Machine Learning Pipeline for Predicting Bone Marrow Edema Along the Sacroiliac Joints on Magnetic Resonance Imaging

Roels, Joris; Craemer, Ann‐Sophie De; Renson, Thomas; Hooge, M. de; Gevaert, Arne; Berghe, Thomas Van Den; Jans, Lennart; Herregods, Nele; Carron, Philippe; Bosch, Filip Van den; Saeys, Yvan; Elewaut, Dirk

doi:10.1002/art.42650

Cited by 8 publications

(1 citation statement)

References 32 publications

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“…After removing duplicates and reviewing titles and abstracts, 26 studies were selected for full-text review. Of these, 16 were removed after reading the full text, leaving ten studies [21][22][23][24][25][26][27][28][29][30] for inclusion in the final review (online supplemental figure 1). Online supplemental table 1 presents the details of the search queries and number of searches per database.…”

Section: Resultsmentioning

confidence: 99%

Performances of machine learning algorithms in discriminating sacroiliitis features on MRI: a systematic review

Moon,

Lee,

Hwang

et al. 2023

RMD Open

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ObjectivesSummarise the evidence of the performance of the machine learning algorithm in discriminating sacroiliitis features on MRI and compare it with the accuracy of human physicians.MethodsMEDLINE, EMBASE, CIHNAL, Web of Science, IEEE, American College of Rheumatology and European Alliance of Associations for Rheumatology abstract archives were searched for studies published between 2008 and 4 June 2023. Two authors independently screened and extracted the variables, and the results are presented using tables and forest plots.ResultsTen studies were selected from 2381. Over half of the studies used deep learning models, using Assessment of Spondyloarthritis International Society sacroiliitis criteria as the ground truth, and manually extracted the regions of interest. All studies reported the area under the curve as a performance index, ranging from 0.76 to 0.99. Sensitivity and specificity were the second-most commonly reported indices, with sensitivity ranging from 0.56 to 1.00 and specificity ranging from 0.67 to 1.00; these results are comparable to a radiologist’s sensitivity of 0.67–1.00 and specificity of 0.78–1.00 in the same cohort. More than half of the studies showed a high risk of bias in the analysis domain of quality appraisal owing to the small sample size or overfitting issues.ConclusionThe performance of machine learning algorithms in discriminating sacroiliitis features on MRI varied owing to the high heterogeneity between studies and the small sample sizes, overfitting, and under-reporting issues of individual studies. Further well-designed and transparent studies are required.

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Section: Resultsmentioning

confidence: 99%

Performances of machine learning algorithms in discriminating sacroiliitis features on MRI: a systematic review

Moon,

Lee,

Hwang

et al. 2023

RMD Open

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Development and clinical validation of a deep learning‐based knee CT image segmentation method for robotic‐assisted total knee arthroplasty

Liu,

Li,

Zou

et al. 2024

Robotics Computer Surgery

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BackgroundThis study aimed to develop a novel deep convolutional neural network called Dual‐path Double Attention Transformer (DDA‐Transformer) designed to achieve precise and fast knee joint CT image segmentation and to validate it in robotic‐assisted total knee arthroplasty (TKA).MethodsThe femoral, tibial, patellar, and fibular segmentation performance and speed were evaluated and the accuracy of component sizing, bone resection and alignment of the robotic‐assisted TKA system constructed using this deep learning network was clinically validated.ResultsOverall, DDA‐Transformer outperformed six other networks in terms of the Dice coefficient, intersection over union, average surface distance, and Hausdorff distance. DDA‐Transformer exhibited significantly faster segmentation speeds than nnUnet, TransUnet and 3D‐Unet (p < 0.01). Furthermore, the robotic‐assisted TKA system outperforms the manual group in surgical accuracy.ConclusionsDDA‐Transformer exhibited significantly improved accuracy and robustness in knee joint segmentation, and this convenient and stable knee joint CT image segmentation network significantly improved the accuracy of the TKA procedure.

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The role of deep learning in diagnostic imaging of spondyloarthropathies: a systematic review

Omar,

Watad,

McGonagle

et al. 2024

Eur Radiol

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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 analyzed 21 studies employing deep learning in SpA imaging diagnosis across MRI, CT, and X-ray modalities. These models, particularly advanced CNNs and U-Nets, demonstrated high accuracy in diagnosing SpA, differentiating arthritis forms, and assessing disease progression. Performance metrics frequently surpassed traditional methods, with some models achieving AUCs up to 0.98 and matching expert radiologist performance. Conclusion This systematic review underscores the effectiveness of deep learning in SpA imaging diagnostics across MRI, CT, and X-ray modalities. The studies reviewed demonstrated high diagnostic accuracy. However, the presence of small sample sizes in some studies highlights the need for more extensive datasets and further prospective and external validation to enhance the generalizability of these AI models. Key Points QuestionHow can deep learning models improve diagnostic accuracy in imaging for spondyloarthropathies (SpA), addressing challenges in early detection and differentiation from other forms of arthritis? FindingsDeep learning models, especially CNNs and U-Nets, showed high accuracy in SpA imaging across MRI, CT, and X-ray, often matching or surpassing expert radiologists. Clinical relevanceDeep learning models can enhance diagnostic precision in SpA imaging, potentially reducing diagnostic delays and improving treatment decisions, but further validation on larger datasets is required for clinical integration.

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Machine Learning Pipeline for Predicting Bone Marrow Edema Along the Sacroiliac Joints on Magnetic Resonance Imaging

Cited by 8 publications

References 32 publications

Performances of machine learning algorithms in discriminating sacroiliitis features on MRI: a systematic review

Performances of machine learning algorithms in discriminating sacroiliitis features on MRI: a systematic review

Development and clinical validation of a deep learning‐based knee CT image segmentation method for robotic‐assisted total knee arthroplasty

The role of deep learning in diagnostic imaging of spondyloarthropathies: a systematic review

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