Search Results

1012WCA Digital Flat Panel X-Ray Detector is indicated for digital imaging solution designed for human anatomy including head, neck, cervical spine, arm, leg and peripheral (foot, hand, wrist, fingers, etc.). It is intended to replace film based radiographic diagnostic systems and provide a case diagnosis and treatment planning for physicians and other health care professionals. Not to be used for mammography.

1012WCA is a wired/wireless digital X-ray flat panel detector that can acquire radiographic images of human anatomy when used with existing radiographic x-ray systems. The wireless LAN((IEEE 802.11a/g/n) communication signals images captured to the system and improves the user operability through high-speed processing. This X-ray imaging detector consists of a scintillator directly coupled to an a-Si TFT sensor. 1012WCA is designed specifically to be integrated with a console PC system and X-Ray generator to digitalize x-ray images into RAW files. The RAW files can be made to DICOM compatible image files which can be viewed by console SW for a radiographic image diagnosis and analysis.

The document describes the Rayence Co., Ltd. 1012WCA Digital Flat Panel X-ray Detector, which is a modified version of the predicate device Xmaru1210P. The primary aim of the submission is to demonstrate substantial equivalence to the predicate device.

Here's an analysis of the acceptance criteria and the study that proves the device meets them:

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

The document does not explicitly state formal "acceptance criteria" in a quantitative sense with pass/fail thresholds. Instead, it aims to demonstrate substantial equivalence to the predicate device. The performance comparison is therefore against the predicate device (Xmaru1210P).

Summary of Study:

The study aimed to demonstrate substantial equivalence between the 1012WCA device and its predicate device, Xmaru1210P, rather than meeting quantitative acceptance criteria defined against an absolute standard. This was achieved through:

• Non-clinical testing: Comparing MTF, DQE, and NPS of both devices using IEC 62220-1 methods.
• Clinical consideration/expert review: Comparing clinical images from both devices by a licensed US radiologist.

2. Sample size used for the test set and the data provenance

• Sample Size for Test Set: Not explicitly stated as a number of images or cases for the clinical expert review. The document mentions "clinical images are taken from both devices" and evaluated "according to age group and anatomical structures." It does not provide a specific count.
• Data Provenance: The images were reviewed by a "licensed US radiologist," implying the data was relevant to clinical practice in the US. No information is given regarding whether the data was retrospective or prospective, or the specific country of origin of the patients whose images were used, beyond the radiologist's location.

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: "Licensed US radiologist." No further detail regarding years of experience or specialization is provided.

4. Adjudication method for the test set

• Adjudication Method: "An expert opinion" from a single licensed US radiologist was used. This does not describe a formal adjudication method like 2+1 or 3+1, which typically involves multiple readers and a tie-breaking mechanism. With only one expert, there is no need for adjudication.

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

• MRMC Study: No, a multi-reader multi-case (MRMC) comparative effectiveness study was not done. The study involved a single expert reviewing images from both devices, not comparing human readers with and without AI assistance (as this is a detector, not an AI diagnostic tool).
• Effect Size of Human Readers with/without AI assistance: Not applicable, as no AI assistance was involved and the study design was not an MRMC comparative effectiveness study in that context.

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

• This is a digital X-ray detector, not an AI algorithm. Therefore, the concept of "standalone (algorithm only)" performance without human-in-the-loop is not directly applicable in the same way it would be for an AI diagnostic software.
• However, the non-clinical tests (MTF, DQE, NPS) represent an objective "standalone" performance assessment of the detector's physical imaging characteristics, independent of human interpretation. These tests indicated that the 1012WCA demonstrated better DQE and overall resolution/sharpness compared to the predicate, even if MTF alone was slightly lower in some aspects.

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

• For the clinical evaluation, the "ground truth" was the expert opinion of a licensed US radiologist. It was implicitly that this expert's assessment of image quality and diagnostic utility served as the basis for comparison between the two devices. There is no mention of pathology, outcomes data, or other objective "ground truth" measures for the clinical aspect.
• For the non-clinical bench testing, the ground truth was based on standardized quantitative metrics like MTF, DQE, and NPS, which are derived from physical measurements according to IEC 62220-1.

8. The sample size for the training set

• Not applicable. This device is a digital X-ray detector, not an AI algorithm that requires a "training set" in the machine learning sense. Its performance is based on its physical design and engineering, not learned from data.

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

• Not applicable, as there is no training set for this type of device.

Ask a specific question about this device