ArticleResearch in diagnostic and interventional imaging2024
Development of a deep learning model for the automated detection of green pixels indicative of gout on dual energy CT scan.
Article in Research in diagnostic and interventional imaging, 2024. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 5 papers.
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Who cites it
5 citing papers in PubMed.
- DECTGoutSys: Reducing False Positive Gout Diagnoses via a Machine Vision Pipeline for Crystal Tophi Identification+Classification in Dual-Energy Computed Tomography (DECT).Journal of imaging informatics in medicine · 2026Article
- Deep learning models for pulmonary embolism segmentation on dual-energy CT: performance analysis and image quality correlation.BMC medical imaging · 2026Article
- Precision imaging biomarkers for gout: artifact-specific spectral CT parameters for personalized differentiation of tophi in a prospective study.Therapeutic advances in musculoskeletal disease · 2026Article
- Deep learning analysis for rheumatologic imaging: current trends, future directions, and the role of human.Journal of rheumatic diseases · 2025Review
- AI for imaging evaluation in rheumatology: applications of radiomics and computer vision-current status, future prospects and potential challenges.Rheumatology advances in practice · 2025Review
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Authors and funding
12 authors.
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Abstract
Background: Dual-energy CT (DECT) is a non-invasive way to determine the presence of monosodium urate (MSU) crystals in the workup of gout. Color-coding distinguishes MSU from calcium following material decomposition and post-processing. Most software labels MSU as green and calcium as blue. There are limitations in the current image processing methods of segmenting green-encoded pixels. Additionally, identifying green foci is tedious, and automated detection would improve workflow. This study aimed to determine the optimal deep learning (DL) algorithm for segmenting green-encoded pixels of MSU crystals on DECTs. Methods: DECT images of positive and negative gout cases were retrospectively collected. The dataset was split into train ( Results: Segresnet showed superior performance, achieving a DSC of 0.9999 for the background pixels, 0.7868 for the green pixels, and an average DSC of 0.8934 for both types of pixels, respectively. According to the post-processed results, the Segresnet reached voxel-level sensitivity and specificity of 98.72 % and 99.98 %, respectively. Conclusion: In this study, we compared two DL-based segmentation approaches for detecting MSU deposits in a DECT dataset. The Segresnet resulted in superior performance metrics. The developed algorithm provides a potential fast, consistent, highly sensitive and specific computer-aided diagnosis tool. Ultimately, such an algorithm could be used by radiologists to streamline DECT workflow and improve accuracy in the detection of gout.
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