Breast Ultrasound Image Reviewed With Assistance of Deep Learning Algorithms

Summary

This study evaluates a second review of ultrasound images of breast lesions using an interactive "deep learning" (or artificial intelligence) program developed by Samsung Medical Imaging, to see if this artificial intelligence will help the Radiologist make more accurate diagnoses.

Conditions

• Breast Cancer
• Breast Lesions
• Breast Mass

Interventions

DEVICE

Ultrasound Image review with CADe

This software is a computer-aided detection (CADe) software application, designed to assist radiologist to analyze breast ultrasound images. S-Detect automatically segments and classifies shape, orientation, margin, lesion boundary, echo pattern, and posterior feature characteristics of user-selected region of interest. The device uses deep learning methods to perform tissue segmentation and classification of images.

DEVICE

Ultrasound Image review with CADx

This software is also a computer-assisted diagnostic(CADx) software application, designed to assist a medical doctor in determining diagnosis by presenting whether a lesion is malignant in a breast ultrasound image obtained from an ultrasound imaging device.

DEVICE

Ultrasound Image manual review

The images will be reviewed by the radiologists using BIRADS scheme without any assistance of artificial assistance. This review will be done off-line using a separate program in entirely manual mode. During this review, BIRADS descriptor choices by each radiologist and the time it takes for the radiologist to make such decision will be stored.

PROCEDURE

Biopsy

Suspicious lesions found on breast ultrasound are then followed either by ultrasound guided biopsy or ultrasound imaging every 6 months for two years. For those who undergo biopsy, ultrasound provides images which are used to localize the lesion and guide the placement of the biopsy needle. The sample is sent to pathology for diagnosis, while the ultrasound guidance images are stored. For those who have imaging follow-up, ultrasound images of the breast mass are obtained, digitally stored and interpreted by the radiologist typically using BIRADS scheme.

Sponsors & Collaborators

• University of Rochester

  collaborator OTHER

• Samsung Medison

  lead INDUSTRY

Principal Investigators

• Avice O'Connell · Department of Imaging Sciences, University of Rochester

• Kevin Parker · Department of Electrical & Computer Engineering, University of Rochester

Study Design

Allocation

RANDOMIZED

Purpose

DEVICE_FEASIBILITY

Masking

SINGLE

Model

CROSSOVER

Eligibility

Min Age

19 Years

Sex

ALL

Healthy Volunteers

Yes

Timeline & Regulatory

Start

2018-09-20

Primary Completion

2019-11-30

Completion

2020-01-31

Countries

• United States

Study Locations