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IQQA-Liver Multimodality is a PC-based, self-contained, non-invasive image analysis software application for reviewing multiphase images derived from CT scanners and MR scanners. Combining image viewing, processing and reporting tools, the software is designed to support the visualization, evaluation and reporting of liver and physician-identified lesions.

The software supports a workflow based on automated image registration for viewing and analyzing multiphase volume datasets. It includes tools for interactive segmentation and labeling of liver segments and vascular structures. The software provides functionalities for manual or interactive segmentation of physician-identified lesions, interactive definition of virtual resection plane, and allows for regional volumetric analysis of such lesions in terms of size, position, margin and enhancement pattern, providing information for physician's evaluation and treatment planning.

The software is designed for use by trained professionals, including physicians and technicians. Image source: DICOM.

The IQQA-Liver Multimodality Software is a self-contained, noninvasive radiographic image analysis application that is designed to run on standard PC hardware. The image data utilized is derived from sources including CT and MR scanners, and of DICOM format. Combining image processing, viewing and reporting tools, the software supports the visualization, evaluation and reporting of liver and physician identified liver lesions. Viewing tools include various standard visualization modes (e.g. original DICOM 2D image viewing, window level adjustment, svnchronized viewing of multi-phase sets, Multi-Planar Reformation (MPR) in any plane (orthogonal, oblique, curved), 3D views in rendering mode (MIP. MinIP, volume rendering), and their relationship to originally acquired images from modality). Analysis and evaluation tools include segmentation of structures utilizing user input of seeding points, user tracing and interactive editing, interactive labeling of segmented areas. quantitative measurement derived from segmentation and labeling results, and the measurement of distance between physician specified structures and landmarks. Reporting tools in the software automatically assemble information (including physician identified lesion focations, measurement information. physician-input lesion characterization. Iesion ROI images across multi-phases, information of liver lobes and vessels, and illustrative snapshots of the GUI taken by user) for physician's confirmation and for further diagnosis note input.

The IQQA-Liver Multimodality software supports a workflow based on automated registration for viewing and analyzing multi-phase volume datasets. The software automatically matches the spatial location of original DICOM images across multi-phases, and provides synchronized viewing of multi-phase dataset to aid visualization. The software further includes tools for interactive segmentation and interactive labeling of liver segments and vascular structures (such as liver lobes. vessels and ducts and major branches), thus facilitation of spatial relationship between suspicious liver lesions and specified anatomical structures/landmarks. The tools also allow for interactive segmentation of physician-identified lesions using user input of seed points and boundary editing, interactive definition of virtual resection plane, interactive ' adjustment of user specified margin size around the lesion, and regional analysis of such lesions with respect to size, shape, position, margin, enhancement pattern etc, thus providing information to support physician's assessment of lesion and treatment plans.

The software is designed for use by trained professionals, including physicians and technicians. Physicians make all final patient management decisions.

1. Acceptance Criteria and Device Performance:

The provided document does not explicitly list acceptance criteria in a table format with corresponding reported device performance values. It states that "Test results support the conclusion that actual device performance satisfies the design intent." However, it does not quantify what that "design intent" or "acceptance criteria" actually were.

What is provided is a general statement about the successful outcome of testing and the fulfillment of the intended use, and the FDA's concurrence that the device is substantially equivalent to predicate devices. More specific performance metrics and their comparison to acceptance thresholds are not detailed in this summary.

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

The document states: "EDDA Technology has conducted software testing at two clinical sites." However, it does not specify the sample size (number of cases or patients) used for this clinical testing.

The data provenance is implicitly retrospective as it mentions "physicians use the v2.0 software application to review multiphase CT and MR images of the liver". The document does not specify the country of origin of the data.

3. Number and Qualifications of Experts for Ground Truth:

The document states: "The purpose of the testing is to have physicians use the v2.0 software application to review multiphase CT and MR images of the liver... and provide feedback along the line of the intended use of the system."

It does not specify the number of physicians or their specific qualifications (e.g., years of experience, subspecialty in radiology) used to establish ground truth or provide feedback. It only refers to them as "physicians."

4. Adjudication Method for the Test Set:

The document does not describe any specific adjudication method used for the test set (e.g., 2+1, 3+1 consensus). It only mentions that physicians used the software, validated major functionalities, and provided feedback. This suggests a less formal "feedback" approach rather than a structured adjudication process for ground truth establishment.

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

A MRMC comparative effectiveness study was not explicitly described in the provided document. The study described was focused on physicians using the software to validate functionalities and provide feedback, not on comparing human reader performance with and without AI assistance to quantify an effect size.

6. Standalone (Algorithm Only) Performance Study:

The document does not describe a standalone performance study where the algorithm's performance was evaluated without human interaction. The clinical testing involved "physicians use the v2.0 software application," implying a human-in-the-loop scenario. The preceding software testing mentioned ("Software testing and validation were done according to written test protocols") likely refers to internal functional and verification testing rather than performance validation against a clinical ground truth in a standalone mode.

7. Type of Ground Truth Used:

The ground truth for the clinical testing appears to be based on physician-identified findings and evaluations. The document states that "physicians use the v2.0 software application to review multiphase CT and MR images... and provide feedback along the line of the intended use of the system." This suggests that the "ground truth" was established by the interpreting physicians and their clinical assessment/feedback, rather than a definitive, independent source like pathology or long-term outcomes data.

8. Sample Size for the Training Set:

The document does not specify the sample size used for the training set. The summary focuses on the validation/testing activities.

9. Ground Truth Establishment for the Training Set:

The document does not describe how the ground truth for the training set was established. This information is typically proprietary and often not included in 510(k) summaries.

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The IQQA-Liver is a PC-based, self-contained, non-invasive image analysis software application for reviewing serial multi-phase CT acquisitions of the liver. Combining image viewing, processing and reporting tools, the software is designed to support physicians in the visualization, evaluation and reporting of liver and physician-identified liver lesions.

The software supports a workflow based on automated image registration for viewing and analyzing multi-phase volume datasets. It also includes tools for interactive segmentation and labeling of liver segments and vascular structures. The software provides functionalities for manual or interactive segmentation of physician-identified lesions, and allows for regional volumetric analysis of such lesions in terms of size, shape, position and enhancement pattern, providing information for physician's assessment of lesion characterization.

The software is designed for use by trained physicians. Image source: DICOM.

The IQQA-Liver Software is a self-contained, non-invasive radiographic image analysis application that is designed to run on standard PC hardware. The image input is DICOM. Combining image processing, viewing and reporting tools, the software supports physicians in the visualization, evaluation and reporting of liver and physician identified liver lesions. Viewing tools include 2D axial image viewing, window level adjustment, a pre-defined optimized liver window level setting, synchronized viewing of multi-phase datascts, MPR and MIP. Analysis and evaluation tools include segmentation of structures utilizing user input of seeding points, interactive labeling of segmented areas, quantitative measurement derived from segmentation and labeling results, and the measurement of distance between physician specified structures to landmarks. Reporting tools in the software automatically assemble information (including physician identified lesion locations, measurement information, physician-input lesion characterization, lesion ROl images across multi-phases, and illustrative snapshots of the GUI taken by physicians) for physician's confirmation and for further diagnosis note input. The IQQA-Liver software supports a workflow based on automated registration for viewing and analyzing multi-phase volume datasets. The software automatically matches the spatial location of axial images across multi-phases, and provides synchronized viewing of multi-phase dataset to aid visualization. The software further includes tools for interactive segmentation and interactive labeling of liver segments and vascular structures (such as liver lobes, vessels and major branches), thus facilitating the visualization of spatial relationship between suspicious liver lesions and specified anatomical structures/landmarks. The tools also allow for interactive segmentation of physician-identified lesions using user input of seed points, and regional analysis of such lesions with respect to size, shape, position and enhancement pattern, thus providing information to help physician's assessment of lesion characterization. The software is designed for use by trained physicians only. Physicians make all final patient management decisions.

The provided text is a 510(k) summary for the EDDA Technology IQQA-Liver Software. While it describes the device's intended use, comparison to predicate devices, and general statements about testing, it does not contain detailed information about specific acceptance criteria and a study proving those criteria are met.

The document states:

• "Testing was performed according to internal company procedures. Software testing and validation were done according to written test protocols established before testing was conducted. Test results were reviewed by designated technical professionals before software proceeded to release. Test results support the conclusion that actual device performance satisfies the design intent."

This is a general statement of compliance, but it does not provide the quantitative acceptance criteria, the details of a study, or the specific performance metrics.

Therefore, I cannot populate the table or answer most of your detailed questions based on the provided text.

Here's what can be inferred or explicitly stated from the document, though it falls short of your request for specific acceptance criteria and study details:

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

• Cannot be provided. The document does not list specific, quantifiable acceptance criteria or reported performance results (e.g., accuracy, precision, sensitivity, specificity, or error rates for segmentation, volume analysis, etc.). It only broadly claims that "actual device performance satisfies the design intent."

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

• Cannot be determined. The document does not specify the sample size of the test set or the origin (country, retrospective/prospective) of the data used for testing.

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

• Cannot be determined. The document does not mention the use of experts for establishing ground truth or their qualifications. Given the device's function involves physician-identified lesions and analysis tools, it's implied that physician input is central, but no formal ground truth establishment process is described for testing.

4. Adjudication method for the test set

• Cannot be determined. The document does not describe any adjudication methods.

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

• Cannot be determined. The document does not mention a multi-reader, multi-case (MRMC) study or any comparative effectiveness study measuring human reader improvement with or without AI assistance. The device is described as "supporting physicians" but not necessarily replacing or directly augmenting their diagnostic accuracy in a quantifiable comparison presented here.

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

• Cannot be determined. The document explicitly states the device is "designed for use by trained physicians" and that "Physicians make all final patient management decisions." This framework suggests a human-in-the-loop design, but it doesn't preclude standalone testing of individual algorithms within the software, though such testing is not detailed.

7. The type of ground truth used

• Cannot be determined with certainty. The software relies on "physician-identified liver lesions" and "interactive segmentation of physician-identified lesions using user input of seed points." This suggests that "expert consensus" or "physician input" forms the basis of the data the software processes, but it does not describe how a ground truth was established for testing or validation purposes. It's likely that a clinical reference (e.g., pathology, clinical follow-up) would have been used for any robust validation, but this is not stated.

8. The sample size for the training set

• Cannot be determined. The document focuses on the device's intended use and comparison to predicates, not on its development or training process.

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

• Cannot be determined. Similar to point 8, this information is not present in the provided 510(k) summary.

In summary, the provided 510(k) document is a regulatory summary focused on substantial equivalence to predicate devices and does not detail the technical performance studies and acceptance criteria typically found in clinical trial reports or more comprehensive technical files.

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The IQQA-Chest is a PC-Based, self-contained, non-invasive image analysis package used during the review of digital chest radiographic images. Combining image viewing, evaluation and reporting tools, the software is designed to support the physician in the identification of lung lesions (e.g. nodules), as well as the confirmation, evaluation and documentation of such physician-identified lesions. The IQQA-Chest software package supports a workflow based on automated segmentation for the visual identification of possible lesions. The tools also allow for regional analysis of possible lesions in terms of size, shape and position, thus aiding the physician in the characterization of physician-identified suspicious lesions. Image source: DICOM

The IQQA-Chest Software Package is a self-contained, non-invasive thoracic radiographic image analysis package that is designed to run on standard PC hardware. Combining image viewing tools (e.g. image window level, pan, zoom, enhancement viewing), evaluation tools (e.g. automatic/interactive segmentation, quantitative measurements derived from marking and segmentation), and reporting tools (e.g. saved lesion, measurement information, physician-input nodule characterization, and etc), the software package is designed to support the physician in the identification of lung lesions (e.g. nodules), as well as the the physician in the laonificance or entation of such physician-identified lesions. The IQQA-Chest software package supports a workflow based on automated segmentation IQQA-Chost software package bapple lesions (nodule enhanced viewing). Based on physician's request, the tool segments locations in the lung area containing circular prysional o requeed, invites fulfiling intensity signal and circular shape constraints) that would typically correlate with lung nodules. The tools also allow for regional that would typessible lesions with respect to size, shape and position, aiding the anaryold or pobo characterization of physician-identified suspicious lesions.

The provided text describes the IQQA-Chest Software Package's 510(k) summary. However, it does not contain specific acceptance criteria, detailed study designs, or performance metrics beyond a general statement of equivalency.

Therefore, I cannot populate the requested tables and information adequately. The document states:

• "Testing was performed according to internal company procedures."
• "Software testing and validation were done according to written test protocols established before testing was conducted."
• "Test results were reviewed by designated technical professionals before software proceeded to release."
• "Test results support the conclusion that actual device performance satisfies the design intent."

This indicates that internal testing was conducted, but the specifics such as criteria, methods, and results are not provided in this 510(k) summary.

Here's what I can provide based on the given text, highlighting the missing information:

Acceptance Criteria and Device Performance

Study Information

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

• Test set sample size: Not specified.
• Data provenance: Not specified (e.g., country of origin, retrospective or prospective).

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.

4. Adjudication method for the test set:

• Adjudication method: Not specified (e.g., 2+1, 3+1, none).

5. If a multi-reader multi-case (MRMC) comparative effectiveness study was done:

• Was an MRMC study done? Not specified.
• Effect size of improvement with AI vs. without AI assistance: Not applicable, as no MRMC study or effect size is reported. The device is described as "designed to support the physician in the identification of lung lesions," implying assistive capabilities, but no comparative effectiveness data is presented.

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

• Was a standalone study done? Not explicitly stated, though the device's function involves "automated segmentation for the visual identification of possible lesions," which is an algorithm-only function. However, performance metrics for this standalone function are not provided.

7. The type of ground truth used:

• Type of ground truth: Not specified (e.g., expert consensus, pathology, outcomes data).

8. The sample size for the training set:

• Training set sample size: Not specified.

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

• Ground truth establishment method: Not specified.

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