Company Filing History:
Years Active: 2023
Title: Innovations of Ming Ma in Machine Learning for Treatment Planning
Introduction
Ming Ma is an accomplished inventor based in Stanford, CA (US). He has made significant contributions to the field of medical treatment planning through his innovative patent. His work focuses on utilizing machine learning to enhance the accuracy and efficiency of dose prediction in radiation therapy.
Latest Patents
Ming Ma holds a patent titled "Dosimetric features-driven machine learning model for DVHs/dose prediction." This invention provides a treatment planning prediction method that predicts a Dose-Volume Histogram (DVH) or Dose Distribution (DD) for patient data. The method incorporates a machine-learning computer framework that includes a Planning Target Volume (PTV) only treatment plan. By using a dosimetric parameter obtained from the prediction of the PTV-only treatment plan, the method outputs a DVH and/or DD for the patient. This approach alleviates the complexities of quantifying anatomical features and directly harnesses the correlation between the PTV-only plan and the clinical plan in the dose domain. The invention offers a robust and efficient solution to the critical DVHs prediction problem in treatment planning and plan quality assurance. Ming Ma has 1 patent to his name.
Career Highlights
Ming Ma is affiliated with Leland Stanford Junior University, where he continues to advance his research and innovations in the field of medical technology. His work has garnered attention for its potential to improve patient outcomes in radiation therapy.
Collaborations
Ming Ma collaborates with notable colleagues such as Yong Yang and Lei Xing, who contribute to the research and development of innovative solutions in medical treatment planning.
Conclusion
Ming Ma's contributions to the field of machine learning in treatment planning exemplify the intersection of technology and healthcare. His innovative patent addresses significant challenges in dose prediction, showcasing the potential for improved patient care through advanced methodologies.