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The Surgical Theater, LLC SuRgical Planner (SRP) is intended for use as a software interface and image segmentation system for the transfer of image information from a CT, MR, or X-ray 3D Angiography (XA) medical scanner to an output file. It can also be used for pre-operative planning and surgical training in a virtual environment.

The SuRgical Planner (SRP) is intended for use as a software interface and image segmentation system for the transfer of imaging information from a CT, MR or X-ray 3D Angiography (XA) medical scanner to an output file. It can also be used for pre-operative planning and surgical training in a virtual environment.

The SuRgical Planner is not intended to be used for diagnosis.

The SRP software has the ability of creating 3D models of the patient data from 2D scan slices. Additionally, it provides the user with the ability to input, display, color, and manipulate the 2D scan slices via a 3D representation. The software transforms 2D medical images into a dynamic interactive 3D scene with multiple point of views on a high-definition (HD) touch screen monitor. The use of a virtual reality (VR) headset provides the surgeon a 3D stereoscopic display of the same scene inside the VR headset. While wearing the VR headset, the surgeon can perform a virtual / simulated "fly-through" inside the 3D scene using controllers to perform such actions as rotate, zoom in and zoom out. The SRP with VR headset and controllers is not intended for use during surgery. The SRP is intended for use for pre-operative planning.

The SRP product does not include any custom hardware and is a software-based device that runs on a high-performance desktop PC assembled using "commercial off-the-shelf" components that meet minimum performance requirements. The design is based on an advanced, touch screen friendly, Graphical User Interface (GUI) that runs an underlying simulation engine to process medical images in DICOM format, and an image generator software engine.

The provided text is a 510(k) summary for the Surgical Theater, LLC SuRgical Planner (SRP) software. It describes the device, its intended use, comparison to a predicate device, and performance data. However, the document primarily focuses on regulatory compliance and substantial equivalence rather than a detailed study evaluating the device's performance against specific acceptance criteria.

Therefore, much of the requested information regarding specific acceptance criteria, detailed study design, sample sizes for test and training sets, expert qualifications, and ground truth establishment is not available in the provided text. The document states that "Software verification and validation testing were conducted" and that "A formal verification and validation plan was executed to confirm that the modified SRP continues to meet its intended use and performance requirements." However, it does not provide the specifics of these plans or their results in a way that directly answers the questions about acceptance criteria and performance metrics.

Here's a breakdown of what can be extracted and what is missing:

Acceptance Criteria and Study Details for SuRgical Planner (SRP)

1. Table of Acceptance Criteria and Reported Device Performance

2. Sample Size for Test Set and Data Provenance

• Sample Size: Not specified in the provided document. The document mentions "Software verification and validation testing were conducted," but details on the number of cases or images used in these tests are absent.
• Data Provenance: Not specified. The document states the device processes DICOM images from CT, MR, or X-ray 3D Angiography (XA) medical scanners, but does not indicate the source (e.g., country, hospital, retrospective/prospective) of the data used for testing.

3. Number of Experts and Qualifications for Ground Truth

• Number of Experts: Not specified.
• Qualifications of Experts: Not specified. The document states the device is for "pre-operative planning and surgical training in a virtual environment," implying surgical expertise might be relevant, but this is not confirmed for ground truth establishment.

4. Adjudication Method for Test Set

• Adjudication Method: Not specified. The document outlines general software verification and validation but does not detail how ground truth was established or if an adjudication process was involved for performance evaluation.

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

• MRMC Study: No, an MRMC comparative effectiveness study is not mentioned in the provided text. The document focuses on the device's technical specifications and regulatory equivalence, not on evaluating human reader performance with or without AI assistance.

6. Standalone (Algorithm Only) Performance Study

• Standalone Study: Yes, implicitly. The document states that the "SRP was fully tested, verified and validated by Surgical Theater as part of its own internal design control requirements" to "meet its intended use and performance requirements." While details on specific metrics are missing, this implies a standalone evaluation of the software's functionality and accuracy in image segmentation and 3D model generation, which are core algorithmic tasks. However, explicit performance metrics (e.g., segmentation accuracy, speed) are not provided.

7. Type of Ground Truth Used

• Type of Ground Truth: Not specified. Given the device's function involves image segmentation and 3D model generation, ground truth would likely relate to the accuracy of these outputs against a reference. However, the method for establishing this reference (e.g., manual segmentation by experts, phantom scans with known anatomy) is not detailed.

8. Sample Size for the Training Set

• Sample Size: Not specified. The document does not provide information about a training set, as the SRP functions primarily as an image processing and visualization tool, rather than a machine learning model that requires explicit training data in the traditional sense. It processes existing medical images.

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

• Ground Truth Establishment for Training Set: Not applicable, as detailed training set information is not provided and the device description does not explicitly mention machine learning components that require a training set with established ground truth in the context of predictive or diagnostic tasks. The "training" aspect mentioned in the document refers to surgical training in a virtual environment using the device, not training of the device itself.

In summary, the provided document serves as a regulatory submission emphasizing substantial equivalence and general safety/effectiveness through internal verification and validation processes. It lacks the quantitative performance data and detailed study designs typically found in clinical efficacy studies that would address specific acceptance criteria with measured outcomes.

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The Surgical Theater, LLC SNAP is intended for use as a software interface and image segmentation system for the transfer of imaging information from a CT, MR or X-ray 3D Angiography (XA) medical scanner to an output file. It is also intended for use in simulating surgical treatment options both pre-operatively and intra-operatively with validated systems as identified in the device labeling.

The Surgical Navigation Advanced Platform (SNAP) is intended for use as a software interface and image segmentation system for the transfer of imaging information from CT or MR medical scanner to an output file. It is also intended for use in simulating and evaluating surgical treatment options both pre-operatively and intra-operatively with validated systems as identified in the device labeling.

The Surgical Navigation Advanced Platform (SNAP) transforms medical images into a dynamic, interactive 3D scene, and connects to external 3rd party surgical navigation systems (i.e. "validated systems"), to extract and display intra-operative surgical navigation information (such as the 3D navigation pointer) inside the generated 3D scene. Current navigation systems usually display the navigation data on 2D black and white DICOM imagery within the external navigation system itself. The SNAP displays the same navigation data (pointer position and orientation), as it is received from the external 3rd party navigation system. in a 3D fashion inside the SNAP 3D model of the anatomy as it is reconstructed from the original DICOM slices.

The SNAP allows surgeons to analyze and plan a specific patient's case before surgery, and then take that plan into the operating room (OR) and use it in conjunction with a validated traditional navigation system during surgery. The SNAP then presents the navigation data into the advanced interactive, high quality 3D image, with multiple point of views on a high-definition (HD) touch screen monitor. The surgeon can perform a virtual / simulated "fly-through" inside the 3D scene using controls such as rotate, zoom in and zoom out. During pre-operative use a virtual reality (VR) headset further increases the surgeon's immersion level in the 3D scene by providing a 3D stereoscopic display of the same 3D scene displayed on the touch screen monitor.

The SNAP product does not include any custom hardware and is a software-based device that runs on a high performance desktop PC assembled using "commercial off-the-shelf" components. The design is based on an advanced, touch screen friendly, Graphical User Interface (GUI) that runs an underlying simulation engine to process medical images in DICOM format, and an image generator software engine.

Here's an analysis of the provided text regarding the acceptance criteria and supporting study for the Surgical Navigation Advanced Platform (SNAP):

Based on the provided text, the device is the Surgical Navigation Advanced Platform (SNAP), and the submission is for modifications to an existing cleared device (K140819). The modifications include adding support for X-ray 3D Angiography (XA) scans, incorporating a VR headset for intra-operative use (previously only pre-operative), and adding a video capture PCB and an "Endo View" GUI for displaying live endoscopy video side-by-side with 3D scenes.

It's important to note that the document primarily focuses on demonstrating substantial equivalence to a predicate device (K140819) rather than presenting a standalone study with specific performance metrics against acceptance criteria for the entire device's functionality. The performance data mentioned are related to Electromagnetic Compatibility (EMC) and Software Verification and Validation Testing to ensure the modifications do not negatively impact the device's safety and effectiveness.

Here's a breakdown of the requested information based only on the provided text:

1. Table of Acceptance Criteria and Reported Device Performance

The document does not present a table of specific quantitative acceptance criteria for the overall performance of the SNAP device (e.g., accuracy of segmentation, simulation fidelity). Instead, the acceptance criteria relate to regulatory compliance and the successful execution of specific tests for the modifications.

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

• Sample Size: The document does not specify a distinct "test set" sample size in terms of patient cases or imaging studies for the performance validation. The testing described largely pertains to software verification, EMC, and risk management. The "DICOM data acceptance" for XA scans implies testing with various DICOM files, but a specific number is not provided.
• Data Provenance: Not explicitly stated. The focus is on the software and hardware modifications rather than clinical data.

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

• Number of Experts & Qualifications: Not mentioned in the provided text. The evaluation is focused on technical compliance and validation by the manufacturer, not a clinical ground truth assessment by external experts.

4. Adjudication Method for the Test Set

• Adjudication Method: Not applicable or mentioned. The described testing is technical validation and verification, not a clinical study requiring adjudication of outcomes or interpretations.

5. If a Multi-Reader Multi-Case (MRMC) Comparative Effectiveness Study Was Done, and Effect Size

• MRMC Study: No, an MRMC comparative effectiveness study is not described in the provided text. The submission is for substantial equivalence of device modifications, not a study evaluating human reader performance with or without AI assistance.

6. If a Standalone (Algorithm Only Without Human-in-the-Loop Performance) Was Done

• Standalone Performance: The text does not describe a standalone performance study in the context of an algorithm's diagnostic or predictive capability. The SNAP device is described as a "software interface and image segmentation system" intended for simulation and surgical planning, interacting with a human surgeon. Its performance evaluation focuses on the accurate processing and display of data for human interpretation and use.

7. The Type of Ground Truth Used

• Type of Ground Truth: For the "DICOM data acceptance" related to XA scans, the ground truth would be adherence to the DICOM standard and the internal specifications for image modality and SOP Class. For software verification and validation, the ground truth is the predefined functional and performance requirements established by the manufacturer, verified through testing against those specifications (e.g., whether the software converts the image correctly, whether the display is accurate). There isn't a clinical "ground truth" (like pathology or outcomes data) discussed for this submission.

8. The Sample Size for the Training Set

• Training Set Sample Size: Not applicable or mentioned in the provided text. The SNAP device is described as an "image segmentation system," but the submission does not detail any machine learning or AI algorithm training that would require a distinct training set. The focus is on functionality and safety of the system as a whole.

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

• Ground Truth for Training Set: Not applicable, as no training set for a machine learning algorithm is discussed.

Summary of Scope:

The provided document (K160584) describes a 510(k) submission for modifications to an existing cleared device. The "performance data" presented are primarily focused on demonstrating that these modifications do not introduce new safety or effectiveness concerns and that the modified device remains in compliance with relevant technical standards (EMC) and internal software verification and validation processes. It is a regulatory submission for substantial equivalence, not a detailed clinical study report on the device's diagnostic or therapeutic performance.

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The Surgical Theater, LLC SNAP is intended for use as a software interface and image segmentation system for the transfer of imaging information from a CT or MR medical scanner to an output file. It is also intended for use in simulating and evaluating surgical treatment options both pre-operatively and intra- operatively with validated systems as identified in the device labeling.

The SNAP is software based medical image management system. It is intended for use as a software interface and image segmentation system for the transfer of imaging information from a CT or MR medical scanner to an output file. It is also intended for use in simulating and evaluating surgical treatment options both pre-operatively and intraoperatively with validated systems as identified in the device labeling. The SNAP system is based on the Surgical Theater Surgery Rehearsal Platform (SRP) image management system. The SNAP utilizes the same identical software as the SRP to create 3D models of patient data from 2D scan slices. This provides the user with ability to input, display, color, and manipulate the 2D scan slices via a 3D representation. The SNAP system enhances the SRP's capability by adding additional input and allowing the surgeon to connect to an external Image Guided System and Navigation systems (in general: "IGS"; for example Brainlab Kolibri or Brainlab Curve), and to see the incoming navigation data in the SNAP monitor. The incoming navigation data is then displayed to the surgeon inside the generated 3D model, so the surgeon gets a 3D representation of his surgery navigation session.

The provided document K140819 for the Surgical Navigation Advanced Platform (SNAP) does not contain a specific table of acceptance criteria with numerical performance targets or a detailed study report demonstrating how these criteria were met. Instead, it states that "Verification and validation results confirm that the SNAP Software meets its' performance requirements."

However, based on the information provided, we can infer the acceptance criteria for the device's functionality and its proven performance:

1. Table of Acceptance Criteria and Reported Device Performance:

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

• Sample Size for Test Set: The document states that the SNAP was "validated by two neurosurgeons based on historical DIOCM cases (of patients' cases who had their surgeries done in the past)." The specific number of DICOM cases used is not mentioned in the provided text.
• Data Provenance: The data used consisted of "historical DIOCM cases (of patients' cases who had their surgeries done in the past)." This indicates the data was retrospective. The country of origin is not specified.

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

• Number of Experts: Two neurosurgeons were used for validation.
• Qualifications of Experts: They are identified as "neurosurgeons," implying medical expertise relevant to the device's application. Specific years of experience or other detailed qualifications are not provided.

4. Adjudication method for the test set:

• The document states that the device was "validated by two neurosurgeons." It does not specify an adjudication method (e.g., 2+1, 3+1). It is only mentioned that they performed the validation.

5. If a multi-reader multi-case (MRMC) comparative effectiveness study was done, and if so, what was the effect size of how much human readers improve with AI vs without AI assistance:

• No MRMC comparative effectiveness study was done comparing human reader performance with and without AI assistance (or in this case, SNAP assistance). The SNAP is described as an image management and navigation assistance tool, not an AI diagnostic tool primarily aimed at improving human reader diagnostic performance. The validation focused on the software's functional correctness and suitability for use, particularly the new intra-operative navigation feature. There is no mention of an effect size related to human improvement with assistance.

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

• Yes, a standalone performance assessment was conducted in the sense that the software underwent internal "full testing, verification, and validation by Surgical Theater as part of its' own internal design control requirements" to confirm it meets its performance requirements. The subsequent validation by neurosurgeons assessed the usability and correctness of its output for clinical use, but the core functionality and technical performance were first verified in a standalone manner.

7. The type of ground truth used:

• The ground truth was implicitly derived from the "historical DIOCM cases (of patients' cases who had their surgeries done in the past)." This suggests that the ground truth for validating the 3D reconstructions and navigation display was based on the known anatomy and surgical outcomes of these historical cases, likely interpreted by the validating neurosurgeons. It is most akin to expert consensus/clinical data derived from previously treated cases.

8. The sample size for the training set:

• The document does not specify a sample size for a training set. The device is described as software that reconstructs 3D models from DICOM data based on algorithms, rather than a machine-learning model that would typically require a distinct training set. The SNAP system "utilizes the same identical software as the SRP to create 3D models."

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

• As the document does not mention a distinct training set in the context of machine learning, there is no information provided on how ground truth for a training set was established. The software's underlying algorithms for 3D reconstruction and visualization likely rely on established medical image processing principles and were developed and refined through engineering and standard software development practices, rather than by training on a labeled dataset in the modern AI sense.

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Surgery Theater, LLC Surgery Rehearsal Platform is intended for use as a software interface and image segmentation system for the transfer of imaging information from CT or MR medical scanner to an output file. It is also intended as pre-operative software for simulating surgical treatment options.

The Surgery Rehearsal Platform (SRP) is software based medical image management system. It is intended for use as a software interface and image segmentation system. for the transfer of imaging information from a CT or MR medical scanner, to an output file. It is also intended as pre-operative software for simulation and evaluation of surgical treatment options.

The SRP software has the capability of creating 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.

Here's an analysis of the acceptance criteria and study information for the Surgical Theater Surgery Rehearsal Platform (K123023) based on the provided text:

Important Note: This 510(k) summary primarily focuses on demonstrating substantial equivalence to a predicate device and does not involve a traditional clinical study with detailed performance metrics and ground truth establishment in the way typically seen for diagnostic AI/ML devices. The "acceptance criteria" here are essentially the requirements for demonstrating substantial equivalence and functional performance.

Acceptance Criteria and Reported Device Performance

• Image Sources: CT and MRI (Matches predicate)
• Data Transfer Method: CD or USB (Matches predicate)
• Preoperative Planning: Yes (Matches predicate)
• Patient Contact: No (Matches predicate)
• Human Intervention for Interpretation of Images: Yes (Matches predicate)
• Capability of creating 3D models of patient data from 2D scan slices: Yes (Matches predicate)
• Provides the user with ability to input, display, color, and manipulate the 2D scan slices via a 3D representation: Yes (Matches predicate)
• Image tools such as rotation, scaling and coloring: Yes (Matches predicate) |
  | Functional Verification | Design outputs met design input requirements (confirmed internally by Quality personnel). |
  | Functional Validation (User Needs) | Met user needs and intended use (confirmed internally by Quality personnel, and subsequently by surgeons). |
  | Risk Analysis Compliance | Performed in accordance with ISO 14971 (2007). Risk management file verification and validation conducted (desk audit and system testing). |
  | Overall Safety and Effectiveness | Demonstrated to be "as safe and effective as its predicate device" based on matching indications for use, construction, operational principles, and performance test results. |

Study Information

1. Sample sizes used for the test set and the data provenance:

   • Test Set Sample Size: Not explicitly stated. The document mentions "documented software test procedures" and testing on "each supported system configuration (e.g. 2D vs. 3D Stereoscopic)." However, a specific number of cases or datasets used for testing is not provided.
   • Data Provenance: Not specified. It's likely that synthetic or anonymized clinical data was used for functional testing, but this is not confirmed. The document does not mention the country of origin or whether the data was retrospective or prospective.

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

   • Number of Experts: Not explicitly stated for the test set's "ground truth."
   • Qualifications of Experts: The document states that "the system was validated by surgeons to ensure the system meets end-user requirements." While these surgeons were involved in functional validation, their role in establishing a formal "ground truth" for specific medical findings is not described.

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

   • Adjudication Method: Not applicable or not described in the context of this submission. The validation involved internal quality personnel and surgeons assessing the system's functionality and meeting user needs, not typically a "ground truth" adjudication process for medical findings.

4. 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. This submission does not describe an MRMC comparative effectiveness study where human readers' performance with and without AI assistance was evaluated. The device is for "pre-operative software for simulating/evaluation surgical treatment options," not primarily a diagnostic AI tool meant to improve human reader accuracy.

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

   • Standalone Performance: The device itself is a standalone software, but its performance is measured against its functional requirements and substantial equivalence to a predicate, not against a specific diagnostic or clinical outcome metric. The "Human Intervention for Interpretation of Images" characteristic is listed as "Yes," indicating it is an assistive tool, not fully autonomous.

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

   • Type of Ground Truth: For the "test set" in the context of traditional AI evaluation, a formal ground truth (e.g., pathology, expert adjudicated labels) is not described. The "ground truth" for this device's validation appears to be adherence to design specifications and user requirements, as confirmed by internal quality personnel and surgeons.

7. The sample size for the training set:

   • Training Set Sample Size: Not applicable. This document describes a software device that performs image segmentation and 3D modeling from existing CT/MR scans, and provides tools for simulation. It is not presented as an AI/ML device that requires a training set in the typical sense for learning patterns from labeled data to make predictions.

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

   • Training Set Ground Truth: Not applicable, as no training set for an AI/ML model is described.

Summary of Approach:

The K123023 submission for the Surgical Theater Surgery Rehearsal Platform focuses on demonstrating substantial equivalence to an existing predicate device (Simbionix PROcedure Rehearsal Studio K112387). The "performance data" describes:

• Internal test plans and execution to confirm the device meets specified requirements (functional verification).
• Functional validation testing by quality personnel and surgeons to ensure the system meets user needs and intended use.
• Compliance with risk analysis standards (ISO 14971).

The validation activities ensure the device functions as intended and is comparable to the predicate. It explicitly states, "Test results confirmed that SRP is substantially equivalent to the predicate." This is a pre-AI/ML era submission, and therefore, the testing and validation criteria do not align with the typical "acceptance criteria" and "ground truth" definitions used for AI/ML diagnostic devices.

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