Search Results

AMICAS PACS 6.0 is designed and marketed for soft copy reading, communication and storage of studies produced by digital modalities, to include Digital Mammography. AMICAS PACS 6.0 receives images acquired from DICOM-compliant medical imaging systems, data from FDA-cleared Computer-Aided Detection systems and other FDA-cleared Image processing systems.

AMICAS PACS 6.0 imports images and render said images, upon request, within the viewer component utilizing both lossless (reversible) and lossy (irreversible) compression. To support the diagnostic interpretation of Mammography studies, AMICAS PACS 6.0 will display the full fidelity DICOM image in a non-compressed format. Images will be rendered with patient and clinical information clearly displayed as part of the DICOM Overlay as required by MQSA, on monitors cleared by FDA for use in Digital Mammography. Lossy compressed mammography images and digitized film screen images must not be used for the purpose of primary diagnosis. Mammographic images may only be interpreted using an FDA approved monitor that offers at least 5Mpixel resolution and meets other technical specifications reviewed and accepted by FDA.

Within AMICAS PACS 6.0, the AMICAS Real Time Worklist offers real-time status of radiology activity and provides customizable workflow management capabilities. Communication of critical results is facilitated and documented through optional, and configurable, components within the Real Time Worklist.

AMICAS PACS 6.0 also offers the Patient Dashboard which provides a single view of all patient data, both imaging and non-imaging.

AMICAS Reach is an optional component within AMICAS PACS 6.0 which provides clinicians secure, proactive communication and access to clinical reports and images.

Order and Report information generated by HIS/RIS and report creation systems are received and displayed in PACS via the transmission of HL7 messaging. For this data, AMICAS PACS 6.0 is not the creator, but instead the downstream recipient which relies on the validity of data from said systems.

AMICAS PACS 6.0 must be installed on suitable, commercial-standard hardware. It is the user's responsibility to ensure monitor quality, ambient light conditions and image compression ratios are consistent with the clinical application.

AMICAS PACS 6.0 is software intended to create and display two-dimensional and three-dimensional images of anatomy from a series of digitally acquired images. Typical users of AMICAS PACS 6.0 are radiologists, technologists and clinicians.

AMICAS PACS 6.0 is designed and marketed for soft copy reading, communication and storage of studies produced by digital modalities, to include Digital Mammography. AMICAS PACS 6.0 receives images acquired from DICOM-compliant medical imaging systems, data from FDA-cleared Computer-Aided Detection systems and other FDA-cleared Image processing systems.

AMICAS PACS 6.0 imports images and render said images, upon request, within the viewer component utilizing both lossless (reversible) and lossy (irreversible) compression.

To support the diagnostic interpretation of Mammography studies. AMICAS PACS 6.0 will display the full fidelity DICOM image in a non-compressed format. Images will be rendered with patient and clinical information clearly displayed as part of the DICOM Overlay as required by MQSA, on monitors cleared by FDA for use in Digital Mammography. Lossy compressed mammography images and digitized film screen images must not be used for the purpose of primary diagnosis. Mammographic images may only be interpreted using an FDA approved monitor that offers at least 5Mpixel resolution and meets other technical specifications reviewed and accepted by FDA,

Within AMICAS PACS 6.0, the AMICAS Real Time Worklist offers real-time status of radiology activity and provides customizable workflow management capabilities. Communication of critical results is facilitated and documented through optional, and configurable, components within the Real Time Worklist.

AMICAS PACS 6.0 also offers the Patient Dashboard which provides a single view of all patient data, both imaging and non-imaging.

AMICAS Reach is an optional component within AMICAS PACS 6.0 which provides clinicians secure, proactive communication and access to clinical reports and images.

Order and Report information generated by HIS/RIS and report creation systems are received and displayed in PACS via the transmission of HL7 messaging. For this data, AMICAS PACS 6.0 is not the creator, but instead the downstream recipient which relies on the validity of data from said systems.

AMICAS PACS 6.0 must be installed on suitable, commercial-standard hardware. It is the user's responsibility to ensure monitor quality, ambient light conditions and image compression ratios are consistent with the clinical application.

This document is a 510(k) summary for the AMICAS PACS 6.0, a Picture Archiving Communication System. It does not contain specific acceptance criteria or an explicit study demonstrating the device meets those criteria, as typically found in a clinical trial report.

The information provided describes the device, its intended use, general safety considerations, and the testing methodologies employed during its development (functional testing, unit testing, design validation in real-life client environments by licensed physicians). However, it does not present a measurable set of acceptance criteria with corresponding study results.

Here's an breakdown of why the requested information cannot be fully extracted from the provided text, and what can be inferred:

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

• Acceptance Criteria: Not explicitly stated as a measurable set of performance targets. The safety and effectiveness are assessed against the predicate device, AMICAS Vision Series PACS 5.5, and compliance with various regulatory requirements (DICOM, MQSA, FDA-cleared monitors).
• Reported Device Performance: No specific quantitative performance metrics are provided. The document focuses on the process of testing and validation rather than the results against predefined criteria.

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 for Test Set: Not specified. "Design Validation activities... consist of testing in real-life client environments by Licensed Physicians" implies a test set, but its size is not mentioned.
• Data Provenance: Not explicitly stated. The testing was done in "real-life client environments," suggesting clinical data, but no details on source (e.g., country of origin, retrospective/prospective nature).

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)

• Number of Experts: Not specified. "Licensed Physicians" are mentioned as validating the design, but their number and specific qualifications (e.g., years of experience, specialty) are not detailed.
• Qualifications of Experts: Only "Licensed Physicians" is stated.

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

• No specific adjudication method is mentioned. The design validation involves "Licensed Physicians" but the process by which their feedback or interpretations were combined or adjudicated is not 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 MRMC comparative effectiveness study is mentioned. This document describes a PACS system, not an AI/CAD system for which such studies are common. It receives data from FDA-cleared CAD systems, but does not perform CAD itself or claim to improve human reader performance with its own AI.

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

• Not applicable. AMICAS PACS 6.0 is a PACS system designed for human interpretation, not a standalone algorithm performing automated diagnosis. Its function is to display images and data, facilitating human interpretation.

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

• Not explicitly stated. Given it's a PACS system for display and interpretation, the "ground truth" during its "design validation" would likely involve assessing the accuracy of image display, data integrity, and workflow functionality, as determined by the "Licensed Physicians" against expectations of suitable clinical use. It doesn't involve establishing a diagnostic "ground truth" (like pathology) for the PACS itself.

8. The sample size for the training set

• Not applicable. This is a PACS software, not an AI model that requires a training set in the conventional sense. The "training" here refers to software development and internal testing cycles ("Unit Testing," "Functional testing").

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

• Not applicable for the same reason as point 8. The "ground truth" for software development and testing would be its compliance with "Software Requirements Specifications" and "VDEV" (Vision Development Elaboration Validation) processes.

Summary:

The provided 510(k) summary for AMICAS PACS 6.0 primarily focuses on regulatory compliance, device description, safety considerations, and the internal software development and testing processes. It confirms that the device was deemed "substantially equivalent" to a predicate device (AMICAS Vision Series PACS 5.5). However, it does not contain detailed information on clinical study designs, acceptance criteria with quantitative performance metrics, sample sizes, expert qualifications, or ground truth establishment relevant to clinical diagnostic accuracy studies, as those are typically required for diagnostic algorithms rather than image display and archival systems like a PACS.

Ask a specific question about this device

Vision Series PACS 5.5 is designed and marketed for soft copy reading, communication and storage of studies produced by digital modalities, to include Digital Mammography. Vision Series PACS receives images acquired from DICOM-compliant medical imaging systems, data from FDA-cleared Computer-Aided Detection systems and other FDA-cleared Image processing systems.

Vision Series PACS imports images and render said images, upon request, within the AMICAS LightBeam Diagnostic Workstation utilizing both lossless (reversible) and lossy (irreversible) compression.

To support the diagnostic interpretation of Mammography studies, Vision Series PACS will display the full fidelity DICOM image in a non-compressed format. Images will be rendered with patient and clinical information clearly displayed as part of the DICOM Overlay as required by MQSA, on monitors cleared by FDA for use in Digital Mammography. Lossy compressed mammography images and digitized film screen images should not be used for the purpose of primary diagnosis. Mammographic images may only be interpreted using an FDA approved monitor that offers at least 5Mpixel resolution and meets other technical specifications reviewed and accepted by FDA.

Within Vision Series PACS 5.5, the AMICAS Real Time Worklist offers real-time status of radiology activity and provides customizable workflow management capabilities. Communication of critical results is facilitated and documented through optional, and configurable, components within the Real Time Worklist.

Vision Reach is an optional component within the PACS 5.5 offering which provides clinicians secure, proactive communication and access to clinical reports and images.

Order and Report information generated by HIS/RIS and report creation systems are received and displayed in PACS via the transmission of HL7 messaging. For this data. AMICAS is not the creator, but instead the downstream recipient which relies on the validity of data from said systems.

Vision Series PACS must be installed on suitable, commercial-standard hardware. It is the user's responsibility to ensure monitor quality, ambient light conditions and image compression ratios are consistent with the clinical application.

AMICAS Vision Series PACS 5.5 is software intended to create and display twodimensional and three-dimensional images of anatomy from a series of digitally acquired images.

The provided text is a 510(k) summary for the AMICAS Vision Series PACS 5.5. It focuses on demonstrating substantial equivalence to a predicate device (AMICAS Vision Series PACS 4.3) rather than detailing specific acceptance criteria and a study to prove meeting those criteria. The submission primarily highlights feature comparisons and general safety considerations.

Therefore, much of the requested information cannot be extracted from this document, as the submission does not include a detailed performance study with specific acceptance criteria as would be found in a clinical trial or performance evaluation for a novel AI device.

Here's a breakdown of what can and cannot be answered based on the provided text:

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

The document does not explicitly define acceptance criteria as typically seen for novel device performance (e.g., sensitivity, specificity thresholds). Instead, it presents a comparison of features between the new device and its predicate to demonstrate substantial equivalence.

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 in the document. The filing mentions "Functional testing is an integral part of Amicas, Inc. Product Development process" and "Amicas Vision Series 5.5 is tested with reference to its Software Requirements Specifications," but gives no details about specific test sets, sample sizes, or data provenance.

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 in the document. The document refers to "trained and qualified professionals" (radiologists, technologists, and clinicians) as typical users, but not as experts establishing ground truth for testing.

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

This information is not provided in the document.

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

A Multi-Reader Multi-Case (MRMC) comparative effectiveness study was not conducted or reported for this submission. The device is a Picture Archiving Communication System (PACS), not an AI-assisted diagnostic tool for which such a study would typically be performed to measure reader improvement. The document mentions "data from FDA-cleared Computer-Aided Detection systems and other FDA-cleared Image processing systems" can be received, but the PACS itself is not presented as 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 applicable/not provided. The device is a PACS, which is an imaging display and management system that always involves a human-in-the-loop for interpretation and diagnosis. It's not a standalone algorithm performing a diagnostic task.

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

This information is not provided in the document. Given the nature of a PACS system, testing would likely focus on functionality, image integrity, and display accuracy, rather than clinical diagnostic ground truth as in a disease detection algorithm.

8. The sample size for the training set

This information is not applicable/not provided. The device is a software system (PACS) for image management and display, not an AI or machine learning algorithm that requires a training set.

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

This information is not applicable/not provided. As explained above, the device is not an AI/ML algorithm requiring a training set.

Ask a specific question about this device

Vision Series PACS 4.3 is designed and marketed for soft copy reading, communication, printing and storage of studies produced by digital modalities, to include Digital Mammography.

Vision Series PACS receives images acquired from DICOM-compliant medical imaging systems, data from FDA-cleared Computer-Aided Detection systems and other FDA-cleared Image processing systems.

Vision Series PACS imports images and render said images, upon request, within the AMICAS LightBeam Diagnostic Workstation utilizing both lossless (reversible) and lossy (irreversible) compression.

To support the diagnostic interpretation of Mammography studies, Vision Series PACS will display the full fidelity DICOM image in a non-compressed format. Images will be rendered with patient and clinical information clearly displayed as part of the DICOM Overlay as required by MQSA, on monitors cleared by FDA for use in Digital Mammography. Lossy compressed mammography images and digitized film screen images should not be used for the purpose of primary diagnosis. Mammographic images may only be interpreted using an FDA approved monitor that offers at least 5Mpixel resolution and meets other technical specifications reviewed and accepted by FDA.

Vision Series PACS formats and delivers images over a network to a FDA-cleared DICOM printer as requested and defined by the user.

Vision Series PACS must be installed on suitable, commercial-standard hardware. It is the user's responsibility to ensure monitor quality, ambient light conditions and image compression ratios are consistent with the clinical application.

AMICAS Vision Series PACS 4.3 is software intended to create and display twodimensional and three-dimensional images of anatomy from a series of digitally acquired images.

Here's an analysis of the acceptance criteria and study information for K062477:

Noteworthy Observation: This 510(k) summary primarily focuses on establishing substantial equivalence to a predicate device (Amicas Light Beam Diagnostic Workstation) based on technological characteristics and functional equivalence, rather than a typical performance study with quantitative acceptance criteria against a clinical ground truth. The "Testing" section describes a software verification process inherent to the product development, not a clinical validation study against a pre-defined performance metric.

1. Table of Acceptance Criteria and 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: Not specified. The document refers to "Functional testing" and "Software Requirements Specifications" but does not detail the size or nature of the test dataset (e.g., number of images, cases).
• Data Provenance: Not specified. Given the nature of the submission (software functional equivalence), it's highly likely that testing involved synthetic data, internal test cases, or a limited set of de-identified clinical images, but no details are provided regarding origin or prospective/retrospective collection.

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

• Number of Experts: Not specified.
• Qualifications of Experts: Not specified. It's implied that "trained and qualified professionals" (radiologists, technologists, clinicians) would eventually use the system, but their involvement in establishing test set ground truth is not detailed.

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

• Adjudication Method: Not specified. Given the focus on functional software testing rather than clinical performance evaluation, a formal clinical adjudication method is unlikely to have been applied in the context of this submission.

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

• MRMC Study: No, an MRMC comparative effectiveness study was not done.
• Effect Size: Not applicable, as no such study was conducted or reported. This device is a PACS system designed for image display and management, not an AI-assisted diagnostic tool.

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

• Standalone Performance Study: Not applicable in the traditional sense of an AI algorithm. This device is a Picture Archiving Communication System (PACS), which is a display and management tool. Its "performance" is primarily defined by its functionality, compatibility, and compliance with imaging standards, rather than autonomous diagnostic accuracy. The testing described is against software requirements, not clinical ground truth for diagnostic accuracy.

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

• Type of Ground Truth: Not explicitly stated as clinical ground truth. The "Testing" section refers to verification against "Software Requirements Specifications." This implies that the ground truth for testing was the expected behavior and output as defined by the software's functional requirements documents, rather than a clinical gold standard (e.g., pathology, expert consensus on disease presence).

8. The sample size for the training set

• Sample Size for Training Set: Not applicable. This is a PACS software, not an AI/ML model that undergoes a training phase.

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

• Ground Truth for Training Set Establishment: Not applicable, as this is not an AI/ML device requiring a training set.

Ask a specific question about this device

The AMICAS Diagnostic Workstation (ADW) is software intended for viewing and diagnostic interpretation of images acquired from CT, MR, CR, DR, US and other DICOM-compliant medical imaging systems when installed on suitable commercial-standard hardware. The ADW receives imaging studies over a network from AMICAS servers or directly from CD with images utilizing both lossless (reversible) and lossy (irreversible) compression. It is the user's responsibility to ensure that monitor quality, ambient light conditions and image compression ratios are consistent with the clinical application.

AMICAS Light Beam Workstation (ALBW) is software intended to create and display two-dimensional and three-dimensional images of anatomy from a series of digitally acquired images.

The provided text does not contain detailed information about acceptance criteria or a specific study proving the device meets them in the way typically required for AI/Ml medical devices. This document is a 510(k) summary for a Picture Archiving Communication System (PACS) software, Amicas Light Beam Workstation (ALBW), which handles and displays medical images, not an AI/ML diagnostic algorithm.

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

1. Table of Acceptance Criteria and Reported Device Performance

The document does not detail specific quantitative acceptance criteria or performance metrics (like sensitivity, specificity, AUC) that an AI/ML diagnostic device would typically report. Instead, it focuses on demonstrating substantial equivalence to a predicate device based on functional characteristics.

The "acceptance criteria" here are implied by the features of the predicate device (Voxar Plug'n View 3D, version 1.0). The ALBW device is deemed substantially equivalent because it performs the same core functions. The JPEG2000 compression features represent an enhancement over the predicate, not a criteria it had to meet.

2. Sample Size for Test Set and Data Provenance

The document states: "ALBW is tested with reference to its Software Requirements Specifications, as documented in the Verification Procedure included in this 510(k) filing." However, it does not provide any specific sample size for a test set or information regarding data provenance (e.g., country of origin, retrospective/prospective). This is a general statement about software testing, not a clinical validation study with a defined test set.

3. Number of Experts and Qualifications for Ground Truth

The document does not mention any experts used to establish a ground truth for a test set. This type of analysis is typically performed for diagnostic devices where human expert consensus is needed to determine the correct diagnosis or finding that the device is being evaluated against. Since ALBW is a PACS workstation, its primary function is display and manipulation, not diagnostic interpretation itself, though it is used for interpretation by qualified professionals.

4. Adjudication Method

As no experts were mentioned for establishing ground truth, there is no adjudication method described.

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

No MRMC study is mentioned. This device is a PACS workstation, not an AI assistant intended to improve human reader performance for a specific task. Its primary function is to facilitate viewing and manipulation of images.

6. Standalone Performance Study

No standalone performance study is mentioned in the context of an AI/ML algorithm. The "testing" section refers to software requirements specification verification.

7. Type of Ground Truth Used

Given the nature of the device as a PACS workstation, no specific "ground truth" (like pathology, expert consensus, or outcomes data) is described for performance evaluation. The device is assessed based on its ability to correctly display and process images as per its specifications, not on its diagnostic accuracy against a clinical ground truth.

8. Sample Size for Training Set

The document does not mention any training set size. This indicates that the device is not an AI/ML model that would require a distinct training phase with a dataset.

9. How Ground Truth for Training Set was Established

Since there is no mention of a training set, there is no information on how its ground truth would have been established.

In summary: The provided 510(k) summary is for a PACS workstation (Amicas Light Beam Workstation) from 2002. This predates the widespread regulatory focus on AI/ML in medical devices and the specific types of performance studies and reporting (like those involving sensitivity, specificity, ROC curves, ground truth establishment, training/test sets, and MRMC studies) that are now standard for such devices. The "study" here consists of software verification against functional specifications and demonstrating substantial equivalence to a predicate device based on shared features.

Ask a specific question about this device