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Updated: Sep 11, 2026
Synchronous Triplanar Reconstruction Integrated with Color Doppler Mapping for Precise and Rapid Localization of Thyroid Lesions
Published on: February 9, 2024
Multi-view AI output variability as a teaching resource in a micro-course for thyroid TI-RADS interpretation among
Chao Fu1, Kefei Cui1, Caifeng Si1
1Department of Ultrasound, The First Affiliated Hospital of Zhengzhou University, Zhengzhou, Henan, China.
Introduction:
Thyroid TI-RADS interpretation requires integration of sonographic features across image planes, yet routine teaching often emphasizes the final category more than the cross-plane reasoning process. We evaluated whether a structured micro-course using multi-view AI output variability as a teaching resource could improve TI-RADS interpretation among ultrasound residents.
Methods:
In this single-center randomized controlled trial, 52 ultrasound residents were randomized 1:1 to either a structured micro-course using multi-view AI output variability as a teaching resource or time-matched conventional TI-RADS teaching. The primary outcome was participant-level TI-RADS interpretation accuracy at baseline (T0), immediately after teaching (T1), and 4 weeks later (T2). Secondary outcomes were inter-learner consistency, representative plane hit score, and self-efficacy. Justification quality in discordant cases was analyzed exploratorily.
Results:
Baseline characteristics were comparable between groups. Accuracy showed a significant group-by-time interaction [F (2, 100.0) = 10.86, P < 0.001]. There was no between-group difference at T0 (β = 0.08, 95% CI: -0.74 to 0.89; P = 0.851), but the intervention group performed better at T1 (β = 1.69, 95% CI: 0.88-2.51; P < 0.001). The between-group difference at T2 was not significant (β = 0.73, 95% CI: -0.08 to 1.55; P = 0.078). At T1, the intervention group also had a higher representative plane hit score (15.00 [12.00-17.00] vs. 13.00 [11.00-15.00]; P < 0.001), higher inter-learner consistency at T1 and T2 (ΔAC2 = 0.08 and 0.09, respectively; both 95% CIs excluded zero), and a greater increase in self-efficacy (β = 0.27, 95% CI: 0.09-0.45; P = 0.006). Exploratory analysis showed more favorable justification quality in the intervention group (OR: 10.26, 95% CI: 5.36-19.67; P < 0.001).
Conclusions:
A structured micro-course using multi-view AI output variability as a teaching resource improved immediate TI-RADS interpretation accuracy, but the between-group advantage was not sustained at 4 weeks, despite persistently higher inter-learner consistency. These findings support the short-term educational value of the structured micro-course rather than an independent effect of AI output variability and warrant evaluation of repeated or booster instruction.
