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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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The Skull Based InstaTrak System is intended for image guided assistance during skull base surgical procedures. It is intended to be used during skull base surgery involving procedures on the base of the brain (junction of the face and neurocranium).

The InstaTrak System is an image guidance system indicated for use during skull base procedures. The Skull Base InstaTrak System is identical in principles of operation to the InstaTrak System cleared under K960330 and the Pediatric InstaTrak System cleared under K981998, which are indicated for use during nasal surgery. Using the Skull Base InstaTrak System, the surgeon can readily identify the immediate location and position of the surgical instrument during skull base procedures. The Skull Base InstaTrak System assists the surgeon in avoiding critical nerves and other anatomical structures. The Skull Base InstaTrak System includes several new components that were not included in K960330 or K981998, such as a head frame, transmitter arm, extended straight sterile pointer, mouth gag, pharyngeal retractor and nasal speculum. The remainder of the components used in the Skull Base System are identical to those described in the original submission. The additional components do not affect the overall operation of the system as the principles of operation are identical to that described in K960330. Like that system, the Skull Base InstaTrak System is an image guided surgery system that employs a computer with a top mounted swiveling monitor, software and an electromagnetic tracking system. The System uses a Sun SPARC STATION™. The System's proprietary software builds a CT model by taking axial CT images and reconstructing the coronal and sagittal views. The electromagnetic tracking system correlates the movement of surgical instruments to the CT model. The tip of the instrument is displayed as a set of cross hairs in the axial, coronal, and sagittal planes on the InstaTrak System monitor. With the InstaTrak System, CT images are used to assist the surgeon in guiding the position of the instrument during skull base surgery.

The Skull Base and InstaTrak Systems allow pre-operative viewing of the patients' CT images, contextual visualization of the pathology, intra-operative localization, screen display outputs for video recording and positional guidance. The system is operated by acquiring an axial CT scan while the patient wears the InstaTrak System headset and associated instruments. The axial images are then transferred via a network connection or cartridge to the InstaTrak System. The Headset position which stays fixed relative to the patients' anatomy, is automatically identified in the CT images by an image processing algorithm. Coronal and sagittal images are reconstructed and along with the Axial images, provide the CT model that will be used as a road map in surgery.

Here's a breakdown of the acceptance criteria and study information based on the provided 510(k) summary:

Acceptance Criteria and Device Performance

The provided document {3} states: "Testing was performed using the new components of the Skull Base InstaTrak System to determine if the new components affected device accuracy. The results showed that the device performed within the specification while using the new components."

While the document explicitly states that the device "performed within the specification," it does not provide the specific numerical acceptance criteria or the reported device performance metrics. It only refers to a general "specification" for accuracy.

Study Details

The provided 510(k) summary offers limited details about the study. Here's what can be inferred:

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

   • Sample size: Not specified.
   • Data provenance: Not specified (e.g., country of origin, retrospective or prospective). The summary only states that "Testing was performed using the new components."

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

   • Not specified. The summary does not mention the involvement of experts for establishing ground truth during the accuracy testing.

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

   • Not specified. Given the nature of accuracy testing for an image guidance system, it's likely physical measurements against a known standard were used rather than expert adjudication in the traditional sense, but this is not explicitly stated.

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:

   • No, an MRMC comparative effectiveness study was not done. The device is an image guidance system that assists surgeons, not an AI-driven diagnostic tool that impacts human reader performance in interpreting images. The study focused on the device's accuracy with new components.

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

   • Yes, a standalone performance assessment was effectively done regarding the device's accuracy. The testing aimed to determine if the new components affected device accuracy {3}. This implies evaluating the system's ability to accurately track instruments relative to the CT model, which is a standalone function of the device. However, the exact methodology (e.g., phantom studies, cadaver studies) is not detailed.

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

   • Not explicitly stated. For "accuracy" testing of an image guidance system, the ground truth would likely involve physical measurements against precisely known spatial references or benchmarks (e.g., phantom models with known dimensions and fiducial markers) rather than expert consensus on medical findings, pathology, or outcomes data.

7. The sample size for the training set:

   • Not applicable as this is not an AI/Machine Learning device that undergoes training in the conventional sense. The "training" for such a system would involve engineering and calibration, not a data-driven training set.

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

   • Not applicable for the same reason as above.

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The Pediatric InstaTrak System is an image guided device for use during endoscopic nasal surgery. It is intended to be used during the treatment of 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 Pediatric InstaTrak System is intended for image guided assistance during nasal surgery in pediatric patients. It is intended to be used during the treatment of 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 InstaTrak System intended for pediatrics is identical to the InstaTrak System intended for adults with the exception of a slight modification to the head set component. The headset assembly, verification pad, and aspirator components have been modified for the Pediatric InstaTrak System. The dimensions of these components of the InstaTrak System have been modified to accommodate the smaller head size of pediatric patients. As described in the original submission, the headset is a single use disposable device made of acetal plastic. The headset is held in place on the patient by spring tension at the three points of contact, the external ear canals, and the bridge of the nose. The headset serves as a foundation for anchoring the electromagnetic transmitter in a fixed location with respect to the patients head to compensate for patient motion during the surgical procedure. The transmitter is attached to the headset prior to surgery by a rotating toggle that is inserted through a slot in the middle of the rectangular section. The headset allow s the device to maintain its registration therefore avoiding the need for re-registration during the surgical procedure. It also continues to perform accurately even while the patient's head is moving during the surgical procedure.

The only change between the Pediatric InstaTrak System and the Adult InstaTrak System is a minor change in dimensions of the headset, straight aspirator and verification pad to accommodate the smaller head size of pediatric patients. This is an insignificant modification not affecting safety or effectiveness of the device.

The headset is first worn by the patient during the CT scan and is removed immediately after the scan. Prior to surgery, the transmitter is attached to the headset and the headset is again placed on the patient. This avoids the problem of adhesive based fiducials which requires the CT scan do be done very near the time of surgery to avoid displacement or loss of the markets.

The modifications to the headset dimensions do not affect safety or effectiveness of the device since the intended use and placement are identical to that summarized above and described in more detail in K960330.

The Pediatric InstaTrak System is identical in technological characteristics to the Adult InstaTrak System except for the size of the headset. The Pediatric InstaTrak System and the Viewing Wand are similar in that they both use an articulated mechanical arm. The proposed Pediatric InstaTrak System and the predicate devices are similar in intended use in that they are all intraoperative imageguidance systems. The Viewing Wand links a positioning probe to an image display with a patients CT or MRI image data. The Pediatric InstaTrak System, the Adult InstaTrak System and the Viewing Wand all use a computer, monitor and hard disk storage system. Both the pediatric and adult InstaTrak Systems have fewer indications/features, using only CT images and fewer screen image displays than the predicate Viewing Wand.

Here's an analysis of the acceptance criteria and supporting studies for the Visualization Technology, Inc. Pediatric InstaTrak System based on the provided document:

Acceptance Criteria and Device Performance for Visualization Technology, Inc. Pediatric InstaTrak System

1. Table of Acceptance Criteria and Reported Device Performance

Note: The document explicitly states that the Pediatric InstaTrak System's performance (2.28 mm mean accuracy, 0.78 mm 95% CI) compares favorably to the predicate device, implying that achieving accuracy within or better than the predicate's range was the implicit acceptance criterion. The headset reproducibility was a new pediatric-specific test, and "acceptable results" was the stated outcome.

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

• Sample Size (Pediatric Headset Reproducibility): 61 pediatric patients.
• Sample Size (Clinical Testing for Accuracy): Not explicitly stated how many patients were in the "Adult InstaTrak System" study, merely that it was conducted at "four clinical sites." The 95% confidence interval reported (0.78 mm) suggests a reasonably sized study, but the precise number of subjects or cases is not provided.
• Data Provenance: The document does not specify the country of origin for the data. The clinical testing for accuracy was performed on the Adult InstaTrak System, which is stated to be "essentially identical except for minor dimension changes." This implies a prospective study given it was clinical testing, but it wasn't specifically conducted on the Pediatric InstaTrak System for the accuracy data. The pediatric headset reproducibility study was also clinical testing, suggesting a prospective design.

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

The document does not provide information on the number or qualifications of experts used to establish ground truth for either the pediatric headset reproducibility or the accuracy clinical testing. The accuracy data likely relates to measuring the physical accuracy of the system's tracking against known points or a gold standard measurement, which may not inherently require expert consensus in the same way as, for example, image interpretation.

4. Adjudication Method for the Test Set

The document does not describe any adjudication method for the test set. Given the "accuracy" and "reproducibility" metrics, it's more likely that direct physical measurements were taken and compared to a known standard or repeated measurements, rather than requiring subjective expert adjudication.

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

• No, an MRMC comparative effectiveness study was not done.
• The device is an image-guided surgical system, not an AI-powered diagnostic or assistive tool for "human readers." Its purpose is to guide surgeons during procedures, not to help "readers" interpret images. Therefore, the concept of improving human reader performance with or without AI assistance is not applicable to this device type.

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

• Yes, the performance metrics reported (mean accuracy, 95% confidence interval of the mean, headset reproducibility) represent a standalone measurement of the device's physical tracking and stability. These measurements quantify the system's intrinsic performance in accurately localizing instruments in relation to patient anatomy, independent of the surgeon's skill or decision-making process. The "human-in-the-loop" aspect for this device is the surgeon using the guidance, but the reported data refers to the system's measurement capabilities.

7. The Type of Ground Truth Used

• For Accuracy: The ground truth for accuracy testing would typically involve a known standard, such as a phantom with precisely measured fiducial markers, or a highly accurate coordinate measuring machine (e.g., optical tracking system or robotic arm) for comparison. The document does not explicitly state the specific method but implies a systematic approach to measure the physical deviation of the tracking system.
• For Reproducibility: Ground truth for reproducibility would involve repeated measurements under consistent conditions to assess the device's ability to consistently report the same position over time or after re-application.

8. The Sample Size for the Training Set

The document does not provide any information regarding a training set. This device is a mechanical/electromagnetic tracking system, not an AI or machine learning model that typically requires a distinct training set. If any statistical models were used in its internal workings, the data used to parameterize those models is not disclosed.

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

As no training set is mentioned or applicable in the context of this device's reported evaluation, this question is not relevant.

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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

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