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NCT05899400 Clinical Trial

A Study to Validate and Improve an Automated Image Analysis Algorithm to Detect Tuberculosis in Sputum Smear Slides

Tuberculosis, Pulmonary Clinical Trial

Official title:
A Study to Validate and Improve an Automated Image Analysis Algorithm to Detect Tuberculosis in Sputum Smear Slides

Clinical Trial Details — Status: Completed

Administrative data
NCT number NCT05899400
Other study ID # Diascopic
Secondary ID 1R43EB028736-01
Status Completed
Phase
First received
Last updated
Start date September 15, 2019
Est. completion date December 31, 2022

Study information
Verified date June 2023
Source Diascopic, LLC
Contact n/a
Is FDA regulated No
Health authority
Study type Observational

Clinical Trial Summary

A Study to Validate and Improve an Automated Image Analysis Algorithm to Detect Tuberculosis in Sputum Smear Slides

Recruitment information / eligibility
Status Completed
Enrollment 400
Est. completion date December 31, 2022
Est. primary completion date October 31, 2022
Accepts healthy volunteers No
Gender All
Age group N/A and older
Eligibility Inclusion Criteria: - Samples sourced from Makerere University's Joint Clinical Research Clinic (JCRC) and Laboratory Exclusion Criteria: - No samples from outside Makerere's Domain

Study Design

Related Conditions & MeSH terms

Intervention

Diagnostic Test:
• Diascopic iON Image Analysis System
A scanning digital optical train that images sputum slides for automated analysis by a tuberculosis detecting algorithm

Locations
Country Name City State
Uganda Makerere University Kampala

Sponsors (3)
Lead Sponsor Collaborator
Diascopic, LLC National Institute for Biomedical Imaging and Bioengineering (NIBIB), National Institutes of Health (NIH)

Country where clinical trial is conducted

Uganda, 

Outcome
Type Measure Description Time frame Safety issue
Primary Determined sensitivity and specificity of the iON device by comparing MTB slide results to their associated bacterial culture results from the same specimen(s). Sensitivity, specificity, and average analysis time were determined. An image database of >40,000 images was also created for internal use. Study completed over the grant period 2019 - 2022
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