AI-assisted Method for Efficiently Generating Breast Ultrasound Screening Reports

Abstract

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 efficiently generated personalized breast ultrasound screening preliminary reports, especially for benign and normal cases, which account for the majority. Doctors then make simple adjustments or corrections based on the preliminary AI report to generate the final report quickly. 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%, greatly reducing repetitive work.

Conclusion: Personalized report generation is more widely recognized by doctors in clinical practice than non-intelligent reports based on fixed templates or options to fill in the blanks.

Keywords: AI; BI-RADS; automatic classification; breast cancer; early screening; feature; report generation; ultrasound.