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The InstaTrak 3500 Plus System (K983529, originally cleared under the product name InstaTrak 3000) is intended as an aid to the surgeon for precisely locating anatomical structures anywhere on the human body during either open or percutaneous procedures. It is indicated for any medical condition that may benefit from the use of stereotactic surgery and which provides a reference to rigid anatomical structures such as sinus, skull, long bone, or vertebra, visible on medical images such as CT, MR, or X-ray.

The InstaTrak system with Multiple Dataset Navigation provides the same capability as the existing system, with the additional functionality of utilizing two sets of medical images instead of one. By providing information from multiple datasets, the user can locate and visualize anatomical structures using different imaging modalities. The InstaTrak 3500 Plus System allows the user to view the medical images of the patient's anatomy in response to the mouse or the tracked surgical instrument. Alignment of the patient and medical images is accomplished through the registration process. In all types of surgery the goal is the same, to display to the surgeon based on the medical images, where the position of a tracked surgical tool is with regard to the patient's anatomy. With the additional capability of multiple dataset navigation, the surgeon can now view the position of the tracked instrument using two sets of medical images instead of one. The Multiple Dataset Navigation will provide the user with the ability to co-reqister (fuse) images from multiple datasets such as CT and MR. Using the existing InstaTrak 3500 Plus System software, the user will register one of the datasets, referred to as the Reference Dataset, to the patient. Navigation is then possible on the fused images, with secondary (registered) dataset(s) acting as a visualization enhancement for both surgical planning and intra-operative quidance. The sensors and instruments used for navigation are identical to those utilized by the existing InstaTrak system. Navigation will be disabled until the datasets have been successfully co-registered. Patient registration is the process by which the coordinate systems of the medical images and the pointing instrument are aligned. This is performed on the primary (reference) dataset. Both the method of registration on the primary (reference) dataset and the resulting accuracy are identical to that described in K983529. The current system provides displays for a single set of medical images. The Multiple Dataset Navigation option will provide displays for multiple sets of medical images. The addition of the Multiple Dataset Navigation operating mode does not change any of the major components of the InstaTrak System. There are no new receivers, transmitters, or instrument attachment configurations associated with this operational mode. Addition of the Multiple Dataset Navigation mode is a software change only.

The provided 510(k) summary for the GE Medical Systems Navigation and Visualization InstaTrak 3500 Plus with Multiple Dataset Navigation does not contain the specific information requested about acceptance criteria, device performance tables, sample sizes, ground truth establishment, or study designs (MRMC, standalone).

The document is a 510(k) summary, which focuses on demonstrating substantial equivalence to a predicate device rather than providing a detailed technical description of performance validation studies.

Here's what can be extracted based on the limitations of the provided text:

1. Table of Acceptance Criteria and Reported Device Performance:

The document does not explicitly state acceptance criteria or provide a table of reported device performance metrics in the way typically seen for diagnostic or AI algorithms (e.g., sensitivity, specificity, accuracy). Instead, it focuses on the equivalence of its registration process and navigation capabilities to existing systems.

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

This information is not provided in the 510(k) summary. The document states that the patient registration method and resulting accuracy for the primary (reference) dataset are "identical to that described in K983529." To find any details about testing or sample sizes, one would need to refer to the K983529 submission, which is not included here.

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

This information is not provided in the 510(k) summary. Given the device's function as an image-guided surgical system for localization, ground truth would likely involve physical measurements or intraoperative verification, rather than expert interpretation of images for diagnosis.

4. Adjudication Method for the Test Set:

This information is not provided.

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

This type of study is typically conducted for diagnostic devices where human readers interpret images. For an image-guided surgical navigation system that aids in locating anatomical structures, an MRMC study in the traditional sense would likely not be relevant or performed. The document focuses on the technical capability of fusing multiple datasets and the accuracy of registration as being equivalent to a predicate. It does not assess the diagnostic performance of human readers with or without AI assistance.

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

The document describes the "Multiple Dataset Navigation" as a software change that provides the "ability to co-register (fuse) images from multiple datasets." The navigation is then "possible on the fused images." The core function described relates to the accuracy of registration. The document states: "Both the method of registration on the primary (reference) dataset and the resulting accuracy are identical to that described in K983529." This implies that the accuracy of the underlying registration algorithm (a standalone component) would have been assessed as part of the original K983529 submission. However, specific details of that assessment are not provided in this summary.

7. The Type of Ground Truth Used:

For the core function of image registration, ground truth would typically involve physical phantoms with known fiducial markers, where the true alignment is precisely measurable. For clinical application, ground truth for actual navigation accuracy is often derived from intraoperative verification using physical measurements or comparison to anatomical landmarks during surgery. The document itself does not specify the type of ground truth used for the claim of "identical accuracy" to K983529.

8. The Sample Size for the Training Set:

This information is not provided. As this device predates the widespread use of deep learning, the concept of a "training set" in the modern AI sense might not apply. The software change focuses on a new capability (multiple dataset navigation) building upon an existing, validated system.

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

This information is not provided.

Summary of Device and Evidence Presented in Document:

The 510(k) summary describes the "InstaTrak 3500 Plus with Multiple Dataset Navigation" as an upgrade (software change only) to an existing image-guided surgical system (InstaTrak 3500 Plus, K983529). The primary new functionality is the ability to co-register (fuse) and navigate using two sets of medical images (e.g., CT and MR) instead of one.

The document claims substantial equivalence to predicate devices (BrainLAB's Vectorvision iPlan and Medtronic's StealthStation with StealthMerge) that also offer multiple dataset fusion.

The key statement regarding performance is:
"Patient registration is the process by which the coordinate systems of the medical images and the pointing instrument are aligned. This is performed on the primary (reference) dataset. Both the method of registration on the primary (reference) dataset and the resulting accuracy are identical to that described in K983529."

This statement implicitly argues that because the underlying patient registration method and its accuracy are unchanged from the previously cleared device (K983529), and the new multiple dataset navigation builds upon this existing, validated platform, no new safety or effectiveness concerns are raised. The validation for the core accuracy would therefore refer back to the K983529 submission, which is not detailed here.

Therefore, this document does not present a standalone study with new acceptance criteria and performance data for this particular software upgrade, but rather leverages the established performance of the base system and the equivalence to predicate devices for the new functionality.

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The InstaTrak 3000 System is intended as an aid to the surgeon for precisely locating anatomical structures anywhere on the human body during either open or percutaneous procedures. It is indicated for any medical condition that may benefit from the use of stereotactic surgery and which provides a reference to rigid anatomical structures such as sinus, skull, cranial, a long bone or vertebra, visible on medical images such as CT, MR, or Xray.

The InstaTrak 3000 System is an image guidance system indicated for use during sinus, skull base, cranial and axial skeletal procedures. The InstaTrak 3000 with FluoroTrak is similar to the InstaTrak 3000 System cleared under K983529. The changes to the system include software enhancements and the addition of a calibration fixture. Using the InstaTrak 3000, the surgeon can readily identify the immediate location and position of the surgical instrument during the indicated procedure. The InstaTrak 3000 assists the surgeon in avoiding critical nerves and other anatomical structures. The InstaTrak 3000 offers multiple modes of operation that includes sinus, skull base, cranial, axial skeletal, to the user based on the indications the user desires. Software is available to the user for using any one, two, or all three of the operational modes. A selection of the operational modes is made by the user prior to the procedure depending needs of the user. The original InstaTrak 3000 System allows the user to view the reconstructed 2D images of the patient's anatomy in response to the mouse or the tracked surgical instrument. Alignment of the patient and images is accomplished through the registration process. In all types of surgery the goal is the same, to indicate to the surgeon based on the pre-operative medical images, where the position of a tracked surgical tool is with regard to the patient's anatomy. The InstaTrak 3000 with FluoroTrak is based on the same hardware and software used in the original InstaTrak System and provides all of the above features. It utilizes the same clinically proven electromagnetic tracking technology as its predecessor.

Here's an analysis of the provided text regarding the acceptance criteria and study for the InstaTrak 3000 System with FluoroTrak Module:

Unfortunately, the provided text does not contain detailed acceptance criteria or a comprehensive study report with the specific information requested in your prompt. The document is a 510(k) summary for premarket notification, which focuses on establishing substantial equivalence to predicate devices rather than providing a full performance study report with quantitative acceptance criteria and detailed statistical results.

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

1. Table of Acceptance Criteria and Reported Device Performance

Missing Information: The document states "within the specification" but does not explicitly list the quantitative acceptance criteria (e.g., specific accuracy thresholds in millimeters) for location, surgical instrument position, or any other performance metric.

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

The document states: "Testing was performed using the new component of the InstaTrak 3000 with FluoroTrak to determine if the new component affected device accuracy."

Missing Information:

• Sample Size: The number of tests, cases, or subjects used for this testing is not mentioned.
• Data Provenance: There is no information about whether the data was prospective or retrospective, or the country of origin.

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

Missing Information: The document does not describe the methodology for establishing ground truth for the performance testing, nor does it mention the involvement or qualifications of any experts for this purpose.

4. Adjudication Method for the Test Set

Missing Information: No adjudication method (e.g., 2+1, 3+1, none) is mentioned as part of the performance testing.

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

No. A Multi-Reader Multi-Case (MRMC) comparative effectiveness study comparing human readers with and without AI assistance was not mentioned or implied in this document. This device is an image-guided surgical system, not an AI diagnostic tool that assists human readers in interpreting images.

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

The performance testing described focuses on the device (InstaTrak 3000 with FluoroTrak module) and its accuracy when using a new component. It is implied to be a standalone performance evaluation of the system, not specifically an algorithm-only study in the context of AI. The core function of the device is image guidance for a surgeon, so human-in-the-loop

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The InstaTrak 3000 System is intended as an aid to the surgeon for precisely locating anatomical structures anywhere on the human body during either open or percutaneous procedures. It is indicated for any medical condition that may benefit from the use of stereotactic surgery and which provides a reference to rigid anatomical structures such as sinus, skull, cranial, a long bone or vertebra, visible on medical images such as CT or MR.

The InstaTrak System is an image guidance system indicated for use during sinus, skull base, cranial and axial skeletal procedures. The InstaTrak 3000 is essentially identical to the InstaTrak System cleared under K960330 and the Pediatric InstaTrak System cleared under K981998 which are both indicated for use during sinus surgery. The changes to the system include a computer upgrade, software enhancements, additional indications and the addition of several components. Using the InstaTrak 3000, the surgeon can readily identify the immediate location and position of the surgical instrument during the indicated procedure. The InstaTrak 3000 assists the surgeon in avoiding critical nerves and other anatomical structures. The InstaTrak 3000 offers multiple modes of operation that includes sinus, skull base or axial skeletal, to the user based on the indications the user desires. Software is available to the user for using any one, two, or all three of the operational modes. A selection of the operational modes is made by the user prior to the procedure depending needs of the user. The original InstaTrak System allows the user to view the reconstructed 2D images of the patient's anatomy in response to the mouse or the tracked surgical instrument. Alignment of the patient and medical images is accomplished through either an automatic or fiducial registration. The indications for use include sinus cleared under K960330 and pediatric sinus surgery (K981998). In all types of surgery the goal is the same, to indicate to the surgeon based on the pre-operative medical images, where the position of a tracked surgical tool is with regard to the patient's anatomy. The InstaTrak 3000 is based on the same hardware and software used in the original InstaTrak System and provides all of the above features. It utilizes the same clinically proven electromagnetic tracking technology as its predecessor. A newer version of a Sun computer has been substituted to provide 3D display capability which includes 3D models and planar images on top of 3D models, oblique and trajectory matching views. Additionally, a surgical planning capability has been added. This allows the surgeon to plan a trajectory prior to surgery and to observe the pre-surgical track in relation to the actual track during the surgical procedure. A new registration technique has been added whereby the surface of the anatomy can be registered to. New instruments have been added to which tracking sensors have been built in or may be attached. These, along with the surface registration and the new displays allow the system to be used in the proposed indications encompassing axial skeletal, and cranial surgery, in addition to the cleared and pending indications.

The InstaTrak 3000 is an image guidance system that aids surgeons in locating anatomical structures during surgical procedures. The provided 510(k) summary (K983529) describes the device and its claimed substantial equivalence to predicate devices. However, the summary does not contain detailed information regarding the specific acceptance criteria or a comprehensive study report with quantitative performance metrics as typically expected for medical device evaluations.

Based on the provided text, here's a breakdown of the available information:

1. Table of Acceptance Criteria and Reported Device Performance

The 510(k) summary lacks a formal table of acceptance criteria and reported numerical device performance metrics. The performance testing mentioned is qualitative and focuses on a specific aspect:

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

The 510(k) summary does not specify the sample size used for the performance testing. It also does not provide information on data provenance (e.g., country of origin, retrospective or prospective nature). The testing appears to have been conducted internally by the manufacturer ("Testing was performed...").

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

The 510(k) summary does not mention the use of experts to establish ground truth for any test set, nor does it detail their qualifications. The testing described focuses on the device's accuracy with new components, implying a technical evaluation rather than a clinical one involving expert consensus on patient data.

4. Adjudication Method for the Test Set

Since the summary does not detail the use of experts or a clinical test set, an adjudication method (like 2+1, 3+1) is not applicable and not mentioned.

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

The 510(k) summary does not describe a Multi-Reader Multi-Case (MRMC) comparative effectiveness study. There is no mention of comparing human reader performance with or without AI assistance, or any effect size of such improvement.

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

The InstaTrak 3000 is an "image guidance system" intended to "aid the surgeon." This inherently implies a human-in-the-loop device. Therefore, a standalone (algorithm-only) performance study as typically understood for diagnostic AI devices is not described and would likely not be the primary evaluation method given the device's nature. Its function is to provide information to a human operator.

7. Type of Ground Truth Used

The type of "ground truth" for the performance testing mentioned appears to be related to the "device accuracy" with new components. This would likely involve engineered or laboratory-based measurements against known physical parameters or standards, rather than clinical ground truth like pathology, expert consensus, or outcomes data. The summary does not specify the exact nature of this "specification" or how accuracy was measured against it.

8. Sample Size for the Training Set

The 510(k) summary does not provide information regarding a training set or its sample size. This is a premarket notification for a navigation system, not a machine learning or AI-driven diagnostic algorithm that typically relies on extensive training data. While the system has "software enhancements," the summary primarily emphasizes its technological similarity to predicate devices and the function of new components.

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

As no training set is mentioned, information on how its ground truth was established is not provided.

Summary of Study Described:

The study referenced in the 510(k) summary (Section 7, "PERFORMANCE TESTING") was a limited internal validation focused on the new components of the InstaTrak 3000 System.

• Objective: To determine if the newly added components (nasal specula, mouth gag, pharyngeal retractor, straight extended aspirator, sterile disposable pointer, transmitter, receiver, and head frame) affected the device's accuracy.
• Methodology (implied): The new components were incorporated into the system, and accuracy tests were performed.
• Results: The manufacturer concluded that "the device performed within the specification while using the new components."
• Limitations (from the provided text):
  • No quantitative accuracy metrics are provided.
  • The "specification" against which performance was measured is not defined.
  • No details on the number of tests, test conditions, or specific methodology are given.
  • The testing was not a clinical trial with patient-specific ground truth or human reader evaluations.

In essence, the 510(k) summary confirms that a technical performance test was conducted to ensure the new hardware components did not degrade the system's accuracy, based on the manufacturer's internal specifications. It does not provide the detailed evidence typically found in studies for AI-driven diagnostic devices, which often involve large datasets, expert ground truth, and comprehensive statistical analysis of clinical performance measures. The substantial equivalence argument primarily relies on the core technology being largely identical to predicate devices.

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Intranasal or sinuses
Acute and chronic sinusitis, endoscopic dacryocystorhinostomy, optic nerve and orbital decompression, the removal of polyps, the biopsy and removal of tumors, and the repair of CSF leaks, pituitary disorder, and encephalocele

The device consists of a wheeled cabinet enclosure with a 20-inch color monitor mounted on the top. Mounted within the cabinet is a computer and a spacial tracking device. The electromagnetic, six-degree-of-freedom tracking device is linked to the computer, which provides the monitor with a display of the patient's CT image data and superimposed crosshairs, indicating the position of the tip of the surgical instrument used with the device. The device is controlled via software.

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

Note: The document is a 510(k) summary from 1996, so the depth of information regarding rigorous clinical trial methodology (especially regarding modern AI/machine learning studies) is limited. The device described uses image-guided surgery technology, not an AI algorithm in the contemporary sense.

1. Table of Acceptance Criteria and Reported Device Performance

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

• Sample Size for Test Set: Not explicitly stated as a number of patients or cases. The document mentions a "multicenter study conducted at four clinical sites." For the laboratory testing, general "laboratory testing was conducted" with no sample size specified.
• Data Provenance: The multicenter study was conducted at "four clinical sites." The country of origin is not specified, but the applicant (Visualization Technology, Inc.) is based in Boston, MA, USA, suggesting a US-based study. The data is reported as originating from prospective clinical testing (a multicenter study) and laboratory nonclinical testing.

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

This type of information is not provided in the document. The device is a surgical guidance system, and device accuracy in real-time is the primary metric, not diagnostic accuracy requiring expert interpretation of results. Therefore, traditional "ground truth" establishment by experts like radiologists isn't directly applicable in the same way it would be for a diagnostic AI device.

4. Adjudication Method for the Test Set

This information is not provided. Given the nature of a surgical guidance system's accuracy measurement, an adjudication method in the context of expert consensus (like for diagnostic AI) is not directly relevant. Accuracy measurements are quantitative.

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

No, a Multi-Reader Multi-Case (MRMC) comparative effectiveness study was not done in the context of human readers improving with AI assistance. This document describes an image-guided surgery system, not an AI diagnostic tool. The comparison made is between the device's accuracy and that of a predicate device, not between human performance with and without AI.

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

Yes, performance was evaluated in a standalone manner. The device's accuracy (both in laboratory and clinical settings) was measured directly. While the device assists a surgeon, the measurement of its accuracy itself is a standalone assessment of the device's capability to correctly determine location. The "human-in-the-loop" aspect relates to the surgeon using the device, but the accuracy figures are a direct assessment of the system's output.

7. The Type of Ground Truth Used

The ground truth used for assessing accuracy would be a precisely measured true physical location in the laboratory setting, and assumed to be the actual anatomical position in the clinical setting (relative to the CT scan data). This is a physical, objective "ground truth" rather than an interpretive one from experts or pathology.

8. The Sample Size for the Training Set

This information is not applicable/not provided. The InstaTrak device described is not an AI/machine learning device that requires a "training set" in the modern sense. It's an electromagnetic tracking system that uses CT image data. The "software feature recognition algorithm" mentioned for autoheadset registration might imply some level of learning, but no training set size or methodology is described.

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

This information is not applicable/not provided for the same reasons as point 8.

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