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The 2430TCA Digital Flat Panel X-Ray Detector is indicated for digital imaging solution designed for a mammographic system. It is intended to replace film or screen based mammographic systems in screening mammography. Xmaru W is an integrated software solution indicated for use with the 2430TCA detector.

2430TCA is a digital mammography X-ray detector that is based on flat-panel technology. This mammographic image detector and processing unit consists of a CsI scintillator coupled to a TFT sensor. This device needs to be integrated with a mammographic imaging system. It can be utilized to capture and digitalize X-ray images for mammographic screening. The RAW files can be further processed as DICOM compatible image files by separate console SW, Xmaru W, for a mammographic screening. 2430TCA detector is connected to the viewing station via a LAN cable.

The provided text is a 510(k) summary for the Rayence 2430TCA with Xmaru W, a digital mammography system. While it discusses the device's characteristics and compares it to a predicate device (2430MCA with Xmaru W), it does NOT contain detailed information about acceptance criteria for an AI/software component, nor a specific study proving it meets such criteria in terms of AI performance.

The document primarily focuses on demonstrating substantial equivalence of the detector (2430TCA) to its predicate (2430MCA) based on physical characteristics, imaging performance (MTF, DQE, NPS), and human expert review of images. It also mentions general software (Xmaru W) but doesn't detail any AI functionality or its validation.

Therefore,Based on the provided FDA 510(k) summary, I cannot provide the requested information about acceptance criteria and a study proving an AI component of the device meets those criteria.

The document discusses the substantial equivalence of a Full-Field Digital Mammography System (including a detector and image processing software). It focuses on the hardware (2430TCA detector) and its image quality parameters (MTF, DQE, NPS) compared to a predicate device. While it mentions "Xmaru W is an integrated software solution," it does not describe any specific AI or machine learning functionality within this software, nor does it discuss validation studies for such a component.

The "Summary of Performance Testing" section describes:

• Human expert review of plain radiographic images from the 2430TCA and 2430MCA, concluding "overall, better image quality of the same anatomical position in the separate patients" for 2430TCA.
• Non-clinical tests (MTF, DQE, NPS) performed on the detector, not an AI algorithm.

Therefore, many of the specific points you've asked for (e.g., sample size for test set, number of experts for ground truth, adjudication method, MRMC study, standalone performance, training set details) are not present in this document because it is focused on the performance of a digital X-ray detector and its fundamental image quality, not an AI algorithm.

If this device were to have an AI component for advanced image analysis (e.g., CADe for lesion detection), that information would typically be in a separate section detailing the AI's performance validation, often with a different set of acceptance criteria and study designs that align with the specific AI function (e.g., sensitivity, specificity, FROC analysis, reader studies). This document does not suggest the presence or validation of such an AI component for diagnostic aid.

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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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0909FCC and 0909FCC-HS are indicated for digital imaging solution designed including head, neck, cervical spine, arm, leg and peripheral (foot, hand, wrist, fingers, etc.). The detectors are intended to replace the x-ray imager on film based radiographic diagnostic systems. Not to be used for mammography.

0909FCC and 0909FCC-HS are digital solid state X-ray detectors based on flat-panel technology. This radiographic image detector and processing unit consists of a scintillator coupled to Indium Ginc Oxide (IGZO) on TFT sensor. This device is connected to the user PC via wired LAN (ethernet cable) and it needs to be integrated with a radiographic imaging system. It does not operate as an X-ray generator controller but 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(Xmaru RF Lite) for a radiographic diagnosis and analysis. 0909FCC is a basic model. 0909FCC-HS is identical with a basic model except for marking of sheet not related to safety. 0909 FCC and 0909 FCC-HS are intended to capture dynamic images. The Xmaru RF lite imaging software is included in the system but the computer that receives the digital images is not provided with the system.

0909FCC and 0909FCC-HS are compatible with the X-ray generator, HTC-35B by Poskom Co.,Ltd, which is not provided with the system.

The provided text does not contain information about acceptance criteria for a diagnostic AI device, nor does it describe a study proving such a device meets acceptance criteria. The document is a 510(k) premarket notification for a digital flat panel X-ray detector (0909FCC, 0909FCC-HS), which is a hardware component for X-ray imaging systems, not an AI diagnostic device.

The summary of the document focuses on demonstrating substantial equivalence of the new detectors to a previously cleared predicate device (1212FCA). The "performance testing" section describes non-clinical bench testing related to image quality metrics of the X-ray detector itself, such as MTF (Modulation Transfer Function), DQE (Detective Quantum Efficiency), and NPS (Noise Power Spectrum), as per IEC 62220-1. These are physical performance characteristics of the imaging hardware, not diagnostic performance of an AI algorithm interpreting images.

Therefore, I cannot provide the requested information regarding acceptance criteria, study details, sample sizes, expert ground truth, adjudication methods, MRMC studies, or standalone AI performance, as these elements are not present in the provided text.

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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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Vatech Clismile is indicated for orthodontic movement of natural teeth, excluding mandibular bicuspid teeth.

This product is an orthodontic bracket and is used with orthodontic wire materials and other orthodontic products to treat malocclusion of the upper and lower teeth.

It is used to correct teeth by transmitting physical force to each tooth, and is a selfligation bracket using a ceramic body and a metal cap made of a metal material. Shelf-life is not applicable because of the low likelihood of time-dependent product degradation.

The provided text describes a 510(k) premarket notification for a medical device called "Vatech Clismile," which is an orthodontic bracket. The document focuses on demonstrating substantial equivalence to a predicate device, "In-Ovation C." The information does not present a study with detailed acceptance criteria and performance reports in the context of an AI/ML powered device. Instead, it focuses on the equivalence of physical and materials characteristics.

Therefore, many of the requested fields cannot be filled as they pertain to the evaluation of an AI-powered medical device through clinical or performance studies with ground truth establishment, which is not the subject of this document.

However, I can extract the relevant information regarding the technical characteristics and the performance testing mentioned to demonstrate equivalence.

Here's the information based on the provided text, with many fields marked as "Not applicable" due to the nature of the document.

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

The document does not explicitly state "acceptance criteria" in a quantitative sense as might be expected for software or AI performance, but rather focuses on equivalence of physical/material properties and adherence to relevant standards.

2. Sample size used for the test set and the data provenance (e.g. country of origin of the data, retrospective or prospective)

• Sample Size for Test Set: Specific sample sizes are not explicitly stated for all tests. For "Dimensional analysis," specific models "Clismile 12-1223" and "IN-OVATION C H100-132-81" (corresponding to the maximum canine) were used for comparative assessment. The document states "Bracket generally uses a one-kit product consisting of 20 brackets." and that "Clismile is also composed of the same number of sets" as the predicate.
• Data Provenance: Not specified, but the manufacturing company is Rayence Co., Ltd. from Gyeonggi-do, Korea. The tests are likely internal company evaluations or performed by accredited labs.
• Retrospective/Prospective: Not specified, but given the context of a 510(k) submission, these would be controlled tests performed for regulatory submission.

3. Number of experts used to establish the ground truth for the test set and the qualifications of those experts (e.g. radiologist with 10 years of experience)

Not applicable. This document does not describe a study involving expert readers or establishment of ground truth in a clinical assessment context. The evaluations are primarily engineering and materials performance tests.

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

Not applicable. (No expert adjudication involved for this type of technical performance testing).

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

Not applicable. This is not an AI-powered device and no MRMC study was conducted.

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

Not applicable. This is not an AI algorithm.

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

The "ground truth" for the tests performed is based on:

• Engineering specifications and measurements (e.g., dimensions, torque, angulation).
• Material composition analysis.
• Standardized test methods (e.g., ISO 27020 for brackets, ISO 10993 series for biocompatibility, ISO 22674 for metallic materials).
• Direct comparison to the predicate device's established characteristics.

8. The sample size for the training set

Not applicable. There is no AI/ML model for which a training set would be required.

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

Not applicable. There is no AI/ML model.

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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.

The 0909FCB flat panel detector employs a CsI:Tl scintillator for X-ray-to-light converter. Columnstructurized CsI:Tl scintillator have high resolution performance due to much less light blurring, and is thus available for a longer period of time. CMOS active pixel detector makes extremely low noise level and highly sensitive performance. Due to seamless one chip CMOS, there is no data missing or artifacts. The high physical and functional performance of 0909FCB gives competitive image quality. The RAW files can be further processed as DICOM compatible image files by separate console SW for a radiographic diagnosis and analysis.

The 1212FCA 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 IGZO 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 separate console SW for a radiographic diagnosis and analysis.

The subject detectors are connected to a viewing station by ethernet connection cable. There is no wireless option available.

The 0909 FCB and 1212FCA SSXI detectors should be tested and used with compatible X-ray generators which are not part of the imaging receptor device package.

The provided text describes the acceptance criteria and performance of two digital flat-panel X-ray detectors: 0909FCB and 1212FCA. The study is presented as part of a 510(k) premarket notification to the FDA, demonstrating substantial equivalence to predicate devices.

Here's a breakdown of the requested information:

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

The document primarily focuses on demonstrating "equivalent or better performance" compared to predicate devices for specific technical metrics rather than predefined acceptance criteria with numerical thresholds. The "reported device performance" is given in comparison to the predicate devices.

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

• Sample Size: The document does not explicitly state a numerical sample size for the "test set" in terms of patient images or specific test subjects. For 0909FCB, it mentions "a broad review of plain radiographic images taken with 0909FCB and 1212FCA." For the non-clinical tests (MTF, DQE, NPS), these are typically performed on test phantoms rather than patient data.
• Data Provenance: The document does not specify the country of origin of the data. The tests are described as non-clinical (phantom-based) and clinical comparisons. The nature of the clinical comparison "obtained from a similar patient" suggests retrospective or concurrent imaging without specifying a patient cohort.

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

The document mentions "physicians and other health care professionals" for intended use and "a correct conclusion" regarding image evaluation, but it does not specify the number or qualifications of experts used to establish ground truth or evaluate the images for the performance study. It's implied that the clinical comparison was based on expert assessment, but details are absent.

4. Adjudication method for the test set:

The document does not describe any formal adjudication method (e.g., 2+1, 3+1) for the clinical image comparison or for establishing ground truth. The statement "little difficulty in evaluating a wide range of anatomic structures necessary to provide a correct conclusion" suggests an unquantified qualitative assessment.

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 was performed or described in the provided text. The devices are digital X-ray detectors, not AI-assisted diagnostic software. The focus is on demonstrating equivalent or better image quality compared to predicate hardware.

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

The performance evaluation primarily involved standalone technical performance metrics (MTF, DQE, NPS) measured on test phantoms, which represents the "algorithm only" or device-only performance characteristics. A clinical comparison of resulting images was also conducted, implicitly without a human-in-the-loop component beyond the initial imaging and subsequent qualitative assessment.

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

For the non-clinical performance tests (MTF, DQE, NPS), the "ground truth" is established by standardized measurement methodologies described by IEC 62220-1. For the qualitative "clinical comparison," the ground truth seems to be implicitly based on the ability of image quality to support a "correct conclusion" for diagnosis and treatment planning, rather than a formal expert consensus or pathology correlation, and is not explicitly defined.

8. The sample size for the training set:

These are hardware devices (X-ray detectors) and not AI algorithms that require a "training set." Therefore, no training set or sample size for a training set is applicable or mentioned.

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

As these are hardware devices and not AI algorithms requiring a training set, this question is not applicable.

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The 2430MCA Digital Flat Panel X-Ray Detector is indicated for digital imaging solution designed for a mammographic system. It is intended to replace film or screen based mammographic systems in screening mammography. Xmaru W is an integrated software solution indicated for use with the 2430MCA detector.

2430MCA is a digital mammography X-ray detector that is based on flat-panel technology. This mammographic image detector and processing unit consists of a CsI scintillator coupled to a CMOS sensor. This device needs to be integrated with a mammographic imaging system. It can be utilized to capture and digitalize X-ray images for mammographic screening. The RAW files can be further processed as DICOM compatible image files by separate console SW, Xmaru W for a mammographic screening. 2430MCA detector is connected by wire to a viewing station via ethernet connection.

Here's a breakdown of the acceptance criteria and study information based on the provided text:

1. Table of Acceptance Criteria and Reported Device Performance:

The document doesn't explicitly state quantitative acceptance criteria in a pass/fail format for clinical performance. Instead, it relies on a qualitative assessment: "provide images of equivalent or superior diagnostic capability to the predicate device."

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

The text states: "...clinical images obtained from the subject device and the predicate device are reviewed by three MQSA qualified US radiologists..." and "...taking sample radiographs of similar age groups and anatomical structures..."

• Sample Size: Not explicitly stated as a number. The term "sample radiographs" is used, implying a subset of images rather than a comprehensive, statistically powered study.
• Data Provenance: Clinical images were obtained from the subject and predicate devices. No specific country of origin is mentioned beyond the radiologists being "US radiologists." The study appears to be retrospective in the sense that existing images from both devices were reviewed, rather than a prospective study designed specifically for this comparison.

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

• Number of Experts: Three (3)
• Qualifications of Experts: MQSA qualified US radiologists. (MQSA stands for Mammography Quality Standards Act, indicating they are qualified to interpret mammograms in the US).

4. Adjudication Method for the Test Set:

• Adjudication Method: "concurrent review... by three MQSA qualified US radiologists to render an expert opinion." This implies a consensus or majority opinion approach rather than a specific 2+1 or 3+1 rule. The outcome was a collective opinion that the images were of "acceptable overall clinical image quality" and "equivalent or superior diagnostic capability."

5. If a Multi-Reader Multi-Case (MRMC) Comparative Effectiveness Study was done, 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 conducted. This study focused on comparing the image quality of the proposed device against a predicate device, as interpreted by human readers, not on how an AI system improves human reader performance. There is no mention of AI assistance in the context of human reader improvement.

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

• Standalone Performance: Not applicable. The device is a digital X-ray detector and integrated software (Xmaru W) for processing, viewing, searching, storing, annotating, measuring, and stitching images. It is not an AI algorithm performing diagnosis independently. The comparison here is between the image output of two hardware/software systems, assessed by human experts.

7. The type of ground truth used:

• Ground Truth Type: Expert consensus. The "ground truth" for the comparison was the "expert opinion" of three MQSA qualified US radiologists regarding the "diagnostic capability" and "overall clinical image quality" of the images produced by the subject and predicate devices. There is no mention of pathology or outcomes data being used as ground truth for this comparison.

8. The sample size for the training set:

• Training Set Sample Size: Not applicable. The document describes a comparison study of a new medical device (digital X-ray detector with software) against a predicate device. It does not describe the development or training of an AI algorithm, so there is no "training set" in this context.

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

• Training Set Ground Truth Establishment: Not applicable, as there is no training set for an AI algorithm described in the document.

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1717FCC 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.

1717FCC 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 amorphous silicon (a-Si) / Indium Gallium Zinc Oxide (IGZO) on TFT sensor. This device is connected to the user PC via wired LAN (ethernet cable) and it needs to be integrated with a radiographic imaging system. It does not operate as an X-ray generator controller but 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(Xmaru RF) for a radiographic diagnosis and analysis.

The provided text describes a 510(k) summary for the Rayence 1717FCC Digital Flat Panel X-Ray Detector, claiming substantial equivalence to predicate devices (1717SCC, K171420) and a reference device (DRF 4343, K080859). The performance claims primarily revolve around demonstrating equivalent or better image quality and technical specifications compared to these predicate devices, rather than establishing acceptance criteria against a specific clinical performance threshold.

Therefore, the typical structure for answering questions about acceptance criteria and clinical study results for a new AI/CAD device (which usually involves specific metrics like sensitivity, specificity, AUC, human reader improvement, etc.) is not directly applicable to this document. This submission is for a hardware device (a digital X-ray detector), not an AI algorithm, and the primary method of demonstrating "acceptance" is through showing substantial equivalence to existing hardware rather than meeting specific clinical performance metrics.

However, I can interpret the request in the context of this 510(k) submission for a hardware device and extract relevant information to address the spirit of your questions as much as possible.

Here's an analysis based on the provided document:

1. Table of Acceptance Criteria and Reported Device Performance

For this hardware device, "acceptance criteria" are based on demonstrating equivalent or superior technical performance and image quality compared to a legally marketed predicate device, rather than diagnostic accuracy metrics.

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1212FCA 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.

1212FCA 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 IGZO TFT sensor. This device needs to be integrated with a radiographic imaging system. It can be utilized to capture and digitalize Xray images for radiographic diagnosis. The RAW files can be further processed as DICOM compatible image files by separate console SW for a radiographic diagnosis and analysis. The subject detectors are not wireless, but they are connected to a viewing station by ethernet connection.

The provided text is a 510(k) Summary for a digital flat panel X-ray detector (1212FCA). It details the device's characteristics and demonstrates its substantial equivalence to a predicate device (1012WCC) through non-clinical performance testing. It focuses on the fundamental image quality parameters of the detector itself, rather than an AI-powered diagnostic algorithm.

Therefore, many of the requested elements regarding AI performance, human reader studies, and sophisticated ground truth establishment for a diagnostic AI are not applicable to this submission. The device is a hardware component that captures X-ray images, which are then used by physicians for diagnosis.

Here's the breakdown of the information available in the document, addressing the prompts where relevant and indicating N/A for those that are not pertinent to this type of device submission:

Device Name: 1212FCA (Digital Flat Panel X-ray Detector)

1. Table of Acceptance Criteria and Reported Device Performance:

The document demonstrates substantial equivalence to the predicate device (1012WCC) by showing comparable or better performance in key image quality metrics. The "acceptance criteria" here is implicitly "performance on par with or better than the predicate device as measured by established international standards."

Assessment: The document states: "1212FCA demonstrated equivalent or better performance in terms of MTF and DQE as well as NPS compared to 1012WCC, the predicate device, at all spatial frequencies." While the MTF values listed for the proposed device appear lower than the predicate, the narrative explicitly claims "equivalent or better performance." This might imply that specific application contexts or other factors (e.g., pixel pitch differences impacting spatial frequency ranges) were considered in the determination of "equivalent or better" performance, or it could be a slight discrepancy in the provided table vs. the summary statement. For a hardware device, "equivalent or better" based on these standard metrics, conducted under IEC standards, is the typical acceptance.

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

• Sample Size: Not explicitly stated as "sample size" in the context of clinical images or patient data. The performance testing was conducted on the device itself using technical measurements according to IEC standards. This typically involves a controlled phantom or physical measurement setup.
• Data Provenance: N/A for clinical data. The tests were "non-clinical" and conducted on the physical device.

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

• Number of Experts: N/A. Ground truth for hardware performance metrics like MTF, DQE, and NPS is established through standardized physical measurements and calculations (e.g., using phantoms and precise equipment), not by human experts interpreting images.
• Qualifications of Experts: N/A.

4. Adjudication Method for the Test Set:

• Adjudication Method: N/A. There is no human interpretation or diagnostic ground truth to adjudicate.

5. If a Multi-Reader Multi-Case (MRMC) Comparative Effectiveness Study was Done:

• MRMC Study: No. This is a hardware device; MRMC studies are for evaluating the impact of AI or new imaging techniques on human reader performance for diagnosis.
• Effect Size of Human Reader Improvement: N/A.

6. If a Standalone (algorithm only without human-in-the-loop performance) was Done:

• Standalone Performance: N/A. This device is not an algorithm. Its "performance" is its ability to produce high-quality X-ray raw data, which then needs to be processed and interpreted by a human and/or console software.

7. The Type of Ground Truth Used:

• Type of Ground Truth: Technical performance metrics (MTF, DQE, NPS) derived from standardized physical measurements following IEC 62220-1. This is not clinical ground truth (e.g., pathology, outcomes data).

8. The Sample Size for the Training Set:

• Sample Size: N/A. This device is a hardware component; it does not involve a training set as would a machine learning algorithm.

9. How the Ground Truth for the Training Set was Established:

• Ground Truth Establishment: N/A.

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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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