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

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.

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.