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The Flat Panel Digital X-ray Detector 17HK700G-W is indicated for digital imaging solution designed for general radiographic system for human anatomy. It is intended to replace film or screen based radiographic systems in all general purpose diagnostic procedures. Not to be used for mammography.

The 17HK700G-W is the solid state x-ray imager, which can generate radiographic images of any part of the body. These devices intercept x-ray photons and the scintillator (CsI:TI) emits visible spectrum photons that illuminate an array of photo-detectors that create an electrical signals. After the electrical signals are generated, it is converted to digital value, and the images are displayed on monitors. The digital value can be communicated to the operator console via wiring connection.

The 17HK700G-W consists of the following components: Flat Panel Detector, Control Box, Calibration Software and power cord and cables. The 17HK700G-W can be used for general X-ray system excluding fluoroscopic, angiographic, and mammographic applications.

The provided text describes a 510(k) premarket notification for a Flat Panel Digital X-ray Detector (17HK700G-W) and its substantial equivalence to a predicate device (S4343-W of GC85A). However, it does not contain the detailed information necessary to fully answer all aspects of your request regarding acceptance criteria and the comprehensive study that proves the device meets those criteria.

Specifically, the document primarily focuses on demonstrating substantial equivalence to an existing predicate device rather than outright proving performance against specific acceptance criteria for a novel AI/Algorithm-driven diagnostic device.

Here's what can be extracted and what information is missing:

Missing Information (Crucial for a typical AI/Algorithm device study):

• A formal table of acceptance criteria for specific diagnostic performance metrics (e.g., sensitivity, specificity, AUC) for the device's intended use.
• Detailed results of a clinical study demonstrating the device's diagnostic performance against these acceptance criteria. The document states "Clinical data has been provided... but provided further evidence in addition to the laboratory performance data to show that the device works as intended," which suggests a supportive rather than a primary diagnostic efficacy study.
• Sample size used for a test set in a diagnostic performance study.
• Provenance of clinical data (country, retrospective/prospective).
• Number of experts, their qualifications, and adjudication methods for establishing ground truth for a diagnostic test set.
• Whether a Multi-Reader Multi-Case (MRMC) comparative effectiveness study was done, and if so, the effect size.
• Information on a standalone (algorithm only) performance study against diagnostic ground truth.
• The type of ground truth used for diagnostic performance (e.g., pathology, outcomes data).
• Sample size for the training set and how its ground truth was established.

Information Extracted from the Document (related to device performance and testing, but not a full diagnostic study):

1. Table of Acceptance Criteria (as reported or inferred from performance tests):

The document presents technical performance characteristics and non-clinical test summaries rather than diagnostic acceptance criteria. The device is a digital X-ray detector, and its performance is compared to a predicate device based on imaging quality metrics.

2. Sample Size and Data Provenance:

• Test Set Sample Size: Not explicitly stated for a diagnostic efficacy test. The "clinical data" mentioned is described as "further evidence" rather than a primary diagnostic study with a defined test set.
• Data Provenance: Not specified (e.g., country of origin, retrospective or prospective).

3. Number of Experts and Qualifications for Ground Truth:

• Not applicable/mentioned in the context of a diagnostic performance study. The ground truth for this device's validation primarily relies on physics-based imaging performance metrics and engineering standards adherence, as well as comparison to a predicate device.

4. Adjudication Method:

• Not applicable/mentioned for a diagnostic performance study.

5. Multi-Reader Multi-Case (MRMC) Comparative Effectiveness Study:

• Not performed based on the provided text. This is a detector, not an AI-driven image interpretation algorithm that assists human readers.

6. Standalone Performance (Algorithm Only):

• Not applicable in the typical sense of an AI algorithm. The device itself (the detector) is the "standalone" component being evaluated for its image acquisition performance, which is measured through metrics like DQE, MTF, and resolution.

7. Type of Ground Truth Used:

• For the non-clinical performance tests, the "ground truth" refers to established physical standards and measurement techniques for X-ray detector performance (e.g., IEC 62220-1 for DQE).
• For the "clinical data" mentioned, the specific type of ground truth is not detailed, but it's presented as supportive evidence for the device working as intended, not as a primary diagnostic accuracy study against a clinical ground truth (like pathology or clinical outcomes).

8. Sample Size for the Training Set:

• Not applicable/mentioned. This document is about a hardware device (X-ray detector), not an AI algorithm that requires a training set.

9. How Ground Truth for Training Set was Established:

• Not applicable.

Summary based on the document:

This 510(k) submission for the LG 17HK700G-W Flat Panel Digital X-ray Detector is primarily focused on demonstrating substantial equivalence to an existing predicate device (Samsung S4343-W). The "acceptance criteria" can be inferred as successfully demonstrating that the new device's technical specifications and safety/performance (via non-clinical tests and supportive clinical data) are comparable to or better than the predicate, and that it meets general regulatory standards for X-ray equipment. The document does not describe a clinical diagnostic performance study with specific diagnostic outcomes or reader studies often seen for AI/Algorithm-based devices.

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The GU60A & GU60A-65 Digital X-ray Imaging Systems are intended for use in generating radiographic images of human anatomy by a qualified/trained doctor or technician. This device is not intended for mammographic applications.

The GU60A & GU60A-65 digital X-ray imaging systems are to be used to take and store image for diagnosis of patients. It consists of HVG(High voltage generator), U-arm positioner, Detector, X-ray tube, Collimator, AEC(Auto Exposure Control), DAP(Dose Area Product), CIB(Control Interface Box), Remote controller, Grid, Barcode scanner and Auto-stitching stand. These systems are used to capture images by transmitting X-ray to a patient's body. The X-ray passing through a patient's body is sent to the detector and then converted into electrical signals. These signals go through the process of amplification and digital data conversion in the signal process device being sent to the S-Station (Operation Software) and saved in DICOM file, a standard for medical imaging. The captured images are sent to the Picture Archiving & Communication System (PACS) server, and can be used for reading images.

The provided text is a 510(k) Premarket Notification for the SAMSUNG ELECTRONICS Co., Ltd. GU60A & GU60A-65 Digital X-ray Imaging Systems. This document primarily focuses on establishing substantial equivalence to a predicate device (XGEO GU60A, K140332) rather than providing detailed acceptance criteria and a comprehensive study report for the device's performance.

Therefore, the requested information cannot be fully extracted. Here's a breakdown of what can and cannot be provided based on the given text:

Information that can be extracted, partially or fully:

• Acceptance Criteria and Reported Device Performance: This information is not explicitly presented in a table format with specific quantitative acceptance criteria or detailed device performance metrics. The document states that "All test results were satisfying the standards" for safety, EMC, and performance, and "The proposed devices show no difference in non-clinical testing data such as MTF and DQE measurements from the predicate device." It also mentions "A Clinical images review report...which shows the equivalent diagnostic capability to the predicate device." However, no specific numerical or statistical acceptance criteria for diagnostic capability are provided.
• Data Provenance (country of origin, retrospective/prospective): Not specified for the clinical images review.
• Ground Truth (type of): The "Clinical images review report" implies expert consensus from a qualified reviewer for diagnostic capability, but specific details about how ground truth was established are not provided.
• Standalone Performance: The "Non-clinical data" section describes testing such as MTF and DQE measurements (IEC 62220-1), which are measures of a device's standalone performance. The document states "The proposed devices show no difference in non-clinical testing data such as MTF and DQE measurements from the predicate device."

Information that cannot be extracted from the provided text:

• Sample size for the test set: Not mentioned.
• Number of experts used to establish the ground truth for the test set and their qualifications: Not mentioned.
• Adjudication method for the test set: Not mentioned.
• Multi-Reader Multi-Case (MRMC) comparative effectiveness study: Not mentioned.
• Effect size of human readers with vs without AI assistance: Not applicable as no AI component is described.
• Sample size for the training set: Not mentioned. This device does not appear to utilize AI/ML, so a "training set" in that context would not be relevant.
• How the ground truth for the training set was established: Not applicable.

Based on the available text, here's what can be reported:

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

Since specific quantitative acceptance criteria and detailed device performance for diagnostic capability are not provided, this table will reflect the general statements made in the document regarding equivalence and compliance.

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

• Sample Size for Test Set: Not specified.
• Data Provenance: Not specified. The company is based in the Republic of Korea, but the origin of the clinical images for review is not stated, nor is whether the data was retrospective or prospective.

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

• Number of Experts: Not specified.
• Qualifications of Experts: Not specified, although a "qualified/trained doctor or technician" is mentioned in the Indications for Use for operating the system. The "clinical images review report" implies a qualified reviewer, but details are not provided.

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

• Adjudication Method: Not specified.

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 comparative effectiveness study is mentioned, nor is any AI component described for the device. The device is a digital X-ray imaging system, not an AI-assisted diagnostic tool.

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

• Yes, standalone performance testing was done for the physical imaging components.
  • "Non-clinical testing data was provided in conformance to the FDA "Guidance for the Submission of 510(k)'s for Solid-State X-ray Imaging Devices", which includes MTF and DQE measurements as tested by IEC 62220-1."
  • The results indicated that "The proposed devices show no difference in non-clinical testing data such as MTF and DQE measurements from the predicate device."

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

• The "Clinical images review report" implies expert consensus or review by qualified individuals for assessing "equivalent diagnostic capability." However, the exact nature (e.g., specific clinical endpoints, pathology correlation) used to establish ground truth for this review is not detailed.

8. The sample size for the training set

• Not applicable/Not mentioned, as the document describes a hardware device (X-ray system) and not an algorithm requiring a "training set" in the context of AI/ML.

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

• Not applicable/Not mentioned, for the same reason as above.

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