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Ikshana is a software device to display medical images. It includes functions for image review, image manipulation, measurements, and 3D visualization.

Medical images may only be interpreted using an FDA-cleared display monitor that meets technical specifications that are reviewed and accepted by the FDA.

Ikshana is intended to be used as an adjunct to the interpretation of images performed using diagnostic imaging systems and is not intended for primary diagnosis. Display monitors used for reading medical images for diagnostic purposes must be FDA-approved radiology monitors.

Ikshana software is indicated for use by qualified healthcare professionals, including, but not restricted to, radiologists, non-radiology specialists, physicians, and technologists.

When accessing the Ikshana software from a wireless stereoscopic head-mounted display (HMD) or mobile device, the images viewed are for informational purposes only and are not intended for diagnostic use.

Ikshana is a stand-alone modular software platform to be used by clinicians for the visualization of medical images in 3D to allow for surgical planning activities. The device takes 2D medical images and creates accurate 3D representations that clinicians can then view on a stereoscopic display. This modular package is used to

• · Load patient CT/MR DICOM data
• . View DICOM data using a traditional computer monitor or in Augmented Reality (AR) using a head-mounted display, HMD (Microsoft HoloLens 2).

The provided document describes the acceptance criteria and the study conducted for the Ikshana device, particularly focusing on its measurement and segmentation capabilities.

1. Table of Acceptance Criteria and Reported Device Performance:

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

• Measurement Study: A combination of orthopedic and maxillofacial models was used. The specific number of cases for the test set is not explicitly stated, but the study evaluated "multiple users."
• Segmentation Study: 60 trials were conducted using a combination of cardiovascular, orthopedic, and maxillofacial models. The specific number of cases is not explicitly stated beyond "60 trials."
• Data Provenance: Not specified in the provided text (e.g., country of origin or whether it was retrospective/prospective).

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

• Segmentation Study: The results were "validated by subject matter experts." The specific number and qualifications of these experts are not mentioned.
• Measurement Study: The ground truth for the measurement study seems to be derived from a comparison with the reference device, Medical Mimics, which implies its measurements are considered a reference standard, rather than expert consensus on individual cases.

4. Adjudication Method for the Test Set:

• The document does not explicitly describe an adjudication method like 2+1 or 3+1. For the segmentation study, it states results were "validated by subject matter experts," which suggests some form of expert review, but the specific process is not detailed. For the measurement study, the comparison is directly with the reference device's measurements.

5. If a Multi-Reader Multi-Case (MRMC) Comparative Effectiveness Study was done:

• No, a multi-reader multi-case (MRMC) comparative effectiveness study was not explicitly described for assessing the improvement of human readers with AI assistance. The studies focused on the performance of the device itself (measurement and segmentation accuracy/equivalence).

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

• Yes, the performance studies described (Measurement Study and Segmentation Study) appear to be standalone evaluations of the Ikshana software's capabilities. The measurement study compared Ikshana's measurements to another device, and the segmentation study compared Ikshana's segmentation models to a reference device, with expert validation. This assesses the algorithm's performance directly.

7. The Type of Ground Truth Used:

• Measurement Study: The ground truth for the measurement study appears to be established through measurements obtained using the referenced predicate device, Mimics Medical. The study aimed to show "equivalence" with the predicate, implying the predicate's measurements serve as the reference.
• Segmentation Study: The ground truth for the segmentation study was established by comparing Ikshana's segmentation models with those from the previously cleared reference device, Mimics Medical (K183105), and these results were "validated by subject matter experts." This indicates a hybrid approach, using a cleared device as a primary reference and expert review for validation.

8. The Sample Size for the Training Set:

• The document does not provide information about the sample size used for the training set. The focus is on the performance evaluation of the final device.

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

• The document does not provide information on how the ground truth for the training set was established, as details about the training process are not included.

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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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Immersive View Surgical Plan (IVSP®) is intended for use as a software system for the transfer of imaging information from a medical scanner such as a CT based system. The is processed by the ImmersiveView Surgical Plan (IVSP®) system and the result is an output data file that may then be provided as digital models or used as input to a rapid prototyping portion of the system that produces physical outputs including anatomical models, surgical splints, and surgical guides for use in maxillofacial surgery. The Immersive View Surgical Plan (IVSP®) system is also intended as a pre-operative software tool for simulating/evaluating surgical treatment options.

The ImmersiveView Surgical Plan (IVSP®) system is a software based pre-surgical planning system. It is intended for use as a software system for the transfer of imaging information from a medical scanner such as CT based system. Physical outputs include surgical splints, and surgical guides that will be used in maxillofacial surgery. Surgical marking guides and surgical splints are not intended to come in contact with surgical cutting or drilling tools and therefore should not interface with surgical cutting or drilling tools.

ImmersiveTouch receives patient specific medical imaging information which is further utilized by ImmersiveTouch trained employees within the ImmersiveView Surgical Plan (IVSP®) system. This includes software to extract anatomical areas of interest from 3D medical scan images and create patient-specific physical and digital outputs. Throughout the process, a physician reviews and approves the plan prior to delivery of the final outputs.

Physical model outputs include surgical splints, and surgical guides for use in maxillofacial surgery. Surgical splints and surgical guides are designed and manufactured by ImmersiveTouch trained employees. Trained employees utilize the ImmersiveTouch additive manufacturing workflow to design and manufacture surgical guides, and, surgical splints using polymer resin. Surgical splints and surgical guides are provided in a NON-sterile condition and instructions for use provide the steps for cleaning and sterilization prior to use in surgery. Surgical splints and surgical guides are manufactured based on recommendations outlined in the FDA Guidance Document "Technical Considerations for Additive Manufactured Medical Devices."

Prior to use in surgery, the physician confirms the accuracy and level of precision by attaching surgical splints and surgical guides to the anatomical models are designed and manufactured using similar workflow and materials as the subject devices.

Reports are generated for each patient specific case illustrating the plan and accompany the physical outputs that are delivered once verified and approved by the physician.

The provided document is a 510(k) summary for the ImmersiveView Surgical Plan (IVSP®) system. It focuses on demonstrating substantial equivalence to a predicate device rather than detailing a specific clinical study with acceptance criteria and device performance metrics in the way a clinical trial report would.

Therefore, the information you've requested regarding acceptance criteria and a study proving the device meets them, including sample sizes, ground truth establishment, expert qualifications, and MRMC studies, is not present in this 510(k) summary.

Here's what can be extracted and what is explicitly not available based on the provided text:

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

• Not explicitly provided as a quantifiable table with specific performance metrics (e.g., accuracy, sensitivity, specificity) against defined acceptance criteria.
• The document states: "The performance data indicates that the verification and validation testing performed on the ImmersiveView Surgical Plan (IVSP®) system successfully demonstrates that design outputs meet design inputs." This is a high-level statement but doesn't offer specific numerical acceptance criteria or performance results.
• Mechanical Strength Testing: "The result of the testing concluded that subject device was in accordance with the pre-defined acceptance criteria." This implies criteria existed, but they are not detailed.

2. Sample sized used for the test set and the data provenance (e.g. country of origin of the data, retrospective or prospective)

• Not provided. The document refers to "patient specific medical imaging information" but does not give any details on the number of cases or their origin.

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)

• Not provided. The document mentions "a physician reviews and approves the plan prior to delivery of the final outputs" and that "ImmersiveTouch trained employees" utilize the system. It also notes that "the physician confirms the accuracy and level of precision by attaching surgical splints and surgical guides to the anatomical models." However, this does not detail ground truth establishment in a study context, expert numbers, or qualifications.

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

• Not 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

• Not provided. The document focuses on the system as a pre-surgical planning tool and highlights "Human Intervention for Interpretation of images: Yes" for both the subject and predicate devices, indicating a human-in-the-loop approach. However, a comparative effectiveness study measuring improvement with AI assistance is not mentioned.

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

• Not directly addressed in terms of formal performance metrics. The device description indicates human involvement in the planning process: "ImmersiveTouch receives patient specific medical imaging information which is further utilized by ImmersiveTouch trained employees within the ImmersiveView Surgical Plan (IVSP®) system." And "Throughout the process, a physician reviews and approves the plan prior to delivery of the final outputs." This implies it's not a standalone, algorithm-only device in its clinical workflow.

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

• Not explicitly stated in a study context. The closest mention to "ground truth" or verification is a physician confirming the accuracy of physical outputs against anatomical models.

8. The sample size for the training set

• Not applicable/Not provided. This document describes a surgical planning system that utilizes medical images as input to create patient-specific outputs and does not describe an AI or machine learning algorithm that requires a "training set" in the conventional sense. The "image segmentation system" mentioned is part of the overall workflow but no details on its AI/ML training are given.

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

• Not applicable/Not provided. (See point 8)

Summary of available "performance data" categories from the document:

The "Performance Data" section primarily addresses verification and validation activities related to manufacturing and quality control, ensuring the physical outputs meet certain standards, rather than clinical performance metrics of the software itself.

• Device Performance Validation: "processes validation methods such as IQ, OQ, and PQ to ensure that the manufacturing process can effectively produce patient matched devices." Also, "Equipment used for production purposes have been qualified to ensure the equipment used for manufacturing of surgical guides, surgical splints and, anatomical models meet production needs."
• Sterilization Validation: Conducted in accordance with ISO 17665 and FDA guidance to a Sterility Assurance Level (SAL) of 1x10^-6.
• Biocompatibility Validation: Conducted in accordance with ISO 10993-1 and FDA guidance.
• Mechanical Strength Testing: "The result of the testing concluded that subject device was in accordance with the pre-defined acceptance criteria." (Specific criteria and results not detailed).

In conclusion, the document provides evidence for the safety and substantial equivalence of the device through manufacturing process validation, sterilization validation, biocompatibility validation, and mechanical strength testing of the physical outputs. However, it does not include the type of detailed performance study you've asked for, which typically involves comparing diagnostic or functional output capabilities against a ground truth using specific metrics and sample sizes. This level of detail is often found in clinical study reports, which are usually separate from the 510(k) summary focused on substantial equivalence.

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