The lesion detection efficacy of deep learning on automatic breast ultrasound and factors affecting its efficacy: a pilot study.

ABSTRACT:

Objectives

The aim of this study was to investigate the detection efficacy of deep learning (DL) for automatic breast ultrasound (ABUS) and factors affecting its efficacy.

Methods

Females who underwent ABUS and handheld ultrasound from May 2016 to June 2017 (N = 397) were enrolled and divided into training (n = 163 patients with breast cancer and 33 with benign lesions), test (n = 57) and control (n = 144) groups. A convolutional neural network was optimized to detect lesions in ABUS. The sensitivity and false positives (FPs) were evaluated and compared for different breast tissue compositions, lesion sizes, morphologies and echo patterns.

Results

In the training set, with 688 lesion regions (LRs), the network achieved sensitivities of 93.8

SUBMITTER: PhD XL 

PROVIDER: S-EPMC8822545 | biostudies-literature | 2022 Feb

REPOSITORIES: biostudies-literature