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The Cuattro UnoMD, when used with a cleared digital image capture device, provides for the capture of digital images in place of conventional film radiographic examinations. The device is intended to be available for retrofit on existing or planned x-ray machines with a cleared digital image capture device. The device is intended for use by trained and qualified personnel in the acquisition and review of radiographic images. The product is not intended for mammography or fluoroscopy applications.

The Cuattro UnoMD software is a Windows based software application capable of acquiring x-ray images from commonly commercialized digital flat panels. The software can use traditional mouse and keyboard inputs as well as touch screen monitors as an alternative. The use of the software enables the user to use a separate traditional x-ray generator and capture x-ray images without film. The images are processed and then presented to the user on a touch screen computer monitor, within 12 seconds after the x-ray exposure. The software also has capabilities to send images to hospital medical PACS systems and digital media for archival.

The Cuattro UnoMD system, as described in the provided 510(k) summary (K093480), relies on demonstrating substantial equivalence to a legally marketed predicate device (DEL-IMS, K063188) rather than meeting specific performance acceptance criteria through a clinical study with a predefined set of metrics. The submission focuses on non-clinical performance data and bench testing.

Here's an analysis based on the provided text, addressing your questions:

1. Table of Acceptance Criteria and Reported Device Performance

2. Sample size used for the test set and the data provenance

The document explicitly states that "Bench testing has been performed on the device following Cuattro's design control processes, as well as the applicable FDA guidance documents, in particular the guidance on Content of Premarket Submissions for Software Contained in Medical Devices."

• Sample Size: The document does not specify a quantitative sample size for a "test set" in the context of clinical images or patient data. The evaluation was non-clinical performance data and bench testing.
• Data Provenance: Not applicable in the context of a clinical test set. The evaluation focuses on the software's functional characteristics and compliance with standards, not on an analysis of clinical image data from a specific origin.

3. Number of experts used to establish the ground truth for the test set and the qualifications of those experts

Not applicable. This was a 510(k) submission based on non-clinical performance data and bench testing, not a clinical study involving expert interpretation of images or establishment of ground truth for a diagnostic task.

4. Adjudication method for the test set

Not applicable, as there was no test set of clinical images requiring adjudication by experts.

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 comparative effectiveness study was performed or mentioned in the provided text. The submission focuses on the software's functionality and its equivalence to a predicate device, not on its impact on human reader performance. This device is a PACS system component; it's not described as an AI-powered diagnostic aid meant to directly improve human reader accuracy.

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

This point is somewhat addressed. The device is a "software application capable of acquiring x-ray images from commonly commercialized digital flat panels" and "processes and then presents" images. It's an image acquisition and archiving system, not an "algorithm" in the sense of a standalone diagnostic or analytical tool. Its performance is standalone in its core function (acquiring, processing, and presenting images) without human intervention in the image processing itself, but it still requires a human for interpretation. The bench testing performed would evaluate this standalone functionality.

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

Not applicable in the conventional sense. The "ground truth" for this type of submission (focusing on substantial equivalence for an image acquisition/management system) would be:

• Compliance with DICOM 3.0 standards.
• Successful acquisition and display of images.
• Processing time within specifications (e.g., 12 seconds).
• Correct interfacing with cleared digital image capture devices.
• Adherence to software design control processes.

These are verifiable through technical specifications, testing protocols, and direct observation, rather than clinical ground truth like pathology.

8. The sample size for the training set

Not applicable. The Cuattro UnoMD is a software system for image acquisition and management, not an AI/ML diagnostic algorithm that requires a training set of images.

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

Not applicable, as there is no training set mentioned for this type of device.

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