Automatic Image Segmentation and Grading Diagnosis of Sacroiliitis Associated with AS Using a Deep Convolutional Neural Network on CT Images

Zhang, Ke; Luo, Guibo; Li, Wenjuan; Zhu, Yuemin; Pan, Jielin; Li, Ximeng; Liu, Chaoran; Liang, Jianchao; Zhan, Yingying; Zheng, Jing; Li, Shaolin; Cai, Wenli; Hong, Guobin

doi:10.1007/s10278-023-00858-1

Cited by 8 publications

(7 citation statements)

References 30 publications

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“…Further research into the early detection of axSpA from CT images was conducted by Castro-Zunti et al [29] using statistical machine learning and deep learning classifiers; their research showed that a modified InceptionV3 deep learning model outperformed traditional machine learning methods and the performance of a musculoskeletal radiologist in terms of accuracy, sensitivity and specificity, demonstrating the potential of these artificial intelligence methods to facilitate the early diagnosis of axSpA. In addition, a fully automated approach using nnU-Net and a 3D convolutional neural network for sacroiliitis segmentation and grading on CT images achieved high accuracy and outperformed radiologist assessment in certain cases [30]. This method suggests an approach to standardise sacroiliitis grading and improve diagnostic confidence [30].…”

Section: Improving Diagnostic Accuracy In Medical Imaging With Artifi...mentioning

confidence: 99%

“…In addition, a fully automated approach using nnU-Net and a 3D convolutional neural network for sacroiliitis segmentation and grading on CT images achieved high accuracy and outperformed radiologist assessment in certain cases [30]. This method suggests an approach to standardise sacroiliitis grading and improve diagnostic confidence [30]. Van den Berghe et al [31 ▪ ] developed and validated a multicentre deep learning network capable of detecting sacroiliitis-related structural lesions on pelvic CT scans.…”

Section: Improving Diagnostic Accuracy In Medical Imaging With Artifi...mentioning

confidence: 99%

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Artificial intelligence and machine learning in axial spondyloarthritis

Adams,

Bressem,

Poddubnyy

2024

Current Opinion in Rheumatology

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Purpose of review To evaluate the current applications and prospects of artificial intelligence and machine learning in diagnosing and managing axial spondyloarthritis (axSpA), focusing on their role in medical imaging, predictive modelling, and patient monitoring. Recent findings Artificial intelligence, particularly deep learning, is showing promise in diagnosing axSpA assisting with X-ray, computed tomography (CT) and MRI analyses, with some models matching or outperforming radiologists in detecting sacroiliitis and markers. Moreover, it is increasingly being used in predictive modelling of disease progression and personalized treatment, and could aid risk assessment, treatment response and clinical subtype identification. Variable study designs, sample sizes and the predominance of retrospective, single-centre studies still limit the generalizability of results. Summary Artificial intelligence technologies have significant potential to advance the diagnosis and treatment of axSpA, providing more accurate, efficient and personalized healthcare solutions. However, their integration into clinical practice requires rigorous validation, ethical and legal considerations, and comprehensive training for healthcare professionals. Future advances in artificial intelligence could complement clinical expertise and improve patient care through improved diagnostic accuracy and tailored therapeutic strategies, but the challenge remains to ensure that these technologies are validated in prospective multicentre trials and ethically integrated into patient care.

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Section: Improving Diagnostic Accuracy In Medical Imaging With Artifi...mentioning

confidence: 99%

Section: Improving Diagnostic Accuracy In Medical Imaging With Artifi...mentioning

confidence: 99%

Artificial intelligence and machine learning in axial spondyloarthritis

Adams,

Bressem,

Poddubnyy

2024

Current Opinion in Rheumatology

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“…Currently, in the computer vision field, the primarily used deep learning algorithm is convolutional neural networks (CNN) (5,15). This methodology excels in image analysis by identifying repeating patterns through a multi-layered approach (15) (Figure 2).…”

Section: Deep Learning Models In Medical Imagingmentioning

confidence: 99%

The Role of Deep Learning in Diagnostic Imaging of Spondyloarthropathies: A Systematic Review

Omar,

Watad,

McGonagle

et al. 2024

Preprint

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Aim: Diagnostic imaging is an integral part of identifying spondyloarthropathies (SpA), yet the interpretation of these images can be challenging. This review evaluated the use of deep learning models to enhance the diagnostic accuracy of SpA imaging. Methods: Following PRISMA guidelines, we systematically searched major databases up to February 2024, focusing on studies that applied deep learning to SpA imaging. Performance metrics, model types, and diagnostic tasks were extracted and analyzed. Study quality was assessed using QUADAS-2. Results: We included 22 studies demonstrating that deep learning aids in diagnosing and classifying SpA, differentiating arthritis forms, and estimating disease progression and structural changes. These models, particularly those using advanced U-Net architectures, consistently outperformed traditional diagnostic methods, showing a notable increase in diagnostic accuracy. Conclusion: Deep learning models are excellent for augmenting the accuracy of SpA imaging diagnostics. Despite their potential, challenges in overcoming retrospective study biases and integrating these models into clinical practice remain. Future directions should aim to validate these models in real-world clinical settings.

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“…CT is essential for aiding the diagnosis of AS, with specific advantages in detecting structural changes. 13 Zhang et al 14 used the no-new-UNet to segment sacroiliac joint CT images and employed a three-dimensional (3D) CNN based on the grading results of three senior radiologists to reclassify sacroiliac arthritis using a three-classification method. Grades 0–1 were designated as class 1, grade 2 as class 2 and grades 3–4 as class 3.…”

Section: Introductionmentioning

confidence: 99%

Entry point of machine learning in axial spondyloarthritis

Chen,

Liu,

et al. 2024

RMD Open

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Axial spondyloarthritis (axSpA) is a globally prevalent and challenging autoimmune disease. Characterised by insidious onset and slow progression, the absence of specific clinical manifestations and biomarkers often leads to misdiagnosis, thereby complicating early detection and diagnosis of axSpA. Furthermore, the high heterogeneity of axSpA, its complex pathogenesis and the lack of specific drugs means that traditional classification standards and treatment guidelines struggle to meet the demands of personalised treatment. Recently, machine learning (ML) has seen rapid advancements in the medical field. By integrating large-scale data with diverse algorithms and using multidimensional data, such as patient medical records, laboratory examinations, radiological data, drug usage and molecular biology information, ML can be modelled based on real-world clinical issues. This enables the diagnosis, stratification, therapeutic efficacy prediction and prognostic evaluation of axSpA, positioning it as an emerging research topic. This study explored the application and progression of ML in the diagnosis and therapy of axSpA from five perspectives: early diagnosis, stratification, disease monitoring, drug efficacy evaluation and comorbidity prediction. This study aimed to provide a novel direction for exploring rational diagnostic and therapeutic strategies for axSpA.

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Automatic Image Segmentation and Grading Diagnosis of Sacroiliitis Associated with AS Using a Deep Convolutional Neural Network on CT Images

Cited by 8 publications

References 30 publications

Artificial intelligence and machine learning in axial spondyloarthritis

Artificial intelligence and machine learning in axial spondyloarthritis

The Role of Deep Learning in Diagnostic Imaging of Spondyloarthropathies: A Systematic Review

Entry point of machine learning in axial spondyloarthritis

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