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Digital Flat Panel X-Ray Detector 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.

1717WCE, 1717WCE-HR, 1717WCE-HS, 1717WCE-GF X-ray detectors, are wired/wireless digital solid state X-ray detectors that are based on flat panel technology. The wireless LAN (IEEE 802.11 n/ac) communication signals images captured to the system and improves the user operability through high speed processing. These radiographic image detectors are processing unit consist of a scintillator coupled to an TFT sensor. The flat-panel detectors need to be integrated with an x-ray generator (not part of the submission), so it can be utilized to capture and digitize x-ray images for radiographic diagnosis.

1717WCE. 1717WCE-HR. 1717WCE-HS. 1717WCE-GF includes the software (firmware) of basic level of concern. It's the same Image Acquisition and Operating Software used for the predicate device is used but modified to include additional detector models in comparison with the predicate device. Full software documentation has been submitted, as well as the necessary sections to demonstrate device cybersecurity.

The RAW files can be further processed as DICOM compatible image files by separate consol SW (K190866, XmaruView V1 / Rayence Co.,Ltd) for a radiographic diagnosis and analysis.

1717WCE is the basic model. 1717WCE-HR is identical with the basic model except for pixel pitch not related to safety. 1717WCE-HS is identical with the basic model except for marking of sheet. 1717WCE-GF is identical with the basic model except for case color and pixel pitch.

The given text describes a 510(k) submission for a Digital Flat Panel X-ray Detector. The submission aims to demonstrate substantial equivalence to a predicate device. However, the document does not describe a study that uses an AI algorithm as a device or an AI assistance to human readers, so most of the requested information regarding AI-specific criteria (like MRMC studies, standalone AI performance, training set details, or ground truth establishment for AI) is not present.

The document focuses on the technical and clinical performance comparison of the subject device (new X-ray detector models) against a predicate device (older X-ray detector models) through non-clinical and image quality assessments by human reviewers.

Here's the available information based on the provided text, addressing the points where information is available and noting where it's not:

1. Table of acceptance criteria and reported device performance:

The document doesn't present a formal table of "acceptance criteria" for the entire device as one might see for an AI algorithm's specific performance metrics (e.g., AUC, sensitivity, specificity). Instead, it demonstrates substantial equivalence by comparing its technological characteristics and performance to a predicate device. The performance is described qualitatively through comparisons of image quality.

Qualitative Image Quality Assessment (summarized from section 6):

• 1717WCE 99.9um vs. 1417WCE 100um: Subject device (1717WCE 99.9um) showed overall better image quality, clearer anatomical structures (bony and soft tissues of upper and lower extremities). Predicate device (1417WCE 100um) had decreased sharpness, more overexposed appearance, and higher noise.
• 1717WCE 140um vs. 1417WCE 140um: Subject device (1717WCE 140um) showed overall better image quality, clearer anatomical structures. Predicate device (1417WCE 140um) had less sharpness, more overexposed appearance, and higher noise.
• Conclusion: Both 1717WCE 140um and 1717WCE 99.9um demonstrated sufficient image quality for diagnostic purposes, with better image quality than the predicate device.

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

• Sample Size: The document states, "After comparing a broad review of plain radiographic images taken with 1717WCE... and 1417WCE images obtained equivalent quality for the same view obtained from a similar patient." It further mentions reviewing "plain radiographic images taken with 1717WCE 99.9um and the 1417WCE 100um" and "plain radiographic images taken with 1717WCE 140um and the 1417WCE 140um." No specific numerical sample size (e.g., number of images, number of patients) is provided for this qualitative review.
• Data Provenance: Not explicitly stated (e.g., country of origin). The studies appear to be internal performance assessments rather than large-scale clinical trials. The data is retrospective in the sense that existing images were compared.

3. Number of experts used to establish the ground truth for the test set and the qualifications of those experts:

• The document mentions "Upon review of the plain radiographic images..." suggesting a human review. However, it does not specify the number of experts, their qualifications (e.g., radiologist with X years of experience), or how ground truth was established by them. The "ground truth" here seems to be subjective human judgment of image quality for diagnostic purposes rather than a definitive disease presence/absence.

4. Adjudication method (e.g. 2+1, 3+1, none) for the test set:

• No adjudication method is described. The qualitative image quality assessment is presented as a singular conclusion derived from "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, an MRMC comparative effectiveness study was not done. This submission is for an X-ray detector, not an AI algorithm assisting human readers. The qualitative image review is a comparison of image detector performance, not human reader performance with or without AI.

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

• Not applicable. The device is a digital X-ray detector, which produces images. It's not a standalone AI algorithm designed to interpret those images without human involvement.

7. The type of ground truth used:

• Qualitative Human Assessment of Image Quality. The "ground truth" for the performance study is based on visual assessment by unspecified reviewers that the image quality of the subject device is "better" or "sufficient" for diagnostic purposes compared to the predicate device. It is not tied to a confirmed diagnosis (e.g., pathology, surgical findings, or long-term outcomes data). The document also mentions "non-clinical test report for the subject device were prepared and submitted to FDA... by using the identical test equipment and same analysis method described by IEC 62220-1" for metrics like MTF, DQE, and NPS, which are objective image quality measurements.

8. The sample size for the training set:

• Not applicable. This device is an X-ray detector, not an AI or machine learning model that requires a training set.

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

• Not applicable. As the device is not an AI/ML model, there is no "training set" or "ground truth for the training set."

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Digital Flat Panel X-Ray Detector 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.

1417WCE, 1417WCE-HR, 1417WCE-HS, 1417WCE-GF X-ray detectors, are wired/wireless digital solid state X-ray detectors that are based on flat panel technology. The wireless LAN (IEEE 802.11 n/ac) communication signals images captured to the system and improves the user operability through high speed processing. These radiographic image detectors are processing unit consist of a scintillator coupled to an a-Si TFT sensor. These devices need to be integrated with a static radiographic imaging system. It can be utilized to capture and digitalize X-ray images for radiographic diagnosis.

The revised 510k Summary specified that 1417WCE, 1417WCE-HR, 1417WCE-HS, 1417WCE-GF includes the software (firmware) of MODERATE level of concern. It's the same Image Acquisition and Operating Software used for the predictive device is used but modified to include additional detector models in comparison with the predicate device.

The RAW files can be further processed as DICOM compatible image files by separate consol SW (K190866, XmaruView V1 / Rayence Co.,Ltd) for a radiographic diagnosis and analysis. The imaging software XMaru View V1 is not part of the subject device.

1417WCE is the basic model. 1417WCE-HR is identical with the basic model except for the pixel pitch size. 417WCE-HS is identical with the basic model except for the case color. 1417WCE-GF is identical with the basic model except for the case color and the pixel pitch size. The differences are not safety related.

The provided text is a 510(k) summary for Rayence Co., Ltd.'s X-ray detectors (1417WCE, 1417WCE-HR, 1417WCE-HS, 1417WCE-GF). It describes the substantial equivalence of these new devices to a previously cleared predicate device (1417WCC, K171418), rather than proving the device meets a specific set of new clinical acceptance criteria through a clinical study.

The core of this submission is non-clinical performance testing to demonstrate that the new devices perform equivalently or better than the predicate, not a clinical study proving diagnostic accuracy or human performance improvement.

Therefore, many of the requested elements for a clinical study (like sample size for test sets, data provenance, number of experts, adjudication methods, MRMC studies, standalone performance with ground truth methods, and training set details) are not applicable or not explicitly stated in this type of submission.

However, I can extract information related to the acceptance criteria implicitly used for substantial equivalence and the non-clinical study details that support the performance claims.

Here's an analysis based on the provided document:

Acceptance Criteria and Reported Device Performance (Implicit for Substantial Equivalence)

The acceptance criteria for this 510(k) submission are implicitly tied to demonstrating substantial equivalence to the predicate device (1417WCC). This means showing that the new devices are as safe and effective as the predicate. The "performance" in this context refers to technical imaging characteristics rather than diagnostic accuracy in a clinical setting.

Implicit Acceptance Criteria and Reported Device Performance:

Study Details (Non-Clinical Performance Testing for Substantial Equivalence)

The provided document describes non-clinical performance testing and a qualitative image review to support substantial equivalence, rather than a full-scale clinical trial with human subjects and diagnostic outcomes.

1. Sample size used for the test set and the data provenance:

   • Test Set Sample Size: Not specified in terms of number of images or patients for the qualitative review. The document mentions comparing "plain radiographic images" from the new devices and the predicate.
   • Data Provenance: Not explicitly stated (e.g., country of origin). The comparison indicates the images were "taken with" the devices and from "a similar patient," implying they were internally generated or acquired for comparison. It was a retrospective comparison of existing image types (though not necessarily existing patient data in a large dataset).

2. Number of experts used to establish the ground truth for the test set and the qualifications of those experts:

   • Not Applicable in the traditional sense of diagnostic ground truth. For the qualitative image review, the document states: "After comparing a broad review of plain radiographic images... images obtained equivalent quality..." This implies a subjective assessment, likely by internal experts, but the number and qualifications are not specified. This is a technical comparison for substantial equivalence, not a diagnostic accuracy study requiring independent expert ground truth for clinical endpoints.

3. Adjudication method (e.g. 2+1, 3+1, none) for the test set:

   • None described. The qualitative image review mentions a "broad review," implying an assessment was made, but no formal adjudication process (like 2+1 reader agreement) is detailed.

4. 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, an MRMC comparative effectiveness study was NOT done. This submission is for digital X-ray detectors themselves, not an AI-powered diagnostic assistance tool. Therefore, a study on human reader improvement with AI assistance is not applicable.

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

   • Yes, in the sense of technical performance testing. The device's technical performance (MTF, DQE, NPS) was evaluated "algorithm only" (as in, device output without human interpretation in the loop) and compared to the predicate device, following international standards (IEC 62220-1). However, this is not a "standalone performance" study measuring diagnostic accuracy.

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

   • Primarily
     technical specifications and qualitative comparison; no traditional diagnostic ground truth. The "ground truth" for this submission are the measured physical properties of the detectors (e.g., MTF, DQE values determined by standardized phantoms) and the subjective assessment of image quality against the predicate. This is sufficient for demonstrating substantial equivalence for a medical imaging device (detector), not for an AI algorithm that provides a diagnostic output.

7. The sample size for the training set:

   • Not Applicable. This submission is for a physical X-ray detector, not an AI or machine learning algorithm that requires a "training set."

8. How the ground truth for the training set was established:

   • Not Applicable. As there is no training set for this device type, no ground truth needed to be established for it.

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1717SCV and 1717SGV X-ray detectors, 127um and 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.

1717SCV / 1717SGV is a digital solid state X-ray detector that is based on flat-panel technology. 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. It can be utilized to capture and digitalize X-ray images for radiographic diagnosis.

1717SCV and 1717SGV have the same Hardware, Software and components.

The type of scintillator layer are different: Cesium Iodide for 1717SCV and Gadolinium Oxsulfide for 1717SGV. Scintillator is a phosphor that produces scintillations.

The subject detectors are not wireless, but they are connected to a viewing station by ethernet connection. Also, the subject detectors have an Automatic Exposure Control (AEC) feature.

The RAW files can be further processed as DICOM compatible image files by separate console SW (K190866 / Xmaruview V1 (Xmaru Chiroview, Xmaru Podview)/ Rayence Co.,Ltd.) for a radiographic diagnosis and analysis.

The software used with the subject detectors is the same as the software XmaruView V1 used with the predicate K190866.

Here's an analysis of the acceptance criteria and study information based on the provided text:

1. Table of Acceptance Criteria and Reported Device Performance

The provided text does not contain explicitly defined acceptance criteria (e.g., a specific threshold for MTF or DQE that the device must meet). Instead, it states that the devices (1717SCV / 1717SGV) have "similar MTF and DQE performance" when compared to their predicate devices (1717SCC / 1717SGC). The implicit acceptance criterion is that the subject device's performance should be comparable to, or not significantly worse than, the legally marketed predicate devices.

2. Sample Size Used for the Test Set and Data Provenance

The document states that "The non-clinical test report for each subject device was prepared and submitted to FDA separately to demonstrate the substantial equivalency..." It then lists the types of tests performed (MTF, DQE, NPS). However, it does not specify the sample size for the test set or the data provenance (e.g., country of origin, retrospective/prospective). These are non-clinical performance evaluations, likely performed in a lab setting rather than on patient data.

3. Number of Experts Used to Establish Ground Truth and Qualifications

The provided text does not mention any human expert review for establishing ground truth. The performance evaluation is based on technical metrics (MTF, DQE, NPS) derived from physical measurements of the devices, not from interpretation of clinical images by experts.

4. Adjudication Method for the Test Set

As the evaluation is based on technical metrics and not human interpretation of images, an adjudication method for a test set of images is not applicable and therefore not mentioned.

5. Multi-Reader Multi-Case (MRMC) Comparative Effectiveness Study

No, a MRMC comparative effectiveness study was not done. The study described focuses on the physical performance characteristics of the X-ray detectors themselves (MTF, DQE, NPS) rather than their impact on human reader performance or the diagnostic accuracy of images.

6. Standalone (Algorithm Only Without Human-in-the-Loop Performance) Study

The performance testing described (MTF, DQE, NPS) is a standalone (algorithm/device only) performance evaluation. These metrics assess the intrinsic image quality and efficiency of the detector itself, independent of human interpretation.

7. Type of Ground Truth Used

The ground truth used for these performance metrics is based on physical measurements and standardized testing protocols (specifically, IEC 6220-1) rather than expert consensus, pathology, or outcomes data. The "ground truth" for MTF, DQE, and NPS refers to the actual physical properties and performance of the detector under controlled conditions.

8. Sample Size for the Training Set

The document does not mention a training set sample size. The devices (1717SCV / 1717SGV) are X-ray detectors, not AI algorithms that would require a training set in the conventional sense. The performance evaluation focuses on the inherent characteristics of the hardware.

9. How the Ground Truth for the Training Set Was Established

Since there is no mention of a training set for an AI algorithm (as this is a medical device clearance for an X-ray detector), the method for establishing ground truth for a training set is not applicable and therefore not described.

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Edge Air(1417) Digital Flat Panel X-Ray Detector is indicated for digital imaging solution designed for general radiographic system for human anatomy. It is intended to replace film or screen based radiographic system in all general purpose diagnostic procedures. Not to be used for mammography.

Edge 40(1417) is a wired/wireless digital solid state X-ray detector that is based on flat-panel technology. The wireless LAN (IEEE 802.11a/g/w/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. 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 a separate console SW program (K190866 / Xmaruview V1 (Xmaru Chiroview, Xmaru Podview) / Rayence Co., Ltd.) for a diagnostic analysis.

The provided text describes the Edge Air (1417) Digital Flat Panel X-ray Detector, a device intended to replace film or screen-based radiographic systems for general diagnostic procedures, excluding mammography. The submission argues for substantial equivalence to a predicate device, Edge Air (K172681).

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

1. Table of Acceptance Criteria and Reported Device Performance

The submission primarily focuses on demonstrating substantial equivalence to a predicate device, rather than defining explicit acceptance criteria against a fixed standard. However, the comparisons provided between the proposed device and the predicate device can be interpreted as the performance metrics and their desired "acceptance" (i.e., being similar or equivalent to the predicate).

2. Sample Size Used for the Test Set and Data Provenance

The document mentions "clinical images have been reviewed by a licensed radiologist" for the "clinical consideration test." However, it does not specify the exact sample size (number of images or patients) used for this clinical review or the demographics of the "similar age group and anatomical structures."

The data provenance is implicitly prospective for the clinical images used for comparison, as they involved "taking sample radiographs." The country of origin of the data is not specified.

3. Number of Experts Used to Establish the Ground Truth for the Test Set and Qualifications of Those Experts

Only one expert is mentioned: "a licensed radiologist." No specific details about the radiologist's experience (e.g., "10 years of experience") are provided beyond being "licensed" and an "expert opinion."

4. Adjudication Method for the Test Set

The adjudication method appears to be none beyond a single expert's opinion. The text states, "clinical images have been reviewed by a licensed radiologist to render an expert opinion." There is no mention of multiple readers, consensus, or a specific adjudication process for discrepancies.

5. If a Multi-Reader Multi-Case (MRMC) Comparative Effectiveness Study Was Done, and the Effect Size of How Much Human Readers Improve with AI vs. Without AI Assistance

No MRMC comparative effectiveness study was done as described for an AI device. This submission is for a digital flat panel X-ray detector, which is a hardware component for image acquisition, not an AI-powered diagnostic tool. The device itself does not involve AI assistance for human readers in the sense of improving their diagnostic capability. The comparison is for image quality of two different hardware detectors.

6. If a Standalone (i.e., algorithm only without human-in-the-loop performance) Was Done

Again, this question is not directly applicable to the device under review. The Edge Air (1417) is an X-ray detector, not a standalone algorithm. Its "performance" is measured by physical characteristics (MTF, DQE, NPS) and its ability to produce diagnostic images comparable to a predicate device. The imaging software (Xmaruview V1) is mentioned as a separate 510(k) (K190866), but the submission for the detector itself does not detail standalone performance of an algorithm.

7. The Type of Ground Truth Used

For the non-clinical tests (MTF, DQE, NPS), the ground truth is derived from physical measurements against established standards (IEC 62220-1).

For the "clinical consideration test," the ground truth is expert opinion/comparison by a single licensed radiologist, asserting similarity in image quality ("spatial and soft tissue contrast resolutions for both devices are equivalent"). It's a comparative visual assessment against images from the predicate device by an expert.

8. The Sample Size for the Training Set

The concept of a "training set" is not directly applicable here. This is a hardware device (X-ray detector). The imaging software (Xmaruview V1) might have had a training set if it involved AI, but the submission for the detector does not provide this information. The device's manufacturing and performance are based on engineering design and testing, not machine learning training.

9. How the Ground Truth for the Training Set Was Established

Since there is no "training set" for the X-ray detector itself, this question is not applicable.

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