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Edge Air(1417) 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 40(1417) is a wired/wireless digital solid state X-ray detector that is based on flat-panel technology. The wireless LAN (IEEE 802.11a/g/w/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 X-ray images for radiographic diagnosis The RAW files can be further processed as DICOM compatible image files by a separate console SW program (K190866 / Xmaruview V1 (Xmaru Chiroview, Xmaru Podview) / Rayence Co., Ltd.) for a diagnostic analysis.

The provided text describes the Edge Air (1417) Digital Flat Panel X-ray Detector, a device intended to replace film or screen-based radiographic systems for general diagnostic procedures, excluding mammography. The submission argues for substantial equivalence to a predicate device, Edge Air (K172681).

Here's an analysis of the acceptance criteria and the study that proves the device meets them:

1. Table of Acceptance Criteria and Reported Device Performance

The submission primarily focuses on demonstrating substantial equivalence to a predicate device, rather than defining explicit acceptance criteria against a fixed standard. However, the comparisons provided between the proposed device and the predicate device can be interpreted as the performance metrics and their desired "acceptance" (i.e., being similar or equivalent to the predicate).

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

The document mentions "clinical images have been reviewed by a licensed radiologist" for the "clinical consideration test." However, it does not specify the exact sample size (number of images or patients) used for this clinical review or the demographics of the "similar age group and anatomical structures."

The data provenance is implicitly prospective for the clinical images used for comparison, as they involved "taking sample radiographs." The country of origin of the data is not specified.

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

Only one expert is mentioned: "a licensed radiologist." No specific details about the radiologist's experience (e.g., "10 years of experience") are provided beyond being "licensed" and an "expert opinion."

4. Adjudication Method for the Test Set

The adjudication method appears to be none beyond a single expert's opinion. The text states, "clinical images have been reviewed by a licensed radiologist to render an expert opinion." There is no mention of multiple readers, consensus, or a specific adjudication process for discrepancies.

5. If a Multi-Reader Multi-Case (MRMC) Comparative Effectiveness Study Was Done, and the Effect Size of How Much Human Readers Improve with AI vs. Without AI Assistance

No MRMC comparative effectiveness study was done as described for an AI device. This submission is for a digital flat panel X-ray detector, which is a hardware component for image acquisition, not an AI-powered diagnostic tool. The device itself does not involve AI assistance for human readers in the sense of improving their diagnostic capability. The comparison is for image quality of two different hardware detectors.

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

Again, this question is not directly applicable to the device under review. The Edge Air (1417) is an X-ray detector, not a standalone algorithm. Its "performance" is measured by physical characteristics (MTF, DQE, NPS) and its ability to produce diagnostic images comparable to a predicate device. The imaging software (Xmaruview V1) is mentioned as a separate 510(k) (K190866), but the submission for the detector itself does not detail standalone performance of an algorithm.

7. The Type of Ground Truth Used

For the non-clinical tests (MTF, DQE, NPS), the ground truth is derived from physical measurements against established standards (IEC 62220-1).

For the "clinical consideration test," the ground truth is expert opinion/comparison by a single licensed radiologist, asserting similarity in image quality ("spatial and soft tissue contrast resolutions for both devices are equivalent"). It's a comparative visual assessment against images from the predicate device by an expert.

8. The Sample Size for the Training Set

The concept of a "training set" is not directly applicable here. This is a hardware device (X-ray detector). The imaging software (Xmaruview V1) might have had a training set if it involved AI, but the submission for the detector does not provide this information. The device's manufacturing and performance are based on engineering design and testing, not machine learning training.

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

Since there is no "training set" for the X-ray detector itself, this question is not applicable.

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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.

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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.

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Smart Stitching software is indicated for use to allow post-capture positioning and joining of individual images to produce an additional composite image for two dimensional image review.

Smart Stitching provides a function to read multiple medical images at one time by stitching them to one image based on overlapping areas. It supports DICOM 3.0 which is standard of medical image format as well as Tiff and Raw images. It provides additional functions such as image retrieval, storage, and transmission. Smart Stitching is designed to facilitate diagnosis by easily stitching multiple medical images, up to 5 images, from multiple captures to one image. Smart Stitching software does not serve as acquisition software nor has control function for acquisition setting.

This FDA 510(k) summary for the OSKO Smart Stitching Software System provides limited details about specific acceptance criteria and the study that rigorously proves the device meets those criteria with statistical significance. However, based on the provided text, here's an attempt to extract and infer the requested information:

1. Table of Acceptance Criteria and Reported Device Performance

The document does not explicitly state quantitative acceptance criteria or a detailed "reported device performance" against those criteria. It refers to "predetermined acceptance criteria" and successful completion of "in house testing criteria." The primary function described is "auto stitching," so the implicit criterion would be the accurate and functional joining of images.

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

• Sample Size: Not explicitly stated. The document mentions "software validation testing," but no specific number of images or cases used in this testing is provided.
• Data Provenance: Not explicitly stated. Given it's "in house testing criteria" and no clinical testing was performed, the data could be internal test data, possibly simulated or representative images. There is no mention of country of origin, retrospective, or prospective data collection.

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

• Number of Experts: Not mentioned.
• Qualifications of Experts: Not mentioned.
• Ground Truth Establishment: The document refers to "in house testing criteria" and "predetermined acceptance criteria," suggesting that acceptance was determined by the manufacturer's internal quality and testing procedures, rather than an independent expert panel establishing a separate ground truth for a test set.

4. Adjudication Method for the Test Set

Not applicable/Not mentioned. There is no mention of a formal adjudication process involving multiple reviewers for establishing ground truth, as no clinical testing or reader study is described. The acceptance seems to be based on internal software validation against pre-defined engineering and functional specifications.

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

• MRMC Study: No, an MRMC comparative effectiveness study was explicitly not done. The document states: "No clinical testing is performed."
• Effect Size: Not applicable, as no MRMC study was conducted. Thus, there is no effect size reported for human readers improving with AI assistance.

6. If a Standalone Study (algorithm only without human-in-the-loop performance) was Done

Yes, implicitly. The "software validation testing" and "auto stitching function and SW compatibility test" described are standalone tests of the algorithm's functionality and performance against its intended design. These tests were conducted without human-in-the-loop performance evaluation in a clinical setting.

7. Type of Ground Truth Used

The ground truth for the device's functionality appears to be based on:

• Engineering Specifications/Design Intent: The software's ability to "allow post-capture positioning and joining of individual images to produce an additional composite image for two dimensional image review."
• Internal Validation Standards: "all in house testing criteria" and "predetermined acceptance criteria."
• Functional Correctness: Verification that the "software functions as intended" and performs its defined input/output operations.

It is not based on expert consensus, pathology, or outcomes data, as no clinical testing was performed.

8. Sample Size for the Training Set

Not mentioned. The document focuses on validation/testing rather than the development or training process of any machine learning components (although it's referred to as "Smart Stitching Software System, Image Processing," implying some algorithmic intelligence, details of its training are absent).

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

Not mentioned, as details about a training set are not provided.

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The XR5 diagnostic X-ray system is intended for use on adult and pediatric patients for taking diagnostics radiographic exposure of all body parts and operated by a qualified/trained doctor or technician. The XR5 diagnostic X-ray system is designed to be used with conventional film/screen, CR cassettes or digital detectors. NOT intended for Mammography use.

The XR5 diagnostic X-ray system is used for image capture by using X-rays on a patient's body. The XR5 is a conventional X-ray machine, with an intuitive operation console to provide the user an easy way to manage optimal conditions for quality images. The high frequency generator is capable of delivering the exposition dose appropriate for general X-ray and diagnostic images of a patient. This system is made to use other detector options including: conventional films, CR, or Digital Flat Panel Detectors. Please note that the quality of the image in any detector depends on the manufacture of the receptor device. The XR5 diagnostic X-ray system doesn't provide an AEC feature.

The provided text describes the OSKO, INC. XR5 Diagnostic X-ray System, which is a conventional X-ray machine. However, the document (a 510(k) summary) focuses on demonstrating substantial equivalence to a predicate device (Multix Fusion by Siemens Medical Solutions USA, Inc.) rather than outlining specific acceptance criteria or a dedicated study proving the device meets those specific acceptance criteria in terms of clinical performance metrics.

Instead, the document emphasizes:

• Technical equivalence: Comparing specifications like generator type, image acquisition methods, and the use of FDA-cleared digital detectors.
• Compliance with safety and electrical standards: Referencing IEC 60601-1, IEC 60601-2-54, and EN60601-1-2.
• Performance of individual components: The digital detectors are evaluated based on DQE, MTF, and line resolution, with these parameters serving as indicators of their performance.

Therefore, many of the requested items (e.g., specific acceptance criteria for diagnostic accuracy, sample sizes for test/training sets, expert qualifications, MRMC studies, standalone performance) are not detailed in this type of regulatory submission, as it's not a clinical performance study report for diagnostic accuracy of the overall system.

Here's an attempt to answer the questions based only on the provided text, with many fields explicitly stated as "Not Applicable" or "Not Provided" due to the nature of the document:

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

The document does not explicitly define specific "acceptance criteria" for diagnostic performance of the overall XR5 system as a diagnostic device (e.g., sensitivity, specificity, accuracy for a particular condition). Instead, it relies on demonstrating substantial equivalence to a predicate device by comparing technical specifications and performance of key components (digital detectors). The "reported device performance" primarily refers to the technical specifications and the performance metrics of the digital detectors used with the system.

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

• Sample Size for Test Set: Not provided. The document states that "Bench performance parameters for the digital detectors are sufficient to demonstrate substantial equivalence to a predicate device" and that "Clinical images are not necessary to establish substantial equivalence based on the previously FDA cleared detectors." This implies that a formal clinical test set with a specific sample size for diagnostic performance was either not conducted or not deemed necessary for this 510(k) submission.
• Data Provenance: Not provided for any clinical data. Non-clinical performance data for the detectors references the IEC 62220-1:2003 Standard.

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/Not provided. No clinical test set to establish ground truth for diagnostic accuracy is described.

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

Not applicable/Not provided. No clinical test set for diagnostic accuracy 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/Not provided. This device is a conventional X-ray system, not an AI-powered diagnostic tool, and no MRMC study is mentioned.

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

Not applicable/Not provided. This device is a conventional X-ray system, not an algorithm, and no standalone performance study in this context is mentioned.

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

Not applicable/Not provided for diagnostic accuracy of the overall system. The document does not describe the establishment of clinical ground truth for diagnostic accuracy for the XR5 system itself. For the digital detectors, their performance metrics (DQE, MTF, line resolution) are physics-based measurements rather than clinical ground truth on patient outcomes.

8. The sample size for the training set

Not applicable/Not provided. This device is a hardware X-ray system, not an AI algorithm requiring a training set. The firmware is "programmed with ANSI C language," suggesting traditional software development and testing rather than machine learning training.

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

Not applicable/Not provided. As above, this device is a hardware X-ray system; no training set for an AI algorithm is mentioned.

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The ELIAN diagnostic X-ray system is intended for use on adult and pediatric patients radiographic exposure of all body parts and operated by a qualified/trained doctor or technician. NOT intended for Mammography use.

The ELIAN is a digital radiographic image acquisition device. It is a fully integrated image capture and routing system under human operator control. This system may be usable by a technician in a typical radiology environment. The ELIAN system includes a Detector Panel, Soft Ware, Case, Grid, Power Box, Switch Box, Interconnecting Cables, U-arm Mechanical and Generator. The Detector Panel is an indirect conversion device in the form of a rectangular plate in which the input x-ray photons are absorbed in an a-Si layer with CSI. The Power Box functions as a buffer between the Detector Panel and Operating PC while also supplying power to the Detector Panel. The Flugel SW transfers signals between the Detector and X-ray Generator and also indicates the status of the panel using console. Finally, the Flugel SW contains for image data capture and correction of defects on the image data.

The device in question is the ELIAN Digital Diagnostic X-ray System.

1. Acceptance Criteria and Reported Device Performance

The provided document describes a 510(k) submission for substantial equivalence, focusing on comparative performance against predicate devices rather than specific acceptance criteria for standalone performance. However, some performance metrics are reported.

The document states: "The overall test results conclude that ELIAN has outperformed the predicate device Sedecal X-Plus LP Plus Digital (K090238) in terms of modulation transfer function and detective quantum efficiency."

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

The document mentions "Clinical images were provided" but does not specify the sample size for a test set in terms of number of cases or images.

The data provenance is not explicitly stated in terms of country of origin or whether it was retrospective or prospective. It implies a clinical evaluation, but details are absent.

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

The document mentions a "clinical study" that compared diagnostic equivalency but does not specify the number of experts used to establish ground truth or their qualifications. It states, "The result of tests demonstrated that ELIAN produces diagnostic images of equivalent quality as the predicate device..." This implies expert assessment, but no details are provided.

4. Adjudication Method for the Test Set

The document does not describe any adjudication method used for the test set.

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

An MRMC comparative effectiveness study was not specifically mentioned or detailed. The document alludes to a clinical study to "investigate the diagnostic equivalency of detector panels," where ELIAN's images were compared to those from a predicate device across eight body parts. However, it does not describe a setup to measure the improvement of human readers with AI assistance. The ELIAN system is an X-ray imaging device, not an AI-powered diagnostic tool for interpretation assistance in MRMC studies.

6. Standalone Performance Study

The primary performance studies mentioned are non-clinical, focusing on physical parameters like MTF and DQE, in accordance with IEC 62220-1:2003 Standard. The document states: "The overall test results conclude that ELIAN has outperformed the predicate device Sedecal X-Plus LP Plus Digital (K090238) in terms of modulation transfer function and detective quantum efficiency." This indicates a standalone algorithm-only (device-only, in this case) performance assessment against a standard and a predicate, particularly for image quality metrics.

7. Type of Ground Truth Used

For the non-clinical testing (MTF, DQE), the "ground truth" is established through physical measurements and standards, specifically IEC 62220-1:2003.

For the clinical study, the "ground truth" used for comparison appears to be the diagnostic images produced by the predicate device, with the goal of showing "equivalent quality." This suggests a comparative diagnostic performance rather than an absolute ground truth (e.g., pathology, outcomes data).

8. Sample Size for the Training Set

The document does not mention a training set sample size. The ELIAN system is described as an image acquisition device, not a machine learning or AI algorithm that typically requires a training set for model development.

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

Since a training set for an AI algorithm is not mentioned, the method for establishing its ground truth is not applicable/provided.

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