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The EOS is intended for use in general radiographic examinations and applications, excluding the evaluation of lung nodules and examinations involving fluoroscopy, angiography and mammography. EOS allows the radiographic acquisition of either one or two orthogonal X ray images for diagnostic purposes, in one single scan, of the whole body or a reduced area of investigation of a patient in the upright or seated position.

EOS is a digital radiography system in which two sets of xenon gas filled digital detectors and X-ray tubes are positioned orthogonally to generate frontal and lateral images simultaneously by scanning the patient over the area of interest. The diagnostic images are stored in a local database and are displayed on a high-resolution, medical-quality monitor, where the diagnosis is performed. The diagnostic image can be transmitted through a DICOM 3.0 compatible digital network for printing and archiving. The fundamental technological characteristics of the modified EOS are unchanged compared to the cleared EOS.

The provided text describes a 510(k) submission for a modified EOS digital radiography system. However, it does not contain detailed information about acceptance criteria for performance, a specific study proving those criteria were met, or any of the detailed aspects like sample sizes, ground truth establishment, or expert qualifications that your request specifies.

The document states that the fundamental technological characteristics of the modified EOS are unchanged compared to the cleared EOS (K071546). The "Performance Data" section primarily focuses on ensuring the modified device maintains equivalent performance to the predicate device.

Here's a breakdown of what can be extracted based on your request, and what is missing:

1. A table of acceptance criteria and the reported device performance

2. Sample sized used for the test set and the data provenance (e.g. country of origin of the data, retrospective or prospective)

• Not specified. The document mentions "bench testing," "mechanical resistance testing," and "software verification testing" but provides no details on the sample sizes of images or devices used, nor the provenance of any data (e.g., country of origin, retrospective/prospective clinical data).

3. Number of experts used to establish the ground truth for the test set and the qualifications of those experts (e.g. radiologist with 10 years of experience)

• Not applicable based on the provided text. The performance data described is primarily engineering/bench testing and software verification, not a clinical study involving experts establishing ground truth for diagnostic accuracy.

4. Adjudication method (e.g. 2+1, 3+1, none) for the test set

• Not applicable based on the provided text. See point 3.

5. If a multi reader multi case (MRMC) comparative effectiveness study was done, If so, what was the effect size of how much human readers improve with AI vs without AI assistance

• No MRMC study was mentioned. This device is described as a "Digital Radiography System," implying it's an imaging acquisition device, not an AI-powered diagnostic aide for human readers.

6. If a standalone (i.e. algorithm only without human-in-the-loop performance) was done

• Not applicable. The device itself is the imaging system; there's no "algorithm only" performance reported in the context of diagnostic interpretation. The performance data relates to the physical and software functioning of the imaging system.

7. The type of ground truth used (expert consensus, pathology, outcomes data, etc.)

• Not specified/Applicable to the type of testing described. The "ground truth" for the tests mentioned (resolution, accuracy, mechanical resistance, software) would be established by engineering specifications, calibration standards, and functional requirements, not clinical ground truths like pathology or expert consensus.

8. The sample size for the training set

• Not applicable. This document describes a modification to an existing digital radiography system, not the development or training of an AI algorithm. Therefore, there's no "training set" in the context of machine learning.

9. How the ground truth for the training set was established

• Not applicable. See point 8.

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