Marker band guide catheter calibration

An AI-driven system using machine learning and calibration artifacts addresses the challenges of manual and invasive lesion detection by enhancing angiography image interpretation, improving accuracy and efficiency in lesion characterization across different imaging devices and power levels.

WO2026135972A1PCT designated stage Publication Date: 2026-06-25MEDTRONIC VASCULAR INC
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Patent Information

Authority / Receiving Office
WO · WO
Patent Type
Applications
Current Assignee / Owner
MEDTRONIC VASCULAR INC
Filing Date
2025-12-01
Publication Date
2026-06-25

AI Technical Summary

Technical Problem

Current techniques for interpreting coronary lesions, such as manual reading of fluoroscopic images and intravascular imaging, are subjective, invasive, costly, and time-consuming, and there is a lack of non-invasive, automated tools for accurate lesion detection and characterization across different imaging devices and power levels.

Method used

An AI-driven system using machine learning and neural networks to enhance the interpretation of angiography images, incorporating calibration techniques for different imaging devices and power levels, and utilizing calibration artifacts to normalize images for accurate lesion classification.

Benefits of technology

Improves the accuracy and efficiency of lesion detection and characterization, reducing the need for costly and time-consuming procedures like CT scans, and facilitating longitudinal monitoring of disease progression.

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Abstract

Example systems, devices, and techniques are described for lesion characterization. An example medical system includes one or more memories configured to store one or more memories configured to store fluoroscopy imaging data of a patient and processing circuitry communicatively coupled to the memory. The processing circuitry is configured to obtain the fluoroscopy imaging data, the fluoroscopy imaging data including imaging data of one or more calibration artifacts. The processing circuitry is configured to execute one or more machine learning models to determine, based on the imaging data of the one or more calibration artifacts, a calibration factor. The processing circuitry is configured to apply the calibration factor to the fluoroscopy imaging data and output a representation of the fluoroscopy imaging data based on the application of the calibration factor to the fluoroscopy imaging data.
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