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Magnetic Resonance Imaging

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Magnetic resonance imaging (MRI) is a noninvasive medical imaging technique based on a phenomenon of nuclear physics discovered in the 1930s, in which matter exposed to magnetic fields and radio waves was found to emit radio signals. In 1970, a physician and researcher named Raymond Damadian noticed that malignant (cancerous) tissue gave off different signals than normal body tissue. He applied for a patent for the first MRI scanning device in clinical use by the early 1980s. The early MRI...

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Related Experiment Video

Updated: May 16, 2025

Quantification of Levator Ani Hiatus Enlargement by Magnetic Resonance Imaging in Males and Females with Pelvic Organ Prolapse

Quantification of Levator Ani Hiatus Enlargement by Magnetic Resonance Imaging in Males and Females with Pelvic Organ Prolapse

Published on: April 17, 2019

Accuracy of Large Language Model-based Automatic Calculation of Ovarian-Adnexal Reporting and Data System MRI Scores

Rajesh Bhayana¹, Ankush Jajodia¹, Tanya Chawla¹

¹University Medical Imaging Toronto, Joint Department of Medical Imaging, University Health Network, Mount Sinai Hospital and Women's College Hospital, Department of Medical Imaging, University of Toronto, Toronto General Hospital, 200 Elizabeth St, Peter Munk Building, 1st Fl, Toronto, ON, Canada M5G 24C.

|April 1, 2025

Summary

A hybrid large language model (LLM) strategy accurately assigned Ovarian-Adnexal Reporting and Data System (O-RADS) MRI scores from reports, outperforming both LLM-only and radiologist performance.

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Area of Science:

Radiology
Artificial Intelligence
Oncology

Background:

Ovarian-Adnexal Reporting and Data System (O-RADS) for MRI aids malignancy risk assessment but faces inconsistent radiologist adoption.
Automating O-RADS score assignment from reports can enhance adoption and diagnostic accuracy.

Purpose of the Study:

To assess the accuracy of optimized large language models (LLMs) in automatically calculating O-RADS scores from MRI reports.

Main Methods:

A retrospective study evaluated two LLM strategies: GPT-4 with O-RADS rules (LLM-only) and a hybrid approach combining GPT-4 feature classification with a deterministic formula.
Accuracy was compared against radiologist-assigned scores using McNemar test on 284 pelvic MRI reports.

Main Results:

The hybrid LLM strategy achieved 97% accuracy in assigning O-RADS MRI scores, surpassing the LLM-only model (90%) and original radiologist scores (88%).
The hybrid model demonstrated superior performance even on reports from before O-RADS implementation.

Conclusions:

A hybrid LLM application integrating LLM feature extraction with deterministic logic offers a highly accurate method for automated O-RADS MRI score assignment.
This approach enhances diagnostic consistency and accuracy compared to existing methods.