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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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