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Emergent photonics for cardiovascular health

Nature Photonics volume 19, pages 671–680 (2025)Cite this article

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Abstract

Cardiovascular disease (CVD) is the leading global cause of death, encompassing heart attacks, strokes and coronary artery disease. While traditional photonic technologies such as angiography, computed tomography and laser ablation have long been used for CVD diagnosis and treatment, newer innovations are transforming the field. Emerging photonic technologies such as photoacoustic imaging, optical wearable sensors, point-of-care testing and optogenetic control offer non-invasive, high-resolution and high-throughput imaging along with precise therapeutic interventions. This Review highlights the broad applications of these photonic technologies in CVD care, discussing their potential to enhance precision and outcomes. It also addresses the challenges of integrating these innovations into clinical practice, focusing on trends including miniaturization and AI integration. These advancements are poised to revolutionize CVD management and reduce its global burden.

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Fig. 1: Traditional and emerging photonic technologies for the diagnosis and treatment of CVD.
Fig. 2: Emergent photonics for cardiovascular diagnostics.
Fig. 3: Emergent photonics for cardiovascular therapeutics.
Fig. 4: Leveraging AI and emergent photonics for cardiovascular care.

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References

  1. Cosselman, K. E., Navas-Acien, A. & Kaufman, J. D. Environmental factors in cardiovascular disease. Nat. Rev. Cardiol. 12, 627–642 (2015).

    Article  Google Scholar 

  2. Kathiresan, S. & Srivastava, D. Genetics of human cardiovascular disease. Cell 148, 1242–1257 (2012).

    Article  Google Scholar 

  3. Nabel, E. G. Cardiovascular disease. N. Engl. J. Med. 349, 60–72 (2003).

    Article  Google Scholar 

  4. Nishiga, M., Wang, D. W., Han, Y., Lewis, D. B. & Wu, J. C. COVID-19 and cardiovascular disease: from basic mechanisms to clinical perspectives. Nat. Rev. Cardiol. 17, 543–558 (2020).

    Article  Google Scholar 

  5. Ordovás, J. M. & Smith, C. E. Epigenetics and cardiovascular disease. Nat. Rev. Cardiol. 7, 510–519 (2010).

    Article  Google Scholar 

  6. Townsend, N. et al. Epidemiology of cardiovascular disease in Europe. Nat. Rev. Cardiol. 19, 133–143 (2022).

    Article  Google Scholar 

  7. Gaidai, O., Cao, Y. & Loginov, S. Global cardiovascular diseases death rate prediction. Curr. Prob. Cardiol. 48, 101622 (2023).

    Article  Google Scholar 

  8. Libby, P. et al. Atherosclerosis. Nat. Rev. Dis. Primers 5, 56 (2019).

    Article  Google Scholar 

  9. Wolberg, A. S. et al. Venous thrombosis. Nat. Rev. Dis. Primers 1, 15006 (2015).

    Article  Google Scholar 

  10. Davì, G. & Patrono, C. Platelet activation and atherothrombosis. N. Engl. J. Med. 357, 2482–2494 (2007).

    Article  Google Scholar 

  11. Eckel, R. H., Bornfeldt, K. E. & Goldberg, I. J. Cardiovascular disease in diabetes, beyond glucose. Cell Metab. 33, 1519–1545 (2021).

    Article  Google Scholar 

  12. Powell-Wiley, T. M. et al. Obesity and cardiovascular disease: a scientific statement from the American Heart Association. Circulation 143, e984–e1010 (2021).

    Article  Google Scholar 

  13. Piché, M.-E., Tchernof, A. & Després, J.-P. Obesity phenotypes, diabetes, and cardiovascular diseases. Circ. Res. 126, 1477–1500 (2020).

    Article  Google Scholar 

  14. Franco, M., Cooper, R. S., Bilal, U. & Fuster, V. Challenges and opportunities for cardiovascular disease prevention. Am. J. Med. 124, 95–102 (2011).

    Article  Google Scholar 

  15. Ruparelia, N., Chai, J. T., Fisher, E. A. & Choudhury, R. P. Inflammatory processes in cardiovascular disease: a route to targeted therapies. Nat. Rev. Cardiol. 14, 133–144 (2017).

    Article  Google Scholar 

  16. Simons, M. & Ware, J. A. Therapeutic angiogenesis in cardiovascular disease. Nat. Rev. Drug Discov. 2, 863–872 (2003).

    Article  Google Scholar 

  17. van Soest, G., Regar, E. & van der Steen, A. F. W. Photonics in cardiovascular medicine. Nat. Photon. 9, 626–629 (2015).

    Article  ADS  Google Scholar 

  18. Nicol, E. D. et al. The future of cardiovascular computed tomography. JACC Cardiovasc. Imaging 12, 1058–1072 (2019).

    Article  Google Scholar 

  19. Araki, M. et al. Optical coherence tomography in coronary atherosclerosis assessment and intervention. Nat. Rev. Cardiol. 19, 684–703 (2022).

    Article  Google Scholar 

  20. Yabushita, H. et al. Characterization of human atherosclerosis by optical coherence tomography. Circulation 106, 1640–1645 (2002).

    Article  Google Scholar 

  21. Ong, D. S. et al. Coronary calcification and plaque vulnerability: an optical coherence tomographic study. Circ. Cardiovasc. Imag. 9, e003929 (2016).

    Article  Google Scholar 

  22. Ughi, G. J. et al. A neurovascular high-frequency optical coherence tomography system enables in situ cerebrovascular volumetric microscopy. Nat. Commun. 11, 3851 (2020).

    Article  ADS  Google Scholar 

  23. Yao, J. et al. High-speed label-free functional photoacoustic microscopy of mouse brain in action. Nat. Methods 12, 407–410 (2015).

    Article  Google Scholar 

  24. Merdasa, A. et al. Photoacoustic imaging of the spatial distribution of oxygen saturation in an ischemia-reperfusion model in humans. Biomed. Opt. Express 12, 2484–2495 (2021).

    Article  Google Scholar 

  25. Zhu, X. et al. Real-time whole-brain imaging of hemodynamics and oxygenation at micro-vessel resolution with ultrafast wide-field photoacoustic microscopy. Light Sci. Appl. 11, 138 (2022).

    Article  ADS  Google Scholar 

  26. Zhong, X. et al. Free-moving-state microscopic imaging of cerebral oxygenation and hemodynamics with a photoacoustic fiberscope. Light Sci. Appl. 13, 5 (2024).

    Article  ADS  Google Scholar 

  27. Vinegoni, C. et al. Indocyanine green enables near-infrared fluorescence imaging of lipid-rich, inflamed atherosclerotic plaques. Sci. Transl. Med. 3, 84ra45 (2011).

    Article  Google Scholar 

  28. Chang, K. & Jaffer, F. Advances in fluorescence imaging of the cardiovascular system. J. Nucl. Cardiol. 15, 417–428 (2008).

    Article  Google Scholar 

  29. van den Hoven, P. et al. A systematic review of the use of near-infrared fluorescence imaging in patients with peripheral artery disease. J. Vasc. Surg. 70, 286–297 (2019).

    Article  Google Scholar 

  30. Hong, G. et al. Near-infrared II fluorescence for imaging hindlimb vessel regeneration with dynamic tissue perfusion measurement. Circ. Cardiovasc. Imag. 7, 517–525 (2014).

    Article  Google Scholar 

  31. Gao, J. et al. Near-infrared II fluorescence imaging and image-guided siRNA therapy of atherosclerosis. J. Med. Chem. 67, 12428–12438 (2024).

    Article  Google Scholar 

  32. Zhou, Y. et al. Intelligent platelet morphometry. Trends Biotechnol. 39, 978–989 (2021).

    Article  Google Scholar 

  33. Zhou, Y. et al. Intelligent classification of platelet aggregates by agonist type. eLife 9, e52938 (2020).

    Article  Google Scholar 

  34. Nishikawa, M. et al. Massive image-based single-cell profiling reveals high levels of circulating platelet aggregates in patients with COVID-19. Nat. Commun. 12, 7135 (2021).

    Article  ADS  Google Scholar 

  35. Zhou, Y. et al. Long-term effects of Pfizer-BioNTech COVID-19 vaccinations on platelets. Cytom. Pt A 103, 162–167 (2023).

    Article  Google Scholar 

  36. Bayoumy, K. et al. Smart wearable devices in cardiovascular care: where we are and how to move forward. Nat. Rev. Cardiol. 18, 581–599 (2021).

    Article  Google Scholar 

  37. Williams, G. J. et al. Wearable technology and the cardiovascular system: the future of patient assessment. Lancet Digit. Health 5, e467–e476 (2023).

    Article  Google Scholar 

  38. Schubert, M. et al. Monitoring contractility in cardiac tissue with cellular resolution using biointegrated microlasers. Nat. Photon. 14, 452–458 (2020).

    Article  ADS  Google Scholar 

  39. Toropov, N. et al. Review of biosensing with whispering-gallery mode lasers. Light Sci. Appl. 10, 42 (2021).

    Article  ADS  Google Scholar 

  40. Han, G.-R. et al. Deep learning-enhanced paper-based vertical flow assay for high-sensitivity troponin detection using nanoparticle amplification. ACS Nano 18, 27933–27948 (2024).

    Article  Google Scholar 

  41. Goncharov, A. et al. Deep learning-enabled multiplexed point-of-care sensor using a paper-based fluorescence vertical flow assay. Small 19, 2300617 (2023).

    Article  Google Scholar 

  42. Ballard, Z. S. et al. Deep learning-enabled point-of-care sensing using multiplexed paper-based sensors. npj Digit. Med. 3, 66 (2020).

    Article  Google Scholar 

  43. Chaturvedi, N. et al. Advances in point-of-care optical biosensing for underserved populations. Trends Anal. Chem. 175, 117731 (2024).

    Article  Google Scholar 

  44. MacRitchie, N. et al. Molecular imaging of atherosclerosis: spotlight on Raman spectroscopy and surface-enhanced Raman scattering. Heart 104, 460–467 (2018).

    Article  Google Scholar 

  45. Low, J. S. Y., Thevarajah, T. M., Chang, S. W., Goh, B. T. & Khor, S. M. Biosensing based on surface-enhanced Raman spectroscopy as an emerging/next-generation point-of-care approach for acute myocardial infarction diagnosis. Crit. Rev. Biotechnol. 40, 1191–1209 (2020).

    Article  Google Scholar 

  46. El-Said, W. A., Fouad, D. M. & El-Safty, S. A. Ultrasensitive label-free detection of cardiac biomarker myoglobin based on surface-enhanced Raman spectroscopy. Sens. Actuat. B 228, 401–409 (2016).

    Article  Google Scholar 

  47. Gong, T. et al. Optical interference-free surface-enhanced Raman scattering co-nanotags for logical multiplex detection of vascular disease-related biomarkers. ACS Nano 11, 3365–3375 (2017).

    Article  Google Scholar 

  48. Yang, H. et al. Noninvasive and prospective diagnosis of coronary heart disease with urine using surface-enhanced Raman spectroscopy. Analyst 143, 2235–2242 (2018).

    Article  ADS  Google Scholar 

  49. Mabbott, S. et al. Detection of cardiovascular disease associated miR-29a using paper-based microfluidics and surface enhanced Raman scattering. Analyst 145, 983–991 (2020).

    Article  ADS  Google Scholar 

  50. Dixon, K. et al. Using machine learning and silver nanoparticle-based surface-enhanced Raman spectroscopy for classification of cardiovascular disease biomarkers. ACS Appl. Nano Mater. 6, 15385–15396 (2023).

    Article  Google Scholar 

  51. Houthoofd, S., Vuylsteke, M., Mordon, S. & Fourneau, I. Photodynamic therapy for atherosclerosis. The potential of indocyanine green. Photodiagn. Photodyn. Ther. 29, 101568 (2020).

    Article  Google Scholar 

  52. Peng, C. et al. Detection and photodynamic therapy of inflamed atherosclerotic plaques in the carotid artery of rabbits. J. Photochem. Photobiol. 102, 26–31 (2011).

    Article  Google Scholar 

  53. Zou, L. et al. Chlorin e6 (Ce6)-loaded plaque-specific liposome with enhanced photodynamic therapy effect for atherosclerosis treatment. Talanta 265, 124772 (2023).

    Article  Google Scholar 

  54. Jenkins, M. P. et al. Reduction in the response to coronary and iliac artery injury with photodynamic therapy using 5-aminolaevulinic acid. Cardiovasc. Res. 45, 478–485 (2000).

    Article  Google Scholar 

  55. Hsueh, B. et al. Cardiogenic control of affective behavioural state. Nature 615, 292–299 (2023).

    Article  ADS  Google Scholar 

  56. Burton, R. A. B. et al. Optical control of excitation waves in cardiac tissue. Nat. Photon. 9, 813–816 (2015).

    Article  ADS  Google Scholar 

  57. Yamashita, D. et al. In vitro analysis of mechanism of pulsed-laser thrombolysis. PLoS ONE 17, e0262991 (2022).

    Article  Google Scholar 

  58. Zhao, X., Li, S., Zhang, C. & Fu, B. MXene-based Q-switched fiber laser and application in laser thrombolysis. Opt. Fiber Technol. 84, 103763 (2024).

    Article  Google Scholar 

  59. Ivankovic, I., Merčep, E., Schmedt, C. G., Deán-Ben, X. L. & Razansky, D. Real-time volumetric assessment of the human carotid artery: handheld multispectral optoacoustic tomography. Radiology 291, 45–50 (2019).

    Article  Google Scholar 

  60. Choi, W. et al. Three-dimensional multistructural quantitative photoacoustic and US imaging of human feet in vivo. Radiology 303, 467–473 (2022).

    Article  Google Scholar 

  61. Karlas, A. et al. Multispectral optoacoustic tomography of lipid and hemoglobin contrast in human carotid atherosclerosis. Photoacoustics 23, 100283 (2021).

    Article  Google Scholar 

  62. Zhang, C. et al. Real-time intelligent classification of COVID-19 and thrombosis via massive image-based analysis of platelet aggregates. Cytom. Pt A 103, 492–499 (2023).

    Article  Google Scholar 

  63. Mikami, H. et al. Ultrafast confocal fluorescence microscopy beyond the fluorescence lifetime limit. Optica 5, 117–126 (2018).

    Article  ADS  Google Scholar 

  64. Nitta, N. et al. Intelligent image-activated cell sorting. Cell 175, 266–276.e213 (2018).

    Article  Google Scholar 

  65. Lei, C., Guo, B., Cheng, Z. & Goda, K. Optical time-stretch imaging: principles and applications. Appl. Phys. Rev. 3, 011102 (2016).

    Article  ADS  Google Scholar 

  66. Lei, C. et al. High-throughput imaging flow cytometry by optofluidic time-stretch microscopy. Nat. Protoc. 13, 1603–1631 (2018).

    Article  Google Scholar 

  67. Wu, J.-L. et al. Ultrafast laser-scanning time-stretch imaging at visible wavelengths. Light Sci. Appl. 6, e16196 (2017).

    Article  Google Scholar 

  68. Hirose, K. et al. Direct evaluation of antiplatelet therapy in coronary artery disease by comprehensive image-based profiling of circulating platelets. Nat. Commun. 16, 4386 (2025).

    Article  Google Scholar 

  69. Goncharov, A. et al. Insertable glucose sensor using a compact and cost-effective phosphorescence lifetime imager and machine learning. ACS Nano 18, 23365–23379 (2024).

    Article  Google Scholar 

  70. Pradhan, R. et al. Insertable biomaterial-based multianalyte barcode sensor toward continuous monitoring of glucose and oxygen. ACS Sens. 9, 6060–6070 (2024).

    Article  Google Scholar 

  71. Turakhia, M. P. et al. Rationale and design of a large-scale, app-based study to identify cardiac arrhythmias using a smartwatch: the Apple Heart Study. Am. Heart J. 207, 66–75 (2019).

    Article  Google Scholar 

  72. Yan, B. P. et al. Contact‐free screening of atrial fibrillation by a smartphone using facial pulsatile photoplethysmographic signals. J. Am. Heart Assoc. 7, e008585 (2018).

    Article  Google Scholar 

  73. Jang, H.-J. et al. Deep learning-based kinetic analysis in paper-based analytical cartridges integrated with field-effect transistors. ACS Nano 18, 24792–24802 (2024).

    Article  Google Scholar 

  74. Lin, C.-W. et al. Improving sensitivity and reproducibility of surface-enhanced Raman scattering biochips utilizing magnetoplasmonic nanoparticles and statistical methods. ACS Sensors 9, 305–314 (2024).

    Article  Google Scholar 

  75. Park, S. et al. Sensitive and reproducible detection of SARS-CoV-2 using SERS-based microdroplet sensor. J. Chem. Eng. 446, 137085 (2022).

    Article  Google Scholar 

  76. Yu, X. et al. Defect-engineered coordination compound nanoparticles based on Prussian blue analogues for surface-enhanced Raman spectroscopy. ACS Nano 18, 30987–31001 (2024).

    Article  Google Scholar 

  77. Liu, L. et al. Highly scalable, wearable surface-enhanced Raman spectroscopy. Adv. Opt. Mater. 10, 2200054 (2022).

    Article  Google Scholar 

  78. Rao, V. K. et al. An ultralow-cost, durable, flexible substrate for ultrabroadband surface-enhanced Raman spectroscopy. Adv. Photon. Res. 5, 2300291 (2024).

    Article  Google Scholar 

  79. Li, X., Lovell, J. F., Yoon, J. & Chen, X. Clinical development and potential of photothermal and photodynamic therapies for cancer. Nat. Rev. Clin. Oncol. 17, 657–674 (2020).

    Article  Google Scholar 

  80. Dolmans, D. E. J. G. J., Fukumura, D. & Jain, R. K. Photodynamic therapy for cancer. Nat. Rev. Cancer 3, 380–387 (2003).

    Article  Google Scholar 

  81. Hayase, M. et al. Photoangioplasty with local motexafin lutetium delivery reduces macrophages in a rabbit post-balloon injury model. Cardiovasc. Res. 49, 449–455 (2001).

    Article  ADS  Google Scholar 

  82. Goda, K., Jalali, B., Lei, C., Situ, G. & Westbrook, P. AI boosts photonics and vice versa. APL Photon. 5, 070401 (2020).

    Article  ADS  Google Scholar 

  83. LeCun, Y., Bengio, Y. & Hinton, G. Deep learning. Nature 521, 436–444 (2015).

    Article  ADS  Google Scholar 

  84. Isozaki, A. et al. AI on a chip. Lab Chip 20, 3074–3090 (2020).

    Article  Google Scholar 

  85. Shastri, B. J. et al. Photonics for artificial intelligence and neuromorphic computing. Nat. Photon. 15, 102–114 (2021).

    Article  ADS  Google Scholar 

  86. Lin, X. et al. All-optical machine learning using diffractive deep neural networks. Science 361, 1004–1008 (2018).

    Article  ADS  MathSciNet  Google Scholar 

  87. Le, D. et al. Deep learning-based optical coherence tomography angiography image construction using spatial vascular connectivity network. Comms. Eng. 3, 28 (2024).

    Article  Google Scholar 

  88. Wang, J. et al. Self-improving generative foundation model for synthetic medical image generation and clinical applications. Nat. Med. 31, 609–617 (2025).

    Article  Google Scholar 

  89. Safari, M., Fatemi, A. & Archambault, L. Medfusiongan: multimodal medical image fusion using an unsupervised deep generative adversarial network. BMC Med. Imag. 23, 203 (2023).

    Article  Google Scholar 

  90. Rashidi, M., Kalenkov, G., Green, D. J. & McLaughlin, R. A. Improved microvascular imaging with optical coherence tomography using 3D neural networks and a channel attention mechanism. Sci. Rep. 14, 17809 (2024).

    Article  Google Scholar 

  91. Luo, M. et al. Deep learning for anterior segment OCT angiography automated denoising and vascular quantitative measurement. Biomed. Signal Process. Control 83, 104660 (2023).

    Article  ADS  Google Scholar 

  92. Gao, Y., Xu, W., Chen, Y., Xie, W. & Cheng, Q. Deep learning-based photoacoustic imaging of vascular network through thick porous media. IEEE Trans. Med. Imag. 41, 2191–2204 (2022).

    Article  Google Scholar 

  93. Zheng, W. et al. Deep learning enhanced volumetric photoacoustic imaging of vasculature in human. Adv. Sci. 10, 2301277 (2023).

    Article  Google Scholar 

  94. Kim, J. et al. Deep learning acceleration of multiscale superresolution localization photoacoustic imaging. Light Sci. Appl. 11, 131 (2022).

    Article  ADS  Google Scholar 

  95. Luo, Y. et al. Quantitative particle agglutination assay for point-of-care testing using mobile holographic imaging and deep learning. Lab Chip 21, 3550–3558 (2021).

    Article  Google Scholar 

  96. Katsoulakis, E. et al. Digital twins for health: a scoping review. npj Digit. Med. 7, 77 (2024).

    Article  Google Scholar 

  97. Fujimoto, J. & Swanson, E. The development, commercialization, and impact of optical coherence tomography. Invest. Ophthalmol. Vis. Sci. 57, OCT1–OCT13 (2016).

    Article  Google Scholar 

  98. Park, J. et al. Clinical translation of photoacoustic imaging. Nat. Rev. Bioeng. https://doi.org/10.1038/s44222-024-00240-y (2024).

    Article  Google Scholar 

  99. Velazquez Berumen, A. et al. Defining priority medical devices for cancer management: a WHO initiative. Lancet Oncol. 19, e709–e719 (2018).

    Article  Google Scholar 

  100. Beran, D., Ewen, M., Lipska, K., Hirsch, I. B. & Yudkin, J. S. Availability and affordability of essential medicines: implications for global diabetes treatment. Curr. Diab. Rep. 18, 48 (2018).

    Article  Google Scholar 

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Acknowledgements

This work was supported by AMED grant numbers JP20wm0325021 and JP23hma922009 (K.G.), JSPS Core-to-Core Program grant number JPJSCCA20190007 (K.G.), JSPS KAKENHI grant numbers 19H05633, JP20H00317, JP21K15640, JP21K15640 and JP23H02810 (K.G.), the White Rock Foundation (K.G.), the Ogasawara Foundation (K.G.), the Nakatani Foundation (K.G.), KAKETSUKEN (K.G.) and UTOPIA grant number JP233fa627001 (Y.Z.). We gratefully acknowledge Serendipity Lab for facilitating collaboration opportunities. A.O. acknowledges NSF (PATHS-UP ERC).

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Authors and Affiliations

  1. Department of Chemistry, The University of Tokyo, Tokyo, Japan

    Yuqi Zhou & Keisuke Goda

  2. Department of Neurosurgery, Graduate School of Medicine, Tohoku University, Miyagi, Japan

    Atsuhiro Nakagawa

  3. Department of Biodesign, Center for Research, Innovation, Education, Tohoku University Hospital (CRIETO), Miyagi, Japan

    Atsuhiro Nakagawa

  4. Institute for Genetic Medicine, Hokkaido University, Sapporo, Japan

    Masahiro Sonoshita

  5. Department of Pathology, Harvard Medical School, Boston, MA, USA

    Guillermo J. Tearney

  6. Electrical & Computer Engineering Department, University of California, Los Angeles, Los Angeles, CA, USA

    Aydogan Ozcan

  7. Department of Bioengineering, University of California, Los Angeles, Los Angeles, CA, USA

    Aydogan Ozcan & Keisuke Goda

  8. California NanoSystems Institute, University of California, Los Angeles, Los Angeles, CA, USA

    Aydogan Ozcan

  9. Institute of Technological Sciences, Wuhan University, Hubei, China

    Keisuke Goda

  10. International Center for Synchrotron Radiation Innovation Smart (SRIS), Tohoku University, Miyagi, Japan

    Keisuke Goda

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All authors contributed to the writing of the manuscript. K.G. edited the manuscript and led the manuscript development team.

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Correspondence to Keisuke Goda.

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K.G. is a shareholder of CYBO and LucasLand. K.G. and M.S. are shareholders of FlyWorks K.K. and FlyWorks America, Inc. The other authors declare no competing interests.

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Zhou, Y., Nakagawa, A., Sonoshita, M. et al. Emergent photonics for cardiovascular health. Nat. Photon. 19, 671–680 (2025). https://doi.org/10.1038/s41566-025-01714-0

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