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    LOW-CAPACITANCE NANOPORE SENSORS ON INSULATING SUBSTRATES
    99.
    发明申请
    LOW-CAPACITANCE NANOPORE SENSORS ON INSULATING SUBSTRATES 有权

    公开(公告)号：US20230091639A1

    公开(公告)日：2023-03-23

    申请号：US17908795

    申请日：2021-03-03

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

    Inventor： Chao WANG ,  Pengkun XIA

    IPC: C23C16/04 ,  G01N33/487 ,  G01N27/04 ,  C12Q1/6869 ,  B01L3/00 ,  C23C16/34 ,  C23C16/02

    Abstract: Fabricating a nanopore sensor includes depositing a first and second oxide layers on first and second sides of a sapphire substrate. The second oxide layer is patterned to form an etch mask having a mask opening in the second oxide layer. A crystalline orientation dependent wet anisotropic etch is performed on the second side of the sapphire substrate using the etch mask to form a cavity having sloped side walls through the sapphire substrate to yield an exposed portion of the first oxide layer, each of the sloped side walls being a crystalline facet aligned with a respective crystalline plane of the sapphire substrate. A silicon nitride layer is deposited on the first oxide layer. The exposed portion of the first oxide layer in the cavity is removed, thereby defining a silicon nitride membrane in the cavity. An opening is formed through the silicon nitride membrane.

    SYSTEMS, METHODS, AND APPARATUSES FOR SYSTEMATICALLY DETERMINING AN OPTIMAL APPROACH FOR THE COMPUTER-AIDED DIAGNOSIS OF A PULMONARY EMBOLISM
    100.
    发明申请
    SYSTEMS, METHODS, AND APPARATUSES FOR SYSTEMATICALLY DETERMINING AN OPTIMAL APPROACH FOR THE COMPUTER-AIDED DIAGNOSIS OF A PULMONARY EMBOLISM 有权

    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.

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