System and Method for Automated Ovarian Follicular Monitoring

Methods and products for automated real-time ovarian follicular detection, monitoring and analysis are provided. The devices and methods allow for remote or local analysis, while minimizing or eliminating the need for technician review of the output images. The methods are useful in human and non-human subjects including companion animals and other animals such as endangered species.

Researchers

Emery N Brown / Rose Faghih / Aaron Styer

Departments: Department of Brain and Cognitive Sciences
Technology Areas: Artificial Intelligence (AI) and Machine Learning (ML) / Biotechnology: Biomedical Devices & Systems, Sensors & Monitoring / Communication Systems: Wireless / Computer Science: Bioinformatics
Impact Areas: Healthy Living

  • system and method for automated ovarian follicular monitoring
    United States of America | Granted | 11,622,744

Publications

Faghih, R. T., A. K. Styer, and E. N. Brown. "Automated Ovarian Follicular Monitoring: A Novel Real-Time Approach." Annual International Conference of the IEEE Engineering in Medicine and Biology Society 2017 (July 2017): 632–35. https://doi.org/10.1109/EMBC.2017.8036904.

 

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