ArticleInternal and emergency medicine2021
A convolutional neural network with transfer learning for automatic discrimination between low and high-grade synovitis: a pilot study.
Article in Internal and emergency medicine, 2021. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 19 papers.
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Who cites it
19 citing papers in PubMed, 32 citations in OpenAlex.
- Vision transformer-based ordinal model for semi-quantitative synovitis grading on musculoskeletal ultrasound: a retrospective study with internal and external validation.Annals of medicine · 2026Article
- Large Language Models Meet Gynecologic Ultrasound: Advancing the Characterization of ADNEXal Masses.Journal of imaging · 2026Article
- Electronic Health Record-Based Machine Learning Model for Predicting Disease Activity in Patients with Rheumatoid Arthritis.Health data science · 2026Article
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- Unveiling Artificial Intelligence's Power: Precision, Personalization, and Progress in Rheumatology.Journal of clinical medicine · 2024Review
- AI am a rheumatologist: a practical primer to large language models for rheumatologists.Rheumatology (Oxford, England) · 2023Review
- Deep Learning Algorithm Detects Presence of Disorganization of Retinal Inner Layers (DRIL)-An Early Imaging Biomarker in Diabetic Retinopathy.Translational vision science & technology · 2023Article
- Machine learning applied to MRI evaluation for the detection of lymph node metastasis in patients with locally advanced cervical cancer treated with neoadjuvant chemotherapy.Archives of gynecology and obstetrics · 2023Article
- A machine learning approach applied to gynecological ultrasound to predict progression-free survival in ovarian cancer patients.Archives of gynecology and obstetrics · 2022Observational
- Dermatopathology of Malignant Melanoma in the Era of Artificial Intelligence: A Single Institutional Experience.Diagnostics (Basel, Switzerland) · 2022Article
- MIXTURE of human expertise and deep learning-developing an explainable model for predicting pathological diagnosis and survival in patients with interstitial lung disease.Modern pathology : an official journal of the United States and Canadian Academy of Pathology, Inc · 2022Article
- The Use and Utility of Machine Learning in Achieving Precision Medicine in Systemic Sclerosis: A Narrative Review.Journal of personalized medicine · 2022Review
- Computational pathology for musculoskeletal conditions using machine learning: advances, trends, and challenges.Arthritis research & therapy · 2022Review
- A Machine Learning Approach to Predict Remission in Patients With Psoriatic Arthritis on Treatment With Secukinumab.Frontiers in immunology · 2022Article
- Validity of Machine Learning in Predicting Giant Cell Arteritis Flare After Glucocorticoids Tapering.Frontiers in immunology · 2022Article
- Deep Learning-Based Classification of Inflammatory Arthritis by Identification of Joint Shape Patterns-How Neural Networks Can Tell Us Where to "Deep Dive" Clinically.Frontiers in medicine · 2022Article
- Artificial Intelligence in Dermatopathology: New Insights and Perspectives.Dermatopathology (Basel, Switzerland) · 2021Review
- Exploring the Oral Microbiome in Rheumatic Diseases, State of Art and Future Prospective in Personalized Medicine with an AI Approach.Journal of personalized medicine · 2021Review
Corrections and comments
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Authors and funding
7 authors at 2 institutions in 2 countries.
Funding
No grant is acknowledged in the PubMed record.
Abstract
Ultrasound-guided synovial tissue biopsy (USSB) may allow personalizing the treatment for patients with inflammatory arthritis. To this end, the quantification of tissue inflammation in synovial specimens can be crucial to adopt proper therapeutic strategies. This study aimed at investigating whether computer vision may be of aid in discriminating the grade of synovitis in patients undergoing USSB. We used a database of 150 photomicrographs of synovium from patients who underwent USSB. For each hematoxylin and eosin (H&E)-stained slide, Krenn's score was calculated. After proper data pre-processing and fine-tuning, transfer learning on a ResNet34 convolutional neural network (CNN) was employed to discriminate between low and high-grade synovitis (Krenn's score < 5 or ≥ 5). We computed test phase metrics, accuracy, precision (true positive/actual results), and recall (true positive/predicted results). The Grad-Cam algorithm was used to highlight the regions in the image used by the model for prediction. We analyzed photomicrographs of specimens from 12 patients with arthritis. The training dataset included n.90 images (n.42 with high-grade synovitis). Validation and test datasets included n.30 (n.14 high-grade synovitis) and n.30 items (n.16 with high-grade synovitis). An accuracy of 100% (precision = 1, recall = 1) was scored in the test phase. Cellularity in the synovial lining and sublining layers was the salient determinant of CNN prediction. This study provides a proof of concept that computer vision with transfer learning is suitable for scoring synovitis. Integrating CNN-based approach into real-life patient management may improve the workflow between rheumatologists and pathologists.
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Read under generation 80e0d062 · epoch 390. Bibliography from PubMed, PubMed Central and OpenAlex; grants from NIH RePORTER; trial links from ClinicalTrials.gov; estimates, votes and beliefs from the Socratic graph.