(63 days)
1717SGN and 1717SCN Digital Flat Panel X-Ray Detector are indicated for digital imaging solution designed for general radiographic system for human anatomy. They are intended to replace film or screen based radiographic systems in all general purpose diagnostic procedures. Not to be used for mammography.
1717SGN and 1717SCN digital solid state X-ray detectors are based on flat-panel technology. Both radiographic image detector and processing units consist of a scintillator coupled to an a-Si TFTsensor. A digital flat panel X-ray detector needs to be integrated with a radiographic imaging system. It can be utilized to capture and digitalize X-ray images for radiographic diagnosis. The RAW files can be further processed as DICOM compatible image files by separate console SW (not part of this 510k submission) for a radiographic diagnosis and analysis.
The provided text describes a 510(k) premarket notification for the Rayence Co., Ltd.'s 1717SGN / 1717SCN Digital Flat Panel X-ray Detectors. The submission aims to demonstrate substantial equivalence to predicate devices (LLX240AB01 and LTX240AA01-A manufactured by Samsung Mobile Display Co., Ltd.).
This document outlines the testing conducted to support the claim of substantial equivalency but does not provide specific, quantifiable acceptance criteria in a table format with corresponding pass/fail results or detailed device performance metrics relevant to an AI/Machine Learning context. The focus is on demonstrating that the new devices perform comparably to (or better than) existing predicate devices for general radiographic use, primarily through non-clinical bench testing and a limited clinical consideration.
Therefore, many of the requested elements for an AI/ML device's acceptance criteria and study proving performance cannot be fully extracted or are not applicable from this document, as it pertains to a hardware medical device submission.
Here's an attempt to address the points based on the provided text, highlighting where information is missing or not applicable:
Acceptance Criteria and Study Proving Device Meets Acceptance Criteria
This document describes the 510(k) submission for a digital flat panel X-ray detector, not an AI/Machine Learning diagnostic algorithm. Therefore, the typical acceptance criteria and study designs for AI/ML devices (e.g., sensitivity, specificity, AUC, MRMC studies, ground truth establishment for AI training/testing sets) are not directly applicable or detailed in this submission.
The general acceptance criteria for this hardware device revolve around demonstrating substantial equivalence to predicate devices in terms of:
- Safe and effective operation.
- Having the same intended use.
- Similar technological characteristics.
- Performance that is comparable or superior to the predicate.
1. A table of acceptance criteria and the reported device performance
The document does not present acceptance criteria in a quantitative table format suitable for an AI/ML context (e.g., minimum sensitivity, specificity). Instead, performance is reported comparatively against the predicate devices.
| Acceptance Criterion (Implicit) | Device Performance (1717SGN / 1717SCN) |
|---|---|
| Non-clinical Performance | |
| MTF (Modulation Transfer Function) | Performed "better" than predicate devices (LLX240AB01/LTX240AA01-A). |
| DQE (Detective Quantum Efficiency) | 1717SGN: Higher DQE than LLX240AB01 at all spatial frequencies and superior Signal-to-Noise Ratio (SNR) transfer. |
| 1717SCN: Higher DQE at high spatial frequencies (1 lp/mm to 3 lp/mm). | |
| NPS (Noise Power Spectrum) | Test results provided (details not explicitly stated as comparative data). |
| Clinical Performance (Qualitative Comparison) | |
| Spatial Resolution | "comparable or superior" to predicate. Specifically, "superior" for 1717SCN/1717SCN (likely a typo, refers to subject devices). |
| Soft Tissue Contrast | "comparable or superior" to predicate. Specifically, "superior" for 1717SCN/1717SCN. Soft tissues on extremity films seen with "better clarity". |
| Evaluation of Anatomic Structures | No difficulty in evaluating a wide range of anatomic structures necessary to provide a correct conclusion. |
| Safety & Electrical Performance | Passed IEC 60601-1:2005 + CORR.1(2006) + CORR(2007) and IEC 60601-1-2:2007 (EMC). |
2. Sample size used for the test set and the data provenance
- Test Set (Clinical Consideration): "sample radiographs of similar age groups and anatomical structures" were taken. A specific number of cases or images is not provided.
- Data Provenance: Not explicitly stated, but given the manufacturer is Rayence Co., Ltd. (Korea) and the consulting firm is in Houston, TX, the data could be from various geographies. The clinical consideration involved a "licensed US radiologist", suggesting the review was done in the U.S. There is no indication of retrospective or prospective study design, but the description "After comparing a broad review of plain radiographic images taken with..." suggests an observational review rather than a formal prospective clinical trial.
3. Number of experts used to establish the ground truth for the test set and the qualifications of those experts
- Number of Experts: "a licensed US radiologist" (implies one expert).
- Qualifications: "licensed US radiologist." No specific experience level (e.g., 10 years) is provided.
4. Adjudication method (e.g. 2+1, 3+1, none) for the test set
- Adjudication Method: "reviewed by a licensed US radiologist to render an expert opinion." This implies no formal adjudication among multiple readers, as only one expert is mentioned. The comparison was a qualitative expert review.
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 or AI assistance: This submission is for a digital X-ray detector, not an AI-powered diagnostic tool. Therefore, an MRMC study related to AI assistance was not applicable and not performed.
6. If a standalone (i.e. algorithm only without human-in-the-loop performance) was done
- Not applicable: This is a hardware device. No diagnostic algorithm is being evaluated in a standalone capacity.
7. The type of ground truth used (expert consensus, pathology, outcomes data, etc)
- Type of Ground Truth (for clinical consideration): The "ground truth" was the expert opinion of a single licensed US radiologist comparing images from the new devices against images from the predicate devices. This is a qualitative comparison, not a formal diagnostic ground truth like pathology.
8. The sample size for the training set
- Not applicable: This is a hardware device; there is no mention of a "training set" in the context of an AI/ML algorithm.
9. How the ground truth for the training set was established
- Not applicable: No training set for an AI/ML algorithm was used.
§ 892.1680 Stationary x-ray system.
(a)
Identification. A stationary x-ray system is a permanently installed diagnostic system intended to generate and control x-rays for examination of various anatomical regions. This generic type of device may include signal analysis and display equipment, patient and equipment supports, component parts, and accessories.(b)
Classification. Class II (special controls). A radiographic contrast tray or radiology diagnostic kit intended for use with a stationary x-ray system only is exempt from the premarket notification procedures in subpart E of part 807 of this chapter subject to the limitations in § 892.9.