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The qXR-LN (qXR Lung nodule) is computer-aided detection software to identify and mark regions in relation to suspected pulmonary nodules from 6 to 30 mm in size. The device is intended to be used in the incidental adult population. It is designed to aid the physician to review the frontal (AP/PA) chest radiographs of adults acquired on digital radiographic systems as a second reader and be used with any DICOM viewer or PACS . qXR-LN provides adjunctive information only and is not a substitute for the original chest radiographic image.

qXR-LN is a Computer-Aided Detection (CADe) device that is designed to perform CAD processing in frontal (PA or AP view) Chest X-ray images for indication of locations for high nodule probability, which has an effective detection size from 6 mm to 30 mm. The device is intended to be a secondary aid to the qualified intended user to identify incidental pulmonary lung nodules chest x-ray images.

The device utilizes a deep learning algorithm. The qXR-LN was trained on a large and diverse dataset of 2.5million scans from 5 countries across the world. The training dataset was from more than 25 manufacturers.

Chest X-rays are sent to qXR-LN by the means of transmission functions within the user's image storage system (e.g., Picture Archiving and Communication System (PACS)) or other radiological imaging equipment (e.g., X-ray systems) and processed by the qXR-LN to detect and localise lung nodules. Following receipt of chest radiographs, the software device automatically analyses each image to detect and localise lung nodules.

qXR-LN receives chest x-ray images in digital imaging and communications in medicine (DICOM) as input. The qXR-LN device produces DICOM format outputs that enable users to view the presence and location of lung nodules.

This device is intended to aid the intended user in review of chest x-rays and detect and localise lung nodules as a secondary reader. The results are not intended to be used on a standalone basis for clinical decision-making nor is it intended to rule out the target conditions or otherwise preclude clinical assessment of X-ray cases.

Here's a breakdown of the acceptance criteria and the study proving the device meets them, based on the provided text:

Acceptance Criteria and Device Performance

1. Table of Acceptance Criteria and Reported Device Performance

The acceptance criteria for qXR-LN are not explicitly stated as distinct numerical targets in the same way performance criteria often are. Instead, the document compares its performance to a predicate device (Samsung Auto Lung Nodule Detection) and demonstrates non-inferiority or improvement. The core principle for acceptance is "substantial equivalence" to the predicate, with performance metrics being a key factor in proving this equivalence.

Based on the provided information, the implicit acceptance criteria are framed around demonstrating performance at least equivalent to the predicate, particularly in terms of improving human reader performance and achieving a competitive standalone sensitivity for nodule detection.

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