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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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Agfa's Orthopedic Software is indicated for use with Impax® Workstations in the acquisition, display, digital processing, review, transfer, storage, archiving and printing of medical images and patient demographic information. It is intended for use by physicians to aid in diagnosis, and by medical professionals whenever they require or desire access to medical images and patient demographic information.

The software application allows orthopedic surgeons and specialists to assess images, plan surgical procedures, monitor patient progress and educate patients in a digital environment.

It allows assessments to be made of geometrical skeletal parameters with comparisons against normative references for adults and children in order to draw therapeutic conclusions. It includes modules for the hip, knee, spine, leg, hand, wrist, elbow, shoulder, foot, ankle and fractures (trauma planning). Users can access a library of manufacturers electronic templates intended to assist in the selection and positioning of implants and the marking of tissues prior to surgery.

The new device is nearly identical to the Agfa predicate (OT-3000). It includes tools for performing common hip, knee and spine measurements, and provides access to orthopedic device manufacturers electronic templates. The measurements and tools enable planning of orthopedic procedures, monitoring patient progress and patient communications.

The orthopedic application is compatible with multiple workstations and Impax® software versions. User may purchase a complete set of tools or individual modules depending on their needs.

The basic principles of operation of the new and predicate devices are the same.

This 510(k) summary (K071972) indicates that the "Agfa Orthopedic Software for Impax Workstations" is nearly identical to a previously cleared device (OT-3000, K050751) with the addition of a spine-planning module, which is present in another predicate device (Cedara Software Corporation's I-SoftView Orthopedic Tools Set, K022881). The submission does not contain detailed acceptance criteria or a dedicated study report that explicitly proves the device meets specific performance metrics in a quantitative way. Instead, the focus is on demonstrating substantial equivalence based on technological characteristics and intended use.

However, based on the provided text, we can infer the acceptance criteria and the "study" that supports it:

1. Table of Acceptance Criteria and Reported Device Performance:

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

The document mentions "medical images" were used for testing the new spinal measurements, but does not specify the sample size (number of images or cases) or the data provenance (e.g., country of origin, retrospective or prospective nature) for this testing.

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

The document mentions "medical images" and "laboratory testing" for confirming the correct operation of spinal measurements, but does not specify the number of experts involved or their qualifications (e.g., "radiologist with 10 years of experience") for establishing ground truth for any test set.

4. Adjudication Method for the Test Set:

The document provides no information about an adjudication method (e.g., 2+1, 3+1, none) for any test set. The confirmation of correct operation appears to be based on internal "laboratory testing."

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

No, the document does not indicate that an MRMC comparative effectiveness study was done. The focus is on demonstrating substantial equivalence to predicate devices, not on quantifying improvement in human reader performance with or without AI assistance.

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

The testing mentioned for the new spinal measurements ("Correct operation...has been confirmed with medical images in laboratory testing") likely refers to a standalone assessment of the algorithm's functionality. However, the document does not explicitly state it as a "standalone" study in the context of comparing it to human performance. The device itself is a software application intended to be used by physicians, so its performance is inherently human-in-the-loop.

7. The Type of Ground Truth Used:

The document refers to the "correct operation" of spinal measurements being confirmed. This implies that some form of expert knowledge or established anatomical/mathematical correctness was used as the ground truth against which the software's measurements were compared. However, the specific method of establishing this ground truth (e.g., expert consensus, pathology, other validated measurements) is not detailed. It's likely that clinicians or technical experts verified the accuracy of the software's calculated measurements against expected values or manual measurements on the "medical images."

8. The Sample Size for the Training Set:

The submission does not mention a training set or its sample size. This is common for software that is largely a re-implementation or slight modification of existing functional tools, rather than a novel algorithm requiring extensive machine learning training data. The device's operation is based on identifying physiological landmarks and calculating measurements, which implies programmed rules rather than a learned model from a large training dataset.

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

Since a training set is not mentioned, the method for establishing its ground truth is not applicable.

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The system is indicated for the storage, reading, interpretation, clinical review, analysis, annotation, distribution, printing, editing and processing of digital images and data acquired from any DICOM device by healthcare professionals, including radiologists, physicians, technologists and clinicians.

• With the Teleradiology option it can be used to consult and share information with remote users.
• With the Mammography option it can be used for screening and diagnosis (with MG, "For presentation" images only) from FDA approved modalities in softcopy and printed formats.
• With the Orthopedic option it can be used to perform common orthopedic measurements of the hip, knee, spine (Coxometry, Gonometry, Lipman Cobb).
• With the Slice Imaging option it can be used to process basic 3D rendering: MIP, MPR or series synchronization.
• With the Quality Assurance option it can be used by PACS administrators or technologists to perform quality control activities related to patient images and data.

The new device is a multifunction PACS workstation for displaying and interpretation of medical images. It includes functions similar to the predicates. It can be used as a general radiography or orthopedic workstation. It displays "for presentation" (MG) mammography images. Image processing is handled by the mammography modalities. It can also display 3D images acquired from CT and MR modalities. It provides user teleradiology functionalities to share information and consult with remote users and can be used to perform quality control activities related to patient images and data.

This 510(k) submission for the Impax SE SigmaCom Client (PACS Medical Imaging Display Workstation) does not contain detailed acceptance criteria or a specific study demonstrating its performance against those criteria in the way a clinical validation study for an AI-powered diagnostic device would.

Instead, this submission is centered on demonstrating substantial equivalence to existing legally marketed predicate devices. The "testing" section states: "Agfa's Impax SE SigmaCom Client (PACS Medical Imaging Display Workstation) has been tested for conformance to specifications." This typically refers to internal engineering and software validation testing, rather than a clinical multi-reader, multi-case study or a standalone algorithm performance study.

Therefore, many of the requested sections cannot be filled as the information is not present in the provided document.

Here's an attempt to answer based only on the provided text, indicating where information is missing:

1. Table of Acceptance Criteria and Reported Device Performance

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

• Test Set Sample Size: Not specified. The submission refers to "testing for conformance to specifications," which implies internal validation, but no specific test set or clinical data set size is mentioned.
• Data Provenance: Not specified.

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

• Not applicable/Not specified. This type of submission relies on demonstrating functional equivalence to existing devices rather than performance against a clinical ground truth established by experts.

4. Adjudication method for the test set

• Not applicable/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. This is not an AI-powered device, and no MRMC study is mentioned. The device is a PACS workstation for displaying and interpreting medical images.

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

• Not applicable/Not specified. This is a workstation that aids human interpretation, not a standalone diagnostic algorithm.

7. The type of ground truth used

• Not applicable/Not specified. The "ground truth" for this device's validation appears to be its ability to perform functions as intended and to be comparable to predicate devices, rather than a clinical ground truth for diagnostic accuracy.

8. The sample size for the training set

• Not applicable/Not specified. This is a PACS workstation, not an AI/ML algorithm that requires a training set in the conventional sense.

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

• Not applicable/Not specified.

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Agfa's Computed Radiography Systems with NX 2.X software are indicated for use in providing diagnostic quality images to aid the physician with diagnosis.

The systems can be used with either Musica, Musica2 or Musica2 Platinum image processing to create radiographic images of the skeleton "in luding 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 musculoskelelal regions of adult or pediatric patients.

Agfa's Computed Radiography Systems with NX 2.X software are not indicated for use in mammography.

The predicate and new devices are nearly identical computed radiography imaging systems. NX 2.X systems (new devices) have:

• . An improved image processing package, Musica2 Platinum (optional). provides improved image processing of the thorax, abdomen or musculoskeletal images for adult or pediatric patients.
• . An auto-stitch function that allows users to automatically combine subimages of large patient anatomies (a leg or spine, for example) into a single image.
• The ability to designate one workstation as the central workstation that . has the ability to view exams on all connected workstations.

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 K071162, "Premarket Notification: Computed Radiography Systems With NX 2.X Workstations" from Agfa HealthCare Corporation, does not contain a detailed study with acceptance criteria and reported device performance as typically expected for demonstrating substantial equivalence for significant changes or novel devices.

Instead, this submission primarily focuses on demonstrating substantial equivalence to a predicate device (Agfa's Computed Radiography Systems with NX1.0 workstations, K053634) based on the similarity of technological characteristics and intended use, with minor improvements in image processing (Musica2 Platinum) and workflow features (auto-stitch, central workstation viewing).

Here's a breakdown of the requested information based on the provided text, highlighting what is not present:

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 mentioned.
• Data Provenance (e.g., country of origin, retrospective/prospective): Not mentioned.

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

• Not mentioned. The submission relies on "diagnostic quality images to aid the physician with diagnosis," implying a continued reliance on human interpretation, but no specific study design involving expert readers for a test set is described.

4. Adjudication Method for the Test Set

• Not mentioned.

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

• No. An MRMC study demonstrating the effectiveness of the improved image processing (Musica2 Platinum) compared to the base system or predicate is not described. The submission focuses on the similarity of algorithms and adjusted parameters rather than a comparative effectiveness study.
• Effect Size of Human Reader Improvement: Not applicable, as no MRMC study was conducted or reported.

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

• No detailed standalone performance study is described. The "improved image processing package, Musica2 Platinum" is a component of a CR system that aids human diagnosis, not a standalone diagnostic algorithm. The claim is about image quality improvement, not automated diagnosis.

7. The Type of Ground Truth Used

• Not explicitly defined or mentioned as part of a formal study to establish ground truth for image processing performance. The "diagnostic quality images" implicitly suggest that the images are intended to be interpreted by medical professionals for clinical diagnosis, but how image quality improvements were objectively measured against a ground truth is not detailed.

8. The Sample Size for the Training Set

• Not mentioned. The document states that Musica2 Platinum "operates with algorithm parameters that are adjusted slightly to match images normally encountered in certain clinical applications." This implies some form of optimization or "training," but the data used for this is not quantified.

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

• Not mentioned.

Summary of the Submission's Approach:

The 510(k) K071162 relies on a "predicate device" substantial equivalence pathway, meaning it asserts that the new device is as safe and effective as a legally marketed device (Agfa's CR Systems with NX1.0, K053634). The key arguments for substantial equivalence are:

• Same Indications for Use: The new system has essentially the same intended use.
• Similar Technological Characteristics: Both systems use the same fundamental X-ray to digital image conversion process.
• Minor Enhancements: The NX 2.X system offers an "improved image processing package" (Musica2 Platinum), an "auto-stitch function," and a "central workstation" viewing capability. The image processing improvement is described as "similar algorithms" with "slightly adjusted parameters" for specific clinical applications (thorax, abdomen, musculoskeletal).
• Safety and Performance Testing: General statements about in-house and imaging performance tests, and compliance with EN 60601-1-1 and EN 60601-1-2 standards (electrical and safety).

In conclusion, this 510(k) submission does not present a detailed clinical study with quantitative acceptance criteria and performance metrics for the reasons requested. It relies on demonstrating that the changes are minor enough that the new device remains substantially equivalent to the predicate, with general safety and performance testing supporting its functionality.

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