Evaluation of TaiHao Breast Ultrasound Diagnosis Software

NCT ID: NCT04551105

Last Updated: 2022-11-02

Study Results

Results available

Outcome measurements, participant flow, baseline characteristics, and adverse events have been published for this study.

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Basic Information

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Recruitment Status

COMPLETED

Clinical Phase

NA

Total Enrollment

16 participants

Study Classification

INTERVENTIONAL

Study Start Date

2020-08-15

Study Completion Date

2020-11-01

Brief Summary

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The BR-USCAD DS Module is a computer-assisted detection and diagnosis software based on a deep learning algorithm. This retrospective, fully-crossed, multi-reader, multi-case (MRMC) study aims to compare the performances of readers without and with the aid of the Breast Ultrasound Image Reviewed with Assistance of Computer-Assisted Detection and Diagnosis System (BR-USCAD DS) in interpreting breast ultrasound images of lesions.

Detailed Description

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Conditions

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Breast Cancer Breast Diseases

Study Design

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Allocation Method

RANDOMIZED

Intervention Model

CROSSOVER

Primary Study Purpose

DIAGNOSTIC

Blinding Strategy

NONE

Study Groups

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First session: manual review first and then review with CADx

Reader Group X interpret the "Dataset A" cases in different random order without any assistance of AI first, and then interpret the "Dataset B" cases in different random order with TaiHao AI system.

Group Type ACTIVE_COMPARATOR

Reader Group X - Session 1

Intervention Type DIAGNOSTIC_TEST

Each rater in "Reader Group X" will interpret the "dataset A" cases in different random order without BR-USCAD DS and interpret the "dataset B" cases in different random order with BR-USCAD DS.

First session: review with CADx first and then manual review

Reader Group Y interpret the "Dataset A" cases in different random order with TaiHao AI system first, and then interpret the "Dataset B" cases in different random order without any assistance of AI.

Group Type ACTIVE_COMPARATOR

Reader Group Y - Session 1

Intervention Type DIAGNOSTIC_TEST

Each rater in "Reader Group Y" will interpret the "dataset A" cases in different random order with BR-USCAD DS and interpret the "dataset B" cases in different random order without BR-USCAD DS.

Second session: manual review first and then review with CADx

At least 4 weeks after first session for memory washing out. Reader Group X interpret the "Dataset A" cases in different random order with TaiHao AI system first, and then interpret the "Dataset B" cases in different random order without any assistance of AI.

Group Type ACTIVE_COMPARATOR

Reader Group X - Session 2

Intervention Type DIAGNOSTIC_TEST

Each rater in "Reader Group X" will interpret the "dataset A" cases in different random order with BR-USCAD DS and interpret the "dataset B" cases in different random order without BR-USCAD DS.

Second session: review with CADx first and then manual review

At least 4 weeks after first session for memory washing out. Rader Group Y interpret the "Dataset A" cases in different random order without any assistance of AI first, and then interpret the "Dataset B" cases in different random order with TaiHao AI system.

Group Type ACTIVE_COMPARATOR

Reader Group Y - Session 2

Intervention Type DIAGNOSTIC_TEST

Each rater in "Reader Group Y" will interpret the "dataset A" cases in different random order without BR-USCAD DS and interpret the "dataset B" cases in different random order with BR-USCAD DS.

Interventions

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Reader Group X - Session 1

Each rater in "Reader Group X" will interpret the "dataset A" cases in different random order without BR-USCAD DS and interpret the "dataset B" cases in different random order with BR-USCAD DS.

Intervention Type DIAGNOSTIC_TEST

Reader Group Y - Session 1

Each rater in "Reader Group Y" will interpret the "dataset A" cases in different random order with BR-USCAD DS and interpret the "dataset B" cases in different random order without BR-USCAD DS.

Intervention Type DIAGNOSTIC_TEST

Reader Group X - Session 2

Each rater in "Reader Group X" will interpret the "dataset A" cases in different random order with BR-USCAD DS and interpret the "dataset B" cases in different random order without BR-USCAD DS.

Intervention Type DIAGNOSTIC_TEST

Reader Group Y - Session 2

Each rater in "Reader Group Y" will interpret the "dataset A" cases in different random order without BR-USCAD DS and interpret the "dataset B" cases in different random order with BR-USCAD DS.

Intervention Type DIAGNOSTIC_TEST

Eligibility Criteria

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Inclusion Criteria

* B-mode breast ultrasound image
* Female, age 21 or older
* Breast lesion images acquired before a biopsy or surgery - these images were retrospectively collected with histology report.
* Non-biopsied benign lesions with negative follow-up for a minimum of 24 months
* At least two orthogonal views of a lesion

Exclusion Criteria

* Breast lesion images acquired after biopsy or surgery.
* Any breast surgeries or interventional procedures in the 12 months prior to ultrasound imaging
* Case demonstrating administrative or technical errors
* Multiple lesions in one 2-D ultrasound image
* Breast ultrasound images with Doppler, elastography, or other overlays present
* Case with less than 2-year follow-up and without biopsy confirmation
Minimum Eligible Age

21 Years

Eligible Sex

FEMALE

Accepts Healthy Volunteers

No

Sponsors

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Columbia University

OTHER

Sponsor Role collaborator

Taipei Veterans General Hospital, Taiwan

OTHER_GOV

Sponsor Role collaborator

Virginia Polytechnic Institute and State University

OTHER

Sponsor Role collaborator

TaiHao Medical Inc.

INDUSTRY

Sponsor Role lead

Responsible Party

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Responsibility Role SPONSOR

Principal Investigators

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Shih Chung Lo, Ph.D.

Role: STUDY_DIRECTOR

Arlington Innovation Center: Health Research - Virginia Tech

Locations

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Arlington Innovation Center: Health Research - Virginia Tech

Arlington, Virginia, United States

Site Status

Countries

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United States

Provided Documents

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Document Type: Study Protocol and Statistical Analysis Plan

View Document

Other Identifiers

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TaiHao BR-USCAD VT 20-249

Identifier Type: -

Identifier Source: org_study_id

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