Artificial intelligence in cardiovascular imaging and intervention
Recent progress in artificial intelligence (AI) includes generative models, multimodal foundation models, and federated learning, which enable a wide spectrum of novel exciting applications and scenarios for cardiac image analysis and cardiovascular interventions. The disruptive nature of these nove...
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| Main Authors: | , , , , , |
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| Format: | Article (Journal) |
| Language: | English |
| Published: |
09 August 2024
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| In: |
Herz
Year: 2024, Volume: 49, Issue: 5, Pages: 327-334 |
| ISSN: | 1615-6692 |
| DOI: | 10.1007/s00059-024-05264-z |
| Online Access: | Verlag, lizenzpflichtig, Volltext: https://doi.org/10.1007/s00059-024-05264-z
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| Author Notes: | Sandy Engelhardt, Salman Ul Hussan Dar, Lalith Sharan, Florian André, Eike Nagel, Sarina Thomas |
| Summary: | Recent progress in artificial intelligence (AI) includes generative models, multimodal foundation models, and federated learning, which enable a wide spectrum of novel exciting applications and scenarios for cardiac image analysis and cardiovascular interventions. The disruptive nature of these novel technologies enables concurrent text and image analysis by so-called vision-language transformer models. They not only allow for automatic derivation of image reports, synthesis of novel images conditioned on certain textual properties, and visual questioning and answering in an oral or written dialogue style, but also for the retrieval of medical images from a large database based on a description of the pathology or specifics of the dataset of interest. Federated learning is an additional ingredient in these novel developments, facilitating multi-centric collaborative training of AI approaches and therefore access to large clinical cohorts. In this review paper, we provide an overview of the recent developments in the field of cardiovascular imaging and intervention and offer a future outlook. |
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| Item Description: | Online veröffentlicht: 09. August 2024 Gesehen am 10.10.2024 |
| Physical Description: | Online Resource |
| ISSN: | 1615-6692 |
| DOI: | 10.1007/s00059-024-05264-z |