Tory O Frizzell1, Margit Glashutter2, Careesa C Liu3, An Zeng4, Dan Pan5, Sujoy Ghosh Hajra6, Ryan C N D'Arcy7, Xiaowei Song8. 1. Clinical Research and Evaluation, Surrey Memorial Hospital, Fraser Health, Surrey, BC, Canada; Department of Engineering Science, Simon Fraser University, Burnaby, BC, Canada. 2. Clinical Research and Evaluation, Surrey Memorial Hospital, Fraser Health, Surrey, BC, Canada. 3. Department of Engineering Science, Simon Fraser University, Burnaby, BC, Canada; Rotman Research Institute, Baycrest Health Sciences, Toronto, ON, Canada. 4. Faculty of Computer, Guangdong Technology University, Guangzhou, Guangdong, China. 5. School of Electronics and Information, Guangdong Polytechnic Normal University, Guangzhou, China. 6. Department of Engineering Science, Simon Fraser University, Burnaby, BC, Canada; Aerospace Research Centre, National Research Council Canada, Ottawa, ON, Canada. 7. Department of Engineering Science, Simon Fraser University, Burnaby, BC, Canada; Department of Biomedical Physiology & Kinesiology, Simon Fraser University, Burnaby, BC, Canada. 8. Clinical Research and Evaluation, Surrey Memorial Hospital, Fraser Health, Surrey, BC, Canada; Department of Biomedical Physiology & Kinesiology, Simon Fraser University, Burnaby, BC, Canada. Electronic address: xiaowei.song@fraserhealth.ca.
Abstract
INTRODUCTION: Multiple structural brain changes in Alzheimer's disease (AD) and mild cognitive impairment (MCI) have been revealed on magnetic resonance imaging (MRI). There is a fast-growing effort in applying artificial intelligence (AI) to analyze these data. Here, we review and evaluate the AI studies in brain MRI analysis with synthesis. METHODS: A systematic review of the literature, spanning the years from 2009 to 2020, was completed using the PubMed database. AI studies using MRI imaging to investigate normal aging, mild cognitive impairment, and AD-dementia were retrieved for review. Bias assessment was completed using the PROBAST criteria. RESULTS: 97 relevant studies were included in the review. The studies were typically focused on the classification of AD, MCI, and normal aging (71% of the reported studies) and the prediction of MCI conversion to AD (25%). The best performance was achieved by using the deep learning-based convolution neural network algorithms (weighted average accuracy 89%), in contrast to 76-86% using Logistic Regression, Support Vector Machines, and other AI methods. DISCUSSION: The synthesized evidence is paramount to developing sophisticated AI approaches to reliably capture and quantify multiple subtle MRI changes in the whole brain that exemplify the complexity and heterogeneity of AD and brain aging.
INTRODUCTION: Multiple structural brain changes in Alzheimer's disease (AD) and mild cognitive impairment (MCI) have been revealed on magnetic resonance imaging (MRI). There is a fast-growing effort in applying artificial intelligence (AI) to analyze these data. Here, we review and evaluate the AI studies in brain MRI analysis with synthesis. METHODS: A systematic review of the literature, spanning the years from 2009 to 2020, was completed using the PubMed database. AI studies using MRI imaging to investigate normal aging, mild cognitive impairment, and AD-dementia were retrieved for review. Bias assessment was completed using the PROBAST criteria. RESULTS: 97 relevant studies were included in the review. The studies were typically focused on the classification of AD, MCI, and normal aging (71% of the reported studies) and the prediction of MCI conversion to AD (25%). The best performance was achieved by using the deep learning-based convolution neural network algorithms (weighted average accuracy 89%), in contrast to 76-86% using Logistic Regression, Support Vector Machines, and other AI methods. DISCUSSION: The synthesized evidence is paramount to developing sophisticated AI approaches to reliably capture and quantify multiple subtle MRI changes in the whole brain that exemplify the complexity and heterogeneity of AD and brain aging.
Authors: Minna Alenius; Laura Hokkanen; Sanna Koskinen; Ilona Hallikainen; Tuomo Hänninen; Mira Karrasch; Minna M Raivio; Marja-Liisa Laakkonen; Johanna Krüger; Noora-Maria Suhonen; Miia Kivipelto; Tiia Ngandu Journal: Front Psychol Date: 2022-07-01
Authors: Qiling He; Lin Shi; Yishan Luo; Chao Wan; Ian B Malone; Vincent C T Mok; James H Cole; Melis Anatürk Journal: Front Aging Neurosci Date: 2022-08-05 Impact factor: 5.702