The mechanical interactions among integrated cellular structures in soft tissues dictate the mechanical behaviors and morphogenetic deformations observed in living organisms. However, replicating these multifaceted attributes in synthetic soft materials remains a challenge. In this work, we develop a smart hydrogel system featuring engineered stiff cellular patterns that induce strain-driven heterogeneous subdomains within the hydrogel film. These subdomains arise from the distinct mechanical responses of the pattern and film domains under applied mechanical forces. Unlike previous studies that incorporate reinforced inclusions into soft matrices to tailor material properties, our method manipulates the localization, integration, and interaction of these subdomain building blocks within the soft film. This enables extensive tuning of both local and global behaviors. Notably, we introduce a subdomain-interface mechanism that allows for the concurrent customization and decoupling of mechanical properties and shape transformations within a single material system-an achievement rarely accomplished with current synthetic soft materials. Additionally, our use of in-situ imaging characterizations, including full-field strain mapping via digital imaging correlation and reciprocal-space patterns through fast Fourier transform analysis of real-space pattern domains, provides rapid real-time monitoring tools to uncover the underlying principles governing tailored multiscale heterogeneities and intricate behaviors.

Purpose

To develop radiology domain-specific bidirectional encoder representations from transformers (BERT) models that can identify speech recognition (SR) errors and suggest corrections in radiology reports.

Materials and methods

A pretrained BERT model, Clinical BioBERT, was further pretrained on a corpus of 114 008 radiology reports between April 2016 and August 2019 that were retrospectively collected from two hospitals. Next, the model was fine-tuned on a training dataset of generated insertion, deletion, and substitution errors, creating Radiology BERT. This model was retrospectively evaluated on an independent dataset of radiology reports with generated errors (n = 18 885) and on unaltered report sentences (n = 2000) and prospectively evaluated on true clinical SR errors (n = 92). Correction Radiology BERT was separately trained to suggest corrections for detected deletion and substitution errors. Area under the receiver operating characteristic curve (AUC) and bootstrapped 95% CIs were calculated for each evaluation dataset.

Results

Radiology-specific BERT had AUC values of >.99 (95% CI: >0.99, >0.99), 0.94 (95% CI: 0.93, 0.94), 0.98 (95% CI: 0.98, 0.98), and 0.97 (95% CI: 0.97, 0.97) for detecting insertion, deletion, substitution, and all errors, respectively, on the independently generated test set. Testing on unaltered report impressions revealed a sensitivity of 82% (28 of 34; 95% CI: 70%, 93%) and specificity of 88% (1521 of 1728; 95% CI: 87%, 90%). Testing on prospective SR errors showed an accuracy of 75% (69 of 92; 95% CI: 65%, 83%). Finally, the correct word was the top suggestion for 45.6% (475 of 1041; 95% CI: 42.5%, 49.3%) of errors.

Conclusion

Radiology-specific BERT models fine-tuned on generated errors were able to identify SR errors in radiology reports and suggest corrections.Keywords: Computer Applications, Technology Assessment Supplemental material is available for this article. © RSNA, 2022See also the commentary by Abajian and Cheung in this issue.