SIS Software is an application intended for use in the viewing, presentation of medical imaging, including different modules for image fusion, and intraoperative functional planning where the 3D output can be used with stereotactic image quided surgery or other devices for further processing and visualization. The device can be used in conjunction with other clinical methods as an aid in visualization of the subthalamic nuclei (STN).

Device Description

SIS Software is an application intended for use in the viewing, presentation and documentation of medical imaging, including different modules for image processing, image fusion, and intraoperative functional planning where the 3D output can be used with stereotactic image guided surgery or other devices for further processing and visualization. The device can be used in coniunction with other clinical methods as an aid in visualization of the subthalamic nuclei (STN).

SIS Software uses machine learning and image processing to enhance standard clinical images for the visualization of the subthalamic nucleus ("STN"). The SIS Software supplements the information available through standard clinical methods, providing additional, adjunctive information to surgeons, neurologists and radiologists for use in visualization and planning stereotactic surgical procedures. SIS Software provides a patient specific, 3D anatomical model of the patient's own brain structures that supplements other clinical information to facilitate visualization in neurosurqical procedures. The software makes use of the fact that some structures in the brain are not easily visualized in 1.5T or 3T clinical MRJ, but are better visualized using high-resolution and high-contrast 7T MRI.

The company's software methodology relies on a reference database of high-resolution brain images (7T MRI) and standard clinical brain images (1.5T or 3T MRI). The 7T images allow visualization of anatomical structures that are then used to find regions of interest within the brain (i.e., the STN) on a patient's clinical image.

SIS visualization is incorporated in the standard clinical MR data, thereby not changing the current standard-of-care workflow protocol and does not require any additional visualization software or hardware platforms.

AI/ML Overview

Here's a breakdown of the acceptance criteria and the study that proves the SIS Software meets them, based on the provided text:

1. Table of Acceptance Criteria and Reported Device Performance

The acceptance criteria are focused on the accuracy of the Subthalamic Nuclei (STN) visualization. The study compared the machine-predicted STN to ground truth STN.

Acceptance Criteria (Pre-specified)	Reported Device Performance
90% of Center of Mass Distances not greater than 2.0mm	95% of Center of Mass Distances were not greater than 2.0mm (95% CI: 86.91 - 98.37%)
90% of Surface Distances not greater than 2.0mm	100% of Surface Distances were not greater than 2.0mm (95% CI: 94.25 - 100%)
Significance vs. Standard of Care (20% successful visualizations)	The rate of successful visualizations from SIS Software (95% of center of mass distances not greater than 2.0mm) is significantly greater than the standard of care (p<0.0001).
Average distance between predicted and original (on 7T) for developmental testing	1mm (actual size of the pixel/data resolution)
Overlap between 3D predicted and original STN for developmental testing	Significantly better (p<0.05) in comparison to the overall of a standard atlas and the original STN.
Dice coefficient (not explicitly an acceptance criterion but reported)	0.64 (noted as expected given the small size of the STN).

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

Pivotal Validation Test Set: Images from 34 subjects, resulting in 68 STNs (each subject has two STNs).
Data Provenance: The text does not explicitly state the country of origin. It indicates that the data was retrospective, as it was composed of previously scanned clinical MRI (1.5T and 3T) and High Field (7T) MRI. Crucially, none of these 68 STNs were part of the company's database for algorithm development or optimization/design, ensuring an independent validation set.

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

The document does not specify the number of experts or their qualifications for establishing the ground truth for the pivotal validation test set. It only mentions "ground truth STNs (manually segmented clinical images and 7T images superimposed)."

For the developmental testing phase, it refers to the STN as "segmented on the 7T image of that subject." This implies expert segmentation, but details on the number or qualifications of these experts are not provided.

4. Adjudication Method for the Test Set

The document does not explicitly describe an adjudication method (e.g., 2+1, 3+1, none) for the test set ground truth. It states that the ground truth STNs were "manually segmented clinical images and 7T images superimposed," suggesting a single ground truth was established, likely by an expert or team of experts, but without specifying a review and adjudication process.

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

No, a Multi-Reader Multi-Case (MRMC) comparative effectiveness study was not done. The study focuses on evaluating the standalone performance of the SIS Software against a pre-established ground truth. There is no mention of human readers assisting with or without AI, or any comparison of human reader performance.

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

Yes, a standalone (algorithm only) performance study was conducted. The "Performance Data" section describes how the "subject machine-learning method then predicted the subthalamic nucleus (STN)" and how the "SIS visualization via the subject software" was compared to ground truth. There is no indication of human interaction or interpretation improving the algorithm's output during the validation.

7. The Type of Ground Truth Used

The ground truth used was primarily based on expert defined anatomical structures from High-Field MRI (7T). Specifically, for the pivotal validation testing, it involved "ground truth STNs (manually segmented clinical images and 7T images superimposed)." For developmental testing, it was "the STN as segmented on the 7T image." The 7T MRI is noted to "allow visualization of anatomical structures that are then used to find regions of interest within the brain (i.e., the STN) on a patient's clinical image," implying that the 7T images serve as a higher-fidelity reference for the ground truth.

8. The Sample Size for the Training Set

The document refers to a "reference database of high-resolution brain images (7T MRI) and standard clinical brain images (1.5T or 3T MRI)" which the software methodology "relies on." However, it does not explicitly state the sample size of this training database.

For the developmental testing, which seems to analyze aspects of the training/development process, it mentions "10 subject datasets that were retrospectively selected from the locked SIS reference database." This is a smaller subset used for a specific "leave-one-out" test, not the full size of the training set.

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

The ground truth for the training set (referred to as the "reference database") was established by using 7T MRI images which "allow visualization of anatomical structures that are then used to find regions of interest within the brain (i.e., the STN)." This implies that experts segmented or labeled the STN structures on these high-resolution 7T images to create the ground truth for the training data that the machine learning model learned from.

Summary

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Food and Drug Administration 10903 New Hampshire Avenue Document Control Center - WO66-G609 Silver Spring, MD 20993-0002

February 14, 2017

Surgical Information Sciences, Inc. % Ms. Janice Hogan Regulatory Counsel Hogan Lovells US LLP 1835 Market Street, 29th Floor PHILADELPHIA PA 19103

Re: K162830

Trade/Device Name: SIS Software Regulation Number: 21 CFR 892.2050 Regulation Name: Picture archiving and communications system Regulatory Class: II Product Code: LLZ Dated: January 12, 2017 Received: January 12, 2017

Dear Ms. Hogan:

We have reviewed your Section 510(k) premarket notification of intent to market the device referenced above and have determined the device is substantially equivalent (for the indications for use stated in the enclosure) to legally marketed predicate devices marketed in interstate commerce prior to May 28, 1976, the enactment date of the Medical Device Amendments, or to devices that have been reclassified in accordance with the provisions of the Federal Food, Drug, and Cosmetic Act (Act) that do not require approval of a premarket approval application (PMA). You may, therefore, market the device, subject to the general controls provisions of the Act. The general controls provisions of the Act include requirements for annual registration, listing of devices, good manufacturing practice, labeling, and prohibitions against misbranding and adulteration. Please note: CDRH does not evaluate information related to contract liability warranties. We remind you, however, that device labeling must be truthful and not misleading.

If your device is classified (see above) into either class II (Special Controls) or class III (PMA), it may be subject to additional controls. Existing major regulations affecting your device can be found in the Code of Federal Regulations, Title 21, Parts 800 to 898. In addition, FDA may publish further announcements concerning your device in the Federal Register.

Please be advised that FDA's issuance of a substantial equivalence determination does not mean that FDA has made a determination that your device complies with other requirements of the Act or any Federal statutes and regulations administered by other Federal agencies. You must comply with all the Act's requirements, including, but not limited to: registration and listing (21 CFR Part 807); labeling (21 CFR Part 801); medical device reporting (reporting of medical device-related adverse events) (21 CFR 803); good manufacturing practice requirements as set forth in the quality systems (QS) regulation (21 CFR Part 820); and if applicable, the electronic product radiation control provisions (Sections 531-542 of the Act); 21 CFR 1000-1050.

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If you desire specific advice for your device on our labeling regulation (21 CFR Part 801), please contact the Division of Industry and Consumer Education at its toll-free number (800) 638 2041 or (301) 796-7100 or at its Internet address

http://www.fda.gov/MedicalDevices/Resourcesfor You/Industry/default.htm. Also, please note the regulation entitled, "Misbranding by reference to premarket notification" (21 CFR Part 807.97). For questions regarding the reporting of adverse events under the MDR regulation (21 CFR Part 803), please go to

http://www.fda.gov/MedicalDevices/Safety/ReportaProblem/default.htm for the CDRH's Office of Surveillance and Biometrics/Division of Postmarket Surveillance.

You may obtain other general information on your responsibilities under the Act from the Division of Industry and Consumer Education at its toll-free number (800) 638-2041 or (301) 796-7100 or at its Internet address

http://www.fda.gov/MedicalDevices/ResourcesforYou/Industry/default.htm.

Sincerely yours.

Michael D'Hara

For

Robert Ochs, Ph.D. Director Division of Radiological Health Office of In Vitro Diagnostics and Radiological Health Center for Devices and Radiological Health

Enclosure

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DEPARTMENT OF HEALTH AND HUMAN SERVICES Food and Drug Administration Indications for Use

Form Approved: OMB No. 0910-0120 Expiration Date: January 31, 2017 See PRA Statement on last paqe

510(k) Number (if known)

K162830

Device Name

SIS Software

Indications for Use (Describe)

Typical users of the SIS Software are medical professionals, including but not limited to surgeons, neurologists and radiologists.

Type of Use (Select one or both, as applicable)
Prescription Use (Part 21 CFR 801 Subpart D)	Over-The-Counter Use (21 CFR 801 Subpart C)

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510(k) SUMMARY

Surgical Information Sciences, Inc.'s SIS Software Device

Sponsor's Name, Address, Telephone Number, Contact Person and Date Prepared

Surgical Information Sciences. Inc. 60 South 6th Street, Suite 2410 Minneapolis, MN 55402 Contact Person: Mark Headrick Phone: (612) 335-8683 E-mail: mark.headrick@surqicalis.com

Date Prepared: January 12, 2017

Trade Name of Device

SIS Software

Common or Usual Name / Classification Name

Picture Archiving and Communication System (Product Code: LLZ; 21 C.F.R. 892.2050)

Predicate and Reference Devices

Medtronic's StealthStation (K050438) Medtronic's Stealth Viz Advanced Planning Application with StealthDTl Package (K081512) Brainreader ApS' Neuroreader Image Processing Software (K140828) (Reference device)

Intended Use / Indications for Use

Typical users of the SIS Software are medical professionals, including but not limited to surgeons, neurologists and radiologists.

Technological Characteristics

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specific, 3D anatomical model of the patient's own brain structures that supplements other clinical information to facilitate visualization in neurosurqical procedures. The software makes use of the fact that some structures in the brain are not easily visualized in 1.5T or 3T clinical MRJ, but are better visualized using high-resolution and high-contrast 7T MRI.

Performance Data

Developmental testing of the reference database was performed using 10 subject datasets that were retrospectively selected from the locked SIS reference database. For each of the selected datasets, the patient's 7T MRI and labeled structures were excluded from the dataset, such that only the corresponding clinical MRI image remained for the test. The subject machine-learning method then predicted the subthalamic nucleus (STN) on these removed datasets and this prediction was compared to the STN as segmented on the 7T image of that subject to validate the prediction when the tested subject was removed from the dataset (standard leave-one-out statistical procedure). The average distance between the predicted and the original (on the 7T) was 1mm, the actual size of the pixel (data resolution). The overlap between the 3D predicted and the original STN was significantly better (p<0.05) in comparison to the overall of a standard atlas and the original STN.

The pivotal validation testing of the subject device, including the reference database, included images from 34 subjects to validate the performance of the SIS Software. A set of 68 STNs (from 34 subjects) were scanned with both clinical MRI (1.5T and 3T) and High Field (7T) MRI. None of the 68 STNs were part of the company's database for algorithm development and none were used to optimize or design the company's software. Thus, this validation data set was completely separate from the data set that was used for development. The software development was frozen and labeled before tested on this validation set.

Three measurements were used to compare the SIS visualization via the subject software and ground truth STNs (manually segmented clinical images and 7T images superimposed): (1) Center of mass distance; (2) Surface distance; and (3) Dice coefficient values.

In sum, 90% of the center of mass distances and surface distances were below 1.9mm and 0.8mm, respectively. Specifically, 95% of the center of mass distances and 100% of the surface distances were not greater than 2.0mm. Thus, the study met the pre-specified criteria of 90% of center of mass distances and surface distances not greater than 2.0mm. Furthermore, the proportion of visualizations not greater than 2.0mm was conservatively estimated from the literature to be 20%. Therefore, the rate of successful visualizations from SIS Software (95% of the center of mass distances not greater than 2.0mm) is significantly greater than the standard of care (p<0.0001). The corresponding confidence intervals are as follows:

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(a) 90% of the center of mass distances and surface distances were below 1.9mm and 0.8mm, respectively (95% CI: 80.74 - 95.56%);
(b) 95% of the center of mass distances were not greater than 2.0mm (95% C): 86.91 98.37%):
(c) 100% of the surface distances were not greater than 2.0mm (95% Cl: 94.25 100%).

In addition, the Dice coefficient in this dataset was 0.64, which was expected given the small size of the STN.

In sum, the SIS Software performed as intended and clinical validation data results observed were as expected.

Substantial Equivalence

With regard to technological characteristics, both the SIS Software and the predicates (K050438; K081512) are all software applications that can be used for visualization, presentation and documentation of medical imaging, including different modules for image processing, image fusion, intraoperative functional planning where the 2D or 3D output can be used with stereotactic image guided surgery or other devices for further processing and visualization. In addition, the SIS Software, like the identified predicate and reference devices, use proprietary algorithms to generate 3D segmented anatomical models from patient's MRI scans.

The SIS Software and predicate devices also all perform image fusion of datasets using automated or manual image matching techniques. Below provides a summary comparison between the SIS Software and the predicate and reference devices.

	SIS Software	MedtronicNavigation,Inc.'sStealth VizAdvancedPlanningApplication withStealthDTIPackage(K081512)	MedtronicNavigation,Inc.'sStealthStation(K050438)	BraindreaderApS'NeuroReaderMedicalImageProcessingSoftware(K140828)
Allows for importing ofdigital imaging sets	Yes	Yes	Yes	Yes
Uses proprietarysoftware algorithm togenerate 3D segmentedanatomical models frompatient's MR scans	Yes	Yes	Yes	Yes
Allows for review andanalysis of data invarious 2D and 3Dpresentation formats	Yes	Yes	Yes	Yes

				SIS Software Technological Characteristics Comparison Table
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	SIS Software	MedtronicNavigation,Inc.'sStealth VizAdvancedPlanningApplication withStealthDTIPackage(K081512)	MedtronicNavigation,Inc.'sStealthStation(K050438)	BraindreaderApS'NeuroReaderMedicalImageProcessingSoftware(K140828)
Performs image fusion ofdatasets usingautomated or manualimage matchingtechnique	Yes; atlas-basedmapping (patientspecific);referencedatabase	Yes; atlas-basedmapping	Yes, atlas-basedmapping	Yes; referencedatabase
Segments structures inimages with manual andautomated tools andconverts them into 3Dobjects for display	Yes	Yes	Yes	Yes
Creates hybrid datasetsby filing in segmentedregions slice-by-slice onanatomical datasets	Yes	Yes	Yes	Yes
Exports results toplanning system	Yes	No	Yes	Yes

In sum, the SIS Software has the same intended use and similar indications, technological characteristics, and principles of operation as its predicate and reference devices. The minor technological differences between the subject device and its predicate and reference devices raise no different questions of safety or effectiveness. Performance data demonstrate that the SIS Software performs as intended and is substantially equivalent.

Regulation Number and Section

§ 892.2050 Medical image management and processing system.

(a)
Identification. A medical image management and processing system is a device that provides one or more capabilities relating to the review and digital processing of medical images for the purposes of interpretation by a trained practitioner of disease detection, diagnosis, or patient management. The software components may provide advanced or complex image processing functions for image manipulation, enhancement, or quantification that are intended for use in the interpretation and analysis of medical images. Advanced image manipulation functions may include image segmentation, multimodality image registration, or 3D visualization. Complex quantitative functions may include semi-automated measurements or time-series measurements.(b)
Classification. Class II (special controls; voluntary standards—Digital Imaging and Communications in Medicine (DICOM) Std., Joint Photographic Experts Group (JPEG) Std., Society of Motion Picture and Television Engineers (SMPTE) Test Pattern).