Deep Learning-Based Assessment of Coronary Artery Morphologies for Predicting Responsiveness to Intravascular Lithotripsy

Yiqing Liu, Farhad R. Nezami, Elazer R. Edelman

Open source

DOI: 10.1016/j.jacadv.2026.103155
Published: 2026-09
Container: JACC: Advances
Publisher: Elsevier BV
Open access: unknown

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Cite this work

BibTeX

@article{allodium:10.1016/j.jacadv.2026.103155,
  title = {Deep Learning-Based Assessment of Coronary Artery Morphologies for Predicting Responsiveness to Intravascular Lithotripsy},
  author = {Yiqing Liu and Farhad R. Nezami and Elazer R. Edelman},
  year = {2026},
  journal = {JACC: Advances},
  doi = {10.1016/j.jacadv.2026.103155},
  url = {https://doi.org/10.1016/j.jacadv.2026.103155}
}

RIS

TY  - JOUR
TI  - Deep Learning-Based Assessment of Coronary Artery Morphologies for Predicting Responsiveness to Intravascular Lithotripsy
AU  - Yiqing Liu
AU  - Farhad R. Nezami
AU  - Elazer R. Edelman
PY  - 2026
JO  - JACC: Advances
DO  - 10.1016/j.jacadv.2026.103155
UR  - https://doi.org/10.1016/j.jacadv.2026.103155
ER  -

APA

Liu, Y., Nezami, F. R., & Edelman, E. R. (2026). Deep Learning-Based Assessment of Coronary Artery Morphologies for Predicting Responsiveness to Intravascular Lithotripsy. JACC: Advances. https://doi.org/10.1016/j.jacadv.2026.103155

Source records

crossref · retrieved 2026-09-26T20:58:45.969Z