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Agfa's DX-Si system is indicated for use in providing diagnostic quality images to aid the physician with diagnosis. The DX-Si can be used to column radiographic exposures of the skeleton (including skull, spinal column and extremities) chest, abdomen and other body parts. The DS-Xi is not indicated for use in mammography.

Use with separately cleared accessories allows the DX-Si to be conveniently used in generating urological, tomographic, pediatio and dental images.

The predicate and new devices are nearly identical computed radiography imaging systems. The DX-Si (new device) is a combination of previously cleared systems combined and marketed as a single system. The devices are the DX-S Digitizer with NX workstation and Siemens OEM version of its Multix Top x-ray system.

The new device includes an interface that allows users to select initial xray exposure settings and review exposure parameters from the digitizer workstation.

The basic principles of operation are unchanged.

The provided text describes the Agfa DX-Si integrated digital imaging system, which is a combination of existing cleared devices. The submission focuses on demonstrating substantial equivalence to its predicate devices rather than presenting novel performance studies for a new device. Therefore, much of the requested information about acceptance criteria and detailed study results is not present in the document.

Here's a breakdown of what can be extracted and what is not available due to the nature of the 510(k) submission for substantial equivalence:

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

This information is not explicitly provided in the document. The submission states: "The DX-Si integrated digital imaging system has been tested for proper performance to specifications through various in-house and imaging performance tests." However, the specific acceptance criteria (e.g., image quality metrics, dose limits, diagnostic accuracy thresholds) and the quantified performance results against these criteria are not detailed.

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

This information is not provided. The document does not describe a clinical study with a test set of images.

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)

This information is not provided. As no clinical study with a test set is described, there's no mention of experts establishing ground truth.

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

This information is not provided. Similarly, without a described test set and ground truth establishment, no adjudication method is mentioned.

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 information is not provided. The document makes no mention of an MRMC study or AI assistance. The DX-Si system is described as a conventional digital imaging system, not an AI-powered diagnostic tool.

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

This information is not provided. The DX-Si is a medical imaging system, not an algorithm being tested in a standalone capacity.

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

This information is not provided.

8. The sample size for the training set

This information is not applicable/not provided. The DX-Si is a hardware and software system for image acquisition and viewing. It's not an AI model that requires a "training set" in the machine learning sense. The testing referred to ("in-house and imaging performance tests") would likely involve engineering and image quality assessments rather than training data for an algorithm.

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

This information is not applicable/not provided.

Summary of available information regarding the "study" and acceptance criteria:

The "study" in this context refers to the technological comparison and performance testing against specifications, not a clinical trial.

• Acceptance Criteria: While not explicitly listed in a table, the document implies that the device was deemed acceptable because it demonstrated "proper performance to specifications" and "met the requirements of EN 60601-1-1 and EN 60601-1-2" (electrical safety and electromagnetic compatibility standards). The primary acceptance criterion for 510(k) purposes was demonstrating substantial equivalence to the predicate devices.
• Reported Device Performance: The document states that the device "has been tested... and shown to meet the requirements." No specific quantitative performance metrics (e.g., spatial resolution, DQE, MTF) are provided. The "performance" is implicitly tied to being substantially equivalent to the cleared predicate devices, which are already accepted as providing "diagnostic quality images."

In essence, the 510(k) for K063421 for the DX-Si system relies on demonstrating that the new combined system has "the same technological characteristics" and "the same indications for use" as its previously cleared predicate devices. This type of premarket notification often focuses on engineering testing and comparison to established components rather than de novo clinical trials or detailed performance studies against specific diagnostic acceptance criteria.

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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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AGFA's WEB1000 software is intended for installation on standard hardware meeting minimum specifications. The system is intended for viewing, assembling, organizing, sharing, and displaying patient images and demographic information. Images stored on the WEB1000 can be part of your evolving workflow. The WEB1000 can also be used remotely over a hospital intranet or over the Internet.

When used by trained and qualified professionals the WEB1000 may be used for reviewing and referral image data collected from various modalities including mammography. When used for mammography the WEB1000 should never be used as a diagnostic tool.

WEB1000™ is a software package, which may be marketed as a software only solution, as well was in conjunction with standard PC hardware. WEB1000™ is a PC-based, DICOM-compliant PACS device that is able to receive and display DICOM images. Images sent to WEB1000™ are converted into formats suitable for viewing in a web browser, and stored in a local cache (hard disk). The algorithms used by WEB1000™ to create JPEG and wavelet images follow known and accepted protocols.

Images sent to WEB1000™ can be viewed using a Java applet that runs within a web browser such as Netscape or Internet Explorer. The WEB1000™ applet can be used for the purposes of viewing images over a hospital intranet, or over the Internet from a remote location. Images stored on WEB1000™ are transient, as WEB1000™ is not intended to be an archiving device. WEB1000™ uses standard "off-the-shelf" PC hardware and communicates using the standard TCP/IP stack. The network hardware used to support the TCP/IP stack is superfluous to WEB1000™ . WEB1000™ is intended for reference viewing of medical data. It is not for the purposes of diagnosis. Images viewed from WEB1000™ are used from reference purposes only. Diagnostic reports created from diagnostic viewing application and distributed through WEB1000™ can be used for treatment of a patient.

The provided 510(k) summary for K053458 for the AGFA WEB1000™ device is a summary of safety and effectiveness, focused on establishing substantial equivalence to a predicate device, rather than a detailed report of a study proving the device meets specific performance acceptance criteria.

The submission claims the device is substantially equivalent to General Electric Medical Systems' Centricity™ PACS System (Web Client Component). The basis for this claim is that: "Technological and functional characteristics of the Agfa's WEB1000™ software are identical to those of General Electric Medical Systems' Centricity™ PACS System (Web Client Component)."

Therefore, the document does not contain the information requested in points 1-9 regarding specific acceptance criteria, a study proving those criteria are met, sample sizes, ground truth establishment, or expert involvement. The entire premise of this 510(k) is that because it is functionally identical to a previously cleared device, it is considered safe and effective for its stated intended use.

Based on the provided text, the requested information (1-9) cannot be extracted because the submission relies on substantial equivalence to a predicate device and does not present a de novo study with acceptance criteria and performance data.

Here’s what can be inferred from the document regarding the lack of such a study:

• No acceptance criteria or reported device performance are listed. The document states, "The algorithms used by WEB1000™ to create JPEG and wavelet images follow known and accepted protocols." This is a general statement, not a specific performance metric.
• No mention of a study with a test set, data provenance, number of experts, adjudication method, MRMC study, or standalone performance study. The document focuses on technological and functional comparison to the predicate.
• The type of ground truth used is not applicable as no study with a ground truth is described.
• No sample size for a training set is mentioned, nor is how ground truth for a training set was established. This is because the submission is not describing the development and validation of a novel algorithm requiring these details.

In summary, the 510(k) for K053458 is a substantial equivalence submission, which typically does not include the detailed performance study data requested. The "study" here is the comparison of technological and functional characteristics to a predicate device, rather than a performance study against predefined acceptance criteria.

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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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The Agfa IMPAX OT3000 Orthopedic workstation is designed to help orthopedic surgeons and specialists access images, plan surgical procedures and monitor patient progress in a digital environment. As an add-on component to the IMPAX client, the OT3000 orthopedic application provides digital planning to the PACS system. These images can be aimed at images helping the surgeon plan the actual prosthetic implant. These plans can also be shown to surgical placement of the implant. They will undergo and to help them understand the pathology present. The application consists of an Impax Diagnostic Workstation and templates. The application consists of guides intended for selecting or positioning orthopedic implants or guiding the marking of tissue before cutting.

Concentrating within the specialty of joint replacement, the IMPAX® OT3000 will provide an orthopedic surgeon with the ability to produce presurgical plans and distribute those plans for intra operative guidelines. It will also support the proper workflow necessary to effectively compare pre and and operative radiograph studies for a unique understanding of the patient's surgical outcome. Integrating this workflow with the orthopedic surgeons surgiour ownershow and combining it with the data produced from the patient physical exam, provides a comprehensive data set for the continued prescription of a patient's relevant treatment and therapy.

The proper choice of prosthesis implant, size and placement is critical to postoperative success and minimizing intra operative complications. Proper pre-surgical planning is key for identifying the correct choices and decisions an orthopedic surgeon makes.

The provided text does not contain any information about acceptance criteria, study details, or performance metrics for the Agfa IMPAX® OT3000 Orthopedic Workstation.

The document is a 510(k) summary, which focuses on demonstrating substantial equivalence to a predicate device rather than providing a detailed performance study. It states that "Technological and functional characteristics of the Agfa's IMPAX® OT3000 software are identical to those of the predicate device."

Therefore, I cannot populate the requested table or answer the specific questions about acceptance criteria, study design, sample sizes, ground truth, or MRMC studies.

Summary of what is missing from the provided text:

• Acceptance Criteria and Reported Performance: No specific performance metrics or thresholds are mentioned.
• Study Details: No study is described that evaluates the device's accuracy or effectiveness. The 510(k) submission relies on substantial equivalence to a predicate device (Siemens' EndoMap).
• Sample Sizes: No information on test sets or training sets.
• Data Provenance: No details on where any data (if used for testing) originated.
• Experts and Ground Truth: No mention of experts, how ground truth was established, or adjudication methods.
• MRMC Study: No information about a comparative effectiveness study with human readers.
• Standalone Performance: No standalone performance study is described.
• Type of Ground Truth: Not applicable since no performance study is detailed.
• Training Set Sample Size and Ground Truth: Not applicable since no training or performance study is detailed.

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The CR50.0 is indicated for use to provide diagnostic quality images to aid in physician diagnosis. The CR50.0 is intended to be used mainly in chest, skeletal, and gastro-intestinal x-ray imaging applications.

The CR50.0, the predicate device, is a computed radiology imaging system. Instead of screens and photographic film for producing the diagnostic image, the CR50.0 system utilizes an "imaging plate," a plate coated with photo-stimulable storage phosphors that are sensitive to X-rays and capable of retaining a latent image. This imaging plate is inserted into a device that scans it with a laser and releases the latent image in the form of light which is converted into a digital bit stream. The bit stream of image data is stored locally, printed or sent to a Picture Archiving and Communications System (PACS) in DICOM format. The CR50.0 is very similar to the CR25.0. It has a new scanning system that improves scan time and an image plate with an improved phosphor. However, the basic principles of operation are unchanged.

This document is a 510(k) Summary for a Device Modification (K050810), meaning it pertains to changes made to an already cleared medical device, the CR25.0 (K041701). As such, it primarily focuses on demonstrating that the modified device (CR50.0) is substantially equivalent to its predicate device and does not include a detailed study with specific acceptance criteria and performance metrics documented in the submission itself.

Here's an analysis based on the provided text, addressing your points where possible:

Acceptance Criteria and Device Performance

The document does not explicitly state specific numerical acceptance criteria or detailed performance metrics for the CR50.0 in a comparative table. This is typical for a Special 510(k) submission, where the focus is on asserting that the modifications do not negatively impact safety or effectiveness, and that any required performance testing confirms substantial equivalence.

Instead, the document states:

• "performance data was collected, and this data demonstrates substantial equivalence." (Section D)
• "This Special 510(k) for Device Modification submission has demonstrated Substantial Equivalence..." (Section G)
• "The CR50.0 has been tested for proper performance to specifications through various in-house reliability and imaging performance demonstration tests." (Section F)

The "specifications" for proper performance are not detailed here, but would likely relate to image quality parameters (e.g., spatial resolution, contrast resolution, noise) that are expected to be at least equivalent to, if not better than, the predicate CR25.0. Given the description of "an image plate with an improved phosphor," it's implied that the imaging performance would be maintained or enhanced.

Therefore, a table of acceptance criteria and reported device performance cannot be generated from the provided text. The key acceptance criterion for this 510(k) was demonstrating substantial equivalence to the predicate device CR25.0.

Study Information (Based on available text):

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

• Cannot be provided as detailed in the explanation above. The submission relies on "performance data" demonstrating substantial equivalence, not specific numerical criteria presented in this summary.

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

• Not specified. The document makes no mention of specific clinical or image-based test sets, their sample sizes, or provenance. The testing mentioned in Section F ("in-house reliability and imaging performance demonstration tests") likely involved technical evaluation rather than a clinical reader study.

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 specified. Given the nature of a device modification and the focus on technical performance (as opposed to diagnostic accuracy per se, which is assumed to be equivalent to the predicate), the submission does not describe a clinical ground truth establishment process or the involvement of experts for this purpose.

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

• Not applicable/None specified. No clinical test set requiring adjudication 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:

• No. This device is a Computed Radiography imaging system and not an AI-powered diagnostic algorithm. Therefore, an MRMC study related to AI assistance would not be relevant or expected for this submission.

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

• No. This is an imaging acquisition device, not an algorithm, so the concept of "standalone performance" in the context of an algorithm is not applicable. The device itself performs image acquisition and processing.

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

• Not applicable/Not specified. As explained for point 3, the submission does not detail a process for establishing a clinical "ground truth" for a test set. Technical performance metrics would be assessed against engineering specifications or benchmarks from the predicate device.

8. The sample size for the training set:

• Not applicable/Not specified. This is a hardware/software imaging system, not a machine learning algorithm that requires a "training set" in the conventional sense. The "training" that would occur is internal software development and parameter optimization, not an AI model training process.

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

• Not applicable/Not specified. As explained for point 8, there isn't a "training set" with ground truth in the context of an AI algorithm for this device.

In summary: This 510(k) is for a modification to a Computed Radiography system. It asserts substantial equivalence to a predicate device based on "performance data" from "in-house reliability and imaging performance demonstration tests." It does not involve AI, clinical reader studies, or detailed reporting of specific acceptance criteria and performance metrics within this summary document. The core of this submission is the claim of substantial equivalence due to unchanged basic principles of operation despite hardware improvements (new scanning system, improved phosphor plate).

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The Radiotherapy Solution Based on CR25.0 is indicated for producing simulation and quality control images for use in radiation therapy planning and quality control.

The Radiotherapy Solution Based on CR allows the application of Portal Imaging in a very wide dose range (1 MU - 400 MU's and higher) by using two different Portal Imaging Cassette types, which are optimised for image quality at their intended dose range. The Radiotherapy Solution Based on CR supports both low- and high-dose applications (sometimes called localisation and verification portal imaging). Not only does the system enable the acquisition of the images under the typical Radiotherapy conditions, the specific requirements for these images are also met which allows their use by the typical "next-in-line" radiotherapy applications. Typical "next-in-line" applications for simulation imaging are, for instance, image comparison and bloc compensator/MLC calculations. For portal imaging, a typical "next-in-line" application is image comparison with a reference image (this can be a simulation image or DRR: comparisons are made between hardcopy prints or on a digital workstation).

The provided text describes a 510(k) premarket notification for a medical device called "Radiotherapy Solution Based on CR" (later referred to as "Radiotherapy Solution Based on CR25.0"). This submission focuses on demonstrating substantial equivalence to previously marketed predicate devices, rather than establishing specific performance criteria through a detailed clinical study with acceptance criteria.

The submission is for an accessory to a CR system and emphasizes its technical characteristics and intended use being similar to existing cleared devices. Therefore, the information typically found in a clinical study demonstrating performance against acceptance criteria in terms of accuracy, sensitivity, specificity, or other quantitative metrics is largely absent.

Here's an analysis based on the provided text, highlighting what is available and what is not:

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

This information is not explicitly provided in the submission. The submission states that the device "has been tested for proper performance to specifications through various in-house reliability and imaging performance demonstration tests." However, the specific acceptance criteria for these "specifications" and the quantitative results are not detailed.

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

The text mentions "Clinical performance has been tested in the typical environment of a clinical radiotherapy department, and sample clinical images have been provided in this 510(k)." The sample size for this "clinical performance" test is not specified, nor is the exact data provenance (country of origin, retrospective/prospective). The emphasis is on demonstrating "sample clinical images" as confirmatory evidence of equivalence.

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):

This information is not provided. Given the nature of a 510(k) submission focused on substantial equivalence rather than a de novo clinical performance study, the establishment of ground truth by multiple experts is not detailed. The "clinical performance" testing seems to be more about confirming the device functions as expected in a real-world setting rather than a rigorous diagnostic accuracy study.

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

This information is not 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:

An MRMC comparative effectiveness study was not conducted or reported. The device is an image acquisition and processing system, not an AI-assisted diagnostic tool for human readers. Its primary function is to enable "Portal Imaging" and produce images for "simulation and quality control" in radiotherapy.

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

A standalone performance evaluation of the "algorithm" in the sense of a diagnostic accuracy study is not explicitly detailed. The device is an integrated system (cassettes, image acquisition, and processing) for producing images. The "performance to specifications" would likely involve objective image quality metrics (e.g., spatial resolution, contrast, noise, dose linearity) which are inherent to the algorithm and hardware working together. However, specific acceptance criteria and detailed results for these are not provided in the summary.

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

Given the device's function in radiotherapy imaging (localization and verification, simulation, quality control), the "ground truth" for its performance would likely relate to the accuracy of anatomical representation, consistency of image quality, and ability to meet the requirements for "next-in-line" radiotherapy applications. However, the specific methodology for establishing this "ground truth" (e.g., comparison to known anatomical landmarks, phantoms, established dosimetry) is not detailed in the provided summary. There is no mention of expert consensus, pathology, or outcomes data in the context of establishing ground truth for the clinical performance.

8. The sample size for the training set:

The concept of a "training set" in the context of machine learning is not applicable to this device. This device is an imaging system, not a machine learning model.

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

As the concept of a "training set" is not applicable, this information is not relevant.

In summary:

This 510(k) summary is typical for a device demonstrating substantial equivalence, where the focus is on comparing the new device's intended use and technological characteristics to already cleared predicate devices. It does not provide the detailed performance metrics, acceptance criteria, multi-reader studies, or comprehensive ground truth establishment methodologies that would be expected for a de novo device or an AI-based diagnostic tool requiring rigorous clinical validation. The "testing" mentioned is primarily for "proper performance to specifications" and "clinical performance" to confirm its functionality in a radiotherapy environment, without specific quantitative results against predefined acceptance criteria being disclosed in this summary.

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The CR25.0 is indicated for use to provide diagnostic quality images to aid in physician diagnosis. The CR25.0 is intended to be used mainly in chest, skeletal, and gastro-intestinal x-ray imaging applications.

The ADC Compact Plus, the predicate device, is a computed radiology imaging system. Instead of screens and photographic film for producing the diagnostic image, the ADC Compact Plus system utilizes an "imaging plate." a plate coated with photo-stimulatable storage phosphors that are sensitive to X-rays and capable of retaining a latent image. This imaging plate is inserted into a device that scans it with a laser and releases the latent image in the form of light which is converted into a digital bit stream. The bit stream of image data is stored locally and can also be stored in a PACS network in DICOM format.

The CR25.0 is very similar to the ADC Compact Plus and the ADC Solo. The electronics are being reorganized and made smaller, which will result in lower power requirements. However, the basic principles of operation are unchanged. Instead of upgrading the currently marketed economy system called the ADC Solo, components of the high-end ADC Compact Plus were reintegrated into a compact lower-cost system, resulting is no loss of resolution or other measures of image quality.

This 510(k) summary for the Agfa CR25.0 doesn't contain detailed acceptance criteria or performance data from a specific study. Instead, it relies on demonstrating substantial equivalence to a predicate device (ADC Compact Plus) through a "Special 510(k) for Device Modification." This type of submission typically focuses on showing that modifications to an already cleared device do not introduce new questions of safety or effectiveness.

Here's a breakdown of the requested information based on the provided text, and where gaps exist:

1. Table of Acceptance Criteria and Reported Device Performance

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

• Sample Size for Test Set: Not specified. The document states that "performance data was collected," but details of the test set (number of images, cases, etc.) are not provided.
• Data Provenance: Not specified. It's implied that "in-house" testing was conducted, but the country of origin or whether it was retrospective/prospective is not mentioned.

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

• Not specified. There is no mention of expert review or ground truth establishment in relation to a test set for performance evaluation.

4. Adjudication Method for the Test Set

• Not specified. Since no expert review is mentioned, no adjudication method is detailed.

5. If a Multi-Reader Multi-Case (MRMC) Comparative Effectiveness Study Was Done, and Effect Size

• No, a MRMC comparative effectiveness study is not mentioned. The submission focuses on device modification and substantial equivalence, not a comparative study with human readers.

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

• The CR25.0 is a computed radiography imaging system, not an AI algorithm. Therefore, the concept of "standalone performance" for an algorithm does not directly apply in this context. The device's performance is inherently tied to producing diagnostic-quality images for human interpretation. The "imaging performance demonstration" refers to the system's ability to produce images, not an automated diagnostic output.

7. The Type of Ground Truth Used

• Not explicitly stated. Given the nature of a computed radiography system, "diagnostic quality images" for physician diagnosis is the primary output. The performance evaluation would likely focus on objective image quality metrics (e.g., resolution, signal-to-noise ratio, contrast) that ensure the produced images are suitable for clinical interpretation, rather than a specific disease outcome or pathology. The "ground truth" would be the technical specifications and expected image characteristics of a diagnostic imaging system.

8. The Sample Size for the Training Set

• Not applicable as the CR25.0 is a computed radiography imaging system, not an AI/machine learning algorithm that requires a training set.

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

• Not applicable for the same reason as above (not an AI/machine learning algorithm).

Summary of Study (Based on Provided Text):

The "study" described is not a traditional clinical trial or comparative effectiveness study. It's a demonstration of substantial equivalence for a modified device (CR25.0) to its predicate (ADC Compact Plus).

• Objective: To demonstrate that the CR25.0, a modified version of the ADC Compact Plus, has "no loss of resolution or other measures of image quality" and meets essential performance specifications, thereby maintaining substantial equivalence to the legally marketed predicate device.
• Methodology: The submission states that "performance data was collected" through "various in-house reliability and imaging performance demonstration" tests. It also claims adherence to international standards (EN 60601-1-1 and EN 60601-1-2) for safety and essential performance. The specific details of these tests (e.g., types of phantoms used, metrics measured, experimental protocols) are not included in this summary, as per the format of a Special 510(k). The basis for acceptance is stated as "the declarations in Section IV provide certification that the data demonstrate equivalence."
• Conclusion: The manufacturer concluded that the CR25.0 is substantially equivalent to the predicate device, implying that its performance meets the requirements for a diagnostic imaging system as established by the predicate. FDA's clearance confirms this finding.

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