Search Results
Found 1 results
510(k) Data Aggregation
(51 days)
Flat Panel Digital X-ray Detector 14HQ901G-B 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.
This model is an x-ray imaging device, a system that can acquire and process X-ray images as digital images. It utilizes amorphous silicon and a high-performance scintillator to ensure sharp high-definition image quality with the resolution of 3.6 lp/mm and the pixel pitches of 140 um. This device is a flat panel based X-ray image acquisition device must be used in conjunction with an operating PC and an X-ray generator. This device can be used for digitizing and transferring X-ray images for radiological diagnosis. The data transmission between the detector and PC can be enabled with a wired (cable) or wireless connection
The provided text describes the LG Electronics Inc. Flat Panel Digital X-ray Detector 14HQ901G-B and its substantial equivalence to a predicate device (17HK701G-W). The document primarily focuses on non-clinical tests and technological characteristics comparison rather than detailed clinical study data with specific acceptance criteria relating to diagnostic performance metrics (like sensitivity, specificity, accuracy).
However, the document states: "Clinical data has been provided according to FDA guidance document 'Guidance for the Submission of 510(k)s for Solid Sate X-ray Imaging Devices'. The data was not necessary to establish substantial equivalence based on the modifications to the device but provided further evidence in addition to the laboratory performance data to show that the device works as intended." This implies that while clinical data was submitted, it wasn't the primary basis for the substantial equivalence determination. The primary basis appears to be the laboratory performance data and technological characteristics comparison.
Since detailed acceptance criteria and a description of a clinical study focusing on diagnostic performance metrics (e.g., sensitivity, specificity) with specific values for the proposed device are not provided in the extracted text, I can only extract general statements about clinical data being submitted. The text doesn't provide the quantifiable acceptance criteria for diagnostic performance or the results of such a study.
Therefore, for aspects related to diagnostic performance, I cannot fill in the table with specific values or answer questions about sample size, expert qualifications, or adjudication methods for a diagnostic performance study.
Here's what can be extracted based on the provided text:
1. Table of Acceptance Criteria and Reported Device Performance
The document does not explicitly state specific acceptance criteria for diagnostic performance (e.g., minimum sensitivity or specificity) nor does it provide a table of reported diagnostic performance metrics alongside such criteria. The "performance test" mentioned in Non-Clinical Test Summary refers to IEC 62220-1, which is about the Detective Quantum Efficiency (DQE), a physical imaging characteristic, not a diagnostic performance metric.
| Feature / Metric | Acceptance Criteria (Not Explicitly Stated for Diagnostic Performance) | Reported Device Performance (as per non-clinical tests) |
|---|---|---|
| Diagnostic Performance | Not explicitly stated for clinical diagnostic performance. | Not explicitly stated for clinical diagnostic performance in the provided text. |
| Technological Characteristics (Comparison to Predicate) | ||
| High Contrast Limiting Resolution (LP/mm) | ≥ 3.6 lp/mm (Implied equivalence to predicate) | 3.6 lp/mm (Proposed Device) |
| DQE (Detective Quantum Efficiency) (Typ. @0.1lp/mm) | No specific acceptance criteria stated for improvement over predicate, but higher is generally better. | Typ.78% (Proposed Device) vs. Typ.72% (Predicate Device) |
| MTF (Modulation Transfer Function) (Typ. @0.5lp/mm) | No specific acceptance criteria stated for improvement over predicate. | Typ.84% (Proposed Device) vs. Typ.89% (Predicate Device) (Note: Proposed device is lower but "no differences in performance as a result of performing the clinical test" is claimed) |
| Electrical Safety | Compliance with ES60601-1 | Complies |
| EMC | Compliance with IEC 60601-1-2 | Complies |
| Software Validation | Developed per FDA guidance (MODERATE level of concern) | Verified and Validated |
| Biocompatibility | Compliance with ISO 10993-1 | Complies |
| Cybersecurity | Compliance with FDA guidance | Addressed |
2. Sample size used for the test set and the data provenance
The text does not provide details on the sample size used for any clinical test set or the provenance (e.g., country of origin, retrospective/prospective) of the clinical data. It only states that "Clinical data has been provided according to FDA guidance document".
3. Number of experts used to establish the ground truth for the test set and the qualifications of those experts
The text does not provide this information.
4. Adjudication method (e.g. 2+1, 3+1, none) for the test set
The text does not provide this information.
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
The text does not mention or describe an MRMC comparative effectiveness study or any AI assistance. The device is a "Flat Panel Digital X-ray Detector," not an AI-powered diagnostic tool.
6. If a standalone (i.e. algorithm only without human-in-the-loop performance) was done
The text does not mention or describe a standalone algorithm performance study. The device itself is an X-ray detector, not an algorithm.
7. The type of ground truth used (expert consensus, pathology, outcomes data, etc.)
The text does not provide this information. It only generally states "clinical data has been provided."
8. The sample size for the training set
The text does not provide this information as it does not describe an AI model or a training set.
9. How the ground truth for the training set was established
The text does not provide this information as it does not describe an AI model or a training set.
Ask a specific question about this device
Page 1 of 1