AI assisted method for efficiently generating breast ultrasound screening reports
This addresses a labor-intensive clinical workflow for radiologists by automating repetitive report writing in breast cancer screening.
The researchers tackled the problem of time-consuming manual breast ultrasound screening report writing by developing an AI pipeline that generates preliminary reports from ultrasound images, which doctors then adjust to create final reports. Their approach, tested on 4809 breast tumor instances, improved doctors' work efficiency by up to 90%.
Background: Ultrasound is one of the preferred choices for early screening of dense breast cancer. Clinically, doctors have to manually write the screening report which is time-consuming and laborious, and it is easy to miss and miswrite. Aim: We proposed a new pipeline to automatically generate AI breast ultrasound screening reports based on ultrasound images, aiming to assist doctors in improving the efficiency of clinical screening and reducing repetitive report writing. Methods: AI was used to efficiently generate personalized breast ultrasound screening preliminary reports, especially for benign and normal cases which account for the majority. Based on the preliminary AI report, doctors then make simple adjustments or corrections to quickly generate the final report. The approach has been trained and tested using a database of 4809 breast tumor instances. Results: Experimental results indicate that this pipeline improves doctors' work efficiency by up to 90%, which greatly reduces repetitive work. Conclusion: Personalized report generation is more widely recognized by doctors in clinical practice compared with non-intelligent reports based on fixed templates or containing options to fill in the blanks.