Evaluation of Use of Diagnostic AI for Lung Cancer in Practice

July 20, 2019 updated by: Ensemble Group Holdings, LLC
This study investigates ways of improving radiologists performance of the classification of CT-scans as cancerous or non-cancerous. Participants interact with an AI to classify CT-scans under three different conditions.

Study Overview

Status

Unknown

Conditions

Intervention / Treatment

Detailed Description

The three conditions are as follows: "probabilistic classification", where the radiologist diagnoses scans using an AI cancer likelihood score; "classification plus detection", where the radiologist see detecting lung nodules in addition to the AI's probabilistic classification score before making her own examination of the CT-scan; and "classification with delayed detection", where the radiologist identifies regions of interest independently of the AI and then sees the AI's detected ROIs.

Study Type

Interventional

Enrollment (Anticipated)

15

Phase

  • Not Applicable

Contacts and Locations

This section provides the contact details for those conducting the study, and information on where this study is being conducted.

Study Locations

  • Hong Kong
      • Hong Kong, Hong Kong
        • University of Hong Kong

Participation Criteria

Researchers look for people who fit a certain description, called eligibility criteria. Some examples of these criteria are a person's general health condition or prior treatments.

Eligibility Criteria

Ages Eligible for Study

  • Child
  • Adult
  • Older Adult

Accepts Healthy Volunteers

No

Genders Eligible for Study

All

Description

Inclusion Criteria:

  • The participant performs radiology screenings professionally

Exclusion Criteria:

-

Study Plan

This section provides details of the study plan, including how the study is designed and what the study is measuring.

How is the study designed?

Design Details

  • Primary Purpose: Diagnostic
  • Allocation: Randomized
  • Interventional Model: Crossover Assignment
  • Masking: Single

Arms and Interventions

Participant Group / Arm
Intervention / Treatment
Experimental: Probabilistic Classification
Radiologists see a "score" from 1-100 that represents the AI's prediction of whether the CT-scan comes from a patient with cancer or not before beginning their analysis of the scan.
Behavioral: AI-human interaction
Exploring what kinds of AI-human interaction improve radiologists detection accuracy.
Experimental: Classification Plus Detection
Radiologists see a "score" from 1-100 that represents the AI's prediction of whether the CT-scan comes from a patient with cancer or not before beginning their analysis of the scan. They also see ROIs identified by the AI that represent lung nodules.
Behavioral: AI-human interaction
Exploring what kinds of AI-human interaction improve radiologists detection accuracy.
Experimental: Classification With Delayed Detection
Radiologists see a "score" from 1-100 that represents the AI's prediction of whether the CT-scan comes from a patient with cancer or not before beginning their analysis of the scan. After identifying their own ROIs, the radiologist then can see ROIs identified by the AI that represent lung nodules before making final decisions.
Behavioral: AI-human interaction
Exploring what kinds of AI-human interaction improve radiologists detection accuracy.

What is the study measuring?

Primary Outcome Measures

Outcome Measure
Measure Description
Time Frame
Classification accuracy
Time Frame: up to 4 months after initiation of evaluation of the test set
This compares radiologists' classifications with the ground truth in the tested cases.
up to 4 months after initiation of evaluation of the test set

Secondary Outcome Measures

Outcome Measure
Measure Description
Time Frame
detection concordance
Time Frame: up to 4 months after initiation of evaluation of the test set
Evaluation of concordance between radiologists in the tested cases in detection of lung nodules > 4 mm
up to 4 months after initiation of evaluation of the test set

Collaborators and Investigators

This is where you will find people and organizations involved with this study.

Sponsor

Ensemble Group Holdings, LLC

Study record dates

These dates track the progress of study record and summary results submissions to ClinicalTrials.gov. Study records and reported results are reviewed by the National Library of Medicine (NLM) to make sure they meet specific quality control standards before being posted on the public website.

Study Major Dates

Study Start (Actual)

December 14, 2018

Primary Completion (Anticipated)

December 15, 2019

Study Completion (Anticipated)

December 15, 2019

Study Registration Dates

First Submitted

December 16, 2018

First Submitted That Met QC Criteria

December 17, 2018

First Posted (Actual)

December 19, 2018

Study Record Updates

Last Update Posted (Actual)

July 23, 2019

Last Update Submitted That Met QC Criteria

July 20, 2019

Last Verified

July 1, 2019

More Information

Terms related to this study

Additional Relevant MeSH Terms

Other Study ID Numbers

  • EN-122018

Plan for Individual participant data (IPD)

Plan to Share Individual Participant Data (IPD)?

Undecided

Drug and device information, study documents

Studies a U.S. FDA-regulated drug product

No

Studies a U.S. FDA-regulated device product

No

This information was retrieved directly from the website clinicaltrials.gov without any changes. If you have any requests to change, remove or update your study details, please contact register@clinicaltrials.gov. As soon as a change is implemented on clinicaltrials.gov, this will be updated automatically on our website as well.

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