Brain MRI-Based Artificial Intelligence Software in Patients with Neurodegenerative Diseases: Current Status

Jeong, Sang Ho; Suh, Chong Hyun; Park, Ho Young; Heo, Hwon; Shim, Woo Hyun; Kim, Sang Joon

doi:10.3348/jksr.2022.0048

Cited by 4 publications

(2 citation statements)

References 53 publications

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“…Thus, it can be used relatively easily in many medical institutions. 16 17 Since 2017, several quantitative analysis and classification clinical software using artificial intelligence have been developed in Korea. Comparison results and their potential limitations are well summarized in recently published review papers.…”

Section: Neuroimaging Test: Magnetic Resonance Imaging (Mri)mentioning

confidence: 99%

See 1 more Smart Citation

Diagnostic Tools for Alzheimer’s Disease: A Narrative Review Based on Our Own Research Experience

Park

Kwon

Kim

et al. 2023

Dement Neurocogn Disord

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Alzheimer’s disease (AD), one of the most representative neurodegenerative diseases, has diverse neurobiological and pathophysiological mechanisms. Treatment strategies targeting a single mechanism have repeated faced failures because the mechanism of neuronal cell death is very complex that is not fully understood yet. Since complex mechanisms exist to explain AD, a variety of diagnostic biomarkers for diagnosing AD are required. Moreover, standardized evaluations for comprehensive diagnosis using neuropsychological, imaging, and laboratory tools are needed. In this review, we summarize the latest clinical, neuropsychological, imaging, and laboratory evaluations to diagnose patients with AD based on our own experience in conducting a prospective study.

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Section: Neuroimaging Test: Magnetic Resonance Imaging (Mri)mentioning

confidence: 99%

“…Comparison results and their potential limitations are well summarized in recently published review papers. 16 17 …”

Section: Neuroimaging Test: Magnetic Resonance Imaging (Mri)mentioning

confidence: 99%

Diagnostic Tools for Alzheimer’s Disease: A Narrative Review Based on Our Own Research Experience

Park

Kwon

Kim

et al. 2023

Dement Neurocogn Disord

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Efficacy and safety of shunt surgery in patients with idiopathic normal-pressure hydrocephalus: can we predict shunt response by preoperative magnetic resonance imaging (MRI)?

Yun,

Suh,

Byun

et al. 2024

Clinical Radiology

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Lateralizing Value of Artificial Intelligence-Based Segmentation Software in MRI-Negative Focal Epilepsy

Park,

Son,

Hwang

et al. 2024

J Epilepsy Res

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Background and Purpose: The magnetic resonance images (MRIs) ability of lesion detection in epilepsy is crucial for a diagnosis and surgical outcome. Using automated artificial intelligence (AI)-based tools for measuring cortical thickness and brain volume originally developed for dementia, we aimed to identify whether it could lateralize epilepsy with normal MRIs.Methods: Non-lesional 3-Tesla MRIs of 428 patients diagnosed with focal epilepsy, based on semiology and electroencephalography findings, were analyzed. AI-based segmentation/volumetry software measured the cortical thickness and the hippocampal volume. The laterality index (LI) was calculated.Results: We classified into temporal lobe epilepsy (TLE, n=294), frontal lobe epilepsy (FLE, n=86), occipital lobe epilepsy (OLE, n=29), and parietal lobe epilepsy (PLE, n=22). Onset age and MRI age were 24.0±16.6 (0-84) and 35.6±14.8 (16-84) years old. In FLE, the LI of frontal thickness was significantly different between the left and right FLE groups, with LIs of the right FLE group being right-shifted and those of the left FLE group being left-shifted, indicating that the lesion side was thinner than the non-lesion side (<i>p</i>=0.01). The discriminable group, which included the patients with left FLE and a LI lower than minus one standard deviation, as well as the patients with right FLE and a LI higher than one standard deviation, showed a longer duration of epilepsy than the non-discriminable group (12.7±9.9 vs. 8.3±7.7 years; <i>p</i>=0.03). Specifically, the LI of individual regions of interest showed that the rostral middle frontal cortex was significantly different in FLE. However, the TLE, PLE, OLE, and LIs were not significantly different.Conclusions: AI-based brain segmentation software can be helpful to decide the laterality of non-lesional FLE especially with longer duration of disease.

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Brain MRI-Based Artificial Intelligence Software in Patients with Neurodegenerative Diseases: Current Status

Cited by 4 publications

References 53 publications

Diagnostic Tools for Alzheimer’s Disease: A Narrative Review Based on Our Own Research Experience

Diagnostic Tools for Alzheimer’s Disease: A Narrative Review Based on Our Own Research Experience

Efficacy and safety of shunt surgery in patients with idiopathic normal-pressure hydrocephalus: can we predict shunt response by preoperative magnetic resonance imaging (MRI)?

Lateralizing Value of Artificial Intelligence-Based Segmentation Software in MRI-Negative Focal Epilepsy

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