Rep 2022;22:4614. DOI: https://doi.org/10.1038/s41598-022-08613-2
Bostani A, Mirzaeibonekhater H, Najafi H, et al. MLP-RL-CRD: diagnosis of cardiovascular risk in athletes using a reinforcement learning-based multilayer perceptron. Physiol Meas 2023;44. DOI: https://doi.org/10.1088/1361-6579/ad1459
Rösler W, Altenbuchinger M, Baeßler B, et al. An overview and a roadmap for artificial intelligence in hematology and oncology. J Cancer Res Clin Oncol 2023;149:7997-8006. DOI: https://doi.org/10.1007/s00432-023-04667-5
Singh D, Singh B. Investigating the impact of data normalization on classification performance. Appl Soft Comput J 2020;97:105524. DOI: https://doi.org/10.1016/j.asoc.2019.105524
Zhou Y, Yasumoto A, Huang CJ, et al. Intelligent classification of platelet aggregates by agonist type. Elife 2020;9:e52938. DOI: https://doi.org/10.7554/eLife.52938
Veninga A, Baaten CCFMJ, De Simone I, et al. Effects of platelet agonists and priming on the formation of platelet populations. Thromb Haemost 2022;122:726-38. DOI: https://doi.org/10.1055/s-0041-1735972
Wang P, Sheriff J, Zhang P, et al. A multiscale model for shear-mediated platelet adhesion dynamics: correlating in silico with in vitro results. Ann Biomed Eng 2023;51:1094-97. DOI: https://doi.org/10.1007/s10439-023-03193-2
Fang K, Dong Z, Chen X, et al. Using machine learning to identify clotted specimens in coagulation testing. Clin Chem Lab Med 2021;59:1289-97. DOI: https://doi.org/10.1515/cclm-2021-0081
Yoon GJ, Heo JN, Kim M, et al. Machine learning-based diagnosis for disseminated intravascular coagulation (DIC): development, external validation, and comparison to scoring systems. PLoS One 2018;13:e0195861. DOI: https://doi.org/10.1371/journal.pone.0195861
Marcucci R, Berteotti M, Gori AM, et al. Heparin induced thrombocytopenia: position paper from the Italian Society on Thrombosis and Haemostasis (SISET). Blood Transfus 2021;19:14-23.
Nilius H, Cuker A, Haug S, et al. A machine learning model for reducing misdiagnosis in heparin-induced thrombocytopenia: a prospective, multicentre, observational study. EClinical Medicine 2022;55:101745. DOI: https://doi.org/10.1016/j.eclinm.2022.101745
Bury L, Gresele P. The amazing genetic complexity of anucleated platelets. Bleeding, Thrombosis and Vascular Biology 2022;1:33. DOI: https://doi.org/10.4081/btvb.2022.33
Antunes-Ferreira M, D'Ambrosi S, Arkani M, et al. Tumor-educated platelet blood tests for non small cell lung cancer detection and management. Sci Rep 2023;13:9359. DOI: https://doi.org/10.1038/s41598-023-35818-w
De Girolamo G, Sarti L, Cecoli S, et al. Safety and efficacy of treatment with vitamin K antagonists in patients managed in a network of anticoagulant services or a routine general care. Bleeding, Thrombosis and Vascular Biology 2022;1:9. DOI: https://doi.org/10.4081/btvb.2022.9
Goto S, Pieper KS, Bassand JP, et al. New artificial intelligence prediction model using serial prothrombin time international normalized ratio measurements in atrial fibrillation patients on Vitamin-K antagonists: GARFIELD-AF. Eur Heart J Cardiovasc Pharmacother 2020;6:301-9. DOI: https://doi.org/10.1093/ehjcvp/pvz076
Labovitz DL, Shafner L, Reyes Gil M, et al. Using artificial intelligence to reduce the risk of nonadherence in patients on anticoagulation therapy. Stroke 2017;48:1416-9. DOI: https://doi.org/10.1161/STROKEAHA.116.016281
Lee H, Kim HJ, Chang HW, et al. Development of a system to support warfarin dose decisions using deep neural networks. Sci Rep 2021;11:14745. DOI: https://doi.org/10.1038/s41598-021-94305-2
Mora D, Nieto JA, Mateo J, et al; RIETE Investigators. Machine learning to predict outcomes in patients with acute pulmonary embolism who prematurely discontinued anticoagulant therapy. Thromb Haemost 2022;122:570-7. DOI: https://doi.org/10.1055/a-1525-7220
Nafee T, Gibson CM, Travis R, et al. Machine learning to predict venous thrombosis in acutely ill medical patients. Res Pract Thromb Haemost 2020;4:230-7. DOI: https://doi.org/10.1002/rth2.12292
Wang Q, Yuan L, Ding X, Zhou Z. Prediction and diagnosis of venous thromboembolism using artificial intelligence approaches: a systematic review and meta-analysis. Clin AppI Thromb Haemost 2021;27:1. DOI: https://doi.org/10.1177/10760296211021162
Jabbour S, Fouhey D, Shapard S, et al. Measuring the impact of AI in the diagnosis of hospitalized patients: a randomized clinical vignette survey study. JAMA 2023;330:2275-84. DOI: https://doi.org/10.1001/jama.2023.22295
Khera R, Simon MA, Ross JS. Automation bias and assistive AI: risk of harm from AI driven clinical decision support. JAMA 2023;220:2255-7. DOI: https://doi.org/10.1001/jama.2023.22557
Dauerman HL, Turco JV, Fuster V. Artificial intelligence, Bob Dylan, and cardiovascular scholarship. J Am Coll Cardiol 2023;82:961-3. DOI: https://doi.org/10.1016/j.jacc.2023.07.006
Menz BD, Modi ND, Sorich MJ, Hopkins AM. Health disinformation use case highlighting the urgent need for artificial intelligence vigilance. JAMA Intern Med 2024;184:9296. DOI: https://doi.org/10.1001/jamainternmed.2023.5947
Sahni NR, Carrus B. Artificial intelligence in U.S. healthcare delivery. N Engl J Med 2023;389:348-58. DOI: https://doi.org/10.1056/NEJMra2204673
Bodini M, Rivolta MW, Sassi R. Opening the black box: interpretability of machine learning algorithms in electrocardiology. Phil Trans A Math Phys Eng Sci 2021;379:20202053. DOI: https://doi.org/10.1098/rsta.2020.0253
Petch J, Di S, Nelson W. Opening the black box: the promise and limitations of explainable machine learning in cardiology. Can J Cardiol 2022;38:204-13. DOI: https://doi.org/10.1016/j.cjca.2021.09.004
Meng J, Xing R. Inside the “black box”: embedding clinical knowledge in data-driven machine learning for heart disease diagnosis. Cardiovasc Digit Health J 2022;3:276-88. DOI: https://doi.org/10.1016/j.cvdhj.2022.10.005
Stark L. Medicine’s lessons for AI regulation. N Engl J Med 2023;389:2213-5. DOI: https://doi.org/10.1056/NEJMp2309872
Rajpurkar P, Chen E, Banerjee O, Topol EJ. AI in health and medicine. Nat Med 2022;28:31-8. DOI: https://doi.org/10.1038/s41591-021-01614-0
How to Cite
1.
Gresele P. Artificial intelligence and machine learning in hemostasis and thrombosis. Bleeding Thromb Vascul Biol [Internet]. 2023 Dec. 30 [cited 2025 Sep. 18];2(4). Available from: https://www.btvb.org/btvb/article/view/105
Copyright (c) 2023 The Author(s)
This work is licensed under a Creative Commons Attribution-NonCommercial 4.0 International License.
PAGEPress has chosen to apply the Creative Commons Attribution NonCommercial 4.0 International License (CC BY-NC 4.0) to all manuscripts to be published.
Similar Articles
- Rosina Albisinni, Tommaso Marrazzo, Arta Karruli, Sabrina Manduca, Giuseppe Nobile, Nicola Galdieri, Marisa De Feo, Successful treatment of aortic arch mural thrombosis with low-dose, ultra-slow-flow thrombolysis: a case report and literature review , Bleeding, Thrombosis and Vascular Biology: Vol. 1 No. 3 (2022)
- Kartik Akkihal, Thomas Varkey, James Kelbert, The use of creatine and the development of deep vein thrombosis. A scoping review , Bleeding, Thrombosis and Vascular Biology: Vol. 3 No. 3 (2024)
- Robert Parambi, Nicole Ziliotto, Francesco Bernardi , Marcello Baroni , Richard W Browne , Dejan Jakimovski, Bianca Weinstock-Guttman, Robert Zivadinov, Murali Ramanathan, Crosstalk between hemostasis inhibitors and cholesterol biomarkers in multiple sclerosis , Bleeding, Thrombosis and Vascular Biology: Vol. 1 No. 3 (2022)
- Maria J. Fernandez Turizo, Rushad Patell, Jeffrey I. Zwicker, Identifying novel biomarkers using proteomics to predict cancer-associated thrombosis , Bleeding, Thrombosis and Vascular Biology: Vol. 3 No. s1 (2024)
- Matteo Guarascio, Gerardo Nicola Pititto, Alessia Abenante, Marco Paolo Donadini, Distal deep vein thrombosis: is there a way out of this dark forest? , Bleeding, Thrombosis and Vascular Biology: Vol. 3 No. 2 (2024)
- Niccolò Bitto, Vincenzo La Mura, Anna Ludovica Fracanzani, Armando Tripodi, Is non-alcoholic fatty liver disease a prothrombotic risk factor? , Bleeding, Thrombosis and Vascular Biology: Vol. 2 No. 2 (2023)
- Gualtiero Palareti, Paolo Prandoni, Cristina Legnani, Emilia Antonucci, Serena Zorzi, Alberto Tosetto, Lorenza Bertù, Sophie Testa, Vittorio Pengo, Walter Ageno, Ida Martinelli, Benilde Cosmi, Eugenio Bucherini, Daniela Poli, Rationale and design of a study on D-dimer use to stratify patients after a first unprovoked venous thromboembolism for their risk of recurrence: extended low-dose Apixaban given only to patients with positive D-dimer results , Bleeding, Thrombosis and Vascular Biology: Vol. 1 No. 1 (2022)
- Mosaad Almegren, Clinical and radiological characteristics of cerebral vein thrombosis: a retrospective study , Bleeding, Thrombosis and Vascular Biology: Vol. 2 No. 3 (2023)
- Mehrie H. Patel, Alok A. Khorana, New drugs, old problems: immune checkpoint inhibitors and cancer-associated thrombosis , Bleeding, Thrombosis and Vascular Biology: Vol. 3 No. s1 (2024)
- Cristina Legnani, Michela Cini, Sophie Testa, Alberto Tosetto, Claudia Dellanoce, Stefania Bellesso, Giuseppe Carli, Ilaria Nichele, Laura Lissandrini, Serena Zorzi, Emilia Antonucci, Gualtiero Palareti, Evaluation of Coaguchek® Pro II coagulation testing device performance to assess direct oral anticoagulant action. The DOAC-CHECK study , Bleeding, Thrombosis and Vascular Biology: Vol. 1 No. 3 (2022)
You may also start an advanced similarity search for this article.
