公开(公告)号：US20230081305A1

公开(公告)日：2023-03-16

申请号：US17944881

申请日：2022-09-14

Applicant: Arizona Board of Regents on behalf of Arizona State University

Inventor： Nahid Ul Islam ,  Shiv Gehlot ,  Zongwei Zhou ,  Jianming Liang

IPC: G06T7/00 ,  G06T3/40 ,  G06V10/774 ,  G06V10/77 ,  G06V10/82 ,  A61B6/00 ,  G16H50/20

Abstract: Described herein are means for systematically determining an optimal approach for the computer-aided diagnosis of a pulmonary embolism, in the context of processing medical imaging. According to a particular embodiment, there is a system specially configured for diagnosing a Pulmonary Embolism (PE) within new medical images which form no part of the dataset upon which the AI model was trained. Such a system executes operations for receiving a plurality of medical images and processing the plurality of medical images by executing an image-level classification algorithm to determine the presence or absence of a Pulmonary Embolism (PE) within each image via operations including: pre-training an AI model through supervised learning to identify ground truth; fine-tuning 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; applying the pre-trained PE diagnosis and detection AI model to new medical images to render a prediction as to the presence or absence of the Pulmonary Embolism within the new medical images; and outputting the prediction as a PE diagnosis for a medical patient.

公开(公告)号：US12236592B2

公开(公告)日：2025-02-25

申请号：US17944881

申请日：2022-09-14

Applicant: Arizona Board of Regents on behalf of Arizona State University

Inventor： Nahid Ul Islam ,  Shiv Gehlot ,  Zongwei Zhou ,  Jianming Liang

IPC: G06T7/00 ,  A61B6/50 ,  G06T3/40 ,  G06V10/77 ,  G06V10/774 ,  G06V10/82 ,  G16H50/20

Abstract: Described herein are means for systematically determining an optimal approach for the computer-aided diagnosis of a pulmonary embolism, in the context of processing medical imaging. According to a particular embodiment, there is a system specially configured for diagnosing a Pulmonary Embolism (PE) within new medical images which form no part of the dataset upon which the AI model was trained. Such a system executes operations for receiving a plurality of medical images and processing the plurality of medical images by executing an image-level classification algorithm to determine the presence or absence of a Pulmonary Embolism (PE) within each image via operations including: pre-training an AI model through supervised learning to identify ground truth; fine-tuning 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; applying the pre-trained PE diagnosis and detection AI model to new medical images to render a prediction as to the presence or absence of the Pulmonary Embolism within the new medical images; and outputting the prediction as a PE diagnosis for a medical patient.