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Editorial
. 2021 Dec 27;6(12):961-963.
doi: 10.1016/j.jacbts.2021.10.016. eCollection 2021 Dec.

Synergizing Light and Machine Learning to Comprehensively Reveal Coronary Plaque Composition

Eric A Osborn  1 , Nicholas E Houstis  2 , Farouc A Jaffer  2
Affiliations
Editorial

Synergizing Light and Machine Learning to Comprehensively Reveal Coronary Plaque Composition

Eric A Osborn et al. JACC Basic Transl Sci. .
No abstract available

Keywords: artificial intelligence; coronary artery disease; fluorescence lifetime; optical coherence tomography.

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Conflict of interest statement

This work has been supported by NIH/NHLBI K08-HL130465 (EAO), and NIH/NHLBI R01-HL150538 and R01-HL137593 (FAJ). Dr Osborn is a consultant with Abbott Vascular, and Canon, Philips; and a member of the scientific advisory board for Dyad Medical, and holds equity in Dyad Medical. Dr Jaffer has received funds for sponsored research from Canon, Siemens, Shockwave, Teleflex, and Mercator; is a consultant with Boston Scientific, Siemens, Magenta Medical, IMDS, Asahi Intecc, Biotronik, Philips, and Intravascular Imaging; and has equity interest in Intravascular Imaging Inc, DurVena; and with Massachusetts General Hospital has licensing arrangements with Terumo, Canon, and Spectrawave, for which they have a right to receive royalties. Dr Housitis has reported that he has no relationships relevant to the contents of this paper to disclose.

Figures

None
Graphical abstract
See this image and copyright information in PMC

Comment on

  • doi: 10.1016/j.jacbts.2021.10.005

References

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