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The IQQA-Guide System is a stereotactic accessory for Computed Tomography (CT). It displays simulated image of interventional instruments (such as biopsy needle, ablation needle, probe) on a computer monitor that also shows an imaging-based 3D model of the patient anatomy, and the current and the projected future path of the interventional instruments.

The system supports the imaging-based model derived from physician's confirmed segmentation results of patient's image scans, including intra-operative CT, pre-procedural CT, and CT or MR previously acquired before surgical procedures. Additionally, overlaying ultrasound images (when available) may be displayed with the model of patient anatomy.

The system supports a workflow based on automated image registrations of spatial mapping from one image space to another image space, or from image space to physical space. Physician may interactively adjust and confirm registration results, and evaluate 3D visualization and quantitative information in terms of distance, size, and spatial location associated with patient anatomy and instruments.

The system is intended for intra-operative guidance for surgical procedures. It is intended for use by trained physicians in clinical intervention and for structures where imaging is currently used for visualizing such procedures.

The IQQA-Guide System is a stereotactic accessory for Computed Tomography (CT). It utilizes electromagnetic tracking technology to locate and navigate instruments relative to an imaging-based model of the patient anatomy. IOOA-Guide displays the simulated image of interventional instruments (such as biopsy needle, ablation needle, probe) on a computer monitor that also shows the imaging-based 3D model of the patient anatomy, and the current and the projected future path of the interventional instruments.

IQQA-Guide supports the imaging-based 3D model derived from physician's confirmed segmentation results of patient's image scans, including intra-operative CT, pre-procedural CT, and CT or MR previously acquired before surgical procedures. The model of segmentation results may also be loaded from saved reports of the IQOA-BodyImaging software (K141745). Additionally, overlaying ultrasound images (when available) may be displayed with the model of patient anatomy. The system supports a workflow based on automated image registrations of spatial mapping from one image space to another image space, or from image space to physical space. Physician may interactively adjust and confirm registration results, and evaluate 3D visualization and quantitative information in terms of distance, size, and spatial location associated with patient anatomy and instruments.

The IQQA-Guide system consists of an EM tracking system, software, and a computer system. The system is intended for intra-operative guidance for surgical procedures. It is intended for use by trained physicians in clinical intervention and for structures where imaging is currently used for visualizing such procedures.

The acceptance criteria and study proving the device meets them are described below:

1. Table of Acceptance Criteria and Reported Device Performance

2. Sample Size and Data Provenance for Test Set

The provided text only mentions "experiments involving intervention on phantoms" and "experiments involving patient studies." It does not specify the exact sample size (number of phantoms or patients) used for the test set.

• Data Provenance: The testing was conducted at "two hospital sites". This suggests retrospective or prospective clinical data, though details are not provided. The country of origin is not specified.

3. Number of Experts and Qualifications for Ground Truth

The document mentions that "physicians use the IQQA-Guide during clinical interventional procedures" and "provide feedback along the line of the intended use of the system." It also states "Test results were reviewed by designated technical professionals." However, it does not specify the number of experts or their detailed qualifications (e.g., years of experience, subspecialty) used to establish the ground truth for the test set.

4. Adjudication Method for Test Set

The document does not describe any specific adjudication method (e.g., 2+1, 3+1). It only states that "physicians use the IQQA-Guide" and "provide feedback," and "Test results were reviewed by designated technical professionals."

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

No multi-reader multi-case (MRMC) comparative effectiveness study comparing human readers with AI assistance versus without AI assistance is mentioned in the provided text. The device is a "stereotactic accessory" for guidance, not an AI for image interpretation or diagnosis.

6. Standalone (Algorithm Only) Performance

The device, IQQA-Guide, is described as a "stereotactic accessory" that displays simulated images of interventional instruments and anatomical models. It relies on physician input for confirmed segmentation results and interactive adjustments. Therefore, it is not a standalone algorithm in the sense of making independent diagnostic or interventional decisions without human-in-the-loop. Its performance is evaluated in the context of aiding a physician.

7. Type of Ground Truth Used

The ground truth for the registration accuracy in phantom studies would likely be based on precisely known physical measurements of the phantom and instrument positions. For patient studies, the ground truth for accuracy would be based on "physician's confirmed segmentation results of patient's image scans" and potentially real-time imaging modalities, but the document does not explicitly state the definitive ground truth method for patient accuracy.

8. Sample Size for the Training Set

The document does not mention the sample size used for any training set. The device is described as an upgrade from already cleared software (IQQA-BodyImaging K141745), but information about its original development or training data is not provided in this 510(k) summary.

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

Since no training set is mentioned, information on how its ground truth was established is not available in the provided text.

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IQQA-BodyImaging is a PC-based, self-contained, non-invasive image analysis software application for reviewing body imaging studies (including thoracic, abdominal and pelvic) derived from CT and MR scanners. Combining image viewing, processing and reporting tools, the software is designed to support the visualization, evaluation, and reporting of body imaging studies and physician-identified lesions. The software supports a workflow based on automated image registration for viewing and analyzing multiphase and multiple time-point volume datasets. It includes tools for interactive segmentation and labeling of organ segments and vascular/ductal/airway structures. The software provides functionalities for manual or interactive segmentation of physician-identified lesions, interactive definition of virtual resection plane and virtual needle path, 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, monitoring, and follow-up. The software is designed for use by trained professionals, including physicians and technicians. Image source: DICOM.

The IQQA-BodyImaging 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. The data utilized is derived from CT and MR scanners, and includes thoracic/abdominal/pelvic images. Combining image processing, viewing and reporting tools, the software supports the visualization, evaluation and reporting of body imaging scans and physician identified lesions. Viewing tools include 2D original DICOM image viewing, window level adjustment, pre-defined optimized window level setting, synchronized viewing of multi-phase datasets or volumes from multiple time-points, MPR (orthogonal, oblique and curved), MIP and MinIP, volume rendering. Analysis and evaluation tools include automatic/interactive segmentation of structures utilizing user input of seeding points and bounded boxes, interactive labeling of segmented areas, user tracing and interactive editing, 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 locations, measurement information, physician-input lesion characterization, lesion snapshot images across multi-phases or multiple time-points, information of organ segments and vessels/ducts/airways, and illustrative snapshots of the GUI taken by physicians, for physician's confirmation and further diagnosis and patient management note input. The IQQA-BodyImaging software supports a workflow based on automated registration for viewing and analyzing multiphase or multiple time-point volume datasets. The software automatically matches the spatial location of original DICOM images across contrasted multiphases or multiple time-points, and with physicians' interactive adjustment, to enable synchronized viewing of datasets simultaneously. Physician may also activate the temporal movie display of selected slice locations across multi-phases to aid visualization and evaluation. After identifying and marking lesions on 2D image display, physicians can either manually trace lesion boundary or activate automated tools to segment lesion. The software further includes tools for interactive segmentation and interactive labeling of organ segments and vascular/ductal/airway structures (such as liver lobes, major branches of vessels/ducts/airways), thus facilitating the visualization of spatial relationship between suspicious lesions and specified anatomical structures/landmarks. The software provides functionalities for interactive adjustment of user-defined margin size around the lesion, interactive definition of virtual resection plane, interactive definition of virtual needle path to lesion and local zone, regional analysis of lesions with respect to suze, shape, position, margin, and enhancement pattern etc, synchronized view of lesion and information between planning/baseline study and monitoring/follow-up studies, thus providing information to support physician's to evaluation of physician-identified lesions as well as treatment planning, monitoring and follow-up assessment.

Here's an analysis of the acceptance criteria and study information provided in the document:

1. Table of Acceptance Criteria and Reported Device Performance

The document does not explicitly state formal "acceptance criteria" in a table format with pass/fail thresholds. Instead, it reports performance metrics from various tests. I will present the reported performance as if these were the evaluation metrics against which the device's performance was assessed.

• Thoracic: 0.4%
• Abdominal: 1.5%
• Pelvic: 2.5% |
  | Interactive Registration Error (phantom studies) | Minimizing registration error. | Mean interactive registration error of 0.2394mm with a standard deviation of 0.2261mm on phantom studies scanned at different times with different positioning and orientations. |
  | Initial Automated Registration Error (patient studies with synthetic deformations) | Minimizing registration error. | Mean initial automated registration error of 0.5594mm with a standard deviation of 0.5448mm on patient studies with synthetic deformations. |
  | Interactive Registration Error (retrospective patient studies) | Minimizing registration error. | Mean interactive registration error of 0.5388mm with a standard deviation of 0.7150mm on retrospective patient studies that are scanned at different times during clinical practice. |
  | Major Functionality Validation | N/A (Qualitative feedback) | Software testing conducted at two clinical sites; physicians used the software to review CT and MR body imaging scans, validate major functionalities, and provide feedback along the line of intended use. |
  | Overall Safety and Effectiveness | Device is safe and effective; no new safety risks. | "The IQQA-BodyImaging labeling contains instructions for use and necessary cautions, warnings and notes to provide for safe and effective use of the device. Risk Management is ensured via the company's design control and risk management procedures. Potential hazards are controlled via software development and verification and validation testing." "Test results demonstrate that the device is safe, effective, and does not raise any new potential safety risks." |
  | Substantial Equivalence | Equivalent to predicate devices in technical characteristics, principles of operation, and functional features, without new safety risks. | "IQQA-BodyImaging and predicate devices are substantially equivalent in the areas of technical characteristics, principles of operation, and functional features. The new device does not raise any new potential safety risks and is equivalent in performance to the existing legally marketed devices." |

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

• Sample Size: Not explicitly stated as a single number for all tests.
  • Simulated images: An unspecified number of simulated images described as containing "ellipsoid, crescent, and cylinder shapes."
  • Retrospective clinical patient studies: An unspecified number of "clinical patient studies" of CT and MR for thoracic, abdominal, and pelvic body parts.
  • Phantom image pairs: An unspecified number of "phantom image pairs scanned at different times with different positioning and orientations."
  • Patient studies with synthetic deformations: An unspecified number of "patient studies with synthetic deformations."
  • Retrospective patient studies for registration: An unspecified number of "retrospective patient studies that are scanned at different times during clinical practice."
• Data Provenance:
  • Simulated images: Artificially generated (simulated).
  • Clinical Patient Studies (for intra-observer consistency): Retrospective, specific country of origin not mentioned, but described as "clinical patient studies."
  • Phantom Studies (for registration): Acquired from physical phantoms, specific country/institution not mentioned.
  • Patient Studies (for automated and interactive registration): Retrospective, specific country of origin not mentioned, but described as "patient studies."

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

• Number of Experts:
  • For intra-observer consistency in volume measurement validation, "two physicians" were used.
  • For clinical site validation/feedback, "physicians" at "two clinical sites" were involved, but the specific number involved in establishing ground truth for the performance metrics is not detailed.
• Qualifications of Experts: The document states "physicians" were used. No further details about their specific qualifications (e.g., specialty, years of experience, subspecialty) are provided.

4. Adjudication Method for the Test Set

• Intra-observer consistency: Implies a comparison between the measurements of two physicians, not necessarily an adjudication to establish a "ground truth" but rather to assess agreement between human operators using the device.
• For other tests where "ground truth" is mentioned (e.g., volumetric measurement accuracy on simulated images, registration error relating to phantom studies), the method of establishing this ground truth is not specified as an adjudication process. The ground truth for simulated images would be known by design. For phantom studies, it's typically based on physical measurements or precise setup parameters.
• No specific adjudication method like "2+1" or "3+1" is described.

5. If a Multi-Reader Multi-Case (MRMC) Comparative Effectiveness Study was done, If so, what was the effect size of how much human readers improve with AI vs without AI assistance

• No MRMC comparative effectiveness study is described in the provided text. The document focuses on the performance of the software's sub-components (segmentation, measurement, registration) and intra-observer consistency when physicians use the tools.
• There is no mention of comparing human readers with AI assistance versus without AI assistance to measure an effect size of improvement. The device is positioned as a tool to support physician evaluation, not explicitly to replace or augment their accuracy in a comparative study.

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

• Yes, partially.
  • The "Volumetric Measurement Accuracy" test on simulated images would likely represent a standalone performance evaluation against a known ground truth.
  • The "Initial Automated Registration Error" on patient studies with synthetic deformations also appears to be a standalone measurement of the algorithm's performance before any interactive adjustment.
• However, other tests, like "Intra-observer Consistency" and "Interactive Registration Error," specifically involve human interaction with the device.

7. The Type of Ground Truth Used

• Simulated Images: "Ground truth" was established by the design of the simulated objects (ellipsoid, crescent, cylinder shapes).
• Phantom Studies (for registration): "Ground truth" would likely be derived from the known physical properties or precise measurements of the phantom and its setup.
• For clinical patient studies (intra-observer consistency and general registration performance), the "ground truth" for the observed differences is not explicitly described as pathology or outcomes data. Instead, the evaluations focus on consistency between human measurements or the accuracy of the software against a reference (which for patient studies might be a manual measurement considered as the reference standard, though not specified).

8. The Sample Size for the Training Set

• Not provided. The document describes validation and testing, but it does not mention the sample size used for training the algorithms within the IQQA-BodyImaging Software.

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

• Not provided. Since the training set sample size is not mentioned, neither is the method for establishing its ground truth.

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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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