Ultrasound and other medical imaging devices scan biological structures or tissues of a patient to aid in the diagnosis of serious illnesses such as cancer. Clarity and poor quality of scanned images due to conditions associated with the patient at time of image capture, or the skill of the technician capturing the scanned image can lead to incorrect or misdiagnosis of diseases.
Dr. Rick Mammone and Dr. Christine Podilchuk, researchers at Rutgers University, have developed new, quick, cost-effective, and accurate solution for the enhancement and interpretation of medical images. The solution enables a mobile device to capture a copy of a displayed medical (e.g., via camera) and uses deep learning techniques to identify and correct flawed photograph effects such as a glare, and/or reflections (associated with use of a flash and/or lighting in the vicinity of the medical image).
In addition, the technology uses deep learning to generate annotations identifying the type of cancers found within the image.
This technology can be bundled with four other related technologies from Dr. Mammone and Dr. Podilchuk to create a complete system for interpreting medical images. The other technologies include:
- Easy identification of areas of concern
- Support for images generated by, stored, or captured by imaging devices and a unique ability to capture and enhance medical images on a mobile phone
- Device and software independent
- For diagnosis of a wide array of medical conditions using medical images
- Telemedicine / Teleradiology
- Mobile medical scanning (e.g., mobile ultrasound)
- Medical Image Solutions, Systems & Software
- Electronic Medical/Health Systems (EMR/EHR)
Intellectual Property & Development Status:
US Patent 10,453,570 and pending continuation; Available for use with new or existing image display/diagnostic applications or within a complete system comprised of additional related technologies from the inventors. We are seeking licensing and/or industry partners.