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 3 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%

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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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Development and validation of a deep learning-based automatic segmentation model for assessing intracranial volume: comparison with NeuroQuant, FreeSurfer, and SynthSeg

Suh,

Jung,

Suh

et al. 2023

Front. Neurol.

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Background and purposeTo develop and validate a deep learning-based automatic segmentation model for assessing intracranial volume (ICV) and to compare the accuracy determined by NeuroQuant (NQ), FreeSurfer (FS), and SynthSeg.Materials and methodsThis retrospective study included 60 subjects [30 Alzheimer’s disease (AD), 21 mild cognitive impairment (MCI), 9 cognitively normal (CN)] from a single tertiary hospital for the training and validation group (50:10). The test group included 40 subjects (20 AD, 10 MCI, 10 CN) from the ADNI dataset. We propose a robust ICV segmentation model based on the foundational 2D UNet architecture trained with four types of input images (both single and multimodality using scaled or unscaled T1-weighted and T2-FLAIR MR images). To compare with our model, NQ, FS, and SynthSeg were also utilized in the test group. We evaluated the model performance by measuring the Dice similarity coefficient (DSC) and average volume difference.ResultsThe single-modality model trained with scaled T1-weighted images showed excellent performance with a DSC of 0.989 ± 0.002 and an average volume difference of 0.46% ± 0.38%. Our multimodality model trained with both unscaled T1-weighted and T2-FLAIR images showed similar performance with a DSC of 0.988 ± 0.002 and an average volume difference of 0.47% ± 0.35%. The overall average volume difference with our model showed relatively higher accuracy than NQ (2.15% ± 1.72%), FS (3.69% ± 2.93%), and SynthSeg (1.88% ± 1.18%). Furthermore, our model outperformed the three others in each subgroup of patients with AD, MCI, and CN subjects.ConclusionOur deep learning-based automatic ICV segmentation model showed excellent performance for the automatic evaluation of ICV.

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

Cited by 3 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)?

Development and validation of a deep learning-based automatic segmentation model for assessing intracranial volume: comparison with NeuroQuant, FreeSurfer, and SynthSeg

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