Unmet Need
Medical imaging is an important diagnostic tool used for the detection of a wide array of clinical conditions. The application of Artificial Intelligence (AI) and Deep Learning (DL) is being developed to assist medical imaging professionals manage the ever-growing number of images which they must interpret in their research and clinical practice. A molecular imaging based form of AI may provide real time crucial information to clinicians for diagnosis and even assessing a response to therapy. There is a strong need for molecular image based AI systems to provide real time diagnosis and response assessment to assist physician decision making.
Technology Overview
A Johns Hopkins researcher has developed a real-time AI-assisted quantitative assessment of molecular images that can objectively and automatically identify, segment, and classify tissue types. This technology may provide true automated and semi-automated image-based diagnosis and offer image-based response assessment. In addition to its contribution to the clinical domain, this system could also prove equally valuable in the pre-clinical domain, helping to assess pre-clinical data to increase the efficiency, accuracy, and objectivity of such work, speeding the rate – and lowering the cost – at which research transitions from the pre-clinical to clinical domains.
Stage of Development
Prototype is available in context of [18F]-FDG PET/CT imaging for the indication of Breast Cancer.
Publications
N/A
