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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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The MinXray HFP24 and HFP40 is intended for use by a qualified/trained physician or technician on both adult and pediatric subjects for taking diagnostic x-rays. These units are specifically designed for podiatric and extremity applications.

MinXray HFP24 and HFP40 are x-ray units which operate from 120 V 50-60~ AC. They utilize a newly designed high frequency inverter designed to be mounted on a stand. The usual safety precautions regarding the use of x-rays must be observed by the operator.

The provided text describes a 510(k) premarket notification for the MinXray HFP24 and HFP40 High Frequency Diagnostic X-Ray Units. This type of submission focuses on demonstrating substantial equivalence to a legally marketed predicate device, rather than conducting extensive clinical studies with specific acceptance criteria that would typically be associated with new efficacy claims or significant changes in intended use.

Therefore, the requested information elements related to robust clinical studies, such as specific performance metrics, sample sizes, expert ground truth establishment, MRMC studies, or standalone algorithm performance, are not applicable in this context.

Here's an analysis based on the provided document:

Acceptance Criteria and Device Performance

The core "acceptance criteria" for a 510(k) submission are met by demonstrating substantial equivalence to existing predicate devices. This involves showing that the new device has the same intended use and either the same technological characteristics or, if technological characteristics are different, that these differences do not raise new questions of safety and effectiveness.

1. Table of Acceptance Criteria and Reported Device Performance

• Size/weight: Modified device is generally smaller/lighter, presented as an improvement without new safety concerns.
• Energy Source: Different kVA, but still within typical medical device operation and tested for safety.
• kV steps, mA: Modified kV range (50-70kV) and mA (10 mA) are specific to the "podiatric and extremity applications" noted in the intended use, and are therefore considered appropriate subsets of the predicate's capabilities.
• Focal Spot Size: 0.8 mm (Modified Device) vs. 1.2 mm (Predicate). A smaller focal spot generally improves image resolution. This is considered a technological improvement that does not raise new safety concerns.
• Exposure Times: Adjusted range (e.g., 1.0-2.0 sec vs. 1.0-4.0 sec) within acceptable limits for the intended use.
• X-ray tube: Different model (SNMIF XDT-S70 vs. Toshiba D-124S), but deemed suitable through bench testing and compliance with performance standards. |

2. Sample Size Used for the Test Set and Data Provenance

• Test Set Sample Size: Not explicitly stated as a separate "test set" in the context of clinical images or patients. The evaluation appears to be based on bench testing and "user testing" of the physical device, comparing its specifications and functional performance to the predicate devices. This typically involves engineering verification and validation (V&V) activities.
• Data Provenance: The data provenance is from bench testing and "user testing" conducted by the manufacturer, Mikasa X-Ray Co., LTD. (Japan) and their distributor MinXray, Inc (USA). The nature of "user testing" in this context likely refers to usability and functional validation, not a formal clinical trial with patient data. It is retrospective in the sense of comparing against established predicate device performance and existing standards.

3. Number of Experts Used to Establish the Ground Truth for the Test Set and Qualifications of Those Experts

• This is not applicable for a 510(k) submission based on substantial equivalence of an X-ray unit. There is no mention of a clinical "test set" requiring expert radiographic interpretation to establish ground truth for diagnostic accuracy, as the device is not an AI diagnostic tool or making new diagnostic claims. The "experts" involved would be the engineers and quality assurance personnel performing the bench and user testing, verifying the technical specifications and safety.

4. Adjudication Method for the Test Set

• Not applicable. As there is no clinical "test set" with diagnostic findings requiring ground truth, there is no need for an adjudication method.

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, an MRMC study was not done. This device is an X-ray generator, not an AI-powered diagnostic software. Therefore, questions regarding AI assistance or improvements in human reader performance are not relevant.

6. If a Standalone (i.e., algorithm only without human-in-the-loop performance) Was Done

• No, a standalone algorithm performance study was not done. This device is hardware (an X-ray unit), not a software algorithm.

7. The Type of Ground Truth Used

• For the purpose of this 510(k), the "ground truth" is primarily based on technical specifications, functional performance, and safety standards as verified through engineering tests and comparison to the predicate devices. Compliance with these technical ground truths (e.g., accurate kV output, appropriate filtration, physical dimensions, safety interlocks) constitutes the basis for demonstrating safety and effectiveness.

8. The Sample Size for the Training Set

• Not applicable. This device does not involve machine learning or AI models, so there is no "training set."

9. How the Ground Truth for the Training Set Was Established

• Not applicable. As there is no training set.

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