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510(k) Data Aggregation
(63 days)
14HQ721G-B
The Flat Panel Digital X-ray Detector 14HQ721G-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 highdefinition 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. This device must be used in conjunction with an operating PC and an X-ray qenerator. 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. This device does not have a dynamic exposure feature.
This document describes the LG Electronics Inc. Flat Panel Digital X-ray Detector 14HQ721G-B. The information provided outlines the device's acceptance criteria and the studies conducted to demonstrate its performance and substantial equivalence to a predicate device.
1. Table of Acceptance Criteria and Reported Device Performance
| Performance Metric | Acceptance Criteria (Predicate Device K221394) | Reported Device Performance (14HQ721G-B) |
|---|---|---|
| Scintillator | CsI | CsI |
| Imaging Area | 14 x 17 inches | 14 x 17 inches |
| Pixel Matrix | 2,500 x 3,052 pixels | 2,500 x 3,052 pixels |
| Pixel Pitch | 140 um | 140 um |
| High Contrast Limiting Resolution | 3.6 lp/mm | 3.6 lp/mm |
| Communication | Wired/Wireless | Wired/Wireless (Standard: 802.11 a/b/g/n/ac compliance, Frequency: 2.4 GHz/5GHz, Bandwidth: 20MHz/40MHz/80MHz, MIMO: 2x2) |
| DQE (Detective Quantum Efficiency) | Typ. 66% @ 0.1 lp/mm | Typ. 66% @ 0.1 lp/mm |
| MTF (Modulation Transfer Function) | Typ. 84% @ 0.5 lp/mm | Typ. 84% @ 0.5 lp/mm |
| Resolution | 3.6 lp | 3.6 lp |
| Anatomical Sites | General | General |
| Exposure Mode | Manual, Auto (AED) | Manual, Auto (AED) |
| Built-in AEC | X (Not present) | O (Present) |
| Semi Dynamic mode | O (Present) | O (Present) |
| Rating | 24V --- 2.1A | 24V --- 2.1A |
| Electrical Safety, EMC, & Performance | Compliance with relevant standards | Complies with ES60601-1:2005 (R)2012 & A1:2012 [Incl. AMD2:2021], IEC 60601-1-2 Edition 4.0 2014-02, FDA guidance "Radio Frequency Wireless Technology in Medical Devices", FDA guidance "Guidance for the Submission of 510(k)s for Solid State X-ray Imaging Devices" |
| Software Validation | Verified and Validated | Software designed and developed according to internal process, verified and validated according to FDA guidance "The content of premarket submissions for software contained in medical devices" |
| Biocompatibility | Compliance with ISO 10993-1 | Complies with ISO 10993-1 and series |
| Imaging Performance Test | Conducted according to IEC 62220-1-1 | Conducted according to IEC 62220-1-1 |
| Cybersecurity | Compliance with FDA guidance | Complies with FDA guidance "Content of Premarket Submissions for Management of Cybersecurity in Medical Devices" and "Postmarket Management of Cybersecurity in Medical Devices" |
| Labeling | Compliance with CFR Part 801 | Complies with CFR Part 801, and FDA guidance "Pediatric Information for X-ray Imaging Device Premarket Notifications" |
2. Sample Size Used for the Test Set and Data Provenance
The document explicitly states: "In this submission, clinical testing was not conducted, since nonclinical information is sufficient to show the substantial equivalence of the proposed devices to the predicate devices for the modifications." This implies that there was no specific "test set" of patient data for clinical performance evaluation. The substantial equivalence was primarily based on non-clinical engineering and performance testing comparing the proposed device to the predicate device.
Therefore, information on sample size, country of origin, and retrospective/prospective nature of a clinical test set is not applicable as no clinical test set was used for this submission's substantial equivalence demonstration.
3. Number of Experts Used to Establish the Ground Truth for the Test Set and Qualifications of Those Experts
Not applicable, as no clinical test set was used to establish ground truth for this submission.
4. Adjudication Method for the Test Set
Not applicable, as no clinical test set was used.
5. If a Multi-Reader Multi-Case (MRMC) Comparative Effectiveness Study was done
No MRMC comparative effectiveness study was conducted as clinical testing was not performed. The submission relies on non-clinical data to demonstrate substantial equivalence.
6. If a Standalone (i.e. algorithm only without human-in-the-loop performance) was done
The device itself is a Flat Panel Digital X-ray Detector, which is a hardware component for acquiring X-ray images. It includes firmware (software of moderate level of concern) that was validated. The performance tests (DQE, MTF, resolution) are measures of the standalone imaging capabilities of the device itself, without a human in the loop for interpretation beyond assessing image quality metrics. The comparison of these metrics to the predicate device effectively serves as a standalone performance assessment.
7. The Type of Ground Truth Used
The ground truth for performance was established through objective engineering and physical measurements based on established standards (e.g., IEC 62220-1-1 for DQE) and comparison to the predicate device's specifications. This is a technical ground truth for imaging performance rather than a clinical ground truth based on expert consensus, pathology, or outcomes data from patients.
8. The Sample Size for the Training Set
The document does not provide information on a training set size. This is consistent with the nature of the device being a digital X-ray detector, where performance is typically validated through engineering tests and comparison to a predicate, rather than through a machine learning model that requires a "training set" of data in the conventional sense. The "software update" mentioned includes the addition of the AEC mode, and this software was designed, developed, verified, and validated internally, but no specific training data for an AI algorithm is indicated.
9. How the Ground Truth for the Training Set Was Established
Not applicable, as no training set for a machine learning model is described. The ground truth for the device's technical performance was established through standardized physical measurements and component specifications, as detailed in point 7.
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