Systems, methods, and media for estimating pulmonary embolism data based on medical data
A deep convolutional neural network processes CT scan data to automatically detect and estimate pulmonary embolism data, improving efficiency and accuracy in embolism detection.
WO2025166365A1 Publication Date: 2025-08-07MEDNISCIENT AI INC
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Patent Information
- Application Number
- PCT/US2025/014348
- Authority / Receiving Office
- WO · WO
- Patent Type
- Applications
- Current Assignee / Owner
- Priority Date
- 2024-05-31
- Filing Date
- 2025-02-03
- Publication Date
- 2025-08-07
AI Technical Summary
Technical Problem
Current methods for detecting pulmonary embolisms in CT scans require extensive manual reading by radiologists, which is a slow and expensive process.
Method used
Utilizing a deep convolutional neural network to automatically process CT scan data, identify pulmonary embolisms, and generate estimated embolism data, including location and severity, through a machine learning algorithm.
Benefits of technology
Facilitates rapid and accurate detection of pulmonary embolisms, reducing the reliance on manual analysis and lowering costs.
✦ Generated by Eureka AI based on patent content.
Abstract
Mechanisms for indicating estimated pulmonary embolism data are provided, the mechanisms including: receiving medical data; processing the medical data to provide processed data; evaluating the processed data using a first machine learning algorithm using a hardware processor; generating the estimated pulmonary embolism data based on the evaluating the processed data; and causing the expected outcome to be presented to a user. In some of these mechanisms, the medical data is 3D matrix image data from a computed tomography (CT) scan. In some of these embodiments, processing the medical data includes at least one of decompressing, intensity scaling, and resizing the medical data. In some of these mechanisms, the first machine learning algorithm is a deep convolutional neural network.
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