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iNtuition-Structural Heart Module is a software solution that is intended to assist Cardiologists, and Clinical Specialists with the visualization and measurements of the heart and vessels.

iNtuition-Structural Heart Module enables the user to:

• · Visualize and measure (diameters, lengths, angles, areas and volumes) structures of the heart and vessels for pre-operative planning and sizing for cardiovascular interventions and for post-operative evaluation.
• · Quantify calcium (volume, density)

iNtuition-Structural Heart Module has the following tools and features that facilitate:

• · Automatic and manual centerline detection.
• · Segmentation of cardiovascular structures.
• · Measurement tools (diameters, lengths, areas, volumes, angles) for the dimensions of vessels and structures.
• · Calcium quantification and scoring.
• · Various visualization techniques: 2D/3D/4D visualization, MPR, Stretched MPR, MIP, MinlP, Raysum and MAR.
• · Capture and Report.

iNtuition-Structural Heart Module is an optional image post-processing software module using iNtuition (K121916) standard features and part of its optional features. It is a software device generally used with the off-the-shelf hardware, offered in various configurations, with the simplest configuration being a stand-alone workstation capable of image review, communications, archiving, database maintenance, remote review, reporting and basic 3D capabilities. It can also be configured as a server with some, or with all, or none of its optional features.

Whether provided as a workstation or a server, the iNtuition-Structural Heart Module software is designed to provide access by a local user physically sitting at the computer hosting the iNtuition server software, and/or by one or more remote users who concurrently connect to the server using a freely-downloadable thin client application or through a zero-footprint web viewer (with conference capabilities) over local network or internet.

iNtuition-Structural Heart Module is iNtuition (K121916) based optional feature and employs all standard features offered by iNtuition such as convenient image manipulation tools like drawing of region of interests, manual and automatic segmentation of structures, image assessment and measurement tools - linear, diameter, angle, area and volume and tools that support the creation of reports, transmitting and storing this report in digital form and tracking historical information about the studies analyzed by the software. iNtuition Vessel analysis and calcium scoring features are utilized to support automatic and manual centerline extraction and analysis and calcium quantification.

iNtuition-Structural Heart Module:

• . Supports the visualization and quantification of coronary vessels and cardiac structures for anatomic and pre- or post-operative evaluations through guided clinical workflows.
• . Enables the assessment and measurement of different structures of the heart, e.g. aorta, aortic valves, mitral valve, pulmonic valve, atria and atrial appendages, and ventricles.
• . Provides analysis of the feasibility of a transapical, transfemoral or subclavian approach to structures for replacement or repair procedures via 3D measurements.
• Uses the same iNtuition (K121916) Vessel Analysis and Calcium modules.Enables .
  assessment and measurement of vessels and can help identify calcifications, aneurysms and other anomalies to quickly and reliably prepare for various types of vascular procedures.Supports the creation, transmission and storage of a report in digital form. It can also track historical information about the studies analyzed by the software.Displays results analysis, that can be printed as hardcopy or saved in a variety of formats to a hard disk, network, PACS system or CD/DVD/USB.

The provided text is a 510(k) Premarket Notification submission for the TeraRecon iNtuition-Structural Heart Module. It is a regulatory document declaring substantial equivalence to predicate devices, rather than a detailed study report proving the device meets specific acceptance criteria based on performance metrics.

The document explicitly states: "iNtuition-Structural Heart Module did not require clinical studies to demonstrate its safety and effectiveness." This means that the information you've requested regarding performance data, sample sizes, ground truth establishment, expert adjudication, and MRMC studies for demonstrating the device meets acceptance criteria via a pre-defined study is not present in this regulatory filing.

Therefore, I cannot extract structured information about specific acceptance criteria and a study proving the device meets them from this document. The FDA clearance is based on a claim of substantial equivalence to existing predicate devices, meaning it has similar indications for use and technological characteristics, and therefore does not raise new questions of safety or effectiveness.

The "Performance Data" section (Page 10) only mentions:

• "The verification and Validation tests have been performed to address the indication for use, the technological characteristics claims, requirement specifications and risk management results."
• "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 being formalized and after ensuring that the software fully satisfies all expected and previously defined system requirements and features. Test results support the conclusion that iNtuition- Structural Heart Module performance satisfies the design intent and is equivalent to its predicate devices."

This describes internal software verification and validation activities, which are typically for functional correctness and adherence to design specifications, not a comparative clinical performance study against specific, quantified acceptance criteria for diagnostic accuracy or clinical outcomes that would usually involve human readers or external ground truth.

In summary, the document states that a clinical performance study was not required. Thus, I cannot provide the details you requested in the structured format, as the information is not available in the provided text.

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iNtuition-T1 Mapping and T2/T2* Mapping are software modules that support the derivation and quantification of T1, T2 and T2* values from MR DICOM image pixel intensities and header information. The quantification of these parameters can be used to characterize tissues. Support is provided to overlay the T1, T2, and T2* values using colormaps on related MR images.

Support is provided for using different colormaps to overlay different ranges of T1, T2 or T2* values and restrict the overlay to region of interest on the images can be of simple planar scan like a single slice or volumetric or 4D scans of a body part. iNtuition-T1 Mapping and T2/T2* Mapping are iNtuition software features that can be used in multiple workflows or be used as basic tools for cardiac functionality, the overlaid images can be captured and forwarded to other systems using standards such as DICOM or http protocol. Quantitative analysis is derived and available as text and graphical display.

iNtuition- T1 Mapping and T2/T2* Mapping qualitation can be used in a clinical setting on MR images of an individual patient and can be used to support the clinical decision making for the patient. iNtuition- T1 Mapping and T2/T2* Mapping are designed for use by healthcare professionals and are intended to assist the physician in diagnosis, who is responsible for making all final patient management decisions.

iNtuition-T1 Mapping and T2/T2* Mapping is an optional image post-processing module, part of iNtuition (K121916), which is software only device generally used with the off-the-shelf hardware, offered in various configuration, with the simplest configuration being a stand-alone workstation capable of image review, communications, archiving, database maintenance, remote review, reporting and basic 3D capabilities. It can also be configured as a server with some, all, or none of its optional features disabled. Whether provided as a workstation or a server, the iNtuition software is designed to provide access by a local user physically sitting at the computer hosting the iNtuition server software, and/or by one or more remote users who concurrently connect to the server using a freely-downloadable thin client application or through a zero-footprint web viewer (with conference capabilities) over local network or internet.

iNtuition-T1 Mapping and T2/T2* Mapping feature can derive quantitative values from intensities and header information of specific MR scan sequences that are specifically coded to enable such derivation (such as Look-Locker and MOLLI for T1.) The quantification of these parameters can be used to derive clinical value such as T2*.. Post-processing such as computation of statistics like volume, area, min/max or various combinations of the derived values, over regions of interests or overlay the derived values using a colormap on related images or a region of the images. The region of interests can be defined by the user through manual, semi-automatic or automatic segmentation techniques provided by iNtuition. The derivation and post-processing can be used with planar, volumetric or 4D scan sequences for cardiac functionality.

iNtuition-T1 Mapping and T2/T2* Mapping is an iNtuition based optional features, and employ all standard features offered by iNtuition such as convenient image manipulation tools like drawing region of interest, manual or automatic segmentation of structures and tools that support creation of a report, transmitting and storing this report in digital form, and tracking historical information about the studies analyzed by the software.

This device is intended only to assists the operator in making decisions. The software is not intended to replace the skill and judgment of a qualified medical practitioner and should only be used by people that have been trained in the software's function (iNtuition), capabilities and limitations. The device is intended to provide supporting analytical tools to a physician, to speed decision-making and to improve communication, but the physician's judgment is paramount and it is normal practice for physicians to validate theories and treatment decisions multiple ways before proceeding with a risky course of patient management.

iNtuition-T1 Mapping and T2/T2* Mapping modules may be sold separately or as an extension of iNtuition.

This submission for K180916 does not provide specific acceptance criteria or a study demonstrating that the device meets those criteria, as it is a Traditional 510(k) stating the product is substantially equivalent to a predicate device and did not require clinical studies.

Therefore, many of the requested details cannot be extracted from the provided text.

However, based on the principle of substantial equivalence, the "acceptance criteria" are generally that the device performs as well as the predicate device across its intended use and technological characteristics.

Here's a breakdown of what can be inferred or stated from the document:

1. Table of Acceptance Criteria and Reported Device Performance:

Since no specific performance metrics or acceptance criteria are listed, this table cannot be populated directly. The document repeatedly states that the device is "substantially equivalent" to its predicate and "performs as well as the predicate device" in terms of its intended use and technological characteristics.

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

• Sample Size for Test Set: Not specified.
• Data Provenance: Not specified (e.g., country of origin, retrospective/prospective). The document mentions "Software verification and validation was completed in accordance with internal processes" and "Performance testing was carried out according to internal company procedures." This implies internal testing rather than a large-scale external test set with specific patient data.

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

• Number of Experts: Not specified.
• Qualifications of Experts: Not specified. Software testing was reviewed by "designated technical professionals."

4. Adjudication Method for the Test Set:

• Adjudication Method: Not specified.

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

• MRMC Study: No. The document explicitly states: "The subject of this traditional 510k notification, iNtuition-T1 Mapping and T2/T2* Mapping, did not require clinical studies to show safety and effectiveness of the software." Therefore, no MRMC study was conducted.
• Effect Size of Human Readers Improvement with AI vs. Without AI Assistance: Not applicable, as no MRMC study was performed.

6. Standalone (Algorithm Only Without Human-in-the-Loop Performance) Study:

• Standalone Study: Not explicitly detailed as a separate "study" with performance metrics. The document describes the software's functionality in deriving and quantifying T1, T2, and T2* values, and mentions "Software verification and validation" and "Performance testing" were conducted internally to ensure it met design intent and was equivalent to the predicate. This would constitute standalone testing of the algorithm's output against expected results, but the specifics are not provided.

7. Type of Ground Truth Used:

• Type of Ground Truth: Not explicitly stated. For internal performance testing of quantitative measurements (like T1/T2/T2* values), ground truth would likely be established through:
  • Reference standards/phantoms: Using known values.
  • Comparison to predicate device's output: Ensuring the new device's output matches that of the already cleared predicate.
  • Manual calculations/expert evaluation: For regions of interest.

The document indicates the software supports "derivation and quantification of T1, T2 and T2* values from MR DICOM image pixel intensities and header information," suggesting the ground truth for these values would be based on the principles of MRI physics and potentially established clinical methods for calculating these parameters.

8. Sample Size for the Training Set:

• Sample Size for Training Set: Not specified. The document describes the software as an "optional image post-processing module" that derives quantitative values from specific MR scan sequences. This doesn't inherently suggest a machine learning model that requires a "training set" in the common sense (i.e., for supervised learning). It's more about algorithmic derivation and quantification. If any machine learning components were involved, the training set details are not disclosed.

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

• How Ground Truth for Training Set was Established: Not applicable/not specified, as training set details are not provided and the primary function described is algorithmic derivation rather than machine learning inference.

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iNtuition-TDA, TVA, and Parametric Mapping are software modules which supports assessment of time-dependent behavior of image intensity, dimensions or volume of regions of interest over time, for volumetric or planar dynamic image types such as CT or MR. Parametric mapping tools encode in color various parameters derived from the temporal or spatial characteristics of the planar or volumetric data.

Support is provided for digital image processing to derive metadata or new images from input image sets, for internal use or for forwarding to other devices using the DICOM protocol. Image processing tools are provided to extract metadata to derive parametric images from combinations of multiple input images.

iNtuition-TDA, TVA and Parametric Mapping are iNtution based software features with dedicated workflows and basic tools and thus support post-processing, displaying and manipulation of reports and medical images from acquisition devices and visualization in 2D, 3D and 4D for single or multiple datasets, or combinations thereof.

iNtuition-TDA, TVA, Parametric Mapping are designed for use by healthcare professionals and are intended to assist the physician in diagnosis, who is responsible for making all final patient management decisions.

iNtuition - TDA. IVA. Parametric Mapping are post-processing modules, part of iNtuition, which is a software device generally used with off-the-shelf hardware. offered in various configurations, with the simplest configuration being a stand-alone workstation capable of image review, communications, archiving, database maintenance, remote review, reporting and basic 3D capabilities. It can also be configured as a server with some, all, or none of its optional features disabled. A fully-configured iNtuition system is capable of various image processing and visualization functions to support the physician in medical image reviewing.

iNtuition - TDA, TVA, Parametric Mapping intended used is to provide solutions to various medical image analysis and viewing problems, which come about as modalities generate more and more images. They also support image distribution over networks, and are DICOM compliant.

iNtuition Time-Dependent Analysis (TDA) and Time-Volume Analysis (TVA) features can obtain quantitative information relating to the evolution of the intensity, density or dimensions of certain regions of CT. MR or other images over time. Statistical analysis such as a histogram representation of the image density values in an image is supported, and analysis of changes in volume over time from multi-phase volumetric images; for example, eiection fraction and stroke volume measurement calculation can be performed using the Time-Volume Analysis tools.

iNtuition Parametric Mapping tools encode in color various parameters derived from the temporal or spatial characteristics of the planar or volumetric data.

iNtuition - TDA, TVA and Parametric Mapping are iNtuition-based optional features, and employ all standard features offered by iNtuition, such as convenient tools to support creation of a report, transmitting and storing this report in digital form, and tracking historical information about the studies analyzed with the software.

These three modules can be sold separately or as a part of the bigger iNtuition package.

The provided text does not contain detailed acceptance criteria for quantitative device performance or a study explicitly proving the device meets such criteria. Instead, it focuses on demonstrating substantial equivalence to predicate devices and adherence to internal company procedures and voluntary industry standards.

Here's an analysis based on the information available:

1. Table of Acceptance Criteria and Reported Device Performance:

No specific, measurable acceptance criteria with corresponding performance metrics are reported in the document. The general acceptance is that the device "fully satisfies all expected and previously defined system requirements and features" and is "substantially equivalent to and perform as well as the predicate devices."

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

• Sample size for the test set: Not specified. The document states "Software testing and validation were done according to written test protocols." It doesn't mention a specific test set size (e.g., number of cases or images).
• Data provenance: Not specified. Since clinical studies were not required, it's unlikely that the "test set" involved patient data in a formal clinical trial sense. It likely refers to internal software testing using simulated or previously acquired anonymized data that were part of the predicate device's validation.

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

Not applicable. The document states that clinical studies were not required. The "ground truth" for the software testing would have been based on the expected outputs as defined by the design requirements, rather than expert interpretation of medical images for diagnostic accuracy. "Test results were reviewed by designated technical professionals." Their qualifications are not specified beyond "technical professionals."

4. Adjudication method for the test set:

Not applicable. No mention of an adjudication method, as it wasn't a study involving human interpretation of medical images with a diagnostic endpoint.

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, an MRMC comparative effectiveness study was not done. The document explicitly states: "The subject of this traditional 510k notification, iNtuition-TDA. TVA, Parametric Mapping, did not require clinical studies to show safety and effectiveness of the software." Therefore, there is no information on the effect size of human reader improvement with or without AI assistance.

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

The performance testing described is likely a standalone assessment of the software's functionality and accuracy against its design specifications. The document states, "Test results support the conclusion that actual device performance satisfies the design intent and is equivalent to its predicate devices." However, it doesn't quantify this performance in medical terms (e.g., sensitivity, specificity for a particular pathology), but rather in terms of meeting functional requirements. The device is intended to "assist the physician in diagnosis, who is responsible for making all final patient management decisions," implying it is not a standalone diagnostic device.

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

Given the lack of clinical studies, the "ground truth" for the software's internal testing would have been defined by the expected computational results based on the software's design and algorithms. For example, if the software calculates ejection fraction, the ground truth would be the mathematically correct ejection fraction from a given input, based on a reference method or calculation. It would not be based on expert medical consensus, pathology, or outcomes data in a clinical validation context.

8. The sample size for the training set:

Not applicable. This is a post-processing software module, not a machine learning or AI algorithm that typically requires a large training set for model development. The document does not mention any machine learning components, and thus, no training set or its size is provided. The comparison is based on "similar technological characteristics" to predicate devices.

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

Not applicable, as there is no mention of a training set for machine learning.

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To receive, store, transmit, post-process, display and allow manipulation of reports and medical images from acquisition devices, including optical or other non-DICOM format images. DICOM images with modality type XA, US, CR, DR, SPECT, NM and MG, and images from volumetric medical scanning devices such as EBT, CT, PET or MRI. To provide access to images derived data and derived images via client-server software, web browser and mobile technology.

Visualization in 2D, 3D and 4D are supported for single or multiple datasets, or combinations thereof. Tools are provided to define and edit paths through structures such as centerlines, which may be used to analyze cross-sections of structures, or to provide flythrough visualizations rendered along such a centerline. Segmentation of regions of interest and quantitative analysis tools are provided, for images of vasculature, pathology and morphology, including distance, angle, volume, histogram, ratios thereof, and tracking of quantities over time. A database is provided to track and compare results using published comparison techniques such as RECIST and WHO. Calcium scoring for quantification of atherosclerotic plaque is supported.

Support is provided for digital image processing to derive metadata or new images from input image sets, for internal use or for forwarding to other devices using the DICOM protocol. Image processing tools are provided to extract metadata to derive parametric images from combinations of multiple input images, such as temporal phases, or images co-located in space but acquired with different imaging parameters, such as different MR pulse sequences, or different CT image parameters (e.g. dual energy).

iNtuition is designed for use by healthcare professionals and is intended to assist the physician in diagnosis, who is responsible for making all final patient management decisions.

Interpretation of mammographic images or digitized film screen images is supported only when the software is used without compression and with an FDA-Approved monitor that offers at least 5Mpixel resolution and meets other technical specifications reviewed and accepted by the FDA.

iNtuitionMOBILE provides wireless and portable access to medical images. This device is not intended to replace full workstations and should be used only when there is no access to a workstation. Not intended for diagnostic use when used via a web browser or mobile device.

iNtuition is a software device generally used with off-the-shelf hardware, offered in various configurations, with the simplest configuration being a stand-alone workstation capable of image review, communications, archiving, database maintenance, remote review, reporting and basic 3D capabilities described elsewhere in this document. The system can also be configured as a server with some, all, or none of its optional features disabled. Whether provided as a workstation or a server, the iNtuition software is designed to provide access by a local user physically sitting at the computer hosting the iNtuition server software, and/or by one or more remote users who concurrently connect to the server using a freely-downloadable thin client application (with conference capabilities). iNtuition supports the physician in medical image viewing.

A fully-configured iNtuition system is capable of various image processing and visualization functions, including full-color Volume Rendering, Calcium Scoring, Segmentation Analysis and Tracking (SAT), Vessel Analysis, Flythrough, Multi-phase review, CT/ CTA Subtraction, Lobular Decomposition (LD), iGENTLE, Maxillo-Facial, Volumetric Histogram, Findings Workflow, Fusion CT/ MR/ PET/ SPECT, MultiKV etc. Each of these features may be offered as an independent upgrade option to the basic configuration.

The intended use of the device is to provide solutions to various medical image analysis and viewing problems, which come about as modalities generate more and more images. It also supports image distribution over networks, and is DICOM compliant.

The provided 510(k) summary for the iNtuition device (K121916) explicitly states that no clinical studies were required or performed to prove the safety and effectiveness of the software. This is a critical piece of information. The assessment relies on non-clinical performance tests and a comparison to predicate devices to establish substantial equivalence.

Therefore, many of the requested categories related to clinical studies and ground truth establishment will be "Not Applicable" or "Not Reported" based on the provided document.

Here's the breakdown based on the given text:

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

2. Sample sized 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 reported. The document states "no clinical studies were required to show safety and effectiveness of the software." Performance testing was non-clinical.
• Data Provenance: Not reported, as no clinical data was used for direct safety and effectiveness demonstrations. Non-clinical performance tests would likely use synthetic or internal test data.

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

• Number of Experts: Not applicable/Not reported. Ground truth in a clinical sense was not established for non-clinical performance tests. "Designated technical professionals" reviewed test results for software validation, but their qualifications are not specified beyond that title.

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

• Adjudication Method: Not applicable/Not reported. This relates to clinical studies for establishing ground truth, which were not performed.

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. The summary explicitly states: "The subject of this traditional 510k notification, iNtuition, did not require clinical studies to show safety and effectiveness of the software." Therefore, no MRMC study comparing human readers with or without AI assistance was performed.
• Effect Size: Not applicable.

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

• Standalone Performance: Not explicitly detailed as a separate study. The device is a "software device" and "offers convenient tools to support creation of a report," but its performance metrics are established through non-clinical software validation and comparison to predicate devices, not through a standalone performance study with specific metrics like sensitivity/specificity against a gold standard. The device is "intended to assist the physician in diagnosis, who is responsible for making all final patient management decisions," implying a human-in-the-loop context for diagnostic use.

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

• Type of Ground Truth: Not applicable/Not reported for demonstrating safety and effectiveness. For non-clinical software performance tests, the "ground truth" would be established by the expected output based on the defined system requirements and internal test protocols.

8. The sample size for the training set

• Sample Size for Training Set: Not applicable/Not reported. The device is a general medical imaging system, not an AI/ML device in the modern sense that requires a specific training set to learn from data for a particular diagnostic task. Its functionality is based on established algorithms and image processing techniques.

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

• Ground Truth for Training Set: Not applicable/Not reported, as there is no mention of a training set for an AI/ML model.

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The 0170 Intuition is a stationery x-ray system intended for obtaining radiographic images of various portions of the human body in a clinical environment. The 0170 Intuition is not intended for mammography.

The 0170 Intuition is a stationary x-ray system with a ceiling mounted tube stand, a floor mounted table and a wall stand that has a floor mounted column with a detector holder. The ceiling stand and the table has automatic movements for up and downs, other movements are manual. The standard equipment includes a graphic display showing X-ray tube rotation and film focus or source image distance.

This document is a 510(k) summary for the Arcoma Intuition Model 0170, a stationary x-ray system. It does not contain information about acceptance criteria or a study proving the device meets acceptance criteria.

The document primarily focuses on:

• Device Identification and Classification: Trade name, classification, review panel, product code, regulation number, and device classification.
• Predicate Devices: A list of previously cleared devices to which the Intuition Model 0170 is substantially equivalent.
• Device Description: A brief overview of the physical components and functionalities of the x-ray system.
• Intended Use: The specified clinical application (radiographic imaging of various portions of the human body, excluding mammography).
• FDA Clearance: The letter from the FDA confirming the substantial equivalence determination and allowing the device to be marketed.

Therefore, I cannot provide the requested information, including:

1. A table of acceptance criteria and reported device performance
2. Sample size used for the test set and data provenance
3. Number of experts and their qualifications for ground truth
4. Adjudication method
5. MRMC comparative effectiveness study results
6. Standalone performance results
7. Type of ground truth used (for test set)
8. Sample size for the training set
9. How ground truth for the training set was established

This type of detailed performance testing and clinical study information is typically found in accompanying technical documentation or clinical study reports, which are not part of this 510(k) summary. The 510(k) summary focuses on demonstrating substantial equivalence to predicate devices, which often relies on technical comparisons and non-clinical bench testing, rather than extensive clinical efficacy studies with predefined acceptance criteria for performance metrics like sensitivity and specificity.

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These sets are indicated for the infusion of fluids into the body below the surface of the skin when attached to a fluid reservoir.

Not Found

The provided text is a 510(k) summary for the Unomedical A/S Intuition Infusion Sets. It discusses the device's substantial equivalence to existing products and verifies that it meets specifications. However, the document does not contain the detailed information necessary to answer many of the specific questions asked about acceptance criteria and a study design, especially concerning AI/ML aspects.

Here's what can be extracted and what cannot:

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)

This information is not provided in the document. The document only states "Verification testing confirmed the product meets their specifications" but gives no details about the size or nature of the test set, nor its 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. This type of device (infusion set) does not typically involve expert review for ground truth in the way medical imaging AI does. The "ground truth" for an infusion set would likely be functional performance metrics, material properties, and manufacturing tolerances, all assessed through engineering and quality control tests, not expert consensus on medical images or diagnoses.

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

This information is not provided in the document. Adjudication methods are typically relevant for studies involving human interpretation or subjective assessments, which are not described here for an infusion set.

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, an MRMC comparative effectiveness study was not done. This document describes an infusion set, which is a physical medical device, not an AI/ML algorithm used to assist human readers (e.g., in medical image interpretation). Therefore, the concept of "human readers improve with AI vs without AI assistance" is not applicable to this submission.

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

Not applicable. This device is a physical infusion set, not an algorithm. Therefore, "standalone algorithm performance" is not relevant.

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

The document implies that the ground truth for performance was product specifications. For an infusion set, this would typically involve various engineering, material science, and functional tests (e.g., fluid flow rates, strength of materials, sterility, biocompatibility, force required for insertion, leakage rates). It would not be expert consensus, pathology, or outcomes data in the way these terms are used for diagnostic or AI devices.

8. The sample size for the training set

This information is not provided and is not applicable. This is a physical device, and the concept of a "training set" is relevant for AI/ML algorithms, not for the verification of a medical infusion set.

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

This information is not provided and is not applicable for the same reasons as point 8.

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Intuition Image's indications for use is to prepare and present patient and image data based on CT, MR, Angiographic and other imaging sources including

• image preparation -
• image localization -
• image fusion -
• image segmentation -
• isocenter handling -
• plan review and approval where the result is used for stereotactic radiation treatment planning that is intended for use in stereotactic, conformal, computer planned, LINAC based radiation treatment of cranial, head and neck and extracranial lesions.

Intuition Image is a software tool running on a standard, standalone computer workstation or being accessible via the intranet connection for pre-planning of treatments based on stereotactic systems.
The system provides e.g. tools for the automatic or manual segmentation of anatomical structures, which helps the user such as the radiologist or the neurosurgeon to quickly achieve the desired segmentation results through a variety of automatic and/or manual re-segmentations. Additionally anatomical and functional structures and segmentations of the human brain as defined and described by Talairach/Tournoux and/or Schaltenbrand/Wahren brain atlases can be correlated with the patient's brain data.
The created treatment plans of Intuition Image can be used on its own or in conjunction with other BrainLAB treatment planning systems such as Intuition Dose (to be developed) or BrainSCAN (K994413) for further planning of parameters, which are relevant for Radiotherapy/Radiosurgery. Intuition Image may also serve as a pre-planning station for various third party treatment planning systems.

The provided 510(k) summary for Intuition Image (K032511) does not contain detailed information about specific acceptance criteria or an explicit study proving the device meets these criteria in the way a modern AI/ML device submission would.

The approval is based on "substantial equivalence" to predicate devices (K020631 and K994413), and the provided text highlights general validation and verification according to BrainLAB's procedures. This pre-dates the current rigorous expectations for AI/ML device performance validation.

Therefore, many of the requested details cannot be extracted from the given text. Below is an attempt to answer what can be inferred or explicitly stated, with clear indications where information is missing.

Acceptance Criteria and Device Performance

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

The document does not provide a table of specific acceptance criteria (e.g., minimum accuracy, sensitivity, specificity, or segmentation precision) or quantified performance metrics for Intuition Image. The primary "performance" reported is that the device "has been verified and validated according to BrainLAB's procedures for product design and development" and that this "validation proves the safety and effectiveness of the system."

Study Details (Based on available information)

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.
• Data Provenance: Not specified. The document states BrainLAB AG is in Germany, but this does not confirm the origin of any test data.
• Retrospective or Prospective: Not specified.

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.
• Qualifications of Experts: Not specified. It mentions the "user such as the radiologist or the neurosurgeon" for segmentation, implying these are the intended experts for using the tool, but not necessarily for establishing ground truth in a formal test.

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

• 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 MRMC comparative effectiveness study is described or implied in the provided text. The device is a "pre-planning station" and software tool, not an AI-assisted diagnostic aid in the modern sense that would typically require such a study for its primary claim (though the "automatic segmentation" could be seen as an early form of AI-assistance).

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

• The device description states it provides "tools for the automatic or manual segmentation." While "automatic segmentation" implies a standalone algorithmic component, no specific standalone performance metrics for this automatic function are reported. The focus is on the user leveraging these tools to achieve results.

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

• Not specified. Given the nature of a planning system, it's likely that ground truth for segmentation or localization would involve expert anatomical delineation on imaging, potentially with cross-validation from multiple modalities or surgical confirmation contextually, but this is not detailed.

8. The sample size for the training set:

• This device predates the common terminology and strict requirements for "training sets" as understood in modern AI/ML submissions. Therefore, a training set size is not mentioned, as the validation would have focused on a more traditional software verification and validation approach rather than an AI model training and testing paradigm.

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

• As no training set is mentioned, the method for establishing its ground truth is also not specified.

Summary of Missing Information:

The provided 510(k) summary is typical for its era (2003) and for device types that are primarily software tools aiding clinicians, rather than standalone diagnostic AI algorithms. It emphasizes "substantial equivalence" to predicate devices and general verification/validation of software functionality. The granular details now expected for AI/ML device submissions, such as specific performance metrics, detailed study designs, ground truth establishment methods, and training/test set sizes, are largely absent.

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