Artificial intelligence for differential diagnosis of dementia – Nature.com
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Nature Medicine (2024)
336
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Using routinely collected multimodal clinical data, we developed an artificial intelligence (AI) model to identify dementia and determine factors causing it, including mixed dementias and Alzheimer’s disease. The model’s predictions were confirmed with biomarker evidence and neuropathological findings, and we show that the AI model, when used in conjunction with neurologist assessments, outperformed neurologist assessments alone.
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Burton, A. How do we fix the shortage of neurologists? Lancet Neurol. 17, 502–503 (2018). This commentary discusses the global shortage of neurologists and proposes solutions to address this issue.
Article PubMed Google Scholar
Lester, P. E., Dharmarajan, T. S. & Weinstein, E. The looming geriatrician shortage: ramifications and solutions. J. Aging Health. 32, 1052–1062 (2020). This article highlights the shortage of geriatricians and proposes solutions to improve recruitment and retention in geriatric medicine.
Article PubMed Google Scholar
Qiu, S. et al. Development and validation of an interpretable deep learning framework for Alzheimer’s disease classification. Brain 143, 192–1933 (2020). This article introduces a fully convolutional network designed to distinguish individuals with Alzheimer’s disease from those with normal cognition.
Article Google Scholar
Romano, M. F. et al. Deep learning for risk-based stratification of cognitively impaired individuals. iScience 26, 107522 (2023). This article presents a framework to predict Alzheimer’s disease progression in individuals with mild cognitive impairment using MRI and survival analysis.
Article PubMed PubMed Central Google Scholar
Qiu, S. et al. Multimodal deep learning for Alzheimer’s disease dementia assessment. Nat Commun. 13, 3404 (2022). This article presents a deep learning framework that improves the diagnosis of cognitive impairment and dementia, differentiating Alzheimer’s disease from other etiologies.
Article CAS PubMed PubMed Central Google Scholar
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This is a summary of: Xue, C. et al. AI-based differential diagnosis of dementia etiologies on multimodal data. Nat. Med. https://doi.org/10.1038/s41591-024-03118-z (2024).
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Artificial intelligence for differential diagnosis of dementia. Nat Med (2024). https://doi.org/10.1038/s41591-024-03147-8
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Nature Medicine (2024)
336
11
Metrics details
Using routinely collected multimodal clinical data, we developed an artificial intelligence (AI) model to identify dementia and determine factors causing it, including mixed dementias and Alzheimer’s disease. The model’s predictions were confirmed with biomarker evidence and neuropathological findings, and we show that the AI model, when used in conjunction with neurologist assessments, outperformed neurologist assessments alone.
This is a preview of subscription content, access via your institution
Access Nature and 54 other Nature Portfolio journals
Get Nature+, our best-value online-access subscription
$29.99 / 30 days
cancel any time
Subscribe to this journal
Receive 12 print issues and online access
$209.00 per year
only $17.42 per issue
Buy this article
Prices may be subject to local taxes which are calculated during checkout
Burton, A. How do we fix the shortage of neurologists? Lancet Neurol. 17, 502–503 (2018). This commentary discusses the global shortage of neurologists and proposes solutions to address this issue.
Article PubMed Google Scholar
Lester, P. E., Dharmarajan, T. S. & Weinstein, E. The looming geriatrician shortage: ramifications and solutions. J. Aging Health. 32, 1052–1062 (2020). This article highlights the shortage of geriatricians and proposes solutions to improve recruitment and retention in geriatric medicine.
Article PubMed Google Scholar
Qiu, S. et al. Development and validation of an interpretable deep learning framework for Alzheimer’s disease classification. Brain 143, 192–1933 (2020). This article introduces a fully convolutional network designed to distinguish individuals with Alzheimer’s disease from those with normal cognition.
Article Google Scholar
Romano, M. F. et al. Deep learning for risk-based stratification of cognitively impaired individuals. iScience 26, 107522 (2023). This article presents a framework to predict Alzheimer’s disease progression in individuals with mild cognitive impairment using MRI and survival analysis.
Article PubMed PubMed Central Google Scholar
Qiu, S. et al. Multimodal deep learning for Alzheimer’s disease dementia assessment. Nat Commun. 13, 3404 (2022). This article presents a deep learning framework that improves the diagnosis of cognitive impairment and dementia, differentiating Alzheimer’s disease from other etiologies.
Article CAS PubMed PubMed Central Google Scholar
Download references
Publisher’s note Springer Nature remains neutral with regard to jurisdictional claims in published maps and institutional affiliations.
This is a summary of: Xue, C. et al. AI-based differential diagnosis of dementia etiologies on multimodal data. Nat. Med. https://doi.org/10.1038/s41591-024-03118-z (2024).
Reprints and permissions
Artificial intelligence for differential diagnosis of dementia. Nat Med (2024). https://doi.org/10.1038/s41591-024-03147-8
Download citation
Published:
DOI: https://doi.org/10.1038/s41591-024-03147-8
Anyone you share the following link with will be able to read this content:
Sorry, a shareable link is not currently available for this article.
Provided by the Springer Nature SharedIt content-sharing initiative
Advertisement
© 2024 Springer Nature Limited
Sign up for the Nature Briefing: Translational Research newsletter — top stories in biotechnology, drug discovery and pharma.
This article was autogenerated from a news feed from CDO TIMES selected high quality news and research sources. There was no editorial review conducted beyond that by CDO TIMES staff. Need help with any of the topics in our articles? Schedule your free CDO TIMES Tech Navigator call today to stay ahead of the curve and gain insider advantages to propel your business!
