Evaluation of the Efficacy of Diagnostic Support Algorithms in Chest X-rays- LuAna Trial

Evaluation of the Efficacy of Diagnostic Support Algorithms in Chest X-rays - LungAnalysis (LuAna): LuAna Stepped Wedge Trial

This study aims to evaluate whether the use of AI as a physician support tool is associated with an increase in the detection rate of chest radiographic findings in adults with respiratory complaints, compared to diagnosis performed exclusively by doctors, without AI support. This is a cluster-randomized clinical trial, following the stepped wedge design, and adhering to the guidelines of the Standard Protocol Items: Recommendations for Interventional Trials (SPIRIT). In this study, the Diagnostic Support Solution for Chest X-rays - LungAnalysis (LuAna), developed by the Hospital Israelita Albert Einstein (HIAE) within the PROADI-SUS Banco de Imagens, was used.

The clinical trial will be conducted in multiple centers with a diverse population from the public health system, to ensure that the algorithms are validated across a broad demographic profile. The expected benefits are significant, providing greater security for patients, increasing doctors' confidence in interpreting chest X-rays, promoting efficiency and cost savings for healthcare services, and offering promising prospects for other AI applications in imaging diagnostics.

Study Overview

• Status: Recruiting
• Conditions: Pneumothorax; Lung Injury; Cardiomegaly; Pleural Effusion; Consolidation; Edema Lung
• Intervention / Treatment: Device: App LuAna

Detailed Description

Imaging diagnostic aid tools that use AI and facilitate the identification of findings on chest x-rays can contribute to doctors' care routines and clinicians' and radiologists' reporting routines, as these tools can allow the organization of care queues according to priorities, in addition to identifying subtle findings on the image, thereby reducing errors in reading the RXT and benefiting patients with greater agility in care and a shorter time until diagnosis. However, for reliability, these tools must undergo rigorous validation processes in large populations before implementation.

• Study Type: Interventional
• Enrollment (Estimated): 1470
• Phase: Not Applicable

Contacts and Locations

• Study Contact: Name: Joselisa Paiva, PhD; Phone Number: +55 (11) 9981667340; Email: joselisa.paiva@einstein.br

Study Locations

• Brazil
  • Paraná
    • Curitiba, Paraná, Brazil, 81130-160
      • Recruiting
      • FEAS Curitiba
      • Contact: Romulo Pereira, PI; Phone Number: +55 41999430693

Participation Criteria

Eligibility Criteria

• Ages Eligible for Study: Adult; Older Adult
• Accepts Healthy Volunteers: No

Description

Inclusion Criteria:

• Non-reported chest X-rays (XRts) of individuals aged over 18 years.
• Individuals images with respiratory complaints.
• Chest X-rays taken during the presence of these respiratory symptoms or while being followed up for respiratory disease.
• Chest X-rays taken on any X-ray machine.
• Chest X-rays that include at least one frontal view of the chest.

Exclusion Criteria:

• Those whose chest X-ray was performed due to a history of trauma, pre-operative risk assessment, lung cancer screening, or exclusively for verifying the correct positioning of a peripheral intravenous catheter (PICC).
• Chest X-rays with technical quality below the minimum required for proper interpretation and diagnosis.
• Cases without at least one frontal view.
• X-rays printed on regular paper.

Study Plan

How is the study designed?

Design Details

• Primary Purpose: Other
• Allocation: N/A
• Interventional Model: Single Group Assignment
• Masking: None (Open Label)

Number of Arms

1

Arms and Interventions

Participant Group / Arm	Intervention / Treatment
Experimental: App LuAna; feedback of the artificial intelligence after the inclusion image in app LuAna.	Device: App LuAna; Inclusion of chest x-ray images in the LuAna app to receive feedback on lung findings.

What is the study measuring?

Primary Outcome Measures

Outcome Measure	Measure Description	Time Frame
Detection rate	Detection rate of "Radiological Findings", before and after Artificial Intelligence assistance, compared to gold standard (report validated twice by thoracic radiologists blind to the interpretation of the examining physician and AI result).	through study completion, an average of 1 year

Collaborators and Investigators

• Sponsor: Hospital Israelita Albert Einstein
• Investigators: Principal Investigator: Joselisa Paiva, PhD, Hospital Israelita Albert Einstein

Study record dates

Study Major Dates

• Study Start (Actual): January 5, 2026
• Primary Completion (Estimated): June 1, 2026
• Study Completion (Estimated): December 1, 2026

Study Registration Dates

• First Submitted: November 7, 2024
• First Submitted That Met QC Criteria: November 11, 2024
• First Posted (Actual): November 13, 2024

Study Record Updates

• Last Update Posted (Actual): February 2, 2026
• Last Update Submitted That Met QC Criteria: January 29, 2026
• Last Verified: January 1, 2026

More Information

Terms related to this study

• Keywords: Artificial Intelligence; Chest X-ray; Stepped wedge trial
• Additional Relevant MeSH Terms: Cardiovascular Diseases; Wounds and Injuries; Pathological Conditions, Anatomical; Heart Diseases; Respiratory Tract Diseases; Lung Diseases; Thoracic Injuries; Pleural Diseases; Hypertrophy; Pathological Conditions, Signs and Symptoms; Pulmonary Edema; Lung Injury; Pleural Effusion; Pneumothorax; Cardiomegaly
• Other Study ID Numbers: LuAna Trial

Plan for Individual participant data (IPD)

• Plan to Share Individual Participant Data (IPD)?: NO

Drug and device information, study documents

• Studies a U.S. FDA-regulated drug product: No
• Studies a U.S. FDA-regulated device product: No
• product manufactured in and exported from the U.S.: No