Public articles linked to the same research event.
Journal of imaging informatics in medicine This study developed and dual-center validated an interpretable U-Net-based AI framework for automated quality control (QC) of knee anteroposterior (AP) and lateral (LAT) radiographs, generating QC indices through anatomical segmentation and landmark localization, with mean Dice similarity coefficients of 0.964 and 0.936 in internal and external validation, most intraclass correlation coefficients exceeding 0.90, QC sensitivity of 91.67% to 98.36% and specificity of 89.13% to 99.10%; separately, six radiographers who received 4 weeks of AI-based feedback on 948 radiographs from 474 patients showed exploratory numerical gains in sensitivity, with effect sizes of 0.30 to 0.73.
This study developed and dual-center validated an interpretable U-Net-based AI framework for automated quality control (QC) of knee anteroposterior (AP) and lateral (LAT) radiographs, generating QC indices through anatomical segmentation and landmark localization, with mean Dice similarity coefficients of 0.964 and 0.936 in internal and external validation, most intraclass correlation coefficients exceeding 0.90, QC sensitivity of 91.67% to 98.36% and specificity of 89.13% to 99.10%; separately, six radiographers who received 4 weeks of AI-based feedback on 948 radiographs from 474 patients showed exploratory numerical gains in sensitivity, with effect sizes of 0.30 to 0.73.
This study developed and dual-center validated an interpretable U-Net-based AI framework for automated quality control (QC) of knee anteroposterior (AP) and lateral (LAT) radiographs, generating QC indices through anatomical segmentation and landmark localization, with mean Dice similarity coefficients of 0.964 and 0.936 in internal and external validation, most intraclass correlation coefficients exceeding 0.90, QC sensitivity of 91.67% to 98.36% and specificity of 89.13% to 99.10%; separately, six radiographers who received 4 weeks of AI-based feedback on 948 radiographs from 474 patients showed exploratory numerical gains in sensitivity, with effect sizes of 0.30 to 0.73.
This study developed and dual-center validated an interpretable U-Net-based AI framework for automated quality control (QC) of knee anteroposterior (AP) and lateral (LAT) radiographs, generating QC indices through anatomical segmentation and landmark localization, with mean Dice similarity coefficients of 0.964 and 0.936 in internal and external validation, most intraclass correlation coefficients exceeding 0.90, QC sensitivity of 91.67% to 98.36% and specificity of 89.13% to 99.10%; separately, six radiographers who received 4 weeks of AI-based feedback on 948 radiographs from 474 patients showed exploratory numerical gains in sensitivity, with effect sizes of 0.30 to 0.73.