Evaluation of Use of Diagnostic AI for Lung Cancer in Practice

Ensemble Group Holdings

Status: Unknown

Conditions: Lung Cancer

Treatments: Behavioral: AI-human interaction

Study type: Interventional

Funder types: Industry

Identifiers

NCT03780582

EN-122018

Details and patient eligibility

About

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.

Full 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.

Enrollment: 15 estimated patients

Sex: All

Volunteers: Accepts Healthy Volunteers

Inclusion criteria

• The participant performs radiology screenings professionally

Exclusion criteria

Trial design

Primary purpose: Diagnostic

Allocation: Randomized

Interventional model: Crossover Assignment

Masking: Single Blind

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15 participants in 3 patient groups

Probabilistic Classification

Experimental group

Description:

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.

Treatment:

Behavioral: AI-human interaction

Classification Plus Detection

Experimental group

Description:

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.

Treatment:

Behavioral: AI-human interaction

Classification With Delayed Detection

Experimental group

Description:

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.

Treatment:

Behavioral: AI-human interaction