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The CareView 1800RF detector is indicated for digital imaging solution designed for providing general radiographic diagnosis of human anatomy. It is intended to replace radiographic film/screen systems in all general-purpose diagnostic procedures. This product is not intended for mammography applications.

The CareView 1800RF detector is a class of radiography X-ray flat panel detector that has an imaging area of 434 mm × 434 mm. The detector communicates by a wired connection (Giga-bit Ethernet communication mode).

The detector functions by intercepting X-ray photons. Then the scintillator emits visible spectrum photons that illuminate an array of photo detectors (a-Si) that create electrical signals. The electrical signals are then digitally converted to display an image on the monitor.

The detector should be connected to a computer and X-ray generator to digitize X-ray images and transfer radiography diagnostics. The x-ray generator, an essential part of a full x-ray system, is not part of the subject medical device.

The CareView 1800RF detector can be used for dynamic imaging (fluoroscopy).

The provided document is a 510(k) premarket notification for the CareView 1800RF X-ray Flat Panel Detector. It aims to demonstrate substantial equivalence to a predicate device, CareView 1800Le (K193173), and a reference device, 1717FCC (K210985).

The document states that clinical images are not necessary for the current submission, and that successful results of bench testing should be sufficient to show substantial equivalence for the subject device. This implies that the acceptance criteria are primarily based on non-clinical performance benchmarks and that no clinical study involving human subjects or expert readers was conducted for this submission to prove device performance against acceptance criteria in a clinical context.

Therefore, many of the requested details about acceptance criteria derived from clinical studies, expert involvement, and ground truth establishment cannot be extracted from this document because such studies were explicitly stated as not necessary for this 510(k) submission.

However, the document does provide information regarding the technical specifications of the device and its predicate/reference devices, which implicitly serve as performance benchmarks for substantial equivalence based on bench testing.

Here's a breakdown of the information that can be extracted:

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

Since no clinical study with explicit acceptance criteria for diagnostic performance is provided, the closest relevant information is the comparison of technical specifications between the proposed device and its predicate/reference devices. The "acceptance criteria" here are essentially "comparable technical specifications."

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

Not applicable. The submission explicitly states "Clinical images are not necessary for the current submission. Successful results of bench testing should be sufficient...". Therefore, no clinical test set was used. The substantial equivalence is based on bench testing for electrical safety, EMC, and comparison of technical specifications.

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. No clinical test set or human evaluation study was conducted.

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

Not applicable. No clinical test set.

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. The device is a flat panel detector, not an AI-powered diagnostic tool. Furthermore, no clinical studies were performed.

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

Not applicable. The device is a flat panel detector, not an algorithm, and no clinical studies were done.

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

Not applicable. No clinical studies were done, so no ground truth for diagnostic accuracy was established. The "ground truth" for the technical comparisons would be the specifications and performance characteristics of the predicate and reference devices, as determined by their respective manufacturers and validated through their own testing protocols.

8. The sample size for the training set:

Not applicable. This is a hardware device (X-ray Flat Panel Detector), not an AI algorithm requiring a training set.

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

Not applicable. No training set was used.

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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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