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Solid State X-ray Imager (Flat Panel/Digital Imager) Indicated for use in generating radiographic images of human anatomy. Intended to replace radiographic film/screen systems in all general-purpose diagnostic procedures, excluding dental, fluoroscopic, angiographic, and mammographic applications.

The PZMEDICAL devices are used in medical x-ray imaging systems. The product can only be used by trained personnel of medical facilities. The product is only used for diagnostic x-ray image acquisition applications. The flat panel detector consists of a Csl scintillator screen and thin-film transistors. The scintillator screen converts the x-rays into visible light. Thin-film transistors convert the visible light to an electrical charge. The flat panel detector can then obtain a digital image by analog to digital conversion and associated circuits. These panels can be used to acquire diagnostic x-ray images as an upgrade to traditional film systems. They can be used wired (Ethernet) or wirelessly (Wi-Fi) and are powered either by AC Line or by rechargeable batteries. The various models are constructed similarly to our previous models cleared in K170480. The suffix "ZF" means FINE pixel size, 99 µm Z means standard pixel size, 139 µm. All models have a battery life indicator. The supporting software PZDR is functionally the same as provided with the predicate. The software performs acquisition of the RAW image and converts it to DICOM format and performs operations as described in the comparison table below. It is functionally unchanged from the predicate software.

The provided text does not contain detailed information about specific acceptance criteria and a study proving a device meets these criteria in the context of an AI/ML-driven medical device. Instead, it is a 510(k) summary for a set of Solid State X-ray Imager (Flat Panel/Digital Imager) devices, which are hardware components (digital X-ray detectors) used to generate radiographic images.

The document discusses the substantial equivalence of these new models to a previously cleared predicate device, focusing on technological characteristics and non-clinical testing for general medical device safety and performance standards. It explicitly states: "Clinical testing. Not required for a determination of substantial equivalence."

Therefore, I cannot provide the requested information about acceptance criteria for an AI/ML device, its performance, sample sizes for test sets, expert qualifications, adjudication methods, MRMC studies, standalone performance, or training set details because:

• The device is not described as an AI/ML device. It's a digital X-ray detector, a hardware component.
• The assessment is based on substantial equivalence to a predicate device, not on specific performance criteria for an AI/ML model.
• Clinical testing was "Not required" for this 510(k) submission, meaning detailed performance studies as would be expected for an AI/ML diagnostic tool were not conducted or reported here.

However, I can extract the provided "performance" information based on technological characteristics and present it in a table format, comparing the new device models to the predicate device, as this is the closest available information to "device performance" in the given document.

Acceptance Criteria and Reported Device Performance (based on Technological Characteristics Comparison for Substantial Equivalence)

Since this submission is for a conventional medical imaging device (digital X-ray detectors) and not an AI/ML device requiring clinical performance criteria like sensitivity, specificity, or AUC, the "acceptance criteria" can be inferred from the technological characteristics that demonstrate substantial equivalence to the predicate device. The "reported device performance" are the values for these characteristics.

1. Table of Acceptance Criteria (Inferred from Predicate) and Reported Device Performance

Additional Information (Not Applicable or Not Provided for an AI/ML Device)

2. Sample size used for the test set and the data provenance: Not applicable in the context of performance testing for an AI/ML device, as no clinical performance study was reported. Non-clinical bench testing was performed to demonstrate technical characteristics.
3. Number of experts used to establish the ground truth for the test set and the qualifications of those experts: Not applicable. Ground truth for an AI/ML model was not established as described.
4. Adjudication method for the test set: Not applicable.
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 done, as this is not an AI-assisted device.
6. If a standalone (i.e. algorithm only without human-in-the-loop performance) was done: Not applicable.
7. The type of ground truth used: For the technical performance characteristics listed (e.g., pixel pitch, resolution, DQE, MTF), the "ground truth" is established through physical measurements and engineering specifications according to recognized industry standards (e.g., IEC).
8. The sample size for the training set: Not applicable, as this is not an AI/ML device requiring a training set.
9. How the ground truth for the training set was established: Not applicable.

Summary of Study Type:

The study described is a non-clinical bench testing and technological comparison study to demonstrate substantial equivalence of new digital X-ray detector models to a previously cleared predicate device. It primarily focuses on the physical and technical specifications of the detectors and their associated software's functional equivalence, adhering to general medical device safety and performance standards. Clinical testing was not required for this submission.

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