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To provide diagnostic quality images to aid in physician diagnosis. Intended to provide diagnostic quality images to aid in physician diagnosis for general radiography and gastro-intestinal imaging applications.

The predicate and newly modified devices are computed radiograpy imaging systems. Instead of traditional screens and photographic film for producing the diagnostic image, these systems utilize an "imaging plate," a plate coated with photo-stimulable storage phosphors that are sensitive to X-rays and capable of retaining a latent image. After exposure, this imaging plate is inserted into a digitizer that scans it with a laser and releases the latent image in the form of light that is converted into a digital image file. The image can then be previewed on a computer workstation, adjusted if necessary then stored locally, sent to an archive, printed or sent to a softcopy capable display such as a PACS system.

The CR85-X and the ADC Compact Plus are similar. The CR85-X utilizes an improved light collector to obtain maximum light efficiency. However, the basic principles of operation are unchanged.

The provided text describes a Special 510(k) for a device modification (Agfa's CR85-X Digitizer) and primarily focuses on demonstrating substantial equivalence to a predicate device (Agfa's ADC Compact Plus). As such, it does not detail a study with specific acceptance criteria and performance metrics in the way one might expect for a de novo device submission.

Instead, the submission asserts that the modified device (CR 85-X) has the same indications for use and technological characteristics as the predicate device. For the "few characteristics that may not be precise enough to ensure equivalence," the submission states that "performance data was collected, and this data demonstrates substantial equivalence." However, in keeping with the format of a Special 510(k), these specific performance data were not included in the submission. The declarations provide certification that the data demonstrate equivalence.

Therefore, many of the requested details about acceptance criteria, specific performance metrics, sample sizes, expert involvement, and study types are not explicitly present in the provided document.

Here's an attempt to answer the questions based on the available information, noting when information is missing:

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

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 (Test Set): Not specified in the provided document. The submission states, "performance data was collected," but does not detail the size or nature of the test set.
• Data Provenance: Not specified.

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 specified. The document does not describe the methodology for establishing ground truth for any performance testing.

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

• Not specified.

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. The device is a digitizer for computed radiography (CR) systems, providing digital images. It is not an AI-assisted diagnostic tool for which an MRMC study comparing human readers with and without AI assistance would typically be conducted. The focus is on the imaging system's equivalence in producing diagnostic quality images.

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

• The device itself is a standalone hardware digitizer. Its "performance" refers to its ability to scan exposed X-ray cassettes and convert latent images into digital files of diagnostic quality, functionally equivalent to its predicate. The document implies performance testing of the device's imaging capabilities was done (e.g., "imaging performance demonstration tests"), but no specific details on such a standalone study are provided.

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

• Not specified. Since the focus is on maintaining diagnostic image quality compared to a predicate, the "ground truth" for performance would likely revolve around objective image quality metrics and potentially expert assessment of usability and diagnostic utility, but this is an inference, not stated fact.

8. The sample size for the training set

• Not applicable/Not specified. This device is a hardware digitizer, not an AI/ML algorithm that requires a training set in the conventional sense. Its "training" would be its design and engineering to meet specifications.

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

• Not applicable/Not specified, as it's not an AI/ML algorithm requiring a training set with established ground truth.

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The CR30-X is indicated for use to provide diagnostic quality images to aid in physician diagnosis. The CR30-X is intended to be used mainly in chest, skeletal and gastro-intestinal x-ray imaging applications.

The predicate and newly modified devices are computed radiography imaging systems. Instead of traditional screens and photographic film for producing the diagnostic image, these systems utilize an "imaging plate," a plate coated with photo-stimulatable storage phosphors that are sensitive to X-rays and capable of retaining a latent image. After exposure, this imaging plate is inserted into a digitizer that scans it with a laser and releases the latent image in the form of light that is converted into a digital image file. The image can then be previewed on a computer workstation, adjusted if necessary then stored locally, sent to an archive, printed or sent to a softcopy capable display such as a PACS system.

The CR30-X and the CR25.0 are similar. The CR30-X utilizes an improved light collector to obtain maximum light efficiency. However, the basic principles of operation are unchanged.

The provided text describes a Special 510(k) for a device modification of the Agfa CR30-X Computed Radiography system, not a study performing a traditional comparative effectiveness or standalone performance evaluation against distinct acceptance criteria in the manner often seen for AI/ML devices.

The premise of a Special 510(k) is that the device modification is minor and does not significantly alter the safety or effectiveness of the device, thus requiring less extensive performance data than a traditional 510(k). The core argument is substantial equivalence to a predicate device (Agfa CR25.0) which has already been cleared.

Therefore, many of the requested categories for acceptance criteria and study details are not applicable to this type of submission. The 'acceptance criteria' in this context are primarily related to maintaining the performance characteristics of the predicate device and meeting general electrical and safety standards.

Here's a breakdown based on the provided document:

1. Table of Acceptance Criteria and Reported Device Performance

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

Not applicable in the context of a Special 510(k) for a device modification. The submission relies on demonstrating that the modified device (CR30-X), which primarily features an "improved light collector," maintains substantial equivalence to an already cleared predicate device (CR25.0). No independent "test set" for diagnostic performance is described. The performance data mentioned are "in-house reliability and imaging performance demonstration tests," but details are not provided.

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

Not applicable. This type of submission does not involve external expert adjudication for a ground truth test set in the way an AI/ML diagnostic device submission would.

4. Adjudication Method for the Test Set

Not applicable.

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. This is not an AI-assisted diagnostic device, but a Computed Radiography imaging system.

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

Not applicable. This is a hardware/system modification, not a standalone algorithm.

7. The Type of Ground Truth Used

The concept of a "ground truth" as typically applied to diagnostic AI models (e.g., pathology, outcomes data) is not relevant to this submission. The "truth" in this context refers to the device's ability to produce diagnostic quality images consistently and safely, equivalent to the predicate device. This is assessed via internal technical and performance testing.

8. The Sample Size for the Training Set

Not applicable. This device does not involve a "training set" in the context of AI/ML.

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

Not applicable.

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Agfa's Computed Radiography Systems with NX1.0 workstations are intended for use in the identification, generation, acquisition, processing and filing of computed radiography images in order to make them ready for interpretation by the physician.
Agfa's Computed Radiography Systems with NX1.0 workstations are indicated to provide diagnostic quality images to aid the physician with diagnosis.

The predicate and newly modified devices are computed radiography imaging systems. Instead of traditional screens and photographic film for producing the diagnostic image, these systems system utilize an "imaging plate," a plate coated with photo-stimulable storage phosphors that are sensitive to X-rays and capable of retaining a latent image. After exposure, this imaging plate is inserted into a digitizer that scans it with a laser and releases the latent image in the form of light that is converted into a digital image file. The image can then be previewed on a computer workstation, adjusted if necessary then stored locally, sent to an archive, printed or sent to a softcopy capable display such as a PACS system.
The NX1.0 and QS 3.0 (predicate) workstations are similar. The NX1.0 workstation includes a number of improvements including:

• . A more intuitive user interface.
• Enhanced image processing. .
• Easier installation and updates, .
• Enhanced image manipulation, display and export capabilities, .
  The basic principles of operation are unchanged.

The provided document is a 510(k) summary for a Computed Radiography System with NX1.0 Workstation. It describes device modifications and asserts substantial equivalence to a predicate device. However, it explicitly states that performance data was not included in the submission because it is a Special 510(k) for Device Modification. It instead refers to "declarations in Exhibits I and H" which certify that data demonstrates equivalence, but these exhibits are not provided in the input.

Therefore, many of the requested details about acceptance criteria, specific performance metrics, and the study design are not available in the provided text.

Here's a breakdown of what can be extracted and what is missing:

1. Table of Acceptance Criteria and Reported Device Performance

Not available in the provided document. The document states that performance data was collected to demonstrate substantial equivalence but was not included in the submission. It only generally asserts "proper performance to specifications" through in-house tests.

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

Not available in the provided document. No specific sample sizes for test sets are mentioned. The provenance of any data is also not stated.

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

Not available in the provided document. No information regarding ground truth establishment or expert involvement in any test sets is provided.

4. Adjudication method for the test set

Not available in the provided document. No information about any adjudication methods is provided.

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. The device is a "Computed Radiography System with NX1.0 Workstation," which is an imaging system, not an AI-powered diagnostic tool for human readers. Its purpose is to generate, process, and file CR images for interpretation by a physician. Therefore, an MRMC comparative effectiveness study regarding "human readers improve with AI vs without AI assistance" is not relevant to this device's function or the information provided.

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

Not applicable. This device is an imaging system that produces diagnostic images for human interpretation, not a standalone diagnostic algorithm. Its performance is related to image quality and system functionality, not algorithmic diagnostic accuracy.

7. The type of ground truth used

Not available in the provided document. No details on ground truth for any testing are provided.

8. The sample size for the training set

Not applicable. This is an imaging acquisition and processing system, not an AI/ML algorithm that requires a training set in the conventional sense. The "training" of such a system would involve engineering and calibration, not machine learning model training with a labeled dataset.

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

Not applicable. As the device is not an AI/ML algorithm with a training set, this question is not relevant.

Summary of available information:

The document describes software modifications to an existing Computed Radiography system. It asserts "substantial equivalence" to a predicate device based on similar intended use and technological characteristics. Testing was conducted "for proper performance to specifications through various in-house reliability and imaging performance demonstration tests," and the device meets EN 60601-1-1 and EN 60601-1-2. However, the specific details of these "specifications," the performance metrics, the test methodology, and the results are explicitly stated as not included in the submission as it's a Special 510(k). The document relies on declarations (Exhibits I and H) that certify data demonstrates equivalence, but these exhibits are not provided.

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The Drystar 5500M is a free standing device used to print diagnostic conventional and mammography images on transparent film for viewing on a standard view box. It may be used in any situation in which a hard copy of an image generated by a medical imaging device is required or desirable.

The device is the new Drystar 5500 and it is a dry, B/W printer, using the direct thermal printing principle to produce continuous-tone images with medical diagnostic image quality onto plastic sheets which can be viewed on a light box. The device has two input trays. Each tray can be adjusted to five different sizes (in inches) of film, including 8x10, 10x12, 11x14, 14x14 and 14x17. Three different types of film can be used in this new device, two for general purpose radiography and a new type of film for mammography, Drystar DT 2 M. The new mammography film comes in only two sizes 8x10 and 10x12. It is thicker than the general purpose radiography film in order to provide a wider range of optical densities. The printer also handles borders for mammography images in a different manner than for regular medical images. Otherwise, the device is very similar to the cleared original Drystar 5500.

The provided document describes the Agfa Drystar 5500 printer, a medical image hard copy device, and focuses on its substantial equivalence to previously cleared devices. It does not contain information about studies proving the device meets specific acceptance criteria related to its performance in image quality or diagnostic accuracy studies in the way a diagnostic AI device report would.

Instead, the "acceptance criteria" for this device are primarily related to its functional equivalence to predicate devices and compliance with regulatory standards for safety and electromagnetic compatibility.

Here's an analysis based on the provided text, while acknowledging that a diagnostic performance study as one might expect for an AI device is NOT present:

Acceptance Criteria and Device Performance (as inferred from the document):

Study Details (Based on available information):

The document does not describe a "study" in the sense of a clinical trial or performance evaluation with human readers and ground truth data for diagnostic accuracy. Instead, it refers to regulatory compliance and functional testing.

1. Sample size used for the test set and the data provenance: Not applicable in the context of image quality or diagnostic accuracy studies. The testing mentioned refers to functional, safety, and regulatory compliance.

2. Number of experts used to establish the ground truth for the test set and the qualifications of those experts: Not applicable. There is no mention of a test set with ground truth established by experts for diagnostic performance.

3. Adjudication method for the test set: Not applicable. There is no mention of a test set requiring adjudication in this context.

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: Not applicable. This device is a printer, not an AI diagnostic tool.

5. If a standalone (i.e. algorithm only without human-in-the-loop performance) was done: Not applicable. This device is a printer, not an algorithm.

6. The type of ground truth used: Not applicable for diagnostic performance. The "ground truth" here would be functional specifications, regulatory standards, and physical measurements (e.g., optical density, film size, continuous tone output).

7. The sample size for the training set: Not applicable. This device is a printer, not a machine learning algorithm.

8. How the ground truth for the training set was established: Not applicable.

Summary from the document:

The Agfa Drystar 5500 printer submission focuses on demonstrating substantial equivalence to existing legally marketed predicate devices (Drystar 4500M and previous Drystar 5500). The "study" aspects mentioned are internal functional testing, compliance with the Mammography Quality Standards Act (MQSA) via an automatic QC procedure, and adherence to consensus standards for safety and electromagnetic compatibility. These tests confirm the device's ability to mechanically and functionally operate as intended and meet relevant regulatory requirements for a medical image hard copy device, particularly for mammography output. It is not a study assessing diagnostic performance or AI effectiveness.

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