{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,2,26]],"date-time":"2026-02-26T15:02:23Z","timestamp":1772118143546,"version":"3.50.1"},"reference-count":35,"publisher":"Wiley","issue":"2","license":[{"start":{"date-parts":[[2025,2,9]],"date-time":"2025-02-09T00:00:00Z","timestamp":1739059200000},"content-version":"vor","delay-in-days":8,"URL":"http:\/\/creativecommons.org\/licenses\/by\/4.0\/"}],"content-domain":{"domain":["onlinelibrary.wiley.com"],"crossmark-restriction":true},"short-container-title":["Brain and Behavior"],"published-print":{"date-parts":[[2025,2]]},"abstract":"<jats:title>ABSTRACT<\/jats:title>\n                  <jats:sec>\n                    <jats:title>Purpose<\/jats:title>\n                    <jats:p>This study presents a novel application of deep learning to enhance the accuracy of left\u2013right orientation identification in anatomical brain MRI scans. Left\u2013right orientation misidentification in brain MRIs presents significant challenges due to several factors, including metadata loss or ambiguity, which often occurs during the de\u2010identification 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.<\/jats:p>\n                  <\/jats:sec>\n                  <jats:sec>\n                    <jats:title>Method<\/jats:title>\n                    <jats:p>A three\u2010dimensional 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.<\/jats:p>\n                  <\/jats:sec>\n                  <jats:sec>\n                    <jats:title>Finding<\/jats:title>\n                    <jats:p>The proposed deep\u2010learning framework demonstrated a 99.8% accuracy in identifying the left\u2013right 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\u2013right orientation. The planum temporale is known to exhibit notable left\u2013right asymmetry related to language functions, underscoring the biological validity of the model. The half of the four left\u2013right 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.<\/jats:p>\n                  <\/jats:sec>\n                  <jats:sec>\n                    <jats:title>Conclusion<\/jats:title>\n                    <jats:p>This approach offers a potential solution to the persistent issue of left\u2013right misorientation in brain MRIs and supports the reliability of neuroscientific research by ensuring accurate data interpretation.<\/jats:p>\n                  <\/jats:sec>","DOI":"10.1002\/brb3.70299","type":"journal-article","created":{"date-parts":[[2025,2,11]],"date-time":"2025-02-11T02:18:58Z","timestamp":1739240338000},"update-policy":"https:\/\/doi.org\/10.1002\/crossmark_policy","source":"Crossref","is-referenced-by-count":0,"title":["A Neural Network Approach to Identify Left\u2013Right Orientation of Anatomical Brain MRI"],"prefix":"10.1002","volume":"15","author":[{"given":"Kei","family":"Nishimaki","sequence":"first","affiliation":[{"name":"The Russell H. Morgan Department of Radiology and Radiological Science The Johns Hopkins University School of Medicine  Baltimore Maryland USA"},{"name":"Department of Applied Informatics, Graduate School of Science and Engineering Hosei University  Tokyo Japan"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Hitoshi","family":"Iyatomi","sequence":"additional","affiliation":[{"name":"Department of Applied Informatics, Graduate School of Science and Engineering Hosei University  Tokyo Japan"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"ORCID":"https:\/\/orcid.org\/0000-0002-1200-8611","authenticated-orcid":false,"given":"Kenichi","family":"Oishi","sequence":"additional","affiliation":[{"name":"The Russell H. Morgan Department of Radiology and Radiological Science The Johns Hopkins University School of Medicine  Baltimore Maryland USA"},{"name":"Department of Neurology The Johns Hopkins University School of Medicine  Baltimore Maryland USA"},{"name":"The Richman Family Precision Medicine Center of Excellence in Alzheimer's Disease Johns Hopkins University School of Medicine  Baltimore Maryland USA"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"name":"for the Alzheimer's Disease Neuroimaging Initiative, the Australian Imaging Biomarkers and Lifestyle flagship study of ageing","sequence":"additional","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]}],"member":"311","published-online":{"date-parts":[[2025,2,9]]},"reference":[{"key":"e_1_2_11_2_1","unstructured":"Allen\u2010Zhu Z. andY.Li.2012. \u201cTowards Understanding Ensemble Knowledge Distillation and Self\u2010Distillation in Deep Learning.\u201d Arxiv. 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