Accelerated Brain Atrophy on Serial Computed Tomography

Zahid, Abdullah Bin; Mikheev, Artem; Srivatsa, Neha; Babb, James S.; Samadani, Uzma; Rusinek, Henry

doi:10.1097/rct.0000000000000435

Cited by 18 publications

(5 citation statements)

References 23 publications

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“…A CT-based longitudinal study on veterans tracked the progression of AD over 4-6 years. Absolute brain volume loss accelerated 1.5x faster for AD patients versus non-AD patients (Bin Zahid et al, 2016). The study highlights the possibility of using CT to monitor the progression of cognitive decline and dementia however, more recent studies have found modalities such as MRI or PET to serve a more vital role in tracking disease progression (van Oostveen and de Lange, 2021).…”

Section: Computed Tomographymentioning

confidence: 87%

Should artificial intelligence be used in conjunction with Neuroimaging in the diagnosis of Alzheimer’s disease?

Mirkin

Albensi

2023

Front. Aging Neurosci.

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Alzheimer’s disease (AD) is a progressive, neurodegenerative disorder that affects memory, thinking, behavior, and other cognitive functions. Although there is no cure, detecting AD early is important for the development of a therapeutic plan and a care plan that may preserve cognitive function and prevent irreversible damage. Neuroimaging, such as magnetic resonance imaging (MRI), computed tomography (CT), and positron emission tomography (PET), has served as a critical tool in establishing diagnostic indicators of AD during the preclinical stage. However, as neuroimaging technology quickly advances, there is a challenge in analyzing and interpreting vast amounts of brain imaging data. Given these limitations, there is great interest in using artificial Intelligence (AI) to assist in this process. AI introduces limitless possibilities in the future diagnosis of AD, yet there is still resistance from the healthcare community to incorporate AI in the clinical setting. The goal of this review is to answer the question of whether AI should be used in conjunction with neuroimaging in the diagnosis of AD. To answer the question, the possible benefits and disadvantages of AI are discussed. The main advantages of AI are its potential to improve diagnostic accuracy, improve the efficiency in analyzing radiographic data, reduce physician burnout, and advance precision medicine. The disadvantages include generalization and data shortage, lack of in vivo gold standard, skepticism in the medical community, potential for physician bias, and concerns over patient information, privacy, and safety. Although the challenges present fundamental concerns and must be addressed when the time comes, it would be unethical not to use AI if it can improve patient health and outcome.

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Section: Computed Tomographymentioning

confidence: 87%

Should artificial intelligence be used in conjunction with Neuroimaging in the diagnosis of Alzheimer’s disease?

Mirkin

Albensi

2023

Front. Aging Neurosci.

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“…[6][7][8] Although not as extensively studied as MRI volumetry, CT volumetry can utilise similar segmentation techniques at lower cost and faster acquisition time. 9 Therefore, CT brain volumetry may provide the opportunity for early detection and intervention to arrest cognitive decline.…”

Section: Brain Volume and Intracranial Arterial Calcificationmentioning

confidence: 99%

Opportunistic Extraction of Quantitative CT Biomarkers: Turning the Incidental Into Prognostic Information

et al. 2023

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The past two decades have seen a significant increase in the use of CT, with a corresponding rise in the mean population radiation dose. This rise in CT use has caused improved diagnostic certainty in conditions that were not previously routinely evaluated using CT, such as headaches, back pain, and chest pain. Unused data, unrelated to the primary diagnosis, embedded within these scans have the potential to provide organ-specific measurements that can be used to prognosticate or risk-profile patients for a wide variety of conditions. The recent increased availability of computing power, expertise and software for automated segmentation and measurements, assisted by artificial intelligence, provides a conducive environment for the deployment of these analyses into routine use. Data gathering from CT has the potential to add value to examinations and help offset the public perception of harm from radiation exposure. We review the potential for the collection of these data and propose the incorporation of this strategy into routine clinical practice.

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“…To achieve early clinical diagnosis and quantify the stage of the disease, it is crucial to identify potential biomarkers or neuroimaging indicators [ 2 ]. Ongoing research focuses on utilizing various neuroimaging techniques, including magnetic resonance imaging (MRI) [ 3 – 5 ], functional magnetic resonance imaging (fMRI) [ 6 ], positron emission tomography (PET) [ 7 ], and computed tomography (CT) [ 8 ], to detect AD-related biomarkers and enable early diagnosis.…”

Section: Introductionmentioning

confidence: 99%

A novel spatiotemporal graph convolutional network framework for functional connectivity biomarkers identification of Alzheimer’s disease

Zhang,

Xue,

Zhang

et al. 2024

Alz Res Therapy

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Background Functional connectivity (FC) biomarkers play a crucial role in the early diagnosis and mechanistic study of Alzheimer’s disease (AD). However, the identification of effective FC biomarkers remains challenging. In this study, we introduce a novel approach, the spatiotemporal graph convolutional network (ST-GCN) combined with the gradient-based class activation mapping (Grad-CAM) model (STGC-GCAM), to effectively identify FC biomarkers for AD. Methods This multi-center cross-racial retrospective study involved 2,272 participants, including 1,105 cognitively normal (CN) subjects, 790 mild cognitive impairment (MCI) individuals, and 377 AD patients. All participants underwent functional magnetic resonance imaging (fMRI) and T1-weighted MRI scans. In this study, firstly, we optimized the STGC-GCAM model to enhance classification accuracy. Secondly, we identified novel AD-associated biomarkers using the optimized model. Thirdly, we validated the imaging biomarkers using Kaplan–Meier analysis. Lastly, we performed correlation analysis and causal mediation analysis to confirm the physiological significance of the identified biomarkers. Results The STGC-GCAM model demonstrated great classification performance (The average area under the curve (AUC) values for different categories were: CN vs MCI = 0.98, CN vs AD = 0.95, MCI vs AD = 0.96, stable MCI vs progressive MCI = 0.79). Notably, the model identified specific brain regions, including the sensorimotor network (SMN), visual network (VN), and default mode network (DMN), as key differentiators between patients and CN individuals. These brain regions exhibited significant associations with the severity of cognitive impairment (p < 0.05). Moreover, the topological features of important brain regions demonstrated excellent predictive capability for the conversion from MCI to AD (Hazard ratio = 3.885, p < 0.001). Additionally, our findings revealed that the topological features of these brain regions mediated the impact of amyloid beta (Aβ) deposition (bootstrapped average causal mediation effect: β = -0.01 [-0.025, 0.00], p < 0.001) and brain glucose metabolism (bootstrapped average causal mediation effect: β = -0.02 [-0.04, -0.001], p < 0.001) on cognitive status. Conclusions This study presents the STGC-GCAM framework, which identifies FC biomarkers using a large multi-site fMRI dataset.

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Accelerated Brain Atrophy on Serial Computed Tomography

Cited by 18 publications

References 23 publications

Should artificial intelligence be used in conjunction with Neuroimaging in the diagnosis of Alzheimer’s disease?

Should artificial intelligence be used in conjunction with Neuroimaging in the diagnosis of Alzheimer’s disease?

Opportunistic Extraction of Quantitative CT Biomarkers: Turning the Incidental Into Prognostic Information

A novel spatiotemporal graph convolutional network framework for functional connectivity biomarkers identification of Alzheimer’s disease

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