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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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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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