Future activity prediction of multiple sclerosis with 3D MRI using 3D discrete wavelet transform

Acar, Züleyha Yılmaz; Başçiftçi, Fatih; Ekmekçi, Ahmet

doi:10.1016/j.bspc.2022.103940

Cited by 5 publications

(1 citation statement)

References 38 publications

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“…In their proposed CNN model, an 82.4% accuracy was obtained with the α-radiomics classifier. Acar et al [27] diagnosed MS using the 3D discrete wavelet transform method from 3D MR imagery. The dataset consisted of 3D MR images from 40 patients.…”

Section: Literature Reviewmentioning

confidence: 99%

Exemplar MobileNetV2-Based Artificial Intelligence for Robust and Accurate Diagnosis of Multiple Sclerosis

Ekmekyapar,

Taşcı

2023

Diagnostics

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Multiple sclerosis (MS) is a chronic autoimmune disease of the central nervous system that prominently affects young adults due to its debilitating nature. The pathogenesis of the disease is focused on the inflammation and neurodegeneration processes. Inflammation is associated with relapses, while neurodegeneration emerges in the progressive stages of the disease. Magnetic resonance imaging (MRI) is commonly used for the diagnosis of MS, and guidelines such as the McDonald criteria are available. MRI is an essential tool to demonstrate the spatial distribution and changes over time in the disease. This study discusses the use of image processing techniques for the diagnosis of MS and specifically combines the MobileNetV2 network with exemplar-based learning, IMrMr feature selection, and K-Nearest Neighbors (KNN) classification methods. Experiments conducted on two different datasets (Dataset 1 and Dataset 2) demonstrate that these methods provide high accuracy in diagnosing MS. Dataset 1 comprises 128 patients with 706 MRI images, 131 MS patients with 667 MRI images, and 150 healthy control subjects with 1373 MRI images. Dataset 2 includes an MS group with 650 MRI images and a healthy control group with 676 MRI images. The results of the study include 10-fold cross-validation results performed on different image sections (Axial, Sagittal, and Hybrid) for Dataset 1. Accuracy rates of 99.76% for Axial, 99.48% for Sagittal, and 98.02% for Hybrid sections were achieved. Furthermore, 100% accuracy was achieved on Dataset 2. In conclusion, this study demonstrates the effective use of powerful image processing methods such as the MobileNetV2 network and exemplar-based learning for the diagnosis of MS. These findings suggest that these methods can be further developed in future research and offer significant potential for clinical applications in the diagnosis and monitoring of MS.

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Section: Literature Reviewmentioning

confidence: 99%

Exemplar MobileNetV2-Based Artificial Intelligence for Robust and Accurate Diagnosis of Multiple Sclerosis

Ekmekyapar,

Taşcı

2023

Diagnostics

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Predicting multiple sclerosis disease progression and outcomes with machine learning and MRI-based biomarkers: a review

Yousef,

Malagurski Tortei,

Castiglione

2024

J Neurol

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Multiple sclerosis (MS) is a demyelinating neurological disorder with a highly heterogeneous clinical presentation and course of progression. Disease-modifying therapies are the only available treatment, as there is no known cure for the disease. Careful selection of suitable therapies is necessary, as they can be accompanied by serious risks and adverse effects such as infection. Magnetic resonance imaging (MRI) plays a central role in the diagnosis and management of MS, though MRI lesions have displayed only moderate associations with MS clinical outcomes, known as the clinico-radiological paradox. With the advent of machine learning (ML) in healthcare, the predictive power of MRI can be improved by leveraging both traditional and advanced ML algorithms capable of analyzing increasingly complex patterns within neuroimaging data. The purpose of this review was to examine the application of MRI-based ML for prediction of MS disease progression. Studies were divided into five main categories: predicting the conversion of clinically isolated syndrome to MS, cognitive outcome, EDSS-related disability, motor disability and disease activity. The performance of ML models is discussed along with highlighting the influential MRI-derived biomarkers. Overall, MRI-based ML presents a promising avenue for MS prognosis. However, integration of imaging biomarkers with other multimodal patient data shows great potential for advancing personalized healthcare approaches in MS.

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Transfer-transfer model with MSNet: An automated accurate multiple sclerosis and myelitis detection system

Tatli,

Macin,

Tasci

et al. 2024

Expert Systems with Applications

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Future activity prediction of multiple sclerosis with 3D MRI using 3D discrete wavelet transform

Cited by 5 publications

References 38 publications

Exemplar MobileNetV2-Based Artificial Intelligence for Robust and Accurate Diagnosis of Multiple Sclerosis

Exemplar MobileNetV2-Based Artificial Intelligence for Robust and Accurate Diagnosis of Multiple Sclerosis

Predicting multiple sclerosis disease progression and outcomes with machine learning and MRI-based biomarkers: a review

Transfer-transfer model with MSNet: An automated accurate multiple sclerosis and myelitis detection system

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