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ImmersiveTouch is intended for use as a software interface and image segmentation system for the transfer of medical imaging information to an output file. ImmersiveTouch is also intended for measuring and treatment planning. ImmersiveTouch output can be used for the fabrication of physical replicas of the output file using traditive manufacturing methods. The physical replicas generated from digital output files are not for diagnostic purpose.

ImmersiveTouch should be used in conjunction with expert clinical judgment.

ImmersiveTouch is a stand-alone modular software package that allows user to import, visualize and segment medical images to create accurate 3D representations. The 3D models can be utilized in ImmersiveTouch for measuring, treatment planning and output file to be further used as an input for additive manufacturing.

This modular package includes, but is not limited to the following functions:

• Importing medical images in DICOM format for visualization, segmentation, and analysis.
• Viewing of medical imaging data in the axial. coronal and sagittal views.
• Calculating a digital 3D model and editing the model.
• Measurements on 3D models.
• Treatment Planning on 3D models with cutting planes and the ability to move cut objects.
• File export for 3D Printing.

Here's a breakdown of the acceptance criteria and study information for ImmersiveTouch, based on the provided text:

1. Table of Acceptance Criteria and Reported Device Performance

The document does not explicitly present a table of acceptance criteria with numerical targets. Instead, it describes compliance with "set acceptance criteria" and "pre-established specifications."

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

The document does not specify the exact sample size for the test sets in any of the studies (Measurements, Segmentation, or Output). It also does not explicitly state the data provenance (e.g., country of origin, retrospective or prospective) for the test data.

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

• Segmentation Study: Ground truth was "validated by subject matter experts." The number and qualifications of these experts are not provided.

4. Adjudication Method for the Test Set

The document does not specify an adjudication method (e.g., 2+1, 3+1, none) for any of the studies. For the segmentation study, it only mentions validation by subject matter experts without detailing the process.

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

There is no mention of an MRMC comparative effectiveness study involving human readers and AI assistance. The studies described are focused on device performance in terms of measurements, segmentation, and output consistency, primarily comparing the ImmersiveTouch device to its predicate.

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

The provided text focuses on the standalone performance of the ImmersiveTouch software system (e.g., segmentation, measurements, output generation). The clinical use of ImmersiveTouch is stated to be "in conjunction with expert clinical judgment," implying it's an aid rather than a fully autonomous diagnostic tool for clinical decision-making. The studies describe the inherent performance of the software without directly evaluating the human-AI interaction.

7. The Type of Ground Truth Used

• Segmentation Study: The ground truth for the segmentation study was established by expert visual validation against the predicate device's models.
• Measurements Study: The "ground truth" for the measurements study appears to be internal consistency, comparing inter and intra-user measurements to "set acceptance criteria," implying a defined range of acceptable variability.
• Output Study: The "ground truth" for the output study again seems to be internal consistency, comparing exported models from both software systems to "set acceptance criteria."

8. The Sample Size for the Training Set

The document does not provide any information regarding the sample size used for training for any algorithms within the ImmersiveTouch software. Given that the software focuses on "image segmentation system," it's plausible it uses machine learning models, but training data details are absent.

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

Since no information on training sets is provided, there is no information on how the ground truth for any potential training set was established.

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The ImmersiveTouch simulators are intended as pre-operative software for simulating and evaluating surgical treatment options.

The ImmersiveTouch is software based pre-surgical planning system. It is intended for use as a software interface. It is also intended as pre-operative software for simulation and evaluation of surgical treatment options.

The ImmersiveTouch software has the capability of displaying 3D models of patient data from 2D scan slices. Additionally, it provides the user with ability to input, display, color, and manipulate the 2D scan slices via a 3D representation.

The 3D models of patient data are combined with the use of haptics. This provides the user with ability to modify the haptic properties, creating the sensation of feeling specific tissues.

Here's a breakdown of the acceptance criteria and study information based on the provided document:

1. Table of Acceptance Criteria and Reported Device Performance:

The document does not explicitly state acceptance criteria in a quantitative table format. Instead, it relies on successful testing, verification, and validation against specifications, as well as adherence to established medical device standards. The performance is reported as meeting these standards.

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

The document does not provide details on the sample size used for any specific test set, nor does it mention the data provenance (e.g., country of origin, retrospective or prospective) for any clinical or performance data. It discusses general testing and validation, but not the specifics of a test set with patient data.

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

The document does not mention the use of experts to establish ground truth for a test set. The focus is on technical compliance with standards rather than clinical performance evaluated against expert-derived ground truth.

4. Adjudication Method for the Test Set:

Given that no experts or ground truth establishment are described, there is no mention of an adjudication method.

5. If a Multi-Reader Multi-Case (MRMC) Comparative Effectiveness Study Was Done:

No, a Multi-Reader Multi-Case (MRMC) comparative effectiveness study is not mentioned in the provided document. The document describes the device as a pre-operative software for simulating and evaluating surgical treatment options, and its substantial equivalence is based on technological characteristics compared to predicate devices, not on a human-in-the-loop performance study.

6. If a Standalone (i.e., algorithm only without human-in-the-loop performance) Was Done:

The document describes the "ImmersiveTouch" as "software based pre-surgical planning system" and clarifies that it is "intended for use as a software interface." It focuses on its technical capabilities (displaying 3D models, manipulating 2D scan slices, haptic properties). While it implies standalone function for generating simulations, it does not explicitly detail a standalone performance study with metrics suitable for an algorithm-only evaluation (e.g., accuracy against a defined ground truth for specific tasks). The performance data cited refers to technical and electrical safety standards.

7. The Type of Ground Truth Used:

The document does not specify the type of ground truth used for performance evaluation in the context of clinical accuracy or decision-making. The "performance data" mentioned refers to compliance with safety and electrical standards (IEC 60950-1, IEC 60601-1, IEC 60601-1-2) which are general engineering and safety truths, not patient-specific clinical ground truths (like pathology, expert consensus, or outcomes data).

8. The Sample Size for the Training Set:

The document does not provide any information about a training set since it focuses on the device's technical specifications and safety standards rather than a machine learning or AI algorithm trained on data.

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

As no training set is mentioned or implied, there is no information on how ground truth for a training set was established.

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