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Intended for use by a qualified/trained physician or technician for the purpose of acquiring X-ray images of the desired parts of patient's anatomy (including head, cervical spine, chest, abdomen, lumbar spine, pelvis and extremities).

This device is not intended for mammography and pediatric patients.

This Digital Radiography System (ManntiX K) is a portable digital device developed, designed and manufactured by Shenzhen Browiner Tech Co., Ltd. A detailed comparison table with an equivalent device are in 510(k) summary. The device consists of the following major components: portable X-ray Assembly, X-ray Protective Device, Mobile Stand, Digital Detector, image processing system. The difference between ManntiX B and ManntiX K are as below.

Note:

• 1. ManntiX B and ManntiX K are delivered with one of CareView1500Cwe, Mars1717X and Luna 1012X, or two of them, or three of them.
• 2. A computer system, necessary for image viewing and manipulation, is not part of the device.

The major components of Digital Radiography System include: portable x-ray assembly, x-ray protective device, mobile stand, digital detector, image processing system, image acquisition workstation.

Portable X-ray assembly including collimator and tube, produces X-rays at the clinically required dose; X-ray protective device is used to protect the human body during diagnostic radiography; mobile stand is used to support or hang X-ray source components; digital detector receives X-ray signals and converts them into visible light, which is converted into electrical signals by photoelectric elements and then converted into digital signals by analog-to-digital conversion before being transmitted to the computer; image processing system has multiple functions. including new patient registration, image acquisition and processing, data transmitting, etc; image acquisition workstation is used in addition to the image processing system to adjust exposure parameters, check the status of the entire machine, and view logs.

The provided document is a 510(k) summary for a Digital Radiography System (ManntiX B, ManntiX K). It describes the device, its intended use, and compares it to a predicate device. However, it does not contain information about acceptance criteria and a study proving that the device meets those criteria, especially in the context of an AI/ML-driven device's performance.

The document primarily focuses on:

• Regulatory information: FDA review, regulations, and contact details.
• Device description: Components, technical specifications (generator, detector, image processing), and intended use.
• Comparison to a predicate device: Highlighting similarities in intended use, technology, and functionality to establish substantial equivalence.
• Non-clinical performance tests: Listing compliance with various IEC and ISO standards related to electrical safety, electromagnetic compatibility, radiation protection, software lifecycle, and usability.
• Biocompatibility: Statement of evaluation according to ISO 10993-1.

There is no mention of:

• Specific acceptance criteria for performance metrics (e.g., sensitivity, specificity, accuracy) beyond general safety and electrical performance standards.
• A study proving the device meets such acceptance criteria.
• Sample sizes for test sets, data provenance, expert ground truth adjudication (for AI/ML performance evaluation), MRMC studies, standalone performance, type of ground truth used (pathology, outcomes), or training set details.

This type of 510(k) submission primarily demonstrates that the proposed device is as safe and effective as a legally marketed predicate device, often through a comparison of technical specifications and adherence to recognized standards. It does not typically involve the detailed clinical or AI/ML performance evaluation aspects you've asked about unless the device incorporates novel AI/ML components that significantly alter its diagnostic output or workflow. In this case, the device is a Digital Radiography System, which is an imaging modality, and the details provided relate to the hardware and image acquisition/processing capabilities rather than an AI/ML diagnostic aid.

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Intended for use by a qualified/trained doctor or technician on adult subjects for taking diagnostic radiographic exposures of the skull, spinal column, chest, abdomen, extremities, and other body parts. Applications can be performed with the patient sitting, standing, or lying in the prone or supine position. Not for mammography.

The Digital Radiography System (MobileApex 60E, MobileApex 60F, MobileApex 60G, MobileApex 60H) is comprised of a High Voltage Generator with a maximum power output of 63kW, the Digital Radiography System (MobileApex 60E, MobileApex 60F, MobileApex 60C, MobileApex 60H) can meet different exposure needs for varying positions and body mass. With the Digital Radiography System's (MobileApex 60E, MobileApex 60F, MobileApex 60G, MobileApex 60H) state-of-the-art design and powerful 63kW generator coupled with the Digital Detector (CareView 1500Cwe, CareView 750Cw, Mars1717X, Mars1417X, Luna 1012X) and Digital Radiography Operator Console, users can obtain clear images quickly and easily.

Based on the provided text, the device in question is a Digital Radiography System (MobileApex 60E, MobileApex 60F, MobileApex 60G, MobileApex 60H). The acceptance criteria and the study proving the device meets these criteria are focused on demonstrating substantial equivalence to a predicate device, the MX40 Mobile Digital X-ray System (K181874), rather than proving a specific, quantitative performance metric for a clinical application (like detection of a disease).

The documentation primarily discusses non-clinical performance tests related to electrical safety, electromagnetic compatibility, radiation protection, software validation, and usability. There is no mention of a clinical study to assess diagnostic performance (e.g., accuracy in detecting specific conditions).

Therefore, the following information is extracted and where information is not provided in the text, it is explicitly stated as "Not provided in the text."

Acceptance Criteria and Device Performance for Digital Radiography System

The acceptance criteria for this device are based on demonstrating substantial equivalence to a predicate device in terms of intended use, technological characteristics, and safety and effectiveness, primarily through compliance with recognized standards.

1. Table of Acceptance Criteria and Reported Device Performance

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

• Sample size for test set: Not applicable/Not provided. The summary describes non-clinical engineering and performance testing against standards, not a clinical test set with patient data for diagnostic performance.
• Data provenance: Not applicable/Not provided. The testing described is type testing and verification against standards, not clinical data collection.

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

• Not applicable/Not provided. Ground truth in a diagnostic sense (e.g., for disease detection) was not established as part of this submission's described testing, which is focused on technical performance and safety.

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

• Not applicable/Not provided. No clinical test set requiring adjudication of ground truth is described.

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 a mobile X-ray system, not an AI-powered diagnostic software.

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

• Not applicable. This is a hardware device (X-ray system), not a standalone algorithm.

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

• Not applicable for diagnostic performance. For the described non-clinical performance tests (e.g., electrical safety, radiation output), the "ground truth" is defined by the requirements and specifications of the relevant international and FDA performance standards (e.g., IEC 60601 series, 21 CFR 1020.30/31).

8. The sample size for the training set

• Not applicable/Not provided. This document describes a traditional medical device (X-ray system) and its non-clinical testing, not an AI/ML product that would require a training set.

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

• Not applicable/Not provided. (See point 8).

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Indicated for use in general radiographic images of human anatomy. It is intended to replace radiographic film/screen systems in all general-purpose diagnostic procedures, excluding fluoroscopic, angiographic, and mammographic applications.

The Prudent 1717, Prudent 1417, Prudent 1212 are digital radiography systems, featuring an integrated flat panel digital detector (FPD). It is designed to perform digital radiographic examinations as a replacement for conventional film. This integrated platform provides the benefits of PACS with the advantages of digital radiography for a filmless environment and improves cost effectiveness. The major functions and principle of operation of the updated panels are the same as our previous panel retaining the Wi-Fi wireless features and rechargeable battery operation. The Prudent 1717 is available in 3 pixel sizes: 100/140/168 um whereas the Prudent 1417, Prudent 1212 are available in two pixel sizes: 100/140 µm. The available resolutions vary according to the comparison table below. All of the models are Wi-Fi wired) and rechargeable battery (or AC line) operated. The device employs the same software as cleared in the predicate with only minor changes made.

Here's an analysis of the provided text regarding the acceptance criteria and supporting study for the PIXXGEN Corporation's Prudent digital x-ray detector panels:

1. Table of Acceptance Criteria and Reported Device Performance

The acceptance criteria are implicitly derived from the comparison to predicate devices and adherence to established standards. The reported device performance is presented in comparison to these predicates.

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

• Sample Size for Test Set: The document does not specify a numerical sample size for the "clinical images" test set. It only states "Clinical images collected."
• Data Provenance: The document does not explicitly state the country of origin. It does not explicitly state if the data was retrospective or prospective. However, the term "Clinical images collected" typically implies prospective collection for such validation, but this is not explicitly confirmed.

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

• Number of Experts: "a Board Certified Radiologist" (singular, implying one).
• Qualifications of Experts: "Board Certified Radiologist." No specific experience level (e.g., "10 years of experience") is provided.

4. Adjudication Method for the Test Set

• Adjudication Method: The document states that the images were "evaluated by a Board Certified Radiologist." This suggests a single reader evaluation, which means no multi-reader adjudication method (like 2+1, 3+1) was explicitly performed or mentioned for the clinical image evaluation.

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, a Multi-Reader Multi-Case (MRMC) comparative effectiveness study was not mentioned. The study described is an evaluation of the device's image quality by a single radiologist, not a comparison of human readers' performance with and without AI assistance. This device is a digital x-ray detector panel, not an AI-powered image analysis tool.

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

• The document primarily describes a standalone performance evaluation of the imaging device itself (the detector panel) in terms of objective image quality metrics (DQE, MTF, limiting resolution) and a qualitative assessment of clinical images. Since the device is a detector, it intrinsically operates "standalone" in providing the image data. The "algorithm" here refers to the device's inherent image acquisition and processing capabilities, not an AI algorithm acting on those images. The evaluation by the radiologist is an assessment of the output of the standalone device.

7. The Type of Ground Truth Used

• For Quantitative Metrics (DQE, MTF, Limiting Resolution): These are objective physical measurements governed by established scientific and engineering standards (e.g., Guidance for the Submission of 510(k)s for Solid State X-ray Imaging Devices). The "ground truth" for these is the measurement itself, verified against the alternate predicate devices' published specifications.
• For Clinical Images: The ground truth was established by expert consensus/evaluation by a "Board Certified Radiologist." The assessment was subjective, stating the images were "of excellent diagnostic quality." It is not directly pathology or outcomes data.

8. The Sample Size for the Training Set

• This document describes a medical device (digital x-ray detector panel), not an AI algorithm that requires a separate "training set" in the machine learning sense. Therefore, there is no mention of a training set sample size. The device's "training" refers to its design and engineering to meet specific technical specifications.

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

• As a digital x-ray detector panel, the concept of a "training set" and establishing ground truth for it (in the AI/machine learning context) does not apply. The device's performance is driven by its physical components and embedded firmware/software, which are developed and verified through engineering principles and adherence to standards rather than algorithm training on a dataset.

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