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Quantor View is PACS image viewing software for DICOM images produced by imaging equipment (CR, DR, CT, MRI, Ultrasound and etc). The software allows physicians to provide medical services by accessing image data and patient information from remote locations. It also enables the images to be stored to the local database of your personal computer allowing database management to operate efficiently. Physicians can utilize the software tools for viewing, researching, teaching, teleconferencing and online collaboration.

This is a PC software product used for viewing digital medical images having the following features: DICOM File Support (Read and display all DICOM files (mono-frame, multi-frame), Read and display non-DICOM file (JPEG Lossy, JPEG Lossless, JPEG2000, RLE), Read and write DICOM CD/DVD (DICOMDIR support), Export DICOM files to BMP, TIFF, JPEG, Convert Non-DICOM images to DICOM, Convert multi-frame image to AVI file), DICOM Network Support (DICOM Storage SCU/SCP, DICOM Query/Retrieve SCU/ SCP, DICOM Print SCU with 8, 12 bit support), Viewer Functions (Customizable toolbars, NN, linear, cubic, super sampling interpolation, ROIs: polygons, circles, rectangles and etc., Custom LUT and curve, CT/MR scout line view, Thumbnail images support, Support 1 to 4 monitors, User defined hanging protocol, tool bar, overlay, Local database support, Local backup support).

This document describes the QuantorView, a Picture Archiving and Communications System (PACS) software, seeking 510(k) clearance. The submission relies on demonstrating substantial equivalence to a predicate device, the eFilm Workstation (K012211). Therefore, the core of the "study that proves the device meets the acceptance criteria" is primarily focused on non-clinical performance testing to show that QuantorView functions comparably to the predicate and adheres to relevant standards.

Here's a breakdown of the requested information based on the provided text, recognizing that a 510(k) for a PACS viewing software typically does not involve clinical performance studies in the same way an AI diagnostic algorithm would.

1. Table of Acceptance Criteria and Reported Device Performance

Since this is a substantial equivalence submission for PACS viewing software, the "acceptance criteria" are primarily defined by the functional equivalence to the predicate device and compliance with relevant standards.

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

• Test Set Sample Size: The document does not specify a quantitative "test set sample size" in terms of number of patient cases or images for clinical performance. As this is PACS viewing software, the testing would focus on the software's ability to correctly display and manipulate various types of DICOM images. This would likely involve a diverse set of DICOM files covering different modalities (CR, DR, CT, MRI, Ultrasound, etc.) and image characteristics to ensure proper handling and viewing functionalities. However, no specific number is provided.
• Data Provenance: Not explicitly stated as this is not a clinical study involving patient data from a specific region. The testing would involve generic DICOM files, potentially simulated or anonymized, to test the software's functionality and compliance.

3. Number of Experts Used to Establish Ground Truth and Qualifications

• This information is not applicable or provided for this type of submission. Ground truth establishment with experts is typically for diagnostic AI algorithms where a clinical finding is being evaluated. For PACS viewing software, the "ground truth" is adherence to DICOM standards and accurate representation of image data.

4. Adjudication Method for the Test Set

• Not applicable or provided. Adjudication methods like 2+1 or 3+1 are used in clinical studies with human readers to resolve discrepancies in diagnoses or interpretations. This is not a clinical study evaluating diagnostic performance.

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

• No, an MRMC comparative effectiveness study was not done. The document explicitly states: "Clinical performance testing: Not applicable or required." MRMC studies are used to evaluate the impact of a device (often AI) on human reader performance, which is not the focus of a PACS viewing software 510(k).

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

• Standalone technical performance testing was done. The software itself was tested to ensure it met its functional specifications and complied with DICOM standards. This is evident from "Non-clinical performance testing. Software validation and risk analysis was done... Testing verified compliance with the DICOM 3 compliance statement has been executed." However, this is not a standalone diagnostic algorithm performance evaluation, but rather a standalone software functionality evaluation.

7. The Type of Ground Truth Used

• The "ground truth" for this device's evaluation is primarily technical compliance and adherence to established standards, specifically:
  • DICOM 3.0 Compliance: Ensuring the software correctly interprets, displays, and handles DICOM files according to the standard.
  • Functional Specifications: Verifying that all advertised features (image manipulation, measurement, network support, etc.) work as intended.
  • Predicate Device Functionality: Demonstrating that QuantorView performs equivalently to the legally marketed eFilm Workstation.

8. The Sample Size for the Training Set

• Not applicable or provided. This device is PACS viewing software, not an AI/machine learning algorithm that requires a "training set" in the conventional sense. The software's capabilities are based on coded logic and adherence to standards, not on learning from a dataset.

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

• Not applicable. As there is no training set for an AI algorithm, there is no ground truth established for it.

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This device is a Computed Radiography System and intended for use in producing digital X-Ray images for general radiography purposes. It comprises of scanner, cassette with reusable phosphor storage plate (IP) and workstation software. It scans X-Ray exposed image plate and produces X-Ray image in digital form. Then, digital image is transferred to workstation for further processing and routing. This device is intended to be operated in a radiological environment by qualified staff. This device is not intended for the acquisition of mammographic image data.

The FireCR Spark is Computed Radiography System which produces the X-ray diagnostic image in digital format instead of using traditional screens and film. This device utilizes reusable X-ray storage phosphor plate (IP) that is sensitive to X-ray and stores latent image when it is exposed to X-ray. After X-ray exposure to the X-ray storage phosphor plate, X-ray storage phosphor plate is scanned by means of laser in the device. Latent image in the X-ray storage phosphor plate is released in a form of light by laser scanning. Then the light is collect and converted into a form of digital image. The signal processing is made to the digital image data such as the digital filtering, the gain & offset correction and flat fielding. The image can then be viewed on a computer workstation, adjusted if necessary, then stored locally, sent to an archive, printed or sent to PACS system. After acquisition of latent image from the X-ray storage phosphor plate, it is erased thoroughly to be reused.

Here's an analysis of the provided text regarding the FireCR Spark Computed Radiography Scanner, focusing on the acceptance criteria and the study proving its performance:

Acceptance Criteria and Device Performance for FireCR Spark (K133120)

1. Table of Acceptance Criteria and Reported Device Performance

The provided 510(k) summary primarily focuses on demonstrating substantial equivalence to a predicate device (FireCR) rather than establishing novel acceptance criteria for a new device. Therefore, the "acceptance criteria" are largely framed as matching or improving upon the predicate device's performance.

The crucial "acceptance criterion" for this 510(k) submission is to demonstrate that the FireCR Spark is substantially equivalent to the predicate device, FireCR, and that any differences do not raise new questions of safety or effectiveness. The device's performance, particularly in DQE and MTF, actually exceeds that of the predicate, which is presented as an enhancement rather than a deviation from acceptance.

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

The document states:

• "In clinical considerations, - The rating was considered equivalent by radiologists."
• "As a result of Clinical Study, FireCR Spark is considered that Image quality is equivalent to the Predicate Device."
• "Non-clinical & Clinical considerations according to FDA Guidance "Guidance for the Submission of 510(k)'s for Solid State X-ray Imaging Devices" was performed."

Sample Size for Test Set: The document does not explicitly state the sample size (number of images or patients) used for the clinical study.

Data Provenance: The document does not explicitly state the country of origin of the data or whether it was retrospective or prospective. Given the submitter's address in Korea, it is possible the clinical data originated there, but this is not confirmed.

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

• Number of Experts: The document states that "The rating was considered equivalent by radiologists." It does not specify the number of radiologists involved.
• Qualifications of Experts: The document identifies them as "radiologists" but does not provide details on their experience level (e.g., "radiologist with 10 years of experience").

4. Adjudication Method for the Test Set

The document does not describe any specific adjudication method (e.g., 2+1, 3+1) for the clinical study ratings. It simply states that "The rating was considered equivalent by radiologists."

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

An MRMC comparative effectiveness study was not explicitly stated or performed as described in the provided text. The clinical study aimed to show equivalence in image quality between the FireCR Spark and the predicate device, FireCR, through radiologist ratings, but it does not evaluate the improvement of human readers with AI assistance versus without AI assistance. The device itself is a Computed Radiography Scanner, not an AI-powered diagnostic tool for interpretation assistance.

6. Standalone (Algorithm Only) Performance Study

This section is not applicable as the FireCR Spark is a Computed Radiography Scanner, a hardware device for capturing and digitizing X-ray images, not a standalone algorithm/AI for image interpretation without human interaction. Its performance metrics (DQE, MTF) are intrinsic to the device's image acquisition capabilities.

7. Type of Ground Truth Used

For the clinical study on image quality, the ground truth was based on expert consensus/ratings by radiologists. The document explicitly states: "The rating was considered equivalent by radiologists." and "FireCR Spark is considered that Image quality is equivalent to the Predicate Device."

For the technical performance metrics (DQE, MTF), the "ground truth" would be established through physical measurements and standardized testing methodologies (e.g., per IEC 62220-1).

8. Sample Size for the Training Set

The document describes a hardware device (Computed Radiography Scanner) and its associated acquisition software. It does not mention a "training set" in the context of machine learning or AI. The term "training set" is not relevant to this type of device, which is not an AI algorithm.

9. How Ground Truth for the Training Set Was Established

As there is no mention of a "training set" for an AI algorithm, this question is not applicable to the information provided. The device performs signal processing, digital filtering, gain & offset correction, and flat fielding, which are standard image processing techniques, not machine learning that would require a ground-truthed training set.

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This device is a Computed Radiography System and intended for use in producing digital X-Ray images for general radiography purposes. It comprises of scanner, cassette with reusable phosphor storage plate (IP) and workstation software. It scans X-Ray exposed image plate and produces X-Ray image in digital form. Then, digital image is transferred to workstation for further processing and routing. This device is intended to be operated in a radiological environment by qualified staff. This device is not intended for the acquisition of mammographic image data.

The FireCR Spark is Computed Radiography System which produces the X-ray diagnostic image in digital format instead of using traditional screens and film. This device utilizes reusable X-ray storage phosphor plate (IP) that is sensitive to X-ray and stores latent image when it is exposed to X-ray. After X-ray exposure to the X-ray storage phosphor plate. X-ray storage phosphor plate is scanned by means of laser in the device. Latent image in the X-ray storage phosphor plate is released in a form of light by laser scanning. Then the light is collect and converted into a form of digital image. The signal processing is made to the digital image data such as the digital filtering, the gain & offset correction and flat fielding. The image can then be viewed on a computer workstation, adjusted if necessary, then stored locally, sent to an archive, printed or sent to PACS system. After acquisition of latent image from the X-ray storage phosphor plate, it is erased thoroughly to be reused.

Here's an analysis of the provided text regarding the acceptance criteria and study for the FireCR Spark Computed Radiography Scanner:

1. Table of Acceptance Criteria and Reported Device Performance

Acceptance Criteria for Clinical Study: Implicitly, the FireCR Spark was accepted if its image quality was considered "equivalent" to the predicate device by radiologists.

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

• Sample Size for Test Set: Not explicitly stated in the provided text. The document mentions "Clinical considerations" and "Clinical Study" but doesn't quantify the number of cases or images used.
• Data Provenance: Not explicitly stated. The submission is from a Korean company (3D Imaging & Simulations Corp., Daejeon, Korea), so the data could be from Korea, but this is not confirmed. It is also not specified whether the data was retrospective or prospective.

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

• Number of Experts: Not explicitly stated. The document refers to "radiologists" (plural), indicating more than one, but no specific number is given.
• Qualifications of Experts: Only stated as "radiologists." No detail on their experience (e.g., years of experience, subspecialty) is provided.

4. Adjudication Method for the Test Set

• Adjudication Method: Not explicitly stated. The text only says, "The rating was considered equivalent by radiologists." This suggests a consensus or comparison, but the specific method (e.g., 2+1, 3+1) is not detailed.

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

• MRMC Study: The document implies a comparison between the FireCR Spark and the predicate device by radiologists ("As a result of Clinical Study, FireCR Spark is considered that Image quality is equivalent to the Predicate Device"). However, it does not explicitly describe a formal MRMC comparative effectiveness study designed to quantify improvement with AI assistance. This device is a Computed Radiography Scanner, not an AI-powered diagnostic tool, so the concept of "human readers improve with AI vs without AI assistance" does not directly apply here. The study focused on demonstrating equivalence in image quality to a predicate device.
• Effect Size of Human Readers Improvement with AI: Not applicable, as this is not an AI-assisted diagnostic device.

6. Standalone (Algorithm Only Without Human-in-the-Loop Performance) Study

• Standalone Study: Yes, non-clinical performance data (DQE, MTF, Spatial Resolution, Image Matrix, Dynamic Range, Defect Compensation) were reported for the device itself against the predicate. This is a standalone technical performance evaluation. The clinical study, though involving radiologists, served to confirm the image quality equivalence produced by the algorithm/device for human interpretation, rather than evaluating human-in-the-loop performance.

7. Type of Ground Truth Used

• Type of Ground Truth: For the technical performance metrics (DQE, MTF, Spatial Resolution, etc.), the ground truth is established by physical measurements and engineering specifications.
• For the clinical study, the ground truth was expert consensus/opinion from radiologists regarding image quality equivalence to the predicate device. It does not mention pathology or outcomes data.

8. Sample Size for the Training Set

• Sample Size for Training Set: The document does not mention a "training set" in the context of an AI/ML algorithm. This device is a Computed Radiography Scanner, and its performance is based on its physical and functional characteristics, not on a machine learning algorithm trained on data. Therefore, this question is not directly applicable to the information provided.

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

• How Ground Truth for Training Set Was Established: Not applicable, as there is no mention of a machine learning training set.

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The FireCR Dental imaging system is indicated for capturing, digitization and processing of intra oral x-ray images stored in imaging plate recording media

The FireCR Dental is Computed Radiography Reader which produces the X-ray diagnostic image in digital format instead of using traditional screens and film. This device utilizes reusable X-ray storage phosphor plate (Imaging Plate) that is sensitive to X-ray and stores latent image when it is exposed to X-ray. After X-ray exposure to the X-ray storage phosphor plate, X-ray storage phosphor plate is scanned by means of laser in the device. Latent image in the X-ray storage phosphor plate is released in a form of light by laser scanning. Then the light is collected and converted into a form of digital image. The signal processing is made to the digital image data such as the digital filtering, the gain & offset correction and flat fielding. The image can then be viewed on a computer workstation, adjusted if necessary, then stored locally, sent to an archive, printed or sent to PACS system. After acquisition of latent image from the X-ray storage phosphor plate, it is erased thoroughly to be reused.

Here's a breakdown of the acceptance criteria and study information for the FireCR Dental Imaging System (K131442), based on the provided text:

Acceptance Criteria and Device Performance

The provided document compares the FireCR Dental to a predicate device, DIGORA Optime, across various technical specifications. The acceptance criteria seem to implicitly be substantial equivalence to the predicate device in key performance metrics.

Conclusion on Acceptance Criteria: The FireCR Dental is deemed to meet acceptance criteria by demonstrating substantial equivalence to the predicate device, with some technical specifications (like DQE, MTF, and Dynamic Range) showing slight improvements. The overall conclusion is that its performance is equivalent to the predicate.

Study Information

The document describes a "Clinical Study" and non-clinical/performance testing.

1. Sample size used for the test set and the data provenance:

• Test Set Sample Size: Not explicitly stated. The text mentions "As a result of Clinical Study, FireCR Dental is considered that Image quality is equivalent to the Predicate Device." This implies a patient-level or image-level comparison, but the number of cases or patients is not provided.
• Data Provenance: Not explicitly stated, but the "Clinical Study" implies human data. The location of the study (e.g., country of origin) is not mentioned. It is unclear if the data was retrospective or prospective.

2. Number of experts used to establish the ground truth for the test set and the qualifications of those experts:

• Number of Experts: Not explicitly stated how many "dentists" were involved.
• Qualifications of Experts: Described as "dentists." No further details on their experience (e.g., years of experience, specialization) are provided.

3. Adjudication method for the test set:

• Adjudication Method: Not explicitly stated. The text says "The rating was considered equivalent by dentists," which suggests a consensus or comparison approach, but the specific method (e.g., majority vote, independent review with arbitration) is not detailed.

4. 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. This device is a Computed Radiography Scanner System, not an AI-assisted diagnostic tool. The "clinical study" focused on comparing the image quality of the new device to a predicate device, as evaluated by dentists, rather than on reader performance with or without AI assistance.
• Effect Size of AI: Not applicable, as this is not an AI device.

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

• Standalone Performance: Yes, in a sense. The comparison of technical specifications (DQE, MTF, Spatial Resolution, Dynamic Range) between the FireCR Dental and the predicate device can be considered standalone performance assessments of the device's imaging capabilities. However, this isn't a "standalone algorithm" performance as the device's primary function is image acquisition, not autonomous diagnosis. The device's output (digital images) is intended for human interpretation.

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

• Ground Truth Type: Expert opinion/evaluation by "dentists" on image quality, implicitly against the predicate device's image quality. The statement "FireCR Dental is considered that Image quality is equivalent to the Predicate Device" suggests a comparison where the predicate's image quality serves as the reference, and the dentists' ratings are the mechanism for assessing equivalence. It's not based on pathology or outcomes data.

7. The sample size for the training set:

• Training Set Sample Size: Not applicable. This device is a Computed Radiography scanner, which processes X-ray signals into digital images. It does not use machine learning or AI that would require a "training set" in the conventional sense for training an algorithm.

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

• Ground Truth for Training Set: Not applicable, as there is no mention of a training set or AI model being developed for this device. The device's "processing" involves digital filtering, gain & offset correction, and flat fielding, which are standard image processing techniques not typically "trained" on labeled ground truth data.

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This device is a Computed Radiography Scanner and intended for use in producing digital X-Ray lmages for general radiography purposes. It comprises of scanner, cassette with reusable imaging plate and workstation software. It scans X-Ray exposed image plate and produces X-Ray lmage in digital form. Then, digital image is transferred to workstation for further processing and routing. This device is intended to be operated in a radiological environment by qualified staff. This device is not approved for the acquisition of mammographic image data.

The FireCR is Computed Radiography System which produces the X-ray diagnostic image in digital formation wasn't broutinnal screens and film. This device utilizes reusable X-ray storage phosphor plate (IP) that is sensitive to X-ray and stores latent image when it is exposed to X-ray. After X-ray exposure to the X-ray storage phosphor plate, X-ray storage phosphor plate is scanned by means of laser in the device. Latent image in the X-ray storage phosphor plate is released in a form of light by laser scanning. Then the light is collect and converted into a form of digital image. The signal processing is made to the digital image data such as the digital filtering, the gain & offset correction and flat flelding. The image can then be viewed on a computer workstation, adjusted if necessary, then stored locally, sent to an archive, printed or sent to PACS system. After acquisition of latent image from the X-ray storage phosphor plate, it is erased thoroughly to be reused.

The provided text describes a 510(k) submission for the FireCR Computed Radiography Scanner. However, it does not contain any information about acceptance criteria or a specific study proving the device meets those criteria, nor does it include details about a test set, ground truth experts, or training data.

The document primarily focuses on:

• Device Description: What the FireCR is and how it works.
• Comparison to Predicate Device: Stating substantial equivalence to the AGFA ADC Compact Plus.
• Safety, EMC, and Performance Data: Mentions that testing according to IEC 60601-1 and IEC 60601-1-2 was performed, and FDA Guidance for Solid State X-ray Imaging Devices was followed, with all results being "satisfactory."
• Indications for Use: General radiography, not mammography.
• FDA Communication: The letter from the FDA confirming substantial equivalence and an internal memo about a regulation number reassignment.

Therefore, I cannot populate the table or answer the specific questions about acceptance criteria and study details based on the provided text.

Based on the provided text, the following information is missing and cannot be extracted:

• A table of acceptance criteria and the reported device performance.
• Sample size used for the test set and the data provenance.
• Number of experts used to establish the ground truth for the test set and their qualifications.
• Adjudication method for the test set.
• Information about a multi-reader, multi-case (MRMC) comparative effectiveness study or its effect size.
• Information about a standalone (algorithm only) performance study.
• (Specifically for an AI/algorithm device, which this is not described as) The type of ground truth used (expert consensus, pathology, outcomes data, etc).
• (Specifically for an AI/algorithm device, which this is not described as) The sample size for the training set.
• (Specifically for an AI/algorithm device, which this is not described as) How the ground truth for the training set was established.

The document confirms that safety and performance testing was done according to IEC 60601-1 and IEC 60601-1-2, and non-clinical & clinical considerations were performed according to FDA guidance for Solid State X-ray Imaging Devices. It states that "All test results were satisfactory," but it does not specify what those test results were, what the acceptance criteria for them were, or the details of any clinical studies.

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