Vision transformer-based ordinal model for semi-quantitative synovitis grading on musculoskeletal ultrasound: a retrospective study with internal and external validation

Most errors occurred between adjacent grades, multimodal GSUS plus PDUS input outperformed single-modality models, and AI assistance improved junior-reader agreement and efficiency.</p> <p>This preliminary study confirms that Vision Transformer can achieve accurate and clinically applicable synovitis grading on musculoskeletal ultrasound, showing better robustness than traditional CNNs. This method may help standardize and streamline ultrasound evaluation in clinical practice and multicenter studies.</p> <p></p><p>A Vision Transformer-based model outperforms conventional CNNs for semi-quantitative synovitis grading on musculoskeletal ultrasound;</p><p>Multimodal input combining gray-scale and power Doppler ultrasound improves grading performance;</p><p>AI assistance enhances junior readers’ agreement and efficiency, supporting more standardized and efficient clinical ultrasound assessment.</p><p></p> <p>A Vision Transformer-based model outperforms conventional CNNs for semi-quantitative synovitis grading on musculoskeletal ultrasound;</p> <p>Multimodal input combining gray-scale and power Doppler ultrasound improves grading performance;</p> <p>AI assistance enhances junior readers’ agreement and efficiency, supporting more standardized and efficient clinical ultrasound assessment.</p>