Clinical trial NCT06686251: Evaluation of the Efficacy of Diagnostic Support Algorithms in Chest X-rays- LuAna Trial
Official title: Evaluation of the Efficacy of Diagnostic Support Algorithms in Chest X-rays - LungAnalysis (LuAna): LuAna Stepped Wedge Trial
Study type: INTERVENTIONAL
Overall status: NOT_YET_RECRUITING
Phase: NA
Conditions: Consolidation
Lung Injury
Pleural Effusion
Pneumothorax
Cardiomegaly
Edema Lung
Keywords: Artificial Intelligence
Chest X-ray
Stepped wedge trial
Lead sponsor: Hospital Israelita Albert Einstein
Sponsor class: OTHER
First posted: 2024-11-13
Enrollment: 1470
Sex: ALL
Minimum age (years): 18
Standard ages: ADULT
OLDER_ADULT
Accepts healthy volunteers: no
Allocation: NA
Intervention model: SINGLE_GROUP
Intervention model description: This is a type of "stepped wedge" cluster randomized clinical trial. In the model standard of this design, interventions are implemented in the clusters randomly and graduate.
Primary purpose: OTHER

Brief summary: 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.

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.

Eligibility criteria: 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.

Primary outcomes:
measure: Detection rate
timeFrame: through study completion, an average of 1 year
Secondary outcomes:
Other outcomes:
Locations:
Study officials:
affiliation: Hospital Israelita Albert Einstein
name: Joselisa Paiva, PhD
role: PRINCIPAL_INVESTIGATOR
References: