Medical Imaging Protocol Name Standardization System and Method

ChangeBridge: Patent Apps - Health Informatics (G16H)

Detected March 26th, 2026

Summary

The USPTO has published a new patent application, US20260088154A1, detailing a system and method for standardizing medical imaging protocol names using machine learning. The application was filed on September 25, 2024, by inventors Philippe Gerner and Mathieu Bedez.

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What changed

This document is a publication of a new patent application (US20260088154A1) filed with the USPTO. The application describes a system and method for standardizing medical imaging protocol names through the generation of a synthetic training dataset and the use of a lightweight text classification model. This technology aims to improve the accuracy and efficiency of identifying and categorizing medical imaging protocols.

As this is a patent application, there are no immediate compliance obligations for regulated entities. However, companies involved in developing or utilizing medical imaging software, AI for healthcare, or data management systems may wish to review the application for potential intellectual property insights or future technological developments. The filing date was September 25, 2024.

Source document (simplified)

← USPTO Patent Applications

SYSTEMS AND METHODS FOR MEDICAL IMAGING PROTOCOL NAME STANDARDIZATION

Application US20260088154A1 Kind: A1 Mar 26, 2026

Inventors

Philippe Gerner, Mathieu Bedez

Abstract

A system and method for medical imaging protocol name standardization includes generating a synthetic training dataset from medical standards in public documentation utilizing knowledge elicitation, wherein the synthetic training dataset includes, for a given language and a given imaging modality, a plurality of combinations of possible medical imaging protocol names for respective standard protocol codes of a plurality of standard protocol codes for each standard medical imaging protocol. The system and method also includes generating a lightweight text classification model from the synthetic training dataset utilizing machine learning. The system and method further includes utilizing the lightweight text classification model to receive a medical imaging protocol name and to output a list of most probable protocol codes based on the medical imaging protocol name.