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The Diagnostic X-ray System is intended to be used and operated by: adequately trained, qualified and authorized health care professionals who have full understanding of the safety information and emergency procedures as well as the capabilities and functions of the device. The device is used for radiological guidance and visualization during diagnostic, interventional and surgical procedures on all patients, except neonates (birth to one month), within the limits of the device. The device is to be used in health care facilities both inside and outside the operating room, sterile as well as non-sterile environment in a variety of procedures.

The Diagnostic X-ray System is a mobile (within an imaging facility) general-purpose diagnostic fluoroscopic X-ray system that uses a C-arm and digital techniques for image capture, display and manipulation and is designed to be used in a variety of general-purpose applications requiring real-time fluoroscopic imaging capabilities.

The Diagnostic X-ray System is consists of X-ray source assembly (combined type), collimator, flat-panel detector, image processing workstation, C-arm and mobile rack, Medical Image Workstation Software.

It appears that the provided FDA 510(k) clearance letter and associated summary pertain to a Diagnostic X-ray System (Trade Name: Diagnostic X-ray System, Model: PLX119C) manufactured by Nanjing Perlove Medical Equipment Co., Ltd.

Crucially, the document explicitly states: "No clinical study is included in this submission."

Therefore, I cannot provide details about acceptance criteria derived from a clinical study, as no such study was presented for this device's 510(k) clearance.

However, I can extract the information provided regarding the non-clinical performance and testing. It's important to understand that for a general-purpose diagnostic X-ray system, substantial equivalence is often demonstrated through comparisons of technical specifications and non-clinical performance to a legally marketed predicate device, rather than a full-scale clinical trial proving "improved" diagnostic accuracy in a specific clinical context.

Here's a breakdown of the available information based on your request, with a clear note about the absence of a clinical study for demonstrating performance against acceptance criteria in a clinical setting:

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

Since no clinical study was performed to establish clinical performance acceptance criteria and then demonstrate the device meets them, I can only present the reported non-clinical performance and compliance with relevant standards. The "acceptance criteria" here are implied by meeting recognized standards and demonstrating comparable technical characteristics to the predicate device.

2. Sample size used for the test set and the data provenance (e.g. country of origin of the data, retrospective or prospective)

• Clinical Images: "Clinical images of multiple body parts were taken using radiography and fluoroscopy, with motion".

  • Sample Size: Not specified (described as "multiple body parts").
  • Data Provenance: Not specified, but generally, for 510(k) submissions from foreign manufacturers, testing would occur at a site capable of generating such data, potentially in the country of origin (China, in this case, for the manufacturer). The images would be prospective as they were "taken" for the purpose of demonstrating performance.

• Bench Testing (Detector Imaging Metrics):

  • Sample Size: Not specified.
  • Data Provenance: Not specified, but typically conducted in a controlled laboratory environment by the manufacturer or a contracted testing facility.

3. Number of experts used to establish the ground truth for the test set and the qualifications of those experts (e.g. radiologist with 10 years of experience)

• Clinical Images: "reviewed and assessed by a qualified radiologist."
  • Number of Experts: One ("a qualified radiologist").
  • Qualifications: "qualified radiologist" – further specific experience or board certification is not detailed in the provided text.

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

• Clinical Images: The document states the images were "reviewed and assessed by a qualified radiologist." This implies a single-reader assessment, with no mention of an adjudication process (e.g., if multiple readers disagreed). Therefore, the adjudication method was none.

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. The device is a diagnostic X-ray system, not an AI-powered diagnostic aide. The submission explicitly states: "No clinical study is included in this submission." Therefore, there is no information about human reader improvement with or without AI assistance.

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

• Not applicable. This device is a diagnostic X-ray system and does not appear to contain an AI algorithm for standalone diagnostic performance. The primary focus is on the image acquisition, processing, and display capabilities for human interpretation.

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

• For the "clinical images," the "ground truth" or standard of reference for "acceptable imaging performance" was the assessment by a qualified radiologist. This is a form of expert assessment/consensus (though by a single expert in this case for the review of performance). There is no mention of pathology or outcomes data being used as ground truth for performance evaluation of the imager itself.

8. The sample size for the training set

• Not applicable / Not specified. This device is hardware with associated software for image acquisition and processing. There is no mention of machine learning or deep learning algorithms requiring a distinct "training set" in the context of AI. The software verification and validation are standard procedures for medical device software, not AI training.

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

• Not applicable. As there is no mention of a traditional "training set" for an AI algorithm, the concept of establishing ground truth for it does not apply here. The software development follows general software engineering principles and V&V, not AI model training.

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The PLX5200A is intended for use by a qualified/trained doctor or technologist on both adult and pediatric patients for taking diagnostic radiographic exposures of the skull, spinal column, extremities, and other body parts. Applications can be performed with patient sitting, standing or lying in the prone or supine positions. It is not intended for mammography.

The proposed device is a mobile x-ray system, it uses digital techniques for image capture, display and manipulation and the mobile design allows it to operate on mains or battery power and to be driven or pushed by an operator to various locations within a building or facility. It is commonly used for bedside imaging. This system consists of a mobile base unit, combined X-ray tube assembly, collimator, frame, X-ray flat panel detector, and image processing system.

This device is a Diagnostic X-ray System, which is a hardware device. The provided text indicates that no clinical study was included in this submission. Therefore, there is no acceptance criteria or study data for device performance as requested for AI/software devices.

However, the submission does mention various performance data related to the hardware itself, which are not based on clinical efficacy or diagnostic accuracy. These include:

• Electrical safety and electromagnetic compatibility (EMC): The system complies with IEC 60601-1:2005+A1:2012 / ANSI AAMI ES60601-1:2005/(R)2012 and A1:2012, IEC 60601-1-3:2008+A1:2013, IEC 60601-2-54:2009, IEC 62471 standard for safety, and IEC 60601-1-2: 2014 standard for EMC. Radio frequency (RF) wireless coexistence of equipment testing was performed according to IEEE ANSI C63.27-2017.
• Software Verification and Validation: The Embedded software and the workstation software were considered a "Moderate" level of concern and were verified and validated according to FDA's "Guidance for the Content of Premarket Submissions for Software Contained in Medical Devices." They comply with FDA recognized standard IEC 62304.
• Cybersecurity: Documentation was provided according to "Content of Premarket Submissions for Management of Cybersecurity in Medical Devices."
• DICOM: DICOM declaration was provided according to FDA recognized standard NEMA PS 3.1 - 3.20 (2016).
• Usability Testing: Usability validation was performed and complies with FDA recognized standard IEC 60601-1-6 Edition 3.1 and EC 62366-1 Edition 1.0.
• Risk Management: Activities were performed and documented according to FDA recognized standard ISO 14971 Second edition 2007-03-01.
• Biocompatibility Testing: Conducted in accordance with ISO 10993-1, including Cytotoxicity, Skin Sensitization, and Skin Irritation for the x-ray flat panel detector.

Since this is a hardware device clearance, and not an AI/software as a medical device (SaMD) or an AI-powered diagnostic device, the typical acceptance criteria and study design elements requested (like sample size for test/training sets, expert ground truth, MRMC studies, standalone performance) are not applicable or provided in this 510(k) summary.

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