Ultrasound and other medical imaging devices generate images by scanning biological structures and/or tissues of patients. These images aid in the diagnosis of serious illnesses such as cancer. Issues such poor quality and clarity of scanned images or conditions associated with the patient during scans or skill of the technician can lead to incorrect or misdiagnosis.
Rutgers researchers have developed a novel method to enable mobile devices to accurately display medical images and identify objects of interest in these images. These methods uses a neural networks in conjunction with a deep learning models to improve captured images by correcting problems such as eliminate blurs, image distortions and other flawed photographic effects.
This technology enables an accurate solution for enhancing, transmitting, and interpreting and providing diagnoses for medical images captured by light ultrasound devices.
In addition, the technology can be bundled with five related technologies which include:
- Easy identification of areas of concern
- Device and software independent
- For diagnosis of a wide array of medical conditions using medical images
- Supports images generated by, stored, or captured by imaging devices and a unique ability to capture and enhance medical images on a mobile phone
- Telemedicine / Teleradiology
- Mobile medical scanning (e.g., mobile ultrasound)
- Medical Image Solutions, Systems & Software
- Electronic Medical/Health Systems
Intellectual Property & Development Status:
US Patent 11,043,297; 10,290,084; 10,290,101; 10,311,570; 10,453,570; 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.