Clinical Studies and AI

Clinical studies provide the evidence that a new diagnostic approach is working and can provide superior accuracy or benefit to patients. In many cases, machine learning, deep learning, and artificial intelligence (AI) are powerful approaches to improving clinical diagnosis, although we believe that a tight bond between biophysics and AI produces the best results. Also important are “pre-clinical” studies on animal models of diseases, which frequently are studied first, followed by institutional review board-approved, low-risk studies on humans.

Journal Articles

  1. Mouse brain elastography changes with sleep-wake cycles, aging, and Alzheimer's disease
    G. R. Ge, W. Song, M. J. Giannetto, J. P. Rolland, M. Nedergaard, and K. J. Parker
    NeuroImage, vol. 295 , pp. 120662-1 -120662-10  (2024). View PDF
  2. Brain elastography in aging relates to fluid-solid trendlines
    K. J. Parker, I. E. Kabir, M. M. Doyley, A. Faiyaz, M. N. Uddin, and G. Schifitto
    Phys Med Biol 69(11) , pp. 115037-1 -115037-16  (2024). View PDF
  3. Multiparametrics quantification and visualization of liver fat using ultrasound
    J. Baek, A. El Kaffas, A. Kamaya, K. Hoyt, and K. J. Parker
    WFUMB Ultrasound Open, vol. 1, no. 1 , pp. 100045-1 -100045-9  (2024). View PDF
  4. WATUNet- a deep neural network for segmentation of volumetric sweep imaging ultrasound
    D. Khaledyan, T. J. Marini, A. M. O'Connell, S. Meng, J. Kan, G. Brennan, Y. Zhao, T. M. Baran, and K. J. Parker
    Mach Learn Sci Technol, vol. 5, no. 1 , pp. 015042-1 -015042-21  (2024). View PDF
  5. H-scan discrimination for tumor microenvironmental heterogeneity in melanoma
    J. Baek, S. S. Qin, P. A. Prieto, and K. J. Parker
    Ultrasound Med Biol, vol. 50, no. 2 , pp. 268 -276  (2024). View PDF
  6. Enhancing breast ultrasound segmentation through fine-tuning and optimization techniques- Sharp attention UNet
    D. Khaledyan, T. J. Marini, T. M. Baran, A. M. O'Connell, and K. J. Parker
    PLoS One, vol. 18, no. 12 , pp. e0289195-1 -e0289195-22  (2023). View PDF
  7. Multiparametric ultrasound imaging for early-stage steatosis: comparison with magnetic resonance imaging-based proton density fat fraction
    J. Baek, L. Basavarajappa, R. Margolis, L, Arthur, J. Li, K. Hoyt, and K. J. Parker
    Med Phys, vol. 51, no. 2 , pp. 1313 -1325  (2023). View PDF
  8. Breast lesion detection and visualization utilizing artificial intelligence and the H-scan
    J. Baek, A. M. O'Connell, and K. J. Parker
    Proceedings, 2022 IEEE International Ultrasonics Symposium, doi: 10.1109/IUS54386.2022.9957217 , pp. 1 -4  (2022). View PDF
  9. No sonographer, no radiologist: assessing accuracy of artificial intelligence on breast ultrasound volume sweep imaging scans
    T. J. Marini, B. Castaneda, K. J. Parker, T. M. Baran, S. E. Romero, R. Iyer, Y. Zhao, Z. Hah, M. H. Park, G. Brennan, J. Kan, S. Meng, A. M. Dozier, and A. M. O'Connell
    PLOS Digit Health, vol. 1, no. 11 , pp. e0000148-1 -e0000148-21  (2022). View PDF
  10. Improving breast cancer diagnosis by incorporating raw ultrasound parameters into machine learning
    J. Baek, A. M. O'Connell, and K. J. Parker
    Mach Learn Sci Technol, vol. 3, no. 4 , pp. 045013-1 -045013-19  (2022). View PDF
  11. Breast ultrasound volume sweep imaging: a new horizon in expanding imaging access for breast cancer detection
    T. J. Marini, B. Castaneda, R. Iyer, T. M. Baran, O. Nemer, A. M. Dozier, K. J. Parker, Y. Zhao, W. Serratelli, G. Matos, S. Ali, B. Ghobryal, A. Visca, and A. M. O'Connell
    J Ultrasound Med, vol. 42, no. 4 , pp. 817 -832  (2022). View PDF
  12. A preliminary study of liver fat quantification using reported ultrasound speed of sound and attenuation parameters
    J. Ormachea and K. J. Parker
    Ultrasound Med Biol, vol. 48, no. 4 , pp. 675 -684  (2022). View PDF
  13. Design and analysis methods for trials with AI-based diagnostic devices for breast cancer
    L. Liu, S. H. Jung, and K. J. Parker
    J Pers Med, vol. 11, no. 11 , pp. 1150-1 -1150-13  (2021). View PDF
  14. The quantification of liver fat from wave speed and attenuation
    K. J. Parker and J. Ormachea
    Phys Med Biol, vol. 66, no. 14 , pp. 145011-1 -145011-10  (2021). View PDF
  15. Diagnostic performance of an artificial intelligence system in breast ultrasound
    A. M. O'Connell, T. V. Bartolotta, A. Orlando, S. H. Jung, J. Baek, and K. J. Parker
    J Med Imaging, vol. 41, no. 1 , pp. 97 -105  (2021). View PDF
  16. H-scan, shear wave and bioluminescent assessment of the progression of pancreatic cancer metastases in the liver
    J. Baek, R. Ahmed, J. Ye, S. A. Gerber, K. J. Parker, and M. M. Doyley
    Ultrasound Med Biol, vol. 46, no. 12 , pp. 3369 -3378  (2020). View PDF
  17. Attenuation of shear waves in normal and steatotic livers
    A. K. Sharma, J. Reis, D. C. Oppenheimer, D. J. Rubens, J. Ormachea, Z. Hah, and K. J. Parker
    Ultrasound Med Biol, vol. 45, no. 4 , pp. 895 -901  (2019). View PDF