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Mirada XD is intended to be used by trained medical professionals including, but not limited to, radiologists, nuclear medicine physicians, and physicists,

Mirada XD is a software application intended to display and visualize 2D & 3D multi-modal medical image data. The user may process, render, review, store, print and distribute DICOM 3.0 compliant datasets within the system and/or across computer networks. Supported modalities include CT, PET, MR, SPECT and planar NM. Supported image types include static, gated and dynamic.

The user may also create, display, print, store and distribute reports resulting from interpretation of the datasets.

Mirada XD allows the user to register combinations of anatomical images and display them with fused and non-fused displays to facilitate the comparison of image data by the user. The registration operation can assist the user in assessing changes in image data, either within or between exammations and aims to help the user obtain a better understanding of the combined information that would otherwise have to be visually compared disjointedly.

Mirada XD provides a number of tools such as rulers and region of interests intended to be used for the assessment of regions of an image to support a clinical workflow. Examples of such workflows include, but are not limited to, the evaluation of the presence or absence of lesions, determination of treatment response and follow-up.

Mirada XD allows the user to define, import, transform, store and export regions of interest structures in DICOM RT format for use in radiation therapy planning systems.

XD is a stand-alone desktop software application with tools and features designed to display or view medical images as well as tools for performing quantitative readings of the imaging data.

The use environment for XD is in a clinical environment, typically within dedicated radiology reading rooms or areas.

The software components provide functions for performing operations related to image display, manipulation, analysis, and quantification, including features designed to facilitate segmentation of user-defined regions of interest.

The software system runs on a dedicated workstation and is intended for display and processing, of a Computed Tomography (CT), Magnetic Resonance (MR), Positron Emission Tomography (PET), Single-Photon Emission Computed Tomography (SPECT) or Nuclear Medicine (NM) images, including contrast enhanced and dynamic or multisequence images.

XD is not intended for specific populations; the system can be used to display data of any patient demographic chosen by trained medical professionals including, but not limited to, radiologists, nuclear medicine physicians, and physicists for use in clinical workflows.

The Mirada XD is a medical imaging software application. The provided text describes the device's indications for use, comparison to a predicate device, and performance testing, but does not explicitly state specific acceptance criteria or provide a detailed study report with all the requested information.

Here's an analysis based on the available text, with indications where specific information is missing:

1. Table of Acceptance Criteria and Reported Device Performance

The document does not provide a formal table of acceptance criteria with corresponding performance metrics. It generally states that the device "meets the user needs and requirements" and passed various tests.

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

This information is not provided in the document. The text mentions "performance testing (Bench)" but offers no details on the number of cases, images, or patient data used, nor their origin (e.g., country, retrospective/prospective).

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

This information is not provided in the document. The text refers to "user actions" and "responsibility of the user" for clinical accuracy of segmentations, implying human interpretation, but does not detail the process of establishing ground truth for testing.

4. Adjudication Method for the Test Set

This information is not provided in the document.

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

A multi-reader multi-case (MRMC) comparative effectiveness study was not explicitly mentioned as being performed. The document focuses on performance testing of the device itself and its equivalence to a predicate, rather than an AI-assisted human vs. non-AI-assisted human comparative study. Therefore, there is no information on the effect size of how much human readers improve with AI vs. without AI assistance.

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

The document mentions "performance testing (Bench)" for features like "Thick Slab visualization" and "PET hotspot finder." It also states, "The software's functions are dependent on the user actions as well as on the available information in the provided medical image data." and "The use of the segmentation tools to achieve a satisfactory delineation of any regions of interest is a user operation and the clinical accuracy of segmentation is the responsibility of the user and not an XD function." This suggests that while underlying algorithms are tested, the overall clinical performance is not treated as standalone algorithm performance but rather as a tool for a human user. It's not explicitly stated that a standalone algorithm-only performance study was conducted in a clinical context.

7. Type of Ground Truth Used

The document does not explicitly state the type of ground truth used for testing. Given the nature of the device (medical image management and processing system with tools for segmentation, registration, and quantification), it's likely that ground truth would involve:

• Expert Consensus/Manual Delineation: For segmentation accuracy, experts would typically manually delineate regions of interest.
• Known Physical Measurements/Phantoms: For distance and volumetric measurements.
• Reference Image Registrations: For image registration accuracy.

However, the text emphasizes the user's responsibility for clinical accuracy, making it unclear how ground truth was precisely established for the "performance and accuracy" evaluation mentioned.

8. Sample Size for the Training Set

This information is not provided in the document. The document describes a "Medical image management and processing system" that includes segmentation, registration, and visualization tools. It does not explicitly mention "training sets" in the context of machine learning, suggesting that the primary verification and validation focus was on software functionality and accuracy rather than a machine learning model's performance based on a training set.

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

Since a training set for machine learning is not mentioned, the method for establishing its ground truth is also not provided.

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AutoContour is intended to assist radiation treatment planners in contouring structures within medical images in preparation for radiation therapy treatment planning.

AutoContour consists of 3 main components:

1. An "agent" service designed to run on the Windows Operating System that is configured by the user to monitor a network storage location for new CT datasets that are to be automatically uploaded to:
2. A cloud-based AutoContour automatic contouring service that produces initial contours and
3. A web application accessed via web browser which allows the user to perform registration with other image sets as well as review, edit, and export the structure set containing the contours.

The provided text describes the acceptance criteria and study proving the device meets those criteria. Here's a breakdown of the requested information:

1. Table of Acceptance Criteria & Reported Device Performance

The document states that formal acceptance criteria and reported device performance are detailed in "Radformation's AutoContour Complete Test Protocol and Report." However, this specific report is not included in the provided text. The summary only generally states that "Nonclinical tests were performed... which demonstrates that AutoContour performs as intended per its indications for use" and "Verification and validation tests were performed to ensure that the software works as intended and pass/fail criteria were used to verify requirements."

Therefore, a table of acceptance criteria and reported device performance cannot be constructed from the provided text.

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

The document mentions that "tests were performed on independent datasets from those included in training and validation sets in order to validate the generalizability of the machine learning model." However, the sample size for the test set is not explicitly stated.

Regarding data provenance:

• The document implies the data used was medical image data (specifically CT, and for registration purposes, MR and PET).
• The country of origin is not specified.
• The terms "training and validation sets" and "independent datasets" suggest these were retrospective datasets used for model development and evaluation. There is no mention of prospective data collection.

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

The document does not provide any information about the number of experts used to establish ground truth for the test set or their qualifications.

4. Adjudication Method for the Test Set

The document does not specify any adjudication method (e.g., 2+1, 3+1, none) used for the test 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?

The document explicitly states: "As with the Predicate Devices, no clinical trials were performed for AutoContour." This indicates that an MRMC comparative effectiveness study involving human readers and AI assistance was not conducted. Therefore, no effect size for human reader improvement is reported.

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

The document mentions "tests were performed on independent datasets from those included in training and validation sets in order to validate the generalizability of the machine learning model." This strongly suggests that standalone performance of the algorithm was evaluated. Although specific metrics for this standalone performance are not detailed in the provided text, the validation of a machine learning model against independent datasets implies a standalone evaluation.

7. The Type of Ground Truth Used

The document mentions that AutoContour is intended to "assist radiation treatment planners in contouring structures within medical images." Given this, the ground truth for the contours would typically be expert consensus or expert-annotated contours. However, the document itself does not explicitly state the type of ground truth used (e.g., expert consensus, pathology, outcomes data).

8. The Sample Size for the Training Set

The document mentions "training and validation sets" but does not provide the sample size for the training set.

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

The document mentions "training and validation sets" but does not detail how the ground truth for the training set was established. Similar to the test set, it would likely involve expert contouring, but this is not explicitly stated.

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Aline Ablation Intelligence is a Computed Tomography (CT) and Magnetic Resonance (MR) image processing software package available for use with ablation procedures.
Aline Ablation Intelligence is controlled by the user via a user interface on a workstation.
Aline Ablation Intelligence imports images from CT and MR scanners and facility PACS systems for display and processing during ablation procedures.
Aline Ablation Intelligence is used to assist physicians in planning ablation procedures, including identifying ablation targets and virtual ablation needle placement. Aline Ablation Intelligence is used to assist physicians in confirming ablation zones.
The software is not intended for diagnosis. The software is not intended to predict ablation volumes or predict ablation success.

Aline Ablation Intelligence 1.0.0, is a stand-alone desktop software application with tools and features designed to assist users in planning ablation procedures as well as tools for evaluating ablation procedure's outcome.
The use environment for Aline Ablation Intelligence is the Operating Room and the hospital healthcare environment such as interventional radiology control room.
Aline Ablation Intelligence has five distinct workflow steps:

• Data assignment
• Tumor segmentation
• Needle planning
• Ablation zone segmentation
• Treatment confirmation
  Of these workflow steps two (Tumor Segmentation and Needle Planning) make use of the planning image volume. These workflow steps contain features and tools designed to support the planning of ablation procedures. The other two (Ablation Zone Segmentation, and Treatment Confirmation) make use of the confirmation image volume. These workflow steps contain features and tools designed to support the evaluation of the ablation procedure's outcome in the confirmation image volume.
  Key features of the Aline Ablation Intelligence Software include:
• Workflow steps availability
• Manual and Automated tools for target tissue and ablation zone segmentation
• Overlaying and positioning virtual ablation needles and user-selected estimates of the ablation regions onto the medical images
• Multimodal image fusion and registration
• Compute achieved margins and missed volumes to help the user assess the coverage of the target tissue by the ablation zone
• Data saving and secondary capture generation
  The software components provide functions for performing operations related to image display, manipulation, analysis, and quantification, including features designed to facilitate segmentation of the target tissues and ablation zones.
  The software system runs on a dedicated workstation and is intended for display and processing, of a Computed Tomography (CT) and/or Magnetic Resonance (MR) image, including contrast enhanced images.
  The system can be used on patient data for any patient demographic chosen to undergo the ablation treatment.
  Aline Ablation Intelligence uses several algorithms to perform operations to present information to the user in order for them to evaluate the planned and post ablation zones. These include:
• Segmentation post-processing
• Automatic ROI definition for Local Rigid Registration
• Measurement and Quantification

Here's a breakdown of the acceptance criteria and study information for the Mirada Medical Ltd. Aline Ablation Intelligence, based on the provided text:

1. Table of Acceptance Criteria and Reported Device Performance

The provided document does not explicitly list quantitative acceptance criteria in a table format that would typically be seen in a performance study. Instead, it describes various functionalities and their expected performance characteristics. However, we can infer some criteria and reported performance based on the "Performance" section:

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

The document states that "Performance testing (Bench) was performed, including on the following features, to ensure that performance and accuracy was as expected: Segmentation post-processing Testing, Automatic ROI definition for Local Rigid Registration Testing, Measurement and Quantification Testing."

However, it does not specify the sample size used for this test set nor the data provenance (e.g., country of origin, retrospective or prospective nature of the data).

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

The document does not specify the number of experts used to establish ground truth or their qualifications for the test set. It mentions that clinical accuracy of segmentation and registration are user responsibilities, implying that a formal expert-driven ground truth process for specific clinical metrics in a test set is not explicitly detailed at this level.

4. Adjudication Method for the Test Set

The document does not specify any adjudication method (e.g., 2+1, 3+1, none) for the test set.

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

A MRMC comparative effectiveness study was not explicitly mentioned or described in the provided text. The document focuses on the device's standalone performance and its substantial equivalence to a predicate device based on features and technical characteristics rather than a study evaluating human reader improvement with AI assistance.

6. Standalone (i.e., algorithm only without human-in-the-loop performance) Study

Yes, a standalone performance assessment was conducted, primarily focusing on the algorithms' output when used with human interaction. The "Performance" section describes several "Performance testing (Bench)" activities for specific algorithms:

• Segmentation post-processing Testing
• Automatic ROI definition for Local Rigid Registration Testing
• Measurement and Quantification Testing

While the software itself has human-in-the-loop components (user responsibilities for clinical accuracy of segmentation and registration, user determination of satisfactory visualization), the testing mentioned for these specific features (like accuracy of linear and volumetric measurements relative to image resolution) indicates an evaluation of the algorithm's core output under specific conditions.

7. Type of Ground Truth Used

The document implies that the ground truth for features like linear and volumetric measurements is based on the image resolution and voxel characteristics for the algorithms. For segmentation and registration, the "clinical accuracy... is the responsibility of the user," suggesting that the ground truth for those tasks is ultimately based on user assessment and manual modification when applying the tool. There is no mention of pathology, expert consensus, or outcomes data being independently used to establish ground truth for the performance testing.

8. Sample Size for the Training Set

The document does not specify the sample size used for any training set. It focuses on the validation and verification aspects of the device.

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

The document does not specify how the ground truth for any potential training set was established. The focus is on the performance testing of the final device.

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Simplicit90Y is a standalone software device that is used by trained medical professionals as a tool to aid in evaluation and information management of digital medical images.

Simplicit90Y supports the reading and display of a range of DICOM compliant imaging and related formats including but not limited to CT, PT, NM, SPECT, MR, SC, RTSS. Simplicit90Y enables the saving of sessions in a proprietary format as well as the export of formats including CSV and PDF files.

Simplicit90Y is indicated, as an accessory to TheraSphere®, to provide pre-treatment dosimetry planning support including Lung Shunt Fraction estimation (based on planar scintigraphy) and liver single-compartment MIRD schema dosimetry, in accordance with TheraSphere® labelling. Simplicit90Y provides tools to create, transform, and modify contours/Regions of Interest for calculation of Lung Shunt Fraction and Perfused Volume. Simplicit90Y includes features to aid in TheraSphere® dose vial selection, dose vial ordering and creation of customizable reports.

Simplicit90Y is indicated for post-treatment dosimetry and evaluation following Yttrium-90 (Y-90) microsphere treatment. Simplicit90Y provides tools to create, transform, and modify contours/Regions of Interest for the user to define objects in medical image volumes to support TheraSphere® post-Y-90 treatment calculation. The objects include, but are not limited to, tumors and normal tissues, and liver volumes.

Simplicit90Y is indicated for registration, fusion display and review of medical mages allowing medical professionals to incorporate images, such as CT, MRI, PET, CBCT and SPECT in TheraSphere® Yttrium-90 (Y-90) microspheres pretreatment planning and post-Y-90 treatment evaluation.

For post-Yttrium-90 (Y-90) treatment, Simplicit90Y should only be used for the retrospective determination of dose and should not be used to prospectively calculate dose or for the case where there is a need for retreatment using Y-90 microspheres.

Simplicit®Y is a software device which provides features and tools for use in pre-treatment dosimetry planning of TheraSphere® Y-90 microspheres treatment and post-treatment evaluation of Y-90 microspheres treatment and operates on Windows computer systems.

Simplicit®Y pre-treatment dosimetry planning features include Lung Shunt Fraction estimation (based on planar scintigraphy) and liver single-compartment MIRD schema in accordance with TheraSphere® labelling. Simplicit®Y additionally provides tools to support TheraSphere® dose vial selection, dose vial ordering and includes the creation of customizable reports.

After administration of Y-90 microspheres, Simplicit®V provides post-treatment dosimetry evaluation with multi-compartment and voxel-wise MIRD techniques applying the Local Deposition Model with scaling for known injected activity to assist the clinician in performing assessment of treatment efficacy and quality assurance, including assessment of absorbed dose to structures such as liver, lung and tumors.

Simplicit®Y provides 2D and 3D image display and advanced dosimetry analysis tools including isodose contour line plan and dose volume histograms.

Simplicit®9Y provides tools for multi-modal image fusion using rigid and deformable registration capable of manual, semi-automated and fully automated operation. Simplicit90Y includes evaluation tools for assessment of registration quality.

Simplicit®9Y provides semi-automated and automated tools for segmentation of Region/Volume Of Interest on multi-modal images.

Simplicit®Y supports the reading, rendering and display of a range of DICOM compliant imaging and related formats including but not limited to CT, PT, NM, SC, RTSS. Simplicit90Y enables the saving of sessions in a proprietary format as well as the export of RTSS, CSV and PDF files.

Simplicit®Y should not be used to change a treatment plan after treatment has been delivered with Yttrium-90 (Y-90) microsphere implants.

The provided text does not contain detailed acceptance criteria or a specific study proving the device meets those criteria with numerical results. It focuses on regulatory approval based on substantial equivalence to a predicate device.

However, based on the information provided, here's a breakdown of what can be inferred and what is missing:

1. Table of Acceptance Criteria and Reported Device Performance

This information is not explicitly provided in the document. The text states: "The results of performance, functional and algorithmic testing demonstrate that Simplicit®Y meets the user needs and requirements of the device, which are demonstrated to be substantially equivalent to those of the listed predicate device." This is a general statement of compliance, not a table of specific criteria and corresponding performance metrics.

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

This information is not explicitly provided. The document makes general statements about "performance, functional and algorithmic testing" but does not detail the size or nature of the test sets used.

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

This information is not explicitly provided. The document mentions general validation and verification but does not detail the process of establishing ground truth with experts.

4. Adjudication Method for the Test Set

This information is not explicitly provided.

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

This information is not explicitly provided. The document describes Simplicit90Y as a "standalone software device" and "a tool to aid in evaluation and information management of digital medical images," but it does not mention MRMC studies comparing human readers with and without AI assistance.

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

Yes, the device is described as a "standalone software device." The document implies that "performance, functional and algorithmic testing" was conducted for the device itself.

7. The Type of Ground Truth Used

This information is not explicitly provided. While the device aids in dosimetry planning and evaluation, the method for establishing "ground truth" for the testing mentioned generally is not detailed.

8. The Sample Size for the Training Set

This information is not explicitly provided. The document does not discuss the machine learning aspects of the software, and therefore, does not mention training sets.

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

This information is not explicitly provided.

Summary of what is present in the document:

The provided text is a 510(k) summary for the Simplicit90Y device. The core argument for its acceptance is substantial equivalence to an existing predicate device (MIM – Y90 Dosimetry, K172218) and several reference devices (Mirada RT, Mirada RTx, Mirada XD).

The study that proves the device meets (implicit) acceptance criteria is described as:

• Testing: "Simplicit®Y is validated and verified against its user needs and intended use by the successful execution of planned performance, functional and algorithmic testing included in this submission. The results of performance, functional and algorithmic testing demonstrate that Simplicit®Y meets the user needs and requirements of the device, which are demonstrated to be substantially equivalent to those of the listed predicate device."
• Compliance: "Verification and Validation for Simplicit®Y has been carried out in compliance with the requirements of CFR 21 Part 820 and in adherence to the DICOM standard."
• Conclusion: "In conclusion, performance testing demonstrates that Simplicit®Y is substantially equivalent to, and performs at least as safely and effectively as the listed predicate device. Simplicit®0Y meets requirements for safety and effectiveness and does not introduce any new potential safety risks."

Essentially, the "study" is a set of "performance, functional and algorithmic testing" designed to show that Simplicit90Y performs similarly to its predicate device for its intended use, particularly for:

• Pre-treatment dosimetry planning (Lung Shunt Fraction, liver MIRD schema)
• Post-treatment dosimetry and evaluation (multi-compartment and voxel-wise MIRD techniques)
• Image registration, fusion, and review
• Contouring/Segmentation for regions of interest.

The document highlights that the proposed device has a narrower scope of indications compared to the predicate, focusing specifically on Y-90 dosimetry. This narrower scope, coupled with similar technology, is used to argue that it does not raise new safety or effectiveness issues.

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IGTFusion is a stand-alone software product that provides the physician 3 means for comparison of medical imaging data from multiple DICOM conformant imaging modality sources. It allows the display, registration and fusing of medical images as an aid during use by diagnostic radiology, oncology, radiation therapy planning, interventional radiology and other medical Specialties. IGTFusion is not intended for mammography diagnosis.

The purpose of IGTFusion is to assist the user with the visual evaluation, comparison, and merging of information between anatomical and functional images from a single patient. The user needs to take into consideration the product's limitations and accuracy when integration from the registration results for final interpretation. IGTFusion does not replace the usual procedures for visual comparison of datasets by a user. Fusion images are intended to provide additional information to a user's existing workflow for patient evaluation.

The provided text is a 510(k) summary for the device IGTFusion. It explicitly states that "No clinical studies were conducted." Therefore, there is no information available within this document to address the criteria listed in your request regarding acceptance criteria, device performance, sample size, expert ground truth, adjudication methods, MRMC studies, standalone performance, or ground truth for training/test sets.

The product relies on demonstrating substantial equivalence to predicate devices (VelocityAIS and RTx) based on technological similarities (visualization capabilities, registration, DICOM Compliance, GUI, reporting capabilities) and arguing that any differences (simplified interface, focused on essential representations, no volume rendering/annotations) do not raise new issues of safety and effectiveness.

The document states that the image registration engine has been "extensively tested to show that is as accurate existing solutions without the need of extra tools," but does not provide any data, methodology, or results of this testing.

Therefore, I cannot populate the requested table or answer the specific questions about the study design and results, as this information is not present in the provided document.

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