دورية أكاديمية
Artificial intelligence to generate medical images: augmenting the cardiologist's visual clinical workflow.
| العنوان: | Artificial intelligence to generate medical images: augmenting the cardiologist's visual clinical workflow. |
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| المؤلفون: | Olender ML; Institute for Medical Engineering and Science, Massachusetts Institute of Technology, 77 Massachusetts Avenue, Cambridge, MA 02139 USA.; Department of Mechanical Engineering, Massachusetts Institute of Technology, 77 Massachusetts Avenue, Cambridge, MA 02139 USA., de la Torre Hernández JM; Department of Cardiology, Hospital Universitario Marques de Valdecilla, IDIVAL, Santander, Spain., Athanasiou LS; Institute for Medical Engineering and Science, Massachusetts Institute of Technology, 77 Massachusetts Avenue, Cambridge, MA 02139 USA., Nezami FR; Thoracic and Cardiac Surgery Division, Brigham and Women's Hospital, Harvard Medical School, Boston, MA, USA., Edelman ER; Institute for Medical Engineering and Science, Massachusetts Institute of Technology, 77 Massachusetts Avenue, Cambridge, MA 02139 USA.; Cardiovascular Division, Brigham and Women's Hospital, Harvard Medical School, Boston, MA, USA. |
| المصدر: | European heart journal. Digital health [Eur Heart J Digit Health] 2021 Jun 07; Vol. 2 (3), pp. 539-544. Date of Electronic Publication: 2021 Jun 07 (Print Publication: 2021). |
| نوع المنشور: | Journal Article |
| اللغة: | English |
| بيانات الدورية: | Publisher: Oxford University Press Country of Publication: England NLM ID: 101778323 Publication Model: eCollection Cited Medium: Internet ISSN: 2634-3916 (Electronic) Linking ISSN: 26343916 NLM ISO Abbreviation: Eur Heart J Digit Health Subsets: PubMed not MEDLINE |
| أسماء مطبوعة: | Original Publication: [Oxford] : Oxford University Press, [2020]- |
| مستخلص: | Artificial intelligence (AI) offers great promise in cardiology, and medicine broadly, for its ability to tirelessly integrate vast amounts of data. Applications in medical imaging are particularly attractive, as images are a powerful means to convey rich information and are extensively utilized in cardiology practice. Departing from other AI approaches in cardiology focused on task automation and pattern recognition, we describe a digital health platform to synthesize enhanced, yet familiar, clinical images to augment the cardiologist's visual clinical workflow. In this article, we present the framework, technical fundamentals, and functional applications of the methodology, especially as it pertains to intravascular imaging. A conditional generative adversarial network was trained with annotated images of atherosclerotic diseased arteries to generate synthetic optical coherence tomography and intravascular ultrasound images on the basis of specified plaque morphology. Systems leveraging this unique and flexible construct, whereby a pair of neural networks is competitively trained in tandem, can rapidly generate useful images. These synthetic images replicate the style, and in several ways exceed the content and function, of normally acquired images. By using this technique and employing AI in such applications, one can ameliorate challenges in image quality, interpretability, coherence, completeness, and granularity, thereby enhancing medical education and clinical decision-making. (© The Author(s) 2021. Published by Oxford University Press on behalf of the European Society of Cardiology.) |
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| فهرسة مساهمة: | Keywords: Artificial intelligence; Image generation; Interventional cardiology; Intravascular imaging; Medical imaging; Synthetic imaging |
| تواريخ الأحداث: | Date Created: 20230130 Latest Revision: 20230202 |
| رمز التحديث: | 20231215 |
| مُعرف محوري في PubMed: | PMC9707980 |
| DOI: | 10.1093/ehjdh/ztab052 |
| PMID: | 36713593 |
| قاعدة البيانات: | MEDLINE |
Full Text Finder | تدمد: | 2634-3916 |
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| DOI: | 10.1093/ehjdh/ztab052 |