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Intended for use by a qualified/trained physician or technician on both adult and pediatric subjects for taking diagnostic x-rays. Not for mammography.

This represents the straightforward combination of three devices: One of three cleared MinXray Portable HF X-ray generators: a) HF120/60H PowerPlus cleared in K040046, (and in K141885) OR b) HF100H+ cleared in K052721 OR c) HF1202 PowerPlus cleared in K153059. Plus: A 510(k) cleared (K150929) Digital X-Ray Receptor Panel CareView 1500Cw X-ray Flat Panel Detector. d) e) PLUS: the dicomPACS® software package (Same as our predicate). The x-ray generators are portable units which operate from 120/240V 50-60° AC. The generator unit utilizes a high frequency inverter and can be mounted to a tripod or support arm. The usual safety precautions regarding the use of x-rays must be observed by the operator. The digital panel features the Careray flat panel technology in a sleek and compact unit. The portable panel provides digital X-ray image capture for a wide range of applications. The lightweight design, generous imaging area, and fast processing times of the detector make it easy to capture high quality diagnostic images for routine diagnosis, as well as challenging trauma and bedside exams. It's a portable solution for a faster, more streamlined approach to digital radiography. The only difference between this modified device and our predicate devices is the supplier of the digital x-ray receptor panel.

The provided text describes a 510(k) premarket notification for the MinXray CMDR 2CW (Multiple Models) mobile x-ray system. The submission aims to demonstrate substantial equivalence to a predicate device, the CMDR 2ST/CMDR 2SPE (Multiple Models).

Here's an analysis of the acceptance criteria and study information:

Acceptance Criteria and Reported Device Performance

The core of the acceptance criteria revolves around demonstrating substantial equivalence to the predicate device. This is primarily assessed by comparing the technological characteristics and showing that the new device is as safe and effective as the predicate, with the same indications for use.

The device performance is demonstrated through non-clinical testing, specifically focused on confirming proper system operation and diagnostic image quality.

The primary difference and therefore the key point of evaluation for substantial equivalence was the Digital X-ray Panel.

Study Information

1. Sample size used for the test set and the data provenance:

   • The test set consisted of "several test exposures" using a radiographic phantom.
   • The data provenance is not explicitly stated in terms of country of origin, but it was generated during non-clinical bench testing by MinXray, Inc. This was a prospective test conducted for the purpose of this submission.

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

   • The text does not specify the number or qualifications of experts who evaluated the images during the bench testing. It only states that "The images were evaluated and found to be of diagnostic quality."

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

   • An adjudication method is not described. The evaluation was likely performed internally as part of the bench testing.

4. 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 or AI-assisted study was performed. The device is a mobile x-ray system, not an AI diagnostic tool.

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

   • Not applicable, as this is an x-ray imaging system, not a diagnostic algorithm.

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

   • The ground truth for the non-clinical testing was based on the expected diagnostic quality of images produced from a radiographic phantom, as assessed by comparison to images from the predicate device and general standards of diagnostic quality for x-ray imaging.

7. The sample size for the training set:

   • Not applicable, as this is not a machine learning device. The "training" here refers to the development and testing of the x-ray system components and their integration.

8. How the ground truth for the training set was established:

   • Not applicable in the context of a machine learning training set. For the development and verification of the x-ray system, the "ground truth" was established through engineering specifications, regulatory standards (e.g., UL, IEC, DHHS radiation safety), and the performance characteristics of previously cleared predicate/reference devices (generators, panels, software).

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This radiographic system is intended for use by a qualified/trained physician or technician on both adult and pediatric subjects for taking diagnostic x-rays. Not for mammographic use.

The MinXray HF1202 consists of an X-ray generator (tubehead/control), continuously adjustable light beam collimator, mounting trunnion, exposure cord with 2-stage exposure switch, and AC power cord. If a stand is purchased with the HF1202, such as the MinXray XGS series of gas spring portable mobile stands, instructions for assembly of the stand and the attachment of the HF1202 are included with the stand. This is a high-frequency generator of updated design. The unit has a serial port and a Bluetooth port for communication of technique factors to a PC. The PC cannot initiate an exposure. When used, the PC must employ FDA cleared software and digital receptor panels. Known compatible with Toshiba FDX4343RP and FDX4343RPW digital x-ray panels. The FDX4343RP was cleared in K130883 (and K131211) and the FDX4343RPW was cleared in K143257.

The provided text describes a 510(k) premarket notification for the MinXray HF1202H PowerPlus™ Portable X-ray Equipment, seeking substantial equivalence to a predicate device. This document does not describe a study that uses AI or machine learning, nor does it provide acceptance criteria and performance data in the typical sense for such devices (e.g., sensitivity, specificity, AUC).

Instead, this document describes a traditional medical device (an X-ray machine) and its comparison to a legally marketed predicate device. The core of the equivalence claim is based on bench testing and compliance with recognized standards, rather than performance metrics on a clinical image test set adjudicated by experts.

Therefore, for your requested information:

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

The "acceptance criteria" here are compliance with various IEC standards and demonstrating functional equivalence to the predicate device. The "reported device performance" is the conclusion of compliance with these standards and the comparison of specifications presented in the table on pages 3-4.

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

Not applicable. The "test set" was not a collection of clinical images or patient data but rather the physical device tested against engineering standards and specifications. The document mentions "test images" were reviewed for overall operation, but no specific sample size of these images or their provenance is provided, as they were not used to establish clinical performance metrics.

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

Not applicable. Ground truth, in the context of clinical performance evaluation by experts, was not established for this type of device submission. The evaluation was primarily based on engineering compliance and functional specifications.

4. Adjudication method for the test set

Not applicable, as there was no expert adjudication of a test set of clinical cases.

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

Not applicable. This device is a portable X-ray machine, not an AI or CAD system. No MRMC study was conducted.

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

Not applicable. This is not an AI/algorithm-based device.

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

The "ground truth" for this submission refers to the established engineering standards, regulatory requirements, and the specifications of the predicate device. It's based on technical compliance and functional equivalence rather than clinical outcomes or diagnostic accuracy.

8. The sample size for the training set

Not applicable. This device is not an AI/Machine Learning system, so there is no training set in the conventional sense.

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

Not applicable, as there is no training set.

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