US 12,236,592 B2
Systems, methods, and apparatuses for systematically determining an optimal approach for the computer-aided diagnosis of a pulmonary embolism
Nahid Ul Islam, Mesa, AZ (US); Shiv Gehlot, Darwara (IN); Zongwei Zhou, Tempe, AZ (US); and Jianming Liang, Scottsdale, AZ (US)
Assigned to Arizona Board of Regents on Behalf of Arizona State University, Scottsdale, AZ (US)
Filed by Arizona Board of Regents on behalf of Arizona State University, Scottsdale, AZ (US)
Filed on Sep. 14, 2022, as Appl. No. 17/944,881.
Claims priority of provisional application 63/244,183, filed on Sep. 14, 2021.
Prior Publication US 2023/0081305 A1, Mar. 16, 2023
Int. Cl. G06T 7/00 (2017.01); A61B 6/50 (2024.01); G06T 3/40 (2024.01); G06V 10/77 (2022.01); G06V 10/774 (2022.01); G06V 10/82 (2022.01); G16H 50/20 (2018.01)
CPC G06T 7/0012 (2013.01) [A61B 6/50 (2013.01); G06T 3/40 (2013.01); G06V 10/7715 (2022.01); G06V 10/774 (2022.01); G06V 10/82 (2022.01); G16H 50/20 (2018.01); G06T 2207/20081 (2013.01); G06T 2207/20084 (2013.01); G06T 2207/30061 (2013.01); G06T 2207/30101 (2013.01); G06V 2201/03 (2022.01)] 20 Claims
OG exemplary drawing
 
1. A system comprising:
a memory to store instructions;
a processor to execute the instructions stored in the memory;
wherein the system is specially configured to:
receive a plurality of medical images;
process the plurality of medical images by executing an image-level classification algorithm to determine the presence or absence of Pulmonary Embolism (PE) within each image;
pre-train an AI model through supervised learning to identify ground truth;
fine-tune the pre-trained AI model specifically for PE diagnosis to generate a pre-trained PE diagnosis and detection AI model;
wherein the pre-trained AI model is based on a modified CNN architecture having introduced therein a squeeze and excitation (SE) block enabling the CNN architecture to extract informative features from the plurality of medical images by fusing spatial and channel-wise information;
apply the pre-trained PE diagnosis and detection AI model to new medical images to render a prediction as to the presence or absence of a Pulmonary Embolism within the new medical images; and
output the prediction as a PE diagnosis for a medical patient.