4.7 Article

ASNET: A Novel AI Framework for Accurate Ankylosing Spondylitis Diagnosis from MRI

Related references

Note: Only part of the references are listed.
Article Immunology

Identification of clinical heterogeneity and construction of a novel subtype predictive model in patients with ankylosing spondylitis: An unsupervised machine learning study

Xuhua Sun et al.

Summary: This study utilized unsupervised machine learning to classify patients with ankylosing spondylitis (AS) and constructed a novel subtype predictive model. The study also investigated the differences in the immune microenvironment to better understand the pathogenesis of AS.

INTERNATIONAL IMMUNOPHARMACOLOGY (2023)

Article Engineering, Biomedical

ExHiF: Alzheimer?s disease detection using exemplar histogram-based features with CT and MR images

Ela Kaplan et al.

Summary: The purpose of this study is to classify the neuroradiological features of patients with Alzheimer's disease (AD) using an automatic hand-modeled method with high accuracy. The proposed model, ExHiF, uses feature extraction, feature selection, and multiple classifiers to achieve the classification. The results show that the ExHiF model achieves 100% classification accuracy for AD patients using two datasets.

MEDICAL ENGINEERING & PHYSICS (2023)

Article Physiology

Deep-learning based quantification model for hip bone marrow edema and synovitis in patients with spondyloarthritis based on magnetic resonance images

Yan Zheng et al.

Summary: This study used a deep learning-based MRI evaluation model to identify inflammatory lesions in the hip of patients with spondyloarthritis (SpA). The U-Net model achieved a high segmentation accuracy for the femoral head and inflammatory lesions. The model's scores were significantly correlated with clinical symptoms and MRI scoring results, indicating its potential to improve the accuracy and efficiency of clinical diagnosis for SpA patients with hip involvement.

FRONTIERS IN PHYSIOLOGY (2023)

Article Public, Environmental & Occupational Health

Comprehensive AI-assisted tool for ankylosing spondylitis based on multicenter research outperforms human experts

Hao Li et al.

Summary: In this study, a comprehensive artificial intelligence (AI) tool was developed for the diagnosis and prediction of ankylosing spondylitis (AS). The tool demonstrated impressive performance, surpassing that of human experts, and a clinical prediction model was established for accurate categorization of high-risk and low-risk AS patients.

FRONTIERS IN PUBLIC HEALTH (2023)

Article Medicine, General & Internal

Radiomics for the Detection of Active Sacroiliitis Using MR Imaging

Matthaios Triantafyllou et al.

Summary: Detecting active inflammatory sacroiliitis at an early stage is crucial for effective treatment and prevention of debilitating forms of axial spondyloarthropathy. Conventional imaging techniques have limited sensitivity in detecting acute inflammation, while Magnetic Resonance Imaging (MRI) is complex and challenging. This study uses machine learning to develop a radiomic signature for diagnosing active sacroiliitis, achieving promising results with an Extreme Gradient Boosting (XGBoost) model.

DIAGNOSTICS (2023)

Article Radiology, Nuclear Medicine & Medical Imaging

Radiomic Quantification for MRI Assessment of Sacroiliac Joints of Patients with Spondyloarthritis

Ariane Priscilla Magalhaes Tenorio et al.

Summary: This article investigates MRI features correlated with spondyloarthritis and develops detection models for inflammatory sacroiliitis using different MRI techniques. The results show that the performance of these models is equivalent to that of specialists and can effectively identify inflammatory sacroiliitis.

JOURNAL OF DIGITAL IMAGING (2022)

Article Rheumatology

Automatic quantification and grading of hip bone marrow oedema in ankylosing spondylitis based on deep learning

Qing Han et al.

Summary: This study developed a new automatic algorithm for quantification and grading of AS-hip arthritis using MRI. Through deep learning-based segmentation and classification networks, the model achieved an accuracy rate of 85.7%. The automatic computer-based analysis of MRI shows potential for diagnosis and grading of AS hip BME.

MODERN RHEUMATOLOGY (2022)

Article Rheumatology

Deep learning algorithms for magnetic resonance imaging of inflammatory sacroiliitis in axial spondyloarthritis

Karina Ying Ying Lin et al.

Summary: An MRI deep learning algorithm was developed for detection of inflammatory sacroiliitis in axial SpA. The sensitivity and specificity of the algorithms were comparable with the interpretation by a radiologist, but outperformed that of the rheumatologist.

RHEUMATOLOGY (2022)

Article Engineering, Biomedical

Automated brain disease classification using exemplar deep features

Ahmet Kursad Poyraz et al.

Summary: This paper proposes an exemplar-based automated brain disease detection model using computer vision techniques and deep learning models. The model achieves high classification accuracy using support vector machines, demonstrating its success in brain disease detection.

BIOMEDICAL SIGNAL PROCESSING AND CONTROL (2022)

Article Chemistry, Multidisciplinary

An Accurate Multiple Sclerosis Detection Model Based on Exemplar Multiple Parameters Local Phase Quantization: ExMPLPQ

Gulay Macin et al.

Summary: This study developed a machine learning model for MS diagnosis using a handcrafted feature engineering approach. The model achieved high accuracy and computational efficiency, outperforming established deep learning models. It has the potential to be implemented as an automated diagnostic tool for screening brain MRIs in suspected MS patients.

APPLIED SCIENCES-BASEL (2022)

Article Radiology, Nuclear Medicine & Medical Imaging

Deep Learning Detects Changes Indicative of Axial Spondyloarthritis at MRI of Sacroiliac Joints

Keno K. Bressem et al.

Summary: This study developed a deep neural network to detect changes in the sacroiliac joints indicative of axial spondyloarthritis on MRI. The results showed that the neural network had high accuracy in detecting inflammatory and structural changes.

RADIOLOGY (2022)

Article Computer Science, Interdisciplinary Applications

Novel nested patch-based feature extraction model for automated Parkinson's Disease symptom classification using MRI images

Ela Kaplan et al.

Summary: This study proposes a handcrafted image classification model that accurately classifies different stages of Parkinson's disease, detects comorbid dementia, and discriminates PD-related motor symptoms. The model achieved high accuracies through the extraction of texture features, the use of multiple feature selectors and classifiers.

COMPUTER METHODS AND PROGRAMS IN BIOMEDICINE (2022)

Article Engineering, Biomedical

Towards effective classification of brain hemorrhagic and ischemic stroke using CNN

Anjali Gautam et al.

Summary: This study aims to classify brain CT images using a newly proposed convolutional neural network, showing high accuracy in two experiments and improvement over traditional CNN architectures.

BIOMEDICAL SIGNAL PROCESSING AND CONTROL (2021)

Article Biology

PrimePatNet87: Prime pattern and tunable q-factor wavelet transform techniques for automated accurate EEG emotion recognition

Abdullah Dogan et al.

Summary: A hand-crafted conventional EEG emotion classification network was developed using prime pattern and TQWT techniques to classify human emotions automatically. The proposed PrimePatNet87 model achieved over 99% classification accuracy on multiple publicly available datasets, demonstrating high efficiency and accuracy.

COMPUTERS IN BIOLOGY AND MEDICINE (2021)

Article Biology

Feed-forward LPQNet based Automatic Alzheimer's Disease Detection Model

Ela Kaplan et al.

Summary: In this study, a new automatic AD detection model called LPQNet was proposed, demonstrating high classification accuracy on three different image datasets and showing superiority over other detection models. Additionally, LPQNet can be used to develop a new generation intelligent AD detection application for MRI and CT devices.

COMPUTERS IN BIOLOGY AND MEDICINE (2021)

Review Medicine, General & Internal

Axial spondyloarthritis: new advances in diagnosis and management

Christopher Ritchlin et al.

Summary: Axial spondyloarthritis (axSpA) is an inflammatory disease of the axial skeleton associated with significant pain and disability. The discovery of the IL-23/IL-17 pathway led to the development of highly effective antibodies directed toward IL-17A for the treatment of axSpA. New agents with dual inhibition of IL-17A and F isoforms, along with oral small molecule agents targeting the Jak-STAT pathway, have also shown efficacy in treating axSpA.

BMJ-BRITISH MEDICAL JOURNAL (2021)

Article Immunology

Serum Metabolomics Signatures Associated With Ankylosing Spondylitis and TNF Inhibitor Therapy

Jiayong Ou et al.

Summary: This study investigated the systemic metabolic shifts associated with AS and TNF inhibitors treatment using LC-MS technology. A diagnostic panel comprising five metabolites was developed, capable of distinguishing HCs from AS with high accuracy. The study also revealed the metabolic pathways involved in AS and the effects of TNF inhibitors therapy on metabolism.

FRONTIERS IN IMMUNOLOGY (2021)

Article Rheumatology

Tofacitinib for the treatment of ankylosing spondylitis: a phase III, randomised, double-blind, placebo-controlled study

Atul Deodhar et al.

Summary: Tofacitinib demonstrated significantly greater efficacy compared to placebo in treating active ankylosing spondylitis in adults, with good safety profile and no new safety risks identified.

ANNALS OF THE RHEUMATIC DISEASES (2021)

Article Engineering, Biomedical

Automatic segmentation and grading of ankylosing spondylitis on MR images via lightweight hybrid multi-scale convolutional neural network with reinforcement learning

Shuiping Gou et al.

Summary: In this study, an automatic method for AS lesion segmentation and grading is proposed, which effectively addresses the challenges in AS lesions in MRI with a lightweight hybrid multi-scale convolutional neural network and reinforcement learning, achieving good segmentation results.

PHYSICS IN MEDICINE AND BIOLOGY (2021)

Article Rheumatology

Deep learning for detection of radiographic sacroiliitis: achieving expert-level performance

Keno K. Bressem et al.

Summary: An artificial neural network was developed and validated for detecting radiographic sacroiliitis in patients with axial spondyloarthritis. The neural network showed excellent performance in both validation and test datasets, demonstrating good sensitivity and specificity as well as high agreement with human readers.

ARTHRITIS RESEARCH & THERAPY (2021)

Review Nanoscience & Nanotechnology

Advances in nanomedicine for the treatment of ankylosing spondylitis

Yanhai Xi et al.

INTERNATIONAL JOURNAL OF NANOMEDICINE (2019)

Article Computer Science, Artificial Intelligence

Automatic detection and diagnosis of sacroiliitis in CT scans as incidental findings

Yigal Shenkman et al.

MEDICAL IMAGE ANALYSIS (2019)

Review Computer Science, Interdisciplinary Applications

Relief-based feature selection: Introduction and review

Ryan J. Urbanowicz et al.

JOURNAL OF BIOMEDICAL INFORMATICS (2018)

Proceedings Paper Computer Science, Artificial Intelligence

Densely Connected Convolutional Networks

Gao Huang et al.

30TH IEEE CONFERENCE ON COMPUTER VISION AND PATTERN RECOGNITION (CVPR 2017) (2017)

Review Medicine, General & Internal

Ankylosing Spondylitis and Axial Spondyloarthritis

Joel D. Taurog et al.

NEW ENGLAND JOURNAL OF MEDICINE (2016)

Article Rheumatology

MRI examinations for axial and peripheral spondyloarthritis

X. Baraliakos et al.

ZEITSCHRIFT FUR RHEUMATOLOGIE (2012)

Review Medicine, General & Internal

Ankylosing spondylitis

Juergen Braun et al.

LANCET (2007)

Article Computer Science, Artificial Intelligence

Theoretical and empirical analysis of ReliefF and RReliefF

M Robnik-Sikonja et al.

MACHINE LEARNING (2003)