# A Neural Network Approach to Identify Left–Right Orientation of Anatomical Brain MRI

## TL;DR

A deep learning model accurately identifies left-right orientation in brain MRIs, even when metadata is missing or ambiguous.

## Contribution

A novel 3D convolutional neural network achieves 99.8% accuracy in determining brain MRI orientation.

## Key findings

- The model achieved 99.8% accuracy in identifying left-right orientation across diverse MRI datasets.
- GradCAM visualization highlighted the right planum temporale as a key region for orientation determination.
- Left-right misidentification was linked to brain feature variations like arachnoidal cysts or ventricular asymmetry.

## Abstract

This study presents a novel application of deep learning to enhance the accuracy of left–right orientation identification in anatomical brain MRI scans. Left–right orientation misidentification in brain MRIs presents significant challenges due to several factors, including metadata loss or ambiguity, which often occurs during the de‐identification of medical images for research, conversion between image formats, software operations that strip or overwrite metadata, and the use of older imaging systems that stored orientation differently.

A three‐dimensional convolutional neural network model was trained using 350 MRIs and evaluated on the basis of eight distinct brain MRI databases, totaling 3056 MRIs, to assess its performance across various conditions, including neurodegenerative diseases.

The proposed deep‐learning framework demonstrated a 99.8% accuracy in identifying the left–right orientation, thus, addressing challenges associated with the loss of orientation metadata. GradCAM was used to visualize areas of the brain where the model focused, demonstrating the importance of the right planum temporale and surrounding areas in judging left–right orientation. The planum temporale is known to exhibit notable left–right asymmetry related to language functions, underscoring the biological validity of the model. The half of the four left–right misidentified MRIs involved notable brain feature variations, such as a large arachnoidal cyst adjacent to the temporal lobe or ventricular asymmetry, indicating areas for further investigation.

This approach offers a potential solution to the persistent issue of left–right misorientation in brain MRIs and supports the reliability of neuroscientific research by ensuring accurate data interpretation.

This study presents a novel application of deep learning to enhance the accuracy of left–right orientation identification in anatomical brain MRI scans. The proposed deep‐learning framework demonstrated a 99.8% accuracy in identifying the left–right orientation, thus addressing challenges associated with the loss of orientation metadata.