Search Results

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."

Ask a specific question about this device

1717G Digital Flat Panel X-Ray Detector is indical 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.

1717G 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 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.

Here's a breakdown of the acceptance criteria and study details for the Rayence 1717G Digital Flat Panel X-ray Detector, based on the provided 510(k) summary:

Acceptance Criteria and Device Performance

The acceptance criteria for the 1717G device are fundamentally based on demonstrating substantial equivalence to its predicate device, the 1717SGC, in terms of image quality and safety. The study focuses on comparing the performance metrics and clinical utility of both devices.

Study Details

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. The document mentions "sample radiographs of similar age groups and anatomical structures."
• Data Provenance: Not explicitly stated. The study involved a "licensed US radiologist" and "radiographs of similar age groups and anatomical structures," suggesting the data was likely obtained from human subjects within a clinical or test setting, but the country of origin is not specified. It appears to be prospective data collected for the purpose of the comparison, as "clinical images are taken from both devices" for review.

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

• Number of Experts: One.
• Qualifications of Experts: A "licensed US radiologist." No specific years of experience are mentioned.

4. Adjudication method for the test set

• Adjudication Method: Not applicable in the typical sense of multiple readers reaching a consensus. A single licensed US radiologist performed a comparative review, evaluating and comparing images from both devices. There was no explicit adjudication process described for conflicting opinions, as there was only one reviewer.

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 explicitly stated or conducted as described here. The study involved a single radiologist comparing images from two devices to assess substantial equivalence, not to evaluate human reader improvement with or without AI assistance. This device is an X-ray detector, not an AI software.

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

• Standalone Study: Yes, a form of "standalone" (algorithm/device-only) performance testing was done in the form of non-clinical tests:
  • MTF (Modulation Transfer Function)
  • DQE (Detective Quantum Efficiency)
  • NPS (Noise Power Spectrum)
    These tests directly measure the technical performance characteristics of the detector itself, independent of human interpretation.

7. The type of ground truth used

• Type of Ground Truth: The ground truth for the technical performance (MTF, DQE, NPS) is derived from the standardized comparison against the predicate device using established metrics (IEC 62220-1). For the clinical image quality, the ground truth is established by expert opinion/consensus from a licensed US radiologist comparing the visual quality of images (e.g., spatial resolution, soft tissue contrast) from both devices. The goal was to prove substantial equivalence, not to diagnose specific conditions against a definitive pathology or outcomes ground truth.

8. The sample size for the training set

• Sample Size for Training Set: Not applicable. This device is a digital X-ray detector (hardware), not an AI algorithm that requires a training set in the machine learning sense. The "training" or development involved engineering and design, with the predicate device serving as a reference.

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

• How Ground Truth for Training Set was Established: Not applicable, as this is a hardware device and not an AI algorithm requiring a training set. The "ground truth" for its development would be engineering specifications and performance targets based on the predicate device and industry standards.

Ask a specific question about this device

910SGA Digital Flat Panel X-Ray Detector is indicated for digital imaging solution designed for human anatomy including head, neck, spinal column, 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 professionals. Not to be used for mammography.

910SGA 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 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.

Here's a breakdown of the acceptance criteria and study information for the 910SGA Digital Flat Panel X-ray Detector, based on the provided text:

1. Table of Acceptance Criteria and Reported Device Performance:

The document doesn't explicitly state "acceptance criteria" in a quantitative, numbered list. Instead, it demonstrates substantial equivalence to a predicate device (1210SGA) through direct comparison of technical characteristics and performance metrics. The underlying acceptance is that the device must perform at least as well as, or be substantially equivalent to, the predicate device.

2. Sample Size and Data Provenance (Test Set):

• Sample Size: The document does not specify an exact number of images or cases used for the clinical assessment. It states, "clinical images are taken from both devices" and implies a "sample radiographs of similar age groups and anatomical structures." This suggests a comparative, rather than quantitative, sample size.
• Data Provenance: Not explicitly stated, but the submission is for a Korean company (Rayence Co., Ltd.) seeking FDA clearance, and the expert is a "licensed US radiologist." This suggests the clinical images could have been taken in the US or in Korea and reviewed by a US expert. The document does not specify if the images were retrospective or prospective.

3. Number of Experts and Qualifications (Ground Truth for Test Set):

• Number of Experts: "a licensed US radiologist" (singular).
• Qualifications: "licensed US radiologist." No specific years of experience are mentioned, but "licensed" implies significant training and qualification.

4. Adjudication Method (Test Set):

• Method: Not explicitly stated as a formal adjudication method (like 2+1 or 3+1). The text mentions a "comparative review by an expert for both devices" and an "expert opinion." This implies a single expert's assessment without a formal multi-reader adjudication process described.

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

• Was it done?: No, an MRMC comparative effectiveness study involving human readers with vs. without AI assistance was not explicitly stated or performed. This study focused on the performance of the new device (910SGA) compared to a predicate device (1210SGA), not on the impact of AI assistance on human readers. The device itself is a flat panel detector, not an AI algorithm for image interpretation.

6. Standalone (Algorithm Only) Performance:

• Was it done?: This is not applicable in the context of this submission. The device is a digital flat panel X-ray detector, an imaging hardware component, not an AI algorithm. The performance evaluation focuses on the detector's image acquisition capabilities (MTF, DQE, NPS, and overall image quality for diagnosis) as compared to a predicate device.

7. Type of Ground Truth Used (Test Set):

• Type: Clinical images were reviewed by an expert to render an "expert opinion" on image quality and "substantial equivalency." This falls under expert consensus/opinion rather than pathology or outcomes data. The goal was to confirm the diagnostic quality of the images produced by the device, not to diagnose specific diseases.

8. Sample Size for the Training Set:

• Sample Size: The document does not refer to a "training set" in the context of machine learning or AI. The testing described is for a hardware device (X-ray detector). The "comparison" is between the proposed device and its predicate, using physical and clinical tests.

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

• Method: Not applicable, as there is no mention of a "training set" for an AI algorithm. The testing relates to fundamental imaging performance and clinical image quality comparison of a hardware device.

Ask a specific question about this device

1417WGA 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.

1417WGA 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. 1417WGA 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.

This submission is for a digital flat panel X-ray detector, 1417WGA, which is very similar to a previously cleared device, 1417PGA. The study conducted to demonstrate substantial equivalence primarily relied on comparing the new device against the predicate device.

Here's the breakdown of the acceptance criteria and study information:

1. Table of Acceptance Criteria and Reported Device Performance:

The document doesn't explicitly state "acceptance criteria" in a quantified table format for the new device (1417WGA). Instead, the performance of the 1417WGA is compared directly to the predicate device (1417PGA), with the implication that performing "almost same" as the predicate device meets the acceptance criteria for substantial equivalence.

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

• Non-clinical Test Set: The document does not specify a quantitative sample size for the non-clinical tests (MTF, DQE, NPS). It states that both 1417WGA and 1417PGA were tested using identical equipment and analysis methods.
• Clinical Test Set: The document simply states "clinical images are taken from both devices" (1417WGA and 1417PGA). A specific number of images or patients is not provided.
• Data Provenance: Not explicitly stated, but the company is based in South Korea. The U.S. Designated agent is in Houston, TX. No specific country of origin for the data is mentioned. The study is retrospective as it compares the new device to a predicate device that was already cleared and presumably has a history of use.

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

• Number of Experts: "a licensed US radiologist" (singular).
• Qualifications of Experts: "a licensed US radiologist." No further details on years of experience or specialization are provided.

4. Adjudication Method for the Test Set:

• The document implies a "none" adjudication method in the traditional sense of multiple readers coming to an agreement. It states that "clinical images are taken from both devices and reviewed by a licensed US radiologist to render an expert opinion." This suggests a single expert's opinion was sufficient for the comparison.

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

• No, a MRMC comparative effectiveness study was not explicitly conducted or reported. The clinical evaluation was based on a single expert's review of images from both devices, not a study comparing human reader performance with and without AI assistance. The device itself is a detector, not an AI interpretation tool.

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

• Yes, the non-clinical performance tests (MTF, DQE, NPS) are considered standalone (algorithm/device-only) evaluations as they measure the intrinsic physical performance of the detector. The device itself is a detector, not an "algorithm" in the sense of AI.

7. Type of Ground Truth Used:

• Non-clinical Tests: Ground truth is established by standardized physical measurements (e.g., MTF, DQE, NPS measurements according to IEC 6220-1).
• Clinical Review: Ground truth is established by expert opinion/consensus from a licensed US radiologist comparing images from the two devices according to established diagnostic radiography evaluation procedures.

8. Sample Size for the Training Set:

• This information is not applicable/not provided. The device is an X-ray detector, not a machine learning algorithm that requires a "training set" for its operation. The performance is based on its physical and electronic characteristics.

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

1417WCA 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.

1417WCA 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. 1417WCA 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.

This submission for the 1417WCA Digital Flat Panel X-ray Detector from Rayence Co., Ltd. does not contain a typical acceptance criteria table with performance metrics and a detailed study proving the device meets those criteria in the way AI/CADe devices often do. Instead, this 510(k) summary focuses on demonstrating substantial equivalence to a predicate device (1417PCA) through non-clinical and clinical comparison.

The "acceptance criteria" here are implicitly tied to demonstrating performance equivalent to the predicate device in key imaging characteristics and a general review by an expert for clinical images.

Here's a breakdown of the requested information based on the provided text:

1. Table of Acceptance Criteria and Reported Device Performance

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

• Non-Clinical Test Set: The text mentions "non-clinical test report... prepared and submitted to FDA separately to demonstrate the substantial equivalency between two different detectors." It states the MTF, DQE, and NPS tests were conducted "by using the identical test equipment and same analysis method described by IEC 62220-1." While it doesn't give a specific number of images or runs, these tests typically involve standardized phantoms and measurement methodology, not patient data.
• Clinical Test Set: Clinical images were obtained from "both devices" (1417WCA and 1417PCA). It also states "both the test (1417WCA) and control group (1417PCA) are evaluated according to similar age group and anatomical structures." The specific number of clinical images (sample size) is not provided.
• Data Provenance: The document does not explicitly state the country of origin or whether the clinical data was retrospective or prospective.

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) was used.
• Qualifications: "a licensed US radiologist." No specific years of experience or specialty were detailed beyond "US radiologist."

4. Adjudication method for the test set:

• The text describes a "comparative review by an expert." It does not specify a formal adjudication method like 2+1 or 3+1. The single radiologist provided an "expert opinion" on the clinical images.

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. This submission is for a digital flat panel X-ray detector itself, not an AI/CADe system. The clinical evaluation involved a single radiologist performing a comparative review of images from the new device and the predicate device for substantial equivalence, not assessing human performance with or without AI.

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

• Not applicable. This device is an imaging hardware component (digital flat panel X-ray detector), not an algorithm or AI system. Its "standalone" performance is characterized by physical imaging metrics like MTF, DQE, and NPS.

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

• For the non-clinical tests (MTF, DQE, NPS), the ground truth is derived from physical measurements using standardized phantoms and test methods (IEC 62220-1).
• For the clinical image comparison, the ground truth for establishing "substantial equivalency in terms of image quality" was based on the expert opinion/review of a single licensed US radiologist. It's not explicitly stated that they were looking for specific pathologies or outcomes, but rather overall diagnostic image quality comparison.

8. The sample size for the training set:

• Not applicable. This device is a hardware detector and does not use a "training set" in the context of machine learning or AI algorithms. Its performance is inherent to its physical design and manufacturing.

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

• Not applicable. As stated above, there is no training set for this hardware device.

Ask a specific question about this device

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

1417PGA 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.

The 1417PGA is a portable digital X-ray flat panel detector that can generate images of any part of the body. This X-ray imaging system consists of a scintillator directly coupled to an a-Si TFT sensor. It makes high-resolution, high-sensitive digital images. 1417PGA is designed specifically to be integrated with an operating PC and a X-ray generator to digitalize X-ray images into RAW files. The RAW files can be made to DICOM compatible image files for a radiographic diagnosis and analysis by console SW.

Here's an analysis of the acceptance criteria and the study that proves the device meets them, based on the provided text:

Device: 1417PGA Digital Flat Panel X-ray Detector
Predicate Device: SDX-4336CP by Samsung Mobile Display Co., Ltd. (K102321)

1. Table of Acceptance Criteria and Reported Device Performance

The submission focuses on demonstrating substantial equivalence to the predicate device, rather than defining explicit "acceptance criteria" against a fixed benchmark. The performance is compared directly against the predicate device. The primary performance metrics mentioned are Modulated Transfer Function (MTF) and Detective Quantum Efficiency (DQE).

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

• Non-clinical (MTF, DQE, NPS) Test Set: The sample size is not explicitly stated. The report mentions "the MTF, DQE and NPS test results of 1417PGA and SDX-4336CP by using the identical test equipment and same analysis method described by IEC 62220-1." This suggests controlled laboratory measurements, not a patient-based dataset.
• Clinical Test Set: The sample size for clinical images is not explicitly stated. It mentions that "clinical images are taken from both devices."
• Data Provenance: The submission is from Rayence Co., Ltd. in Korea. The "non-clinical test report and clinical consideration report were prepared and submitted to FDA separately." The clinical images for the expert review were compared "according to age group and anatomical structures." The country of origin for the clinical data is not specified, but the expert reviewer is a "licensed US radiologist."

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

• Non-clinical: Ground truth was established through physical measurements and standard analysis methods (IEC 62220-1). No human experts were involved in establishing ground truth for these objective metrics.
• Clinical: "a licensed US radiologist" was used for the expert opinion on clinical images. The specific qualifications beyond "licensed US radiologist" (e.g., years of experience, subspecialty) are not provided. The number of experts was one.

4. Adjudication Method for the Test Set

• Non-clinical: Not applicable, as ground truth was established by objective measurements based on standards like IEC 62220-1.
• Clinical: Not applicable. The expert review was a single reader's opinion. There is no mention of multiple readers or an adjudication process. The phrasing "a licensed US radiologist to render an expert opinion" suggests a single-reviewer assessment.

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

No, an MRMC comparative effectiveness study was not explicitly stated or described. The clinical assessment involved a single radiologist reviewing images from both devices. There is no mention of measuring how much human readers improve with AI vs. without AI assistance, as this device itself is a digital X-ray detector, not an AI-powered diagnostic tool. The comparison is between two different hardware detectors.

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

Yes, the non-clinical performance testing (MTF, DQE, NPS) represents standalone performance of the detector hardware without human interpretation. These are objective measurements of the device's image quality characteristics. The device itself is a digital X-ray panel, not an algorithm that outputs a diagnostic decision; its "standalone" performance is its ability to acquire high-quality images.

7. The Type of Ground Truth Used

• Non-clinical (MTF, DQE, NPS): The ground truth was established through physical measurements and standardized testing procedures (IEC 62220-1).
• Clinical: The "ground truth" for the clinical comparison was essentially the expert opinion of a single licensed US radiologist comparing the diagnostic image quality of images produced by both the proposed and predicate devices. This is a form of expert consensus, albeit from a single expert.

8. The Sample Size for the Training Set

Not applicable. This device is a digital X-ray detector (hardware), not an AI algorithm that requires a training set. The submission is for a medical imaging device, not a machine learning model.

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

Not applicable, as there is no training set for this hardware device.

Ask a specific question about this device

1417PCA 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.

1417PCA is a portable digital X-ray flat panel detector that can generate images of any part of the body. This X-ray imaging system consists of a scintillator directly coupled to an a-Si TFT sensor. It makes high-resolution, high-sensitive digital images. 1417PCA is designed specifically to be integrated with an operating PC and a X-ray generator to digitalize X-ray images into RAW files. The RAW files can be made to DICOM compatible image files for a radiographic diagnosis and analysis by console SW.

The provided 510(k) summary describes the Rayence Co., Ltd. 1417PCA Digital Flat Panel X-ray Detector and its substantial equivalence to a predicate device. The information below is extracted and organized as requested.

Acceptance Criteria and Device Performance

The acceptance criteria for the 1417PCA device are established through comparison with the predicate device, SDX-4336CP, primarily focusing on superior or equivalent performance in key imaging metrics.

Table 1: Acceptance Criteria and Reported Device Performance

Study Information

2. Sample Size Used for the Test Set and Data Provenance:
The document states that "clinical images are taken from both devices." However, it does not specify the sample size for the test set (number of images or patients).
The data provenance is not explicitly stated regarding country of origin or whether it was retrospective or prospective.

3. Number of Experts Used to Establish the Ground Truth for the Test Set and Qualifications:
The test set images were "reviewed by a licensed US radiologist to render an expert opinion." This indicates that one licensed US radiologist was used. The specific qualifications (e.g., years of experience, subspecialty) are not provided beyond being a "licensed US radiologist."

4. Adjudication Method for the Test Set:
The document mentions a single "licensed US radiologist" reviewing images and rendering an "expert opinion." This implies no formal adjudication method (e.g., 2+1, 3+1) was used, as it appears to be a solitary review.

5. Multi-Reader Multi-Case (MRMC) Comparative Effectiveness Study:
The document does not describe an MRMC comparative effectiveness study involving multiple readers and comparing performance with and without AI assistance to quantify an effect size. The clinical evaluation involved one radiologist comparing images from the proposed and predicate devices.

6. Standalone (Algorithm Only) Performance:
The device is a digital X-ray flat panel detector, not an AI algorithm. The performance evaluation is for the hardware's ability to acquire images. Therefore, the concept of "standalone (algorithm only without human-in-the-loop performance)" is not applicable in the context of this device. The performance metrics like MTF, DQE, and NPS are intrinsic to the detector hardware.

7. Type of Ground Truth Used:
The ground truth for the clinical comparison was established by the expert opinion of a licensed US radiologist after reviewing clinical images from both devices. The images were evaluated based on age group and anatomical structures according to a diagnostic radiography evaluation procedure.

8. Sample Size for the Training Set:
The device described is a digital X-ray detector, which is hardware for image acquisition. It does not involve a "training set" in the context of machine learning algorithms. The performance evaluation focuses on the physical characteristics and image quality of the detector itself.

9. How the Ground Truth for the Training Set Was Established:
As there is no "training set" for this hardware device, this question is not applicable.

Ask a specific question about this device

1717SGC 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.

1717SGC 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 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 is a 510(k) summary for the 1717SGC Digital Flat Panel X-ray Detector. It focuses on demonstrating substantial equivalence to a predicate device and adherence to electrical, mechanical, and safety standards. It does not contain information related to acceptance criteria for algorithm performance or a study proving such criteria are met for an AI/CAD/software device.

Therefore, I cannot populate the table or answer the questions based on the provided text. The document describes a hardware device (X-ray detector), not a software or AI device that would have performance metrics like sensitivity, specificity, or reader improvement.

If you have a document specific to an AI/CAD software device, please provide that, and I would be happy to analyze it.

Ask a specific question about this device

1210SGA Digital Flat Panel X-Ray Detector is indicated for digital imaging solution designed for human anatomy including head, neck. spinal column, 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 professionals. Not to be used for mammography.

1210SGA 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 The RAW files can be further processed as DICOM compatible image files by separate console SW (not part of this SIOK submission) for a radiographic diagnosis and analysis.

The provided text is a 510(k) summary for the 1210SGA Digital Flat Panel X-ray Detector. This type of submission focuses on demonstrating substantial equivalence to a predicate device rather than establishing new safety and effectiveness through clinical trials with specific acceptance criteria related to diagnostic performance.

Therefore, the document does not contain information on the acceptance criteria or a study proving the device meets those criteria in the context of diagnostic performance (e.g., sensitivity, specificity, accuracy).

The device is a digital X-ray detector, and its "performance" in this context is primarily related to electrical, mechanical, and environmental safety, as well as its ability to capture and digitalize X-ray images, similar to its predicate.

Here's a breakdown of what the document does provide, addressing the requested points where possible, and noting where information is absent:

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

   • Acceptance Criteria: Not explicitly stated in terms of diagnostic performance (e.g., sensitivity, specificity). The submission relies on demonstrating substantial equivalence to a predicate device (Xmaru1210P, K101590) and compliance with relevant safety and EMC standards.
   • Reported Device Performance:
     • Electrical, mechanical, environmental safety and performance testing: According to standard EN/IEC 60601-1.
     • EMC testing: Conducted in accordance with standard EN/IEC 60601-1-2(2001).
     • Results: "All test results were satisfactory."

   Summary Table (based on available information):

   Acceptance Criteria (Implicit)	Reported Device Performance
   Compliance with Electrical, Mechanical, and Environmental Safety Standard (EN/IEC 60601-1)	Satisfactory
   Compliance with EMC Standard (EN/IEC 60601-1-2(2001))	Satisfactory
   Substantial Equivalence to Predicate Device (Rayence Co., Ltd., Xmaru1210P, 510(k) K101590)	Concluded by manufacturer and concurred by FDA for stated indications of use

2. Sample size used for the test set and the data provenance: Not applicable. This submission doesn't describe a diagnostic performance study with a test set of patient cases. The "testing" refers to technical compliance and safety testing.

3. Number of experts used to establish the ground truth for the test set and the qualifications of those experts: Not applicable. No diagnostic performance study involving expert interpretation of medical images is described.

4. Adjudication method (e.g., 2+1, 3+1, none) for the test set: Not applicable. No diagnostic performance study requiring adjudication is described.

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 device is a digital X-ray detector, not an AI-assisted diagnostic tool.

6. If a standalone (i.e. algorithm only without human-in-the-loop performance) was done: Not applicable. This is not an algorithmic diagnostic device.

7. The type of ground truth used (expert consensus, pathology, outcomes data, etc.): Not applicable for diagnostic performance. For the technical and safety testing, the "ground truth" would be the specifications and requirements defined by the regulatory standards (e.g., voltage limits, EMI levels, mechanical stability).

8. The sample size for the training set: Not applicable. This is not a machine learning device that requires a training set.

9. How the ground truth for the training set was established: Not applicable.

In summary: The provided 510(k) Special Summary for the 1210SGA Digital Flat Panel X-ray Detector details its technical characteristics and claims substantial equivalence to a predicate device based on compliance with safety and EMC standards. It does not include information about diagnostic performance studies with specific acceptance criteria, test sets, or expert evaluations as would be expected for a diagnostic AI/CAD device.

Ask a specific question about this device