Search Results

Edge is indicated for digital imaging solution designed for general radiographic system for human anatomy. It is intended to replace film or screen based radiographic system in all general purpose diagnostic procedures. Not to be used for mammography.

Edge digital flat panel X-ray detector 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 (K160579 / XmaruView V1 and XmaruPACS/ Rayence Co.,Ltd.) for a radiographic diagnosis and analysis.

The provided text describes the 510(k) submission for the "Edge Digital Flat Panel X-ray Detector" and its substantial equivalence to a predicate device, the "1717SCC_140µm". The performance testing described primarily focuses on demonstrating this equivalence through non-clinical (bench) testing and a clinical consideration report.

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

1. Table of Acceptance Criteria and Reported Device Performance

The document does not explicitly present a table of "acceptance criteria" for the device's performance in the typical sense of a target value that must be met for approval. Instead, the acceptance criteria for the study proving substantial equivalence appears to be based on the similarity of performance characteristics to the predicate device.

The performance characteristics used for comparison are:

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

The document states: "To further demonstrate the substantial equivalency of two devices, clinical images are taken from both subject devices and reviewed by a licensed US radiologist to render an expert opinion."

• Sample Size: Not explicitly stated as a number of images or patients. It mentions "sample radiographs of similar age group and anatomical structures". This suggests a limited sample size rather than a large clinical trial.
• Data Provenance:
  • Country of Origin: US (implied by "licensed US radiologist").
  • Retrospective or Prospective: Not explicitly stated, but the description "clinical images are taken from both subject devices and reviewed" and "sample radiographs... taken" suggests these were specifically acquired for this comparison, implying a prospective data collection for the purpose of the study.

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" - One (1) expert.
• Qualifications: "licensed US radiologist". No specific years of experience or subspecialty are provided.

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

• Adjudication Method: None mentioned. The single radiologist's opinion appears to be the sole basis for the clinical image quality assessment ("reviewed by a licensed US radiologist to render an expert opinion").

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, an MRMC study was not conducted. This study's purpose was to demonstrate substantial equivalence between two X-ray detectors, not to evaluate AI assistance for human readers. The device itself is an X-ray detector, not an AI algorithm for improving reader performance.
• Effect Size: Not applicable as no such study was performed or needed for this device.

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

• Standalone Performance: The device is an X-ray detector. Its performance is standalone in terms of image acquisition (MTF, DQE, etc.). The "clinical consideration" involved human review of the images produced by the device, but this was to compare the image quality of the device to its predicate, not to assess an AI's diagnostic performance without a human. The non-clinical tests (MTF, DQE) are indeed "standalone" measures of the detector's physical performance.

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

• The term "ground truth" isn't explicitly used in the context of diagnostic findings. For the non-clinical tests (MTF, DQE), the "ground truth" is established by adherence to IEC 62220-1 standards for objective physical performance measurements.
• For the "clinical consideration" part, the "ground truth" for image quality comparison was the expert opinion of a single licensed US radiologist. This is a subjective assessment of image characteristics ("spatial and soft tissue contrast resolution are superior"). There's no mention of a diagnostic "ground truth" (e.g., presence/absence of disease confirmed by pathology or other means) being established for the clinical images used for review. The focus was on comparing the quality of the images produced by the devices, not on the diagnostic accuracy of those images for specific conditions.

8. The sample size for the training set

• This information is not applicable and not provided. The "Edge Digital Flat Panel X-ray Detector" is an imaging hardware device, not an AI algorithm that requires a training set. The clinical consideration involved comparing images produced by two hardware devices.

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

• This information is not applicable and not provided as the device is not an AI algorithm requiring a training set.

Ask a specific question about this device

Edge Air 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 system in all general purpose diagnostic procedures. Not to be used for mammography.

Edge Air is a wired/wireless digital solid state X-ray detector that is based on flat-panel technology. The wireless LAN (IEEE 802.11a/g/n/ac) communication signals images captured to the system and improves the user operability through high-speed processing. 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 Xray images for radiographic diagnosis The RAW files can be further processed as DICOM compatible image files by a separate console SW program (K160579 / Xmaru View V1 and Xmaru PACS/ Rayence Co., Ltd.) for a diagnostic analysis.

This document describes the equivalence of the OSKO, INC. Edge Air Digital Flat Panel X-ray Detector to a predicate device, the Rayence Co., Ltd. 1717WCC. The focus is on demonstrating that the Edge Air performs similarly to the predicate device, rather than establishing novel acceptance criteria for improved diagnostic accuracy.

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

1. Table of Acceptance Criteria and Reported Device Performance

Since this is a substantial equivalence submission, the "acceptance criteria" are primarily based on demonstrating similar performance to the predicate device. The performance metrics are reported in terms of comparison.

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

The document mentions "talking sample radiographs of similar age group and anatomical structures" for the clinical image review. However, the specific sample size of images or patients for the test set is not explicitly stated.

The data provenance for the clinical images is not explicitly stated in terms of country of origin, but it is implied to be for human anatomy and diagnostic procedures. It is a prospective comparison of images taken with the Edge Air and the predicate device.

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

One expert was used for the clinical image review. The expert is described as a "licensed radiologist." No specific details about years of experience are provided.

4. Adjudication Method for the Test Set

The adjudication method appears to be a single expert review. The licensed radiologist reviewed images from both the Edge Air and the predicate device to render an opinion on their similarity and diagnostic image quality. There is no mention of multiple readers or consensus methods.

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, a Multi-Reader Multi-Case (MRMC) comparative effectiveness study was not done. This submission is for a digital flat panel X-ray detector, which is a hardware device for image acquisition, not an Artificial Intelligence (AI) diagnostic tool. Therefore, the concept of human readers improving with or without AI assistance is not applicable here.

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

Yes, in essence, standalone performance was assessed through non-clinical tests.
The non-clinical tests (MTF, DQE, NPS) are measures of the device's inherent imaging performance characteristics without human interpretation. These tests were conducted by "using the identical test equipment and same analysis method described by IEC 6220-1." The comparison against the predicate device's specifications serves as the standalone performance evaluation.

7. The Type of Ground Truth Used

For the non-clinical tests (MTF, DQE, NPS), the ground truth is based on standardized physical measurements as described by IEC 6220-1.

For the clinical image review, the ground truth is based on expert opinion/consensus by a licensed radiologist regarding the visual similarity and diagnostic quality of the images. This is a form of expert consensus, although only one expert is mentioned.

8. The Sample Size for the Training Set

The document specifies the Edge Air detector "uses the same flat panel x-ray detector as that used in the predicate device, K162519, and that no changes were necessary to either the hardware or firmware of the device." It also states it uses the "same version of imaging software Xmaru View 1 / Xmaru PACS (K160579)."

Given that this is a 510(k) submission for a device demonstrating substantial equivalence to a predicate, and the hardware and software are identical to the predicate, there is no mention of a separate "training set" for the Edge Air device's image processing. The underlying technology (detector and software) would have been "trained" or developed during the creation of the predicate device and the imaging software (K160579). The context here is not an AI algorithm that requires a distinct training set for this specific submission.

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

As explained above, this submission does not explicitly discuss a new "training set" for the Edge Air device. The device leverages existing, established hardware and software from the predicate. Therefore, the question of how a training set's ground truth was established for the Edge Air is not applicable in this document's context, as it's not a new AI-driven diagnostic system requiring novel training data. The "ground truth" for the predicate device and its associated software would have been established during their respective development and clearance processes.

Ask a specific question about this device