Direct deep learning analysis of three-dimensional automated breast ultrasound videos with reading mode optimization for breast cancer diagnosis.

ABSTRACT:

Purpose

This study aimed to develop and evaluate a deep learning model that directly analyzes three-dimensional automated breast ultrasound videos (DL-3DABUV) to assist breast cancer diagnosis, and to examine the optimal reading mode for clinical implementation.

Methods

This retrospective study included 547 patients (285 benign, 262 malignant), who were randomly assigned to a training set (n=437) and a test set (n=110). The DL-3DABUV model, built using ResNet50 and multi-instance learning, was trained by directly analyzing videos without image selection or manual annotation. Six radiologists (three experienced and three novice) evaluated the test set under three modes: independent-reading (without DL-3DABUV), second-reading (without prior knowledge of DL-3DABUV results), and

SUBMITTER: Guo Y 

PROVIDER: S-EPMC12856069 | biostudies-literature | 2026 Jan

REPOSITORIES: biostudies-literature