Unsupervised detection of individual atrophy in Alzheimer's disease

Jin, Shichen; Zou, Peini; Ying, Han; Jiang, Jiehui

doi:10.1109/embc46164.2021.9630103

Cited by 4 publications

(2 citation statements)

References 7 publications

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“…Quantitative assessment is performed by manually delineating areas of brain atrophy on MRI; however, it is time‐consuming and labor‐intensive, and the accuracy of longitudinal brain atrophy quantification methods cannot be tested. Therefore, deep learning has also been explored to address this problem 34 , 48 , 49 (Figure 5 ). Bernal et al.…”

Section: Application Of Artificial Intelligence In Mri ...mentioning

confidence: 99%

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Application of artificial intelligence‐based magnetic resonance imaging in diagnosis of cerebral small vessel disease

Hu,

Liu,

Xiong

et al. 2024

CNS Neurosci Ther

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Cerebral small vessel disease (CSVD) is an important cause of stroke, cognitive impairment, and other diseases, and its early quantitative evaluation can significantly improve patient prognosis. Magnetic resonance imaging (MRI) is an important method to evaluate the occurrence, development, and severity of CSVD. However, the diagnostic process lacks quantitative evaluation criteria and is limited by experience, which may easily lead to missed diagnoses and misdiagnoses. With the development of artificial intelligence technology based on deep learning, the extraction of high‐dimensional features in imaging can assist doctors in clinical decision‐making, and it has been widely used in brain function and mental disorders, and cardiovascular and cerebrovascular diseases. This paper summarizes the global research results in recent years and briefly describes the application of deep learning in evaluating CSVD signs in MRI imaging, including recent small subcortical infarcts, lacunes of presumed vascular origin, vascular white matter hyperintensity, enlarged perivascular spaces, cerebral microbleeds, brain atrophy, cortical superficial siderosis, and cortical cerebral microinfarct.

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Section: Application Of Artificial Intelligence In Mri ...mentioning

confidence: 99%

“…Therefore, deep learning has also been explored to address this problem 34,48,49 (Figure 5). Bernal et al 50…”

Section: Application Of Ai In Brain Atrophymentioning

confidence: 99%

Application of artificial intelligence‐based magnetic resonance imaging in diagnosis of cerebral small vessel disease

Hu,

Liu,

Xiong

et al. 2024

CNS Neurosci Ther

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Artificial Intelligence for Cognitive Health Assessment: State-of-the-Art, Open Challenges and Future Directions

et al. 2023

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The subjectivity and inaccuracy of in-clinic Cognitive Health Assessments (CHA) have led many researchers to explore ways to automate the process to make it more objective and to facilitate the needs of the healthcare industry. Artificial Intelligence (AI) and machine learning (ML) have emerged as the most promising approaches to automate the CHA process. In this paper, we explore the background of CHA and delve into the extensive research recently undertaken in this domain to provide a comprehensive survey of the state-of-the-art. In particular, a careful selection of significant works published in the literature is reviewed to elaborate a range of enabling technologies and AI/ML techniques used for CHA, including conventional supervised and unsupervised machine learning, deep learning, reinforcement learning, natural language processing, and image processing techniques. Furthermore, we provide an overview of various means of data acquisition and the benchmark datasets. Finally, we discuss open issues and challenges in using AI and ML for CHA along with some possible solutions. In summary, this paper presents CHA tools, lists various data acquisition methods for CHA, provides technological advancements, presents the usage of AI for CHA, and open issues, challenges in the CHA domain. We hope this first-of-its-kind survey paper will significantly contribute to identifying research gaps in the complex and rapidly evolving interdisciplinary mental health field.

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An unsupervised region of interest extraction model for tau PET images and its application in the diagnosis of Alzheimer's disease

Shi

Wang

Jiang

2022

2022 44th Annual International Conference of the IEEE Engineering in Medicine &Amp; Biology Society (EMBC)

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Unsupervised detection of individual atrophy in Alzheimer's disease

Cited by 4 publications

References 7 publications

Application of artificial intelligence‐based magnetic resonance imaging in diagnosis of cerebral small vessel disease

Application of artificial intelligence‐based magnetic resonance imaging in diagnosis of cerebral small vessel disease

Artificial Intelligence for Cognitive Health Assessment: State-of-the-Art, Open Challenges and Future Directions

An unsupervised region of interest extraction model for tau PET images and its application in the diagnosis of Alzheimer's disease

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