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510(k) Data Aggregation
(28 days)
1417WGC 127um and 1417WGC 140um are 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.
While 1417WGC_127um digital X-ray detector is identical to 1417WGC (K141563), both 1417WGC 127um and 1417WGC 140um are wired/wireless digital solid state X-ray detectors which are based on flat-panel technology. The wireless LAN(IEEE 802.11a/g/n/ac) communication signals images captured to the system and improves the user operability through high-speed processing. This radiographic image detector and processing unit consists of a scintillator coupled to an a-Si TFT sensor. This device needs to be integrated with a radiographic imaging system. Both devices do not operate as an X-ray generator controller but both 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 (K160579 / Xmaru View V1 and Xmaru PACS/ Rayence Co.,Ltd.) for a radiographic diagnosis and analysis.
The provided text is related to a 510(k) premarket notification for a digital flat panel X-ray detector. It describes the device, its intended use, and a comparison to a predicate device. The primary study presented here is a demonstration of substantial equivalence, which involves both non-clinical and clinical considerations rather than a strict acceptance criteria study against a specific performance metric.
Based on the provided text, here's an attempt to extract the requested information, acknowledging that specific "acceptance criteria" in numerical terms for clinical performance are not explicitly stated as they might be for an AI algorithm performance study. Instead, the equivalence to the predicate device is the key acceptance criterion.
1. Table of Acceptance Criteria and Reported Device Performance
| Criterion Type | Acceptance Criterion | Reported Device Performance |
|---|---|---|
| Non-Clinical | ||
| MTF (Modulation Transfer Function) | "performed similarly compared with the predicate devices" | "1417WGC_140um has similar MTF... performance at all spatial frequencies, especially from 2 lp/mm to 3.5 lp/mm. The comparison of the MTF... for 1417WGC_140um detector demonstrated that the performed almost same with 1417WGA." |
| DQE (Detective Quantum Efficiency) | "performed similarly compared with the predicate devices" | "...DQE of the both 1417WGA performed similarly compared with the predicate devices... The comparison of the... DQE for 1417WGC_140um detector demonstrated that the performed almost same with 1417WGA." |
| Clinical Equivalence | "demonstrate the substantial equivalency of the subject devices compared to each respective predicate device" in terms of diagnostic image quality. | "the images obtained with the 1417WGC 140um are superior to the same view obtained from a similar patient with 1417WGA, the predicate devices. In general, both the spatial and soft tissue contrast resolution are superior using the 1417WGC_140μm. Specifically, the soft tissues on extremity were seen with better clarity. There is little difficulty in evaluating a wide range of anatomic structures necessary to provide a correct conclusion." |
| Safety & Effectiveness | Device is "safe and effective and substantially equivalent" | "Based on the non-clinical and clinical consideration test and the outcome of a comparative review by an expert for both devices, the sponsor can claim the substantial equivalency between the subject devices and their predicate devices in terms of diagnostic image quality." |
2. Sample size used for the test set and the data provenance
- Sample Size: The text states, "clinical images are taken from both subject devices and reviewed by a licensed US radiologist." However, it does not specify the exact number of images or cases in this clinical test set. It mentions "sample radiographs of similar age groups and anatomical structures."
- Data Provenance: Prospective, as images were "taken from both subject devices" specifically for this comparison. The country of origin for the clinical data is implied to be the US ("reviewed by a licensed US radiologist").
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" (singular, implying one expert).
- Qualifications of Experts: "licensed US radiologist." Specific years of experience are not mentioned.
4. Adjudication method for the test set
- Adjudication Method: The text describes a "comparative review by an expert." This indicates a single expert opinion was used, performing a direct comparison between images from the subject device and the predicate device. There is no mention of consensus methods (e.g., 2+1, 3+1) or multiple readers.
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, a multi-reader, multi-case (MRMC) comparative effectiveness study as typically understood for AI-assisted reading was not conducted. This study compares a new imaging device (digital X-ray detector) to a predicate imaging device, not the effectiveness of human readers with or without AI assistance. Therefore, no effect size for human reader improvement with AI is provided.
6. If a standalone (i.e. algorithm only without human-in-the-loop performance) was done
- Yes, partially. Non-clinical performance tests like MTF and DQE were performed on the device itself (algorithm/device only performance), comparing it to the predicate device. However, the "clinical consideration" involved a human radiologist for diagnostic image quality evaluation, meaning the overall clinical assessment was not purely standalone. The device itself is an image acquisition device, not an AI diagnostic algorithm, so "standalone performance" in the AI sense isn't directly applicable for the clinical evaluation.
7. The type of ground truth used (expert consensus, pathology, outcomes data, etc.)
- For the clinical comparison, the ground truth was based on the expert opinion/impression of a licensed US radiologist regarding diagnostic image quality, specifically comparing the visibility of anatomic structures and clarity of soft tissues between images produced by the subject device and the predicate device.
8. The sample size for the training set
- This document describes safety and effectiveness for a medical device (Digital Flat Panel X-ray Detector), not an AI algorithm that requires a training set. Therefore, there is no training set sample size mentioned or applicable in this context. The device's underlying technology (a-Si TFT sensor, scintillator) is hardware-based.
9. How the ground truth for the training set was established
- N/A, as there is no training set for an AI algorithm for this device.
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