Search Results

The dicomPACS® DX-R with flat panel digital imaging system is intended for use in generating radiographic images of human antomy. This device is intended to replace film/screen systems in all general purpose diagnostic procedures. This device is not intended for mammography applications. This device is intended for use by qualified medical when, in the judgment of the physician, procedures would be contrary to the best interest of the patient.

The dicomPACS®DX-R with flat panel digital imaging system consists of two components, the dicomPACS®DX-R software for viewing captured images on a Windows based computer, and one of three solid state X-ray imaging devices: Toshiba FDX4343R, Toshiba FDX3543RP, or Konica Minolta AeroDR P-11 (1417HQ). The system will display high quality images in less than five seconds over a wide range of X-ray dose settings. The software has the following characteristics: The dicomPACS® DX-R software runs on an off-the shelf PC which forms the operator console. Images captured with the flat panel digital detector are communicated to the operator console via LAN or WLAN connection, depending on the model and the user's choice. dicomPACS®DX-R software uses the software API of the panel manufacturers to control the flat panels and to receive and to calibrate image data. The dicomPACS® DX-R software is an independent product for the acquisition, processing and optimisation of X-ray images (raw images) provided by flat panel (DR) systems or CR systems. In general, such software is also called „console software" as it is installed on the so-called „console PC" of the imaging device. dicomPACS® DX-R carries out the image processing of the raw images provided by the particular device and provides the radiographer / X-ray assistant with an optimum workflow for their work.

{
  "1. A table of acceptance criteria and the reported device performance": {
    "Acceptance Criteria": "Images are clinically acceptable; quality, resolution comparable to or better than predicate devices.",
    "Reported Device Performance": "The panels produce images that are clinically acceptable. The images are of excellent quality, high resolution and are comparable to or better than the images from the predicate devices."
  },
  "2. Sample sized used for the test set and the data provenance (e.g. country of origin of the data, retrospective or prospective)": "The text only mentions \"Images from all three panels were reviewed\". A specific sample size for the test set is not provided. The data provenance is not explicitly stated, but the reviewer is a US Board Certified Radiologist, suggesting the evaluation was relevant to US clinical standards.",
  "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)": "One US Board Certified Radiologist was used to review the images and establish clinical acceptability.",
  "4. Adjudication method (e.g. 2+1, 3+1, none) for the test set": "No specific adjudication method is described, as only one radiologist reviewed the 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 comparative effectiveness study was done. The study conducted was a review of images by a single radiologist to confirm clinical acceptability and comparability to predicate devices, as described in the FDA Guidance Document for Solid State X-ray Imaging Devices.",
  "6. If a standalone (i.e. algorithm only without human-in-the-loop performance) was done": "The study was not a standalone (algorithm only) performance study. It involved a human reviewer (radiologist) assessing the output of the device (images).",
  "7. The type of ground truth used (expert concensus, pathology, outcomes data, etc)": "Expert consensus by a single US Board Certified Radiologist on the clinical acceptability, quality, and resolution of the images.",
  "8. The sample size for the training set": "Not applicable. This device is a digital X-ray imaging system, not an AI/ML algorithm that requires a training set in the conventional sense. The performance evaluation focuses on the image quality produced by the system.",
  "9. How the ground truth for the training set was established": "Not applicable, as this is not an AI/ML device requiring a training set."
}

Ask a specific question about this device

IMIX PanoRad and PanoRad SL X-Ray Systems are indicated for use in generating radiographic images of human anatomy. They have Solid State X-ray Imaging systems intended to replace radiographic film/screen systems in all general-purpose diagnostic procedures. (Not for mammography.)

The modified device can produce digital x-ray images in various configurations.

The provided text is a 510(k) summary for the IMIX ADR Finland OY PanoRad and PanoRad SL Systems. This document aims to demonstrate substantial equivalence to previously cleared devices, rather than establishing acceptance criteria for a new, distinct device's performance.

Therefore, the document does not contain the information requested in your prompt regarding acceptance criteria, device performance studies, and ground truth establishment because it is focused on demonstrating that the revised device is substantially equivalent to existing, legally marketed devices.

Here's why each of your requested points is not present in the provided text:

1. A table of acceptance criteria and the reported device performance: This document doesn't define new performance criteria or report performance against them. Instead, it compares the characteristics of the modified device to a predicate device to show they are essentially the same.
2. Sample size used for the test set and the data provenance (e.g. country of origin of the data, retrospective or prospective): No specific test sets or clinical studies for performance evaluation are described. The filing relies on the established safety and effectiveness of the existing predicate devices and the individual components.
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): Since no new performance studies are detailed, there's no mention of experts establishing ground truth.
4. Adjudication method (e.g. 2+1, 3+1, none) for the test set: Not applicable, as there's no new test set 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: This is not an AI/CAD device. It's an X-ray imaging system, so MRMC studies, especially with AI assistance, are not relevant to this filing.
6. If a standalone (i.e. algorithm only without human-in-the loop performance) was done: Not applicable, as this is a hardware device (X-ray system), not an algorithm.
7. The type of ground truth used (expert consensus, pathology, outcomes data, etc): Not applicable, as there's no new performance study requiring ground truth.
8. The sample size for the training set: Not applicable, as this is not an AI/Machine Learning device that requires a training set.
9. How the ground truth for the training set was established: Not applicable for the same reason as point 8.

In summary, the provided document focuses on demonstrating substantial equivalence of a modified X-ray system to a predicate device by comparing technical specifications and intended use, rather than presenting a de novo performance study with specific acceptance criteria and ground truth validation.

Ask a specific question about this device