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The IMPAX MA3000 Diagnostic PACS Workstation is intended for use with regionally approved digital mammography modalities presenting processed images (DICOM "For Presentation" images) and the display of multi-modality general imaging DICOM images including adjunct breast imaging modality studies (i.e. Breast MR and Breast US).

The IMPAX MA3000 Diagnostic PACS Workstation when intended for diagnostic/screening Mammography viewing must do so only when used with FDA cleared monitors and only when viewing Lossless format images.

The IMPAX MA3000 Diagnostic PACS Workstation is also intended for soft and hardcopy reading and diagnosis by Radiologists.

IMPAX MA3000 is a diagnostic softcopy breast imaging workstation with diagnostic print capability.

The following features are available:

· Display and printing of regionally approved DICOM DR Digital Mammography Images

(MG SOP class)

· Display and printing of regionally approved DICOM CR Digital Mammography Images

(CR SOP class)

The Hardware configuration of Embrace™ will consist of the following:

Dell Precision™ Workstation 650; System (Per Host Machine): Compaq xw6000

Number & Details of CPU's 1 or 2 CPU's depending on configuration

Hard Drive space: 40GB IDE

CD-ROM: Yes

Floppy: Yes

Network interfaces: System comes with an integrated 10/100/1000 Ethernet adapter

Power Supplies: Default

Chassis: Tower

Peripherals: Microsoft IntelliMouse or IntelliMouse Explorer; Keyboard

Embrace™ will support the following monitors:

• BARCO Mammography MeDis 5MP CRT monitor package MGD . 521M
• . BARCO Mammography 5MP and 3MP Flat Panel LCD's (EU)

The provided text is a 510(k) summary for a PACS workstation and does not contain information about acceptance criteria or a study proving that the device meets such criteria. The document describes the device name, classification, indications for use, and a comparison to predicate devices, focusing on substantial equivalence for regulatory approval. It does not include details on performance metrics, sample sizes, expert qualifications, or ground truth establishment relevant to the requested study information.

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Agfa's Computed Radiography Systems with NX 2008 software are indicated for use in providing diagnostic quality images to aid the physiclan with diagnosis.

The systems can be used with either Musica, Musica2 or Musica2 Platinum image processing to create radiographic images of the skeleton (including skull, spinal column and extremities) chest, abdomen and other body parts.

When used with separately cleared accessories the systems can be conveniently used to generate urological, tomographic, pediatric and dental images, and for radiotherapy planning and quality control.

When used with Musica2 Platinum software the systems are indicated for creating high quality images of the thorax, abdomen or musculoskeletal regions of adult or pediatric patients.

Agfa's Computed Radiography Systems with NX 2008 software are not indicated for use in mammography.

The predicate and new devices are nearly identical computed radiography imaging systems. NX 2008 systems (new devices) have updated hardware and software that offers:

• An optional medical grade display. The display is cleared separately . by the display manufacturer.
• The capability to operate as a mixed-use system for both general . radiography and mammography (where approved or licensed). This is not the case for the USA the separately licensed mammography system is not available.
• Improved tools for installation, configuration and management. .
• The ability to receive DICOM veterinary images and data. .

The basic principles of operation of the new and predicate devices are the same. They have the same underlying technological characteristics.

The provided 510(k) summary for Agfa's Computed Radiography Systems with NX 2008 Workstations does not contain detailed information about specific acceptance criteria or a comprehensive study proving the device meets those criteria in the way typically expected for novel AI/ML devices.

Instead, this submission focuses on establishing substantial equivalence to a predicate device (Agfa's Computed Radiography Systems with NX2.0 Workstations, K071162). The core argument is that the new device has "the same underlying technological characteristics" and that any differences "do not alter the intended therapeutic/diagnostic effect."

Therefore, the information below reflects what is available in the document, and notes where specific details common to AI/ML device studies are not provided.

1. Table of Acceptance Criteria and Reported Device Performance

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

• Not explicitly provided. The document states "various internal tests" were conducted, but does not detail a specific test set size for performance evaluation related to diagnostic image quality.
• The data provenance (e.g., country of origin, retrospective/prospective) is also not provided as clinical studies were not involved.

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

• Not applicable / Not provided. The submission explicitly states, "No clinical testing was involved." Therefore, there was no separate test set requiring expert ground truth establishment in the context of diagnostic performance.

4. Adjudication method for the test set

• Not applicable / Not provided. As no clinical testing with a separate test set and expert ground truth was conducted, 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

• No. The document explicitly states "No clinical testing was involved." This submission is for an updated Computed Radiography system, not an AI/ML diagnostic aid.

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

• Not applicable. This device is a Computed Radiography system (hardware and software for image acquisition and processing), not a standalone diagnostic algorithm in the AI sense. Its "performance" is in producing diagnostic quality images for human interpretation.

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

• Not applicable / Not provided. Since there was no clinical testing requiring ground truth for diagnostic performance, this information is not available. The "ground truth" for the engineering performance tests would be against established engineering specifications.

8. The sample size for the training set

• Not applicable / Not provided. This submission describes a CR imaging system, not an AI/ML algorithm that requires a "training set" in the context of machine learning. The system's image processing (Musica, Musica2, Musica2 Platinum) would have been developed and tuned using proprietary methods by Agfa, but this is not analogous to an AI/ML training set as understood for diagnostic algorithms.

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

• Not applicable / Not provided. See explanation for point 8.

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Registration and Fusion (Basic and Extended) is indicated for the simultaneous visualization of multiple medical images of the same patient. The application can assist the user in visually matching and comparing anatomical studies taken at different times or acquired via different imaging modalities (Basic: CT-CT, CT-MR, MR-MR, Extended: CT-PET, MR-PET) as well as assist in making measurements. It is primarily used in the fields of diagnostic radiology, neurology and oncology.

Medical Data Registration and Fusion R 1.0 is a PACS plug-in (accessory). It is an image analysis software package that establishes the geometrical relationship between different DICOM 3.0 compliant 3-D medical data sets from PET, MR and CT imaging (registration"). The matched images are displayed (fusion') by either the use of semitransparent overlays or by displaying them side-by-side. Registration and fusion facilitates the comparison of PET/CT, PET/MRI, CT/CT, MRI/MRI, CT/MRI image data sets for use in: The general radiology department for various lesions. The oncology department for various cancerous lesions. The neurology department for various lesions. The rigid registration used in the application, aims to help the clinician navigate to the same anatomical location in both image sets. The Medical Data Registration and Fusion software runs on Agfa's PACS workstations (Impax 5.3 and 6.3 workstations).

The provided 510(k) summary for Agfa's Registration and Fusion software does not contain specific acceptance criteria or a detailed study proving the device meets those criteria in the way typically expected for a medical AI/CAD device.

Instead, this submission focuses on demonstrating substantial equivalence to a predicate device (Mirage 5.5, K043441) by highlighting similar indications, intended use, and technological characteristics. The "Testing" section is extremely brief and only states: "Registration and Fusion has been tested for compatibility with Agfa's Impax® PACS Systems." This implies functional testing and integration testing, rather than a clinical performance study with predefined acceptance criteria.

The information sought in your request (sample sizes, expert qualifications, adjudication methods, MRMC studies, standalone performance, ground truth types and establishment for training sets) is not present in this 510(k) summary. This is common for devices like PACS accessories that primarily provide visualization and registration tools, where the safety and effectiveness are often derived from the predicate device and the basic functionality of the software.

Therefore, I cannot populate the table or answer the specific questions as the required information is not disclosed in the provided document.

Summary of available information regarding acceptance criteria and study:

The document states:

• Acceptance Criteria: Not explicitly defined or listed in terms of clinical performance metrics (e.g., sensitivity, specificity, accuracy). The implicit acceptance criteria revolve around functional compatibility with Agfa PACS systems and achieving substantial equivalence to the predicate device in terms of intended use and technological characteristics.
• Study Proving Acceptance: The document mentions "testing for compatibility with Agfa's Impax® PACS Systems." This suggests internal functional and integration testing, but not a formal clinical performance study with human readers, ground truth, or statistical analysis of diagnostic accuracy.

Regarding the specific questions you asked:

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

   • Acceptance Criteria: Not explicitly stated.
   • Reported Device Performance: Not reported in terms of clinical metrics. The performance described is functional (image registration and fusion capabilities).

2. Sample sizes used for the test set and the data provenance: Not provided.

3. Number of experts used to establish the ground truth for the test set and the qualifications of those experts: Not provided, as a formal test set with expert-established ground truth for diagnostic performance is not described.

4. Adjudication method for the test set: 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: Not done/not reported. This device is a visualization and registration tool, not an AI/CAD system designed to directly improve diagnostic accuracy in that manner.

6. If a standalone (i.e. algorithm only without human-in-the-loop performance) was done: Not done/not reported for diagnostic performance. The device's function (registration and fusion) is inherently an assist to a human user.

7. The type of ground truth used: Not provided.

8. The sample size for the training set: Not applicable/not provided. This device is not described as an AI/machine learning system that requires a training set in the typical sense. It performs rule-based image registration and fusion.

9. How the ground truth for the training set was established: Not applicable/not provided.

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OrthoGon is a pluq-in software application for PACS workstations. The package is indicated to assist clinicians in their analysis of anatomy and pathology by providing interactive 2D measurement schemes and comparisons against prior measurements and against normative references to aid in the diagnosis of disorders and deformities.

OrthoGon is intended for use in the fields of musculoskeletal radiology. orthopedics, traumatology and pediatrics using 2D X-ray images based on DICOM standards.

The application is intended for use by radiologists, radiographers, technologists, referring physicians and orthopedic surgeons and can be used in operating theaters, mobile and military environments.

OrthoGon assists physicians in their analysis of anatomy and pathology by providing easy to use, interactive measurement schemes.

OrthoGon allows the user to easily perform standard measurements and to compare results to prior measurements and to normal values from the medical literature.

OrthoGon uses X-ray images based on the DICOM standards. The device is compatible with multiple PACS workstations and software versions.

OrthoGon allows the user export results into Excel formatted reports.

The provided K073271 510(k) summary for Agfa OrthoGon does not contain any information about acceptance criteria or a study proving the device meets said criteria.

The document describes Agfa OrthoGon as a software device intended to assist physicians in analyzing anatomy and pathology by providing interactive 2D measurement schemes. It asserts that OrthoGon is substantially equivalent to two predicate devices (Agfa's Orthopedic Software for Impax® Workstations (K071972) and OrthoCrat Limited's TraumaCAD (K042816)).

The "F. TESTING" section simply states: "OrthoGon has been tested for compatibility with Agfa's Impax® PACS Systems." This is a compatibility test, not a performance study evaluating acceptance criteria for diagnostic accuracy or measurement precision.

Therefore, I cannot provide the requested information, as it is not present in the provided text. The document focuses on demonstrating substantial equivalence based on intended use, technological characteristics, and compatibility, rather than presenting a performance study with defined acceptance criteria and results.

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The Drystar AXYS is a free-standing dry film printer used to print diagnostic 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, including digital mammography.

The Drystar AXYS 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 AXYS 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 printer is sold with two film 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.

The provided document is a 510(k) summary for the Drystar AXYS, a medical image hardcopy device (printer). It primarily focuses on demonstrating substantial equivalence to a predicate device (Drystar 5500) rather than providing detailed acceptance criteria and a study proving device performance against those criteria in the context of clinical AI/software performance.

Therefore, many of the requested sections (e.g., sample sizes, expert qualifications, adjudication methods, MRMC studies, standalone performance, training set details) are not applicable or not available in this specific document, as it describes a hardware device that prints images, not an AI or software algorithm that analyzes them.

Here's a breakdown based on the available information:

1. Table of Acceptance Criteria and Reported Device Performance

This document does not define specific "acceptance criteria" for clinical performance of an AI/software device, nor does it report performance metrics relevant to an AI/software's diagnostic accuracy (e.g., sensitivity, specificity, AUC).

Instead, the closest equivalent to "performance" for this device relates to its printing capabilities and compliance with manufacturing standards.

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

• Not Applicable. This document describes a medical printer, not a diagnostic AI/software that would have a "test set" of patient data for performance evaluation. The "testing" mentioned refers to engineering and quality assurance activities for hardware and software functionality, as well as compliance with standards.

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

• Not Applicable. As above, no clinical test set requiring expert-established ground truth is described.

4. Adjudication Method for the Test Set

• Not Applicable. No clinical test set or adjudication is mentioned.

5. If a Multi-reader Multi-case (MRMC) Comparative Effectiveness Study was Done

• No. This document does not mention any MRMC studies. The device is a printer, not a diagnostic aid that would involve human readers interpreting images with and without AI assistance.

6. If a Standalone (Algorithm Only Without Human-in-the-Loop Performance) Study was Done

• Not Applicable. The device is a printer, not an algorithm. Therefore, there is no "standalone performance" study in the context of an AI/software algorithm.

7. The Type of Ground Truth Used

• Not Applicable. For a printer, "ground truth" in the clinical AI sense (e.g., pathology, outcomes data) is not relevant. The "truth" would involve whether the printed image accurately reflects the digital image data, has appropriate density, resolution, and is free from artifacts, which would be assessed through technical specifications and quality control, not clinical ground truth.

8. The Sample Size for the Training Set

• Not Applicable. This is a hardware printer, not an AI model that requires a training set of data.

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

• Not Applicable. As above, no training set or its ground truth is relevant for this device.

Summary of the Study (as described in the 510(k) summary):

The "study" described in the document is primarily an equivalence demonstration. It focuses on showing that the Drystar AXYS is substantially equivalent to a previously cleared predicate device, the Drystar 5500.

The key points of the equivalence demonstration and "testing" mentioned are:

• Comparison to Predicate: Detailed comparison of hardware and software characteristics to the Drystar 5500 (though the detailed table 3.1 is not provided in this excerpt).
• Intended Use Equivalence: Stating that the Drystar AXYS has the same indications for use as the Drystar 5500.
• Technological Equivalence: Asserting that the technological characteristics are "identical" to the predicate device.
• Quality Control Procedures: Stating that the device includes an automatic QC procedure to comply with MQSA. This is a crucial "test" for the mammography application.
• Consensus Standards Compliance: Testing against and meeting "a number of consensus standards for safety and electromagnetic compatibility."

In essence, the "study" for this device is a regulatory submission proving that a new version of a printer performs its basic function (printing diagnostic images) and meets relevant safety and quality standards, effectively demonstrating it is as safe and effective as a device already on the market. It does not involve clinical performance studies in the way one would assess a diagnostic AI or software.

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