Bio

Dr. Vasiliki Bikia is a Postdoctoral Researcher at Stanford University, jointly affiliated with the Institute for Human-Centered Artificial Intelligence (HAI) and the Department of Biomedical Data Science, where she works under the mentorship of Prof. Roxana Daneshjou. She holds an Advanced Diploma in Electrical and Computer Engineering from the Aristotle University of Thessaloniki (AUTH), Greece (2017), and a Ph.D. in Biomedical Engineering from the Swiss Federal Institute of Technology of Lausanne (EPFL), Switzerland (2021). Her doctoral work focused on addressing the clinical need for non-invasive cardiovascular monitoring by combining machine learning with physics-based numerical modeling.

Dr. Bikia's research centers on the development of large multimodal models to improve patient outcome prediction. She is also passionate about building patient-facing chatbots that help individuals better understand complex medical information, ultimately aiming to enhance communication and empower patients in their care journey. Moreover, she has contributed to the Stanford Spezi framework, designing and prototyping the Spezi Data Pipeline tool for enhanced digital health data accessibility and analysis workflows.

Institute Affiliations

Honors & Awards

  • MIT Rising Stars in EECS, Massachusetts Institute of Technology, Cambridge, US (2024)
  • Best PhD Thesis (Nominee), Ecole Polytechnique Fédérale de Lausanne, VD, Switzerland (2021)
  • Aristotle University of Thessaloniki Excellency Award, Aristotle University of Thessaloniki, Thessaloniki, Greece (2016)
  • Seeds Innovation and Technology Competition, National Bank of Greece, Athens, Greece (2016)
  • Microsoft ImagineCup Innovation, World Citizenship and Ability Award, Microsoft, MSR, WA, US (2015)

Stanford Advisors

Contact

  • Academic
    bikia@stanford.edu
    University - Scholar Department: Department of Biomedical Data Science Position: Postdoctoral Scholar

Additional Info

Lab Affiliations

All Publications

  • Developing technologies to assess vascular ageing: a roadmap from VascAgeNet. Physiological measurement Zanelli, S., Agnoletti, D., Alastruey, J., Allen, J., Bianchini, E., Bikia, V., Boutouyrie, P., Bruno, R. M., Climie, R., Djeldjli, D., Gkaliagkousi, E., Giudici, A., Gopcevic, K., Grillo, A., Guala, A., Hametner, B., Joseph, J., Karimpour, P., Kodithuwakku, V., Kyriacou, P. A., Lazaridis, A., Lønnebakken, M. T., Martina, M. R., Mayer, C. C., Nabeel, P. M., Navickas, P., Nemcsik, J., Orter, S., Park, C., Pereira, T., Pucci, G., Rey, A. B., Salvi, P., Seabra, A. C., Seeland, U., van Sloten, T., Spronck, B., Stansby, G., Steens, I., Stieglitz, T., Tan, I., Veerasingham, D., Wassertheurer, S., Weber, T., Westerhof, B. E., Charlton, P. H. 2024; 45 (12)

    Abstract

    Vascular ageing (vascular ageing) is the deterioration of arterial structure and function which occurs naturally with age, and which can be accelerated with disease. Measurements of vascular ageing are emerging as markers of cardiovascular risk, with potential applications in disease diagnosis and prognosis, and for guiding treatments. However, vascular ageing is not yet routinely assessed in clinical practice. A key step towards this is the development of technologies to assess vascular ageing. In this Roadmap, experts discuss several aspects of this process, including: measurement technologies; the development pipeline; clinical applications; and future research directions. The Roadmap summarises the state of the art, outlines the major challenges to overcome, and identifies potential future research directions to address these challenges.

    View details for DOI 10.1088/1361-6579/ad548e

    View details for PubMedID 38838703

    View details for PubMedCentralID PMC11697036

  • Impact of arterial system alterations due to amputation on arterial stiffness and hemodynamics: a numerical study. Scientific reports Obeid, H., Bikia, V., Segers, P., Pare, M., Boutouyrie, P., Stergiopulos, N., Agharazii, M. 2024; 14 (1): 24852

    Abstract

    Subjects with amputation of the lower limbs are at increased risk of cardiovascular mortality and morbidity. We hypothesize that amputation-induced alterations in the arterial tree negatively impact arterial biomechanics, blood pressure and flow behavior. These changes may interact with other biological factors, potentially increasing cardiovascular risk. To evaluate this hypothesis regarding the purely mechanical impact of amputation on the arterial tree, we used a simulation computer model including a detailed one-dimensional (1D) arterial network model (143 arterial segments) coupled with a zero-dimensional (0D) model of the left ventricle. Our simulations included five settings of the arterial network: (1) 4-limbs control, (2) unilateral amputee (right lower limb), (3) bilateral amputee (both lower limbs), (4) trilateral amputee (lower-limbs and right upper-limb), and (5) quadrilateral amputee (lower and upper limbs). Analysis of regional stiffness, as calculated by pulse wave velocity (PWV) for large-, medium- and small-sized arteries, showed that, while aortic stiffness did not change with increasing degree of amputation, stiffness of medium and smaller-sized arteries increased with greater amputation severity. Despite a staged decrease in cardiac output, the systolic and diastolic blood pressure values increased, resulting in an increase in both central and peripheral pulse pressures but with an attenuation of pulse pressure amplification. The most significant increase in peak systolic pressure and decrease in peak systolic blood flow was observed at the site of the abdominal aorta. Wave separation analysis indicated no changes in the shape of the forward and backward wave components. However, the results from wave intensity analysis showed that with extended amputation, there was an increase in peak forward wave intensity and a rise in the inverse peak of the backward wave intensity, suggesting potential alterations in cardiac hemodynamic load. In conclusion, this simulation study showed that biomechanical and hemodynamic changes in the arterial network geometry could interact with additional risk factors to increase the cardiovascular risk in patients with amputations.

    View details for DOI 10.1038/s41598-024-75881-5

    View details for PubMedID 39438559

  • Utility of smart watches for identifying arrhythmias in children. Communications medicine Zahedivash, A., Chubb, H., Giacone, H., Boramanand, N. K., Dubin, A. M., Trela, A., Lencioni, E., Motonaga, K. S., Goodyer, W., Navarre, B., Ravi, V., Schmiedmayer, P., Bikia, V., Aalami, O., Ling, X. B., Perez, M., Ceresnak, S. R. 2023; 3 (1): 167

    Abstract

    Arrhythmia symptoms are frequent complaints in children and often require a pediatric cardiology evaluation. Data regarding the clinical utility of wearable technologies are limited in children. We hypothesize that an Apple Watch can capture arrhythmias in children.We present an analysis of patients ≤18 years-of-age who had signs of an arrhythmia documented by an Apple Watch. We include patients evaluated at our center over a 4-year-period and highlight those receiving a formal arrhythmia diagnosis. We evaluate the role of the Apple Watch in arrhythmia diagnosis, the results of other ambulatory cardiac monitoring studies, and findings of any EP studies.We identify 145 electronic-medical-record identifications of Apple Watch, and find arrhythmias confirmed in 41 patients (28%) [mean age 13.8 ± 3.2 years]. The arrythmias include: 36 SVT (88%), 3 VT (7%), 1 heart block (2.5%) and wide 1 complex tachycardia (2.5%). We show that invasive EP study confirmed diagnosis in 34 of the 36 patients (94%) with SVT (2 non-inducible). We find that the Apple Watch helped prompt a workup resulting in a new arrhythmia diagnosis for 29 patients (71%). We note traditional ambulatory cardiac monitors were worn by 35 patients (85%), which did not detect arrhythmias in 10 patients (29%). In 73 patients who used an Apple Watch for recreational or self-directed heart rate monitoring, 18 (25%) sought care due to device findings without any arrhythmias identified.We demonstrate that the Apple Watch can record arrhythmia events in children, including events not identified on traditionally used ambulatory monitors.

    View details for DOI 10.1038/s43856-023-00392-9

    View details for PubMedID 38092993

    View details for PubMedCentralID 4937287

  • Arterial pulse wave modeling and analysis for vascular-age studies: a review from VascAgeNet AMERICAN JOURNAL OF PHYSIOLOGY-HEART AND CIRCULATORY PHYSIOLOGY Alastruey, J., Charlton, P. H., Bikia, V., Paliakaite, B., Hametner, B., Bruno, R., Mulder, M. P., Vennin, S., Piskin, S., Khir, A. W., Guala, A., Mayer, C. C., Mynard, J., Hughes, A. D., Segers, P., Westerhof, B. E. 2023; 325 (1): H1-H29

    Abstract

    Arterial pulse waves (PWs) such as blood pressure and photoplethysmogram (PPG) signals contain a wealth of information on the cardiovascular (CV) system that can be exploited to assess vascular age and identify individuals at elevated CV risk. We review the possibilities, limitations, complementarity, and differences of reduced-order, biophysical models of arterial PW propagation, as well as theoretical and empirical methods for analyzing PW signals and extracting clinically relevant information for vascular age assessment. We provide detailed mathematical derivations of these models and theoretical methods, showing how they are related to each other. Finally, we outline directions for future research to realize the potential of modeling and analysis of PW signals for accurate assessment of vascular age in both the clinic and in daily life.

    View details for DOI 10.1152/ajpheart.00705.2022

    View details for Web of Science ID 001008201900001

    View details for PubMedID 37000606

    View details for PubMedCentralID PMC7614613

https://orcid.org/0000-0002-4660-1560