This preprint is under consideration at Alzheimer's Research and Therapy. A preprint is a preliminary version of a manuscript that has not completed peer review at a journal. Research Square does not conduct peer review prior to posting preprints. The posting of a preprint on this server should not be interpreted as an endorsement of its validity or suitability for dissemination as established information or for guiding clinical practice.
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Research
Sergio Grueso
Universitat Oberta de Catalunya
Corresponding Author
ORCiD: https://orcid.org/0000-0002-3888-912X
License:
This work is licensed under a CC BY 4.0 License. Read Full License
Background: Increase in life-span in our society is a double-edged sword that entails a growing number of patients with neurocognitive disorders, Alzheimer’s disease being the most prevalent. Advances in medical imaging and computational power, enable new methods for early detection of neurocognitive disorders with the goal of preventing or reducing cognitive decline. Computer-aided image analysis and early detection of changes in cognition is a promising approach for patients with mild cognitive impairment, sometimes a prodromal stage of Alzheimer’s disease.
Methods: We conducted a systematic review following PRISMA guidelines of studies where Machine Learning was applied to neuroimaging data in order to predict the progression from Mild Cognitive Impairment to Alzheimer’s disease. After removing duplicates, we screened 159 studies and selected 47 for a qualitative analysis.
Results: Most studies used Magnetic Resonance Image and Positron Emission Tomography data but also Magnetoencephalography. The datasets were mainly extracted from the Alzheimer’s disease Neuroimage Initiative (ADNI) database with some exceptions. Regarding the algorithms used, the most common were support vector machines, but more complex models such as Deep Learning, combined with multimodal and multidimensional data (neuroimaging, clinical, cognitive, biological, and behavioral) achieved the best performance.
Conclusions: Although performance of the different models still has room for improvement, the results are promising and this methodology has a great potential as a support tool for clinicians and healthcare professionals.
Keywords
Alzheimer’s disease, Conversion, Machine Learning, Magnetic Resonance, Mild Cognitive Impairment, PRISMA, Positron Emission Tomography, Prediction
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This preprint is under consideration at Alzheimer's Research and Therapy. A preprint is a preliminary version of a manuscript that has not completed peer review at a journal. Research Square does not conduct peer review prior to posting preprints. The posting of a preprint on this server should not be interpreted as an endorsement of its validity or suitability for dissemination as established information or for guiding clinical practice.
Learn more about In Review
Research
Sergio Grueso
Universitat Oberta de Catalunya
Corresponding Author
ORCiD: https://orcid.org/0000-0002-3888-912X
Badges
Prescreen
This manuscript passed our Prescreen assessment
Complete author information
Appropriate declaration statements
Potential risks to human health
Prescreen
Peer Review Timeline
CURRENT STATUS: Under Review
Version 1
Posted 15 Mar, 2021
No community comments so far
Editorial decision: Revise Before Review
On 08 Mar, 2021
Editor assigned
On 01 Mar, 2021
Submission checks complete
On 01 Mar, 2021
Editor invited
On 01 Mar, 2021
First submitted
On 01 Mar, 2021
Metrics
Comments: 0
PDF Downloads: ...
HTML Views: ...
License:
This work is licensed under a CC BY 4.0 License. Read Full License
Background: Increase in life-span in our society is a double-edged sword that entails a growing number of patients with neurocognitive disorders, Alzheimer’s disease being the most prevalent. Advances in medical imaging and computational power, enable new methods for early detection of neurocognitive disorders with the goal of preventing or reducing cognitive decline. Computer-aided image analysis and early detection of changes in cognition is a promising approach for patients with mild cognitive impairment, sometimes a prodromal stage of Alzheimer’s disease.
Methods: We conducted a systematic review following PRISMA guidelines of studies where Machine Learning was applied to neuroimaging data in order to predict the progression from Mild Cognitive Impairment to Alzheimer’s disease. After removing duplicates, we screened 159 studies and selected 47 for a qualitative analysis.
Results: Most studies used Magnetic Resonance Image and Positron Emission Tomography data but also Magnetoencephalography. The datasets were mainly extracted from the Alzheimer’s disease Neuroimage Initiative (ADNI) database with some exceptions. Regarding the algorithms used, the most common were support vector machines, but more complex models such as Deep Learning, combined with multimodal and multidimensional data (neuroimaging, clinical, cognitive, biological, and behavioral) achieved the best performance.
Conclusions: Although performance of the different models still has room for improvement, the results are promising and this methodology has a great potential as a support tool for clinicians and healthcare professionals.
Figure 1
Figure 2
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