AI-enhanced Wrist-Hand US Image Acquisition: Development and Initial Clinical Evaluation

Background Despite their widespread use and clinical importance, musculoskeletal (MSK) US examinations remain highly subjective and heavily reliant on the sonologist's expertise. Purpose To develop deep learning models for standard plane recognition and anatomic segmentation in dynamic wrist-hand US examinations and to evaluate their performance in clinical practice. Materials and Methods A total of 66 743 normal US images from 430 volunteers (218 female and 212 male; mean age, 43.3 years ± 10.6 [SD]) were prospectively collected across 20 hospitals (October 2023 to May 2024). A tool based on Residual Network and High-Resolution Network was developed to perform two tasks and was evaluated with the F1 score and mean intersection over union, which were compared with those of baseline models with use of Wilcoxon signed rank and McNemar tests, as appropriate. The clinical evaluation phase involved 36 healthy volunteers who were randomly assigned to undergo artificial intelligence (AI)-assisted or conventional scanning by three junior-level and three senior-level novice MSK sonologists. Kaplan-Meier plots and the χ² test or Fisher exact test were used to assess the impact of AI assistance on efficiency and accuracy. Results The AI tool achieved an average F1 score of 96.2% for standardized plane recognition, outperforming EfficientNet and MobileNet by 1.3%-2.5% (per-image classification correctness, P < .001 for both). It achieved a median mean intersection over union of 0.647, higher than SegFormer (0.634) and DeepLabV3 (0.636) (P < .001 for both), in anatomic segmentation. AI assistance increased standard plane acquisition rate by 9.9% (from 241 of 270 images to 265 of 270, adjusted P < .05) and resulted in a 5.8-fold reduction (29 of 270 to five of 270, adjusted P < .05) in nonstandard image acquisition for novice MSK sonologists and improved scanning efficiency (P < .001) for junior-level novices. Conclusion The model demonstrated robust performance independently and helped improve the quality and efficiency of performance of novice MSK sonologists. Clinical trial registration no. NCT06883669 © RSNA, 2026 Supplemental material is available for this article. See also the editorial by Nazarian in this issue.