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Magnetic Resonance Imaging

Magnetic Resonance Imaging

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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Automated Segmentation of Cortical Grey Matter from T1-Weighted MRI Images

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Using machine learning for sequence-level automated MRI protocol selection in neuroradiology.

Andrew D Brown^1,2, Thomas R Marotta^1,2

¹Department of Medical Imaging, St Michael's Hospital, Toronto, ON, Canada.

Journal of the American Medical Informatics Association : JAMIA

|November 2, 2017

Summary

Automating magnetic resonance imaging (MRI) protocol selection using machine learning can improve accuracy and reduce errors. This AI-driven approach enhances patient safety and healthcare efficiency by correctly selecting imaging sequences.

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Area of Science:

Medical Imaging
Artificial Intelligence
Machine Learning

Background:

Incorrect magnetic resonance imaging (MRI) protocol selection can lead to missed clinical findings, patient harm, and wasted resources.
Accurate protocol selection is crucial for effective medical imaging.

Purpose of the Study:

To develop and evaluate a machine learning (ML) method for automatically selecting MRI sequences based on clinical indications and patient demographics.
To compare the performance of different ML models against a baseline for automated MRI protocoling.

Main Methods:

Utilized machine learning models including support vector machine, gradient boosting machine, and random forest.
Analyzed unstructured text from MRI orders, including clinical indications and patient demographics.
Compared ML models against a baseline predicting the most common protocol.

Main Results:

The gradient boosting machine model achieved the highest performance.
Achieved 95% accuracy, 86% precision, and 80% recall.
Demonstrated significantly better performance than the baseline model.

Conclusions:

Automating MRI sequence selection using machine learning applied to MRI orders is feasible.
This automation has significant implications for safety, quality, and financial aspects of medical imaging.
Potential to improve the overall quality and safety of medical imaging service delivery.