Breast Ultrasound Image Reviewed With Assistance of Deep Learning Algorithms

Study Results

Results pending

The study team has not published outcome measurements, participant flow, or safety data for this trial yet. Check back later for updates.

Basic Information

Get a concise snapshot of the trial, including recruitment status, study phase, enrollment targets, and key timeline milestones.

Recruitment Status

UNKNOWN

Clinical Phase

NA

Total Enrollment

300 participants

Study Classification

INTERVENTIONAL

Study Start Date

2018-09-20

Study Completion Date

2020-01-31

Brief Summary

Review the sponsor-provided synopsis that highlights what the study is about and why it is being conducted.

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.

Related Clinical Trials

Explore similar clinical trials based on study characteristics and research focus.

Using Deep Learning Methods to Analyze Automated Breast Ultrasound and Hand-held Ultrasound Images, to Establish a Diagnosis, Therapy Assessment and Prognosis Prediction Model of Breast Cancer.

NCT04270032

Breast Cancer

UNKNOWN

Development of Assist Tool for Breast Examination Using the Principle of Ultrasonic Sensor

NCT06255808

Breast Cancer Breast Neoplasm Artificial Intelligence +2 more

ACTIVE_NOT_RECRUITING

Multi-Reader Multi-Case, Retrospective Study to Evaluate Effectiveness of CadAI-B for Breast

NCT06352684

Breast Cancer

ACTIVE_NOT_RECRUITING

Multi-center Study of Deep Learning AI in Breast Mass

NCT05443672

Breast Neoplasms

UNKNOWN

3D Ultrasound Breast Imaging

NCT04692818

Breast Tumor

RECRUITING NA

Detailed Description

Dive into the extended narrative that explains the scientific background, objectives, and procedures in greater depth.

Using ultrasound images prospectively acquired, the purpose of this study entails a second review of ultrasound images with suspicious breast lesions using an interactive "deep learning" (or artificial intelligence) program developed by SamsungMedison Co.,Ltd.

The images will be reviewed by the radiologists twice: first without, and then with assistance of artificial intelligence program by SamsungMedison Co., Ltd.

BIRADS system will be used in this study.

The objectives of the study are twofold: to quantify the statistical equivalence of radiologists' opinion and AI's output (CADe), and to check BIRADS score-based diagnostic accuracy (CADx) that is gained by the Radiologists' use of this interactive tool

Conditions

See the medical conditions and disease areas that this research is targeting or investigating.

Breast Cancer Breast Lesions Breast Mass

Study Design

Understand how the trial is structured, including allocation methods, masking strategies, primary purpose, and other design elements.

Allocation Method

RANDOMIZED

Intervention Model

CROSSOVER

This clinical study performed by multiple reader multiple case (MRMC) study design, where as set of clinical readers evaulate under multiple reading condition. All Interpreting physician(reader) independently read all of the cases. (fully-crossed design).

Primary Study Purpose

DEVICE_FEASIBILITY

Blinding Strategy

SINGLE

Outcome Assessors

The study consisted of 10 readers with varying levels of training and experience providing analysis on a randomized set of 300 patients' breast ultrasound data with and without S-Detect for Breast. Two reading periods separated by at least 3-week washout, totaling 600 cases analyzed per reader. PI and her associate have knowledge about patients diagnosis and other information. So, they are exclueded in readers for "reviewing". And all breast US images are de-indentified.

Study Groups

Review each arm or cohort in the study, along with the interventions and objectives associated with them.

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. Radiologists also make assessment decision without any intervention from artificial intelligence. 10 radiologists review manually.

Group Type ACTIVE_COMPARATOR

Ultrasound Image review with CADe

Intervention Type DEVICE

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.

Ultrasound Image review with CADx

Intervention Type DEVICE

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.

Ultrasound Image manual review

Intervention Type DEVICE

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.

Biopsy

Intervention Type PROCEDURE

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.

Review by S-Detect for Breast

The same images will be separately processed by the artificial intelligence system (S-Detect for Breast) by Samsung. The two results, one by the radiologists and the other by artificial intelligence system, will be compared to statistically quantify equivalence (CADe).

Group Type EXPERIMENTAL

Ultrasound Image review with CADe

Intervention Type DEVICE

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.

Ultrasound Image manual review

Intervention Type DEVICE

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.

Review with assistance of S-Detect for Breast

Second, the images will be reviewed by the radiologists with the help of artificial intelligence system, which is an interactive tool automatically providing recommendations on BIRADS descriptor choices that can be modified by the radiologists. The radiologists, after selecting all the descriptors of BIRADS, will decide the assessment categories. These decisions will be compared with the ground truths generated from the biopsy results or a 24-month follow-up (CADx).

Group Type EXPERIMENTAL

Ultrasound Image review with CADx

Intervention Type DEVICE

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.

Ultrasound Image manual review

Intervention Type DEVICE

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.

Biopsy

Intervention Type PROCEDURE

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.

Interventions

Learn about the drugs, procedures, or behavioral strategies being tested and how they are applied within this trial.

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.

Intervention Type 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.

Intervention Type 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.

Intervention Type DEVICE

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.

Intervention Type PROCEDURE

Other Intervention Names

Discover alternative or legacy names that may be used to describe the listed interventions across different sources.

S-Detect S-Detect for Breast CADe Computer-Assisted Detection Device S-Detect S-Detect for Breast CADx Computer-Assisted Diagnostic Device Convetional Ultrasound image

Eligibility Criteria

Check the participation requirements, including inclusion and exclusion rules, age limits, and whether healthy volunteers are accepted.

Inclusion Criteria

* Adult females or males recommended for ultrasound-guided breast lesion biopsy or ultrasound follow-up with at least one suspicious lesion
* Age \> 18 years
* Able to provide informed consent
3. Racial and Ethnic Origin: There are no enrollment exclusions based on economic status, race, or ethnicity. Based on local and United States census data, the expected ethnic distribution will be approximately 26 Hispanic (approx. 16%) and 134 non-Hispanic people. Furthermore, the expected racial distribution is expected to be approximately 126 White (approx. 79% of the whole study), 21 Black or African America (13%), 8 Asian (5%), and 5 of other categories (3%).
4. Vulnerable Subjects: It is unlikely that any UR students or employees will be enrolled unless their primary physician refers them to UR Medicine Breast Imaging at Red Creek for breast ultrasound and a suspicious lesion is found. We do not expect any of these referrals to be from staffs who work directly with the PIs.

Exclusion Criteria

* Unable to read and understand English
* Unable or unwilling to provide informed consent
* A patient with current or previous diagnosis of breast cancer in the same quadrant
* Unable or unwilling to undergo study procedures
3. Subject Characteristics

1. Number of Subjects: 300 subjects from 300 separate breast lesions can be acquired. If a subject has more than 1 suspicious lesion, each may be chosen by the radiologist attending as suitable for "second review".

Minimum Eligible Age

19 Years

Eligible Sex

ALL

Accepts Healthy Volunteers

Yes