SIS System is an application intended for use in the viewing, presentation 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 quided surgery or other processing and visualization. The device can be used in conjunction with other clinical methods as an aid in visualization of the subthalamic nuclei (STN) and globus pallidus externa and interna (GPe and GPi, respectively).

Device Description

The SIS System version 5.1.0, a software only device based on machine learning and image processing, is designed to enhance standard clinical images for the visualization of structures in the basal ganglia area of the brain, specifically the subthalamic nucleus (STN) and globus pallidus externa and interna (GPe/GPi). The output of the SIS system supplements the information available through standard clinical methods by providing additional, adjunctive information to surgeons, neurologists, and radiologists for use in viewing brain structures for planning stereotactic surgical procedures and planning of lead output.

The SIS System version 5.1.0 provides a patient-specific, 3D anatomical model of specific brain structures based on the patient's own clinical MR image using pretrained deep learning neural network models. This method incorporates ultra-high resolution 7T (7 Tesla) Magnetic Resonance images to determine ground truth for the training data set to train the deep learning models. These pre-trained deep learning neural network models are then applied to a patient's clinical image to predict the shape and position of the patient's specific brain structures of interest. The SIS System is further able to locate and identify implanted leads, where implanted, visible in post-operative CT images and place them in relation to the brain structure of interest from the preoperative processing.

The proposed device is a modification to the SIS Software version 3.6.0 that was cleared under K192304. The changes made to the SIS System include (1) an updated algorithm that is based on deep learning Convolutional Neural Network models that were architected and optimized for brain image segmentation; (2) the addition of new targets for visualization, specifically the globus pallidus externa and interna (GPe/GPi); and (3) the addition of a functionality to determine the orientation of a directional lead, following its segmentation from the post-operative CT image.

AI/ML Overview

The provided text describes the acceptance criteria and the study conducted for the SIS System (Version 5.1.0), a software-only device designed to enhance the visualization of specific brain structures (subthalamic nucleus - STN, and globus pallidus externa and interna - GPe/GPi) using deep learning and image processing.

Here's a breakdown of the requested information:

1. Table of Acceptance Criteria and Reported Device Performance:

The document states that "visualization accuracy testing was conducted for the STN and GPi/GPe structures using the same test methods and acceptance criteria for the previously cleared predicate device." However, the specific numerical acceptance criteria for visualization accuracy (e.g., minimum Dice similarity coefficient, maximum distance errors) are not explicitly provided in the text. The only specific performance metric reported is related to electrode orientation detection.

Table: Acceptance Criteria and Reported Device Performance

Performance Metric	Acceptance Criteria (Implied)	Reported Device Performance
STN Visualization Accuracy	(Same as predicate, but specific numerical criteria not provided)	"performed similarly to the predicate device" (no specific numbers given)
GPi/GPe Visualization Accuracy	(Same as predicate, but specific numerical criteria not provided)	"performed similarly to the predicate device" (no specific numbers given)
MRI to CT Registration Accuracy	(Requirement to remain accurate)	"ensure that 3D transformation remains accurate" (no specific numbers)
CT Image Processing (Lead Segmentation)	(Validation of lead segmentation)	"validate the lead segmentation" (no specific numbers)
Electrode Orientation Detection Accuracy (Trusted Detections)	>90% accurate within ± 30°	91% of cases correct within ± 30°
Electrode Orientation Detection Accuracy (Untrusted Detections)	(Not explicitly stated or reported, but mentioned as characterized)	(Not explicitly reported)

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

Test Set Sample Size:
- For electrode orientation detection: 43 CT image series that contained 55 leads.
- For visualization accuracy, MRI to CT registration, and lead segmentation: The text does not explicitly state the sample size for these tests. It only mentions "repeated to validate that the modified software functions as specified."
Data Provenance: Not specified. The document does not mention the country of origin of the data or whether it was retrospective or prospective.

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

The document does not explicitly state the number of experts used to establish ground truth for the test set.
It mentions that "ultra-high resolution 7T (7 Tesla) Magnetic Resonance images to determine ground truth for the training data set." It does not directly link this to human experts for the test set.

4. Adjudication Method for the Test Set:

The document does not mention a specific adjudication method (e.g., 2+1, 3+1) for establishing ground truth for the test set.

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

No, an MRMC comparative effectiveness study comparing human readers with AI vs. without AI assistance was not mentioned in the provided text. The study focuses on evaluating the device's performance in segmentation and lead detection, not its impact on human reader performance.

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

Yes, the performance data described, particularly for visualization accuracy and electrode orientation detection, appears to be that of the standalone algorithm (without human-in-the-loop performance measurement). The assessments of "accuracy within ± 30°" and "performed similarly to the predicate device" refer to the output of the software itself.

7. The Type of Ground Truth Used:

Expert Consensus/High-Resolution Imaging: For the training data set (and implicitly for evaluation, though not explicitly stated for the test set), the ground truth for brain structure segmentation was stated to be derived from "ultra-high resolution 7T (7 Tesla) Magnetic Resonance images." This implies that experts (e.g., neurologists, radiologists) likely delineated these structures on these high-resolution images to create the ground truth.
For electrode orientation, it is stated that the "software was characterized by two probabilities: the probability of a trusted detection being accurate (within ± 30° of the ground truth) and the probability of an untrusted detection being accurate." This suggests a human-defined ground truth for lead orientation against which the algorithm's output was compared.

8. The Sample Size for the Training Set:

The document does not explicitly state the sample size for the training set. It only mentions that "This method incorporates ultra-high resolution 7T (7 Tesla) Magnetic Resonance images to determine ground truth for the training data set to train the deep learning models."

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

The ground truth for the training set was established using "ultra-high resolution 7T (7 Tesla) Magnetic Resonance images". This implies that these high-resolution images served as the reference standard, likely with manual or semi-manual expert annotations of the brain structures (STN, GPe/GPi) to create the ground truth labels for training the deep learning neural network models.

Summary

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Surgical Information Sciences, Inc. % Kelliann Payne Partner Hogan Lovells US LLP 1735 Market Street, 23rd Floor PHILADELPHIA, PA 19103

March 31, 2021

Re: K210071

Trade/Device Name: SIS System (Version 5.1.0) Regulation Number: 21 CFR 892.2050 Regulation Name: Picture archiving and communications system Regulatory Class: Class II Product Code: QIH, LLZ Dated: January 11, 2021 Received: January 11, 2021

Dear Kelliann Payne:

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. Although this letter refers to your product as a device, please be aware that some cleared products may instead be combination products. The 510(k) Premarket Notification Database located at https://www.accessdata.fda.gov/scripts/cdrh/cfdocs/cfpmn/pmn.cfm identifies combination product submissions. 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 of medical device-related adverse events) (21 CFR 803) for

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devices or postmarketing safety reporting (21 CFR 4, Subpart B) for combination products (see https://www.fda.gov/combination-products/guidance-regulatory-information/postmarketing-safety-reportingcombination-products); good manufacturing practice requirements as set forth in the quality systems (QS) regulation (21 CFR Part 820) for devices or current good manufacturing practices (21 CFR 4, Subpart A) for combination products; and, if applicable, the electronic product radiation control provisions (Sections 531-542 of the Act); 21 CFR 1000-1050.

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 https://www.fda.gov/medical-device-safety/medical-device-reportingmdr-how-report-medical-device-problems.

For comprehensive regulatory information about mediation-emitting products, including information about labeling regulations, please see Device Advice (https://www.fda.gov/medicaldevices/device-advice-comprehensive-regulatory-assistance) and CDRH Learn (https://www.fda.gov/training-and-continuing-education/cdrh-learn). Additionally, you may contact the Division of Industry and Consumer Education (DICE) to ask a question about a specific regulatory topic. See the DICE website (https://www.fda.gov/medical-device-advice-comprehensive-regulatoryassistance/contact-us-division-industry-and-consumer-education-dice) for more information or contact DICE by email (DICE@fda.hhs.gov) or phone (1-800-638-2041 or 301-796-7100).

Sincerely.

For

Thalia T. Mills, Ph.D. Director Division of Radiological Health OHT7: Office of In Vitro Diagnostics and Radiological Health Office of Product Evaluation and Quality Center for Devices and Radiological Health

Enclosure

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

510(k) Number (if known)

K210071

Device Name

SIS System (version 5.1.0)

Indications for Use (Describe)

Typical users of SIS System 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

K210071

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

Surgical Information Sciences, Inc. 10405 6th Avenue North, Suite 110 Plymouth, MN 55441 Contact Person: Ann Quinlan-Smith Phone: 612-325-0187 E-mail: ann.quinlan.smith@surqicalis.com

Date Prepared: March 29, 2021

Trade Name of Device: SIS System version 5.1.0

Common or Usual Name/Classification Name:

Automated Radiological Image Processing Software (Product Code: QIH; 21 Primary: C.F.R 892.2050);
Secondary: System, Image Processing, Radiological (Product Code: LLZ; 21 C.F.R 892.2050)

Regulatory Class: Class II

Predicate Device: Surgical Information Sciences SIS Software version 3.6.0 (K192304)

Intended Use / Indications for Use

SIS System 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 conjunction with other clinical methods as an aid in visualization of the subthalamic nuclei (STN) and globus pallidus externa and interna (GPe and GPi, respectively).

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

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Technological Characteristics

Performance Data

Following the modifications, the software verification and validation testing was repeated to validate that the modified software functions as specified and performs similarly to the predicate device.

To validate the updated algorithm, visualization accuracy testing was conducted for the STN and GPi/GPe structures using the same test methods and acceptance criteria for the previously cleared predicate device. In addition, the company repeated the MRI to CT registration testing to ensure that 3D transformation remains accurate. The company also repeated the testing for image processing

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of CT images to validate the lead segmentation. Finally, the electrode orientation detection software was validated on 43 CT image series that contained 55 leads. The software was characterized by two probabilities: the probability of a trusted detection being accurate (within ± 30° of the ground truth) and the probability of an untrusted detection being accurate. When the software trusted the lead detection, it was correct in 91% of cases. This testing demonstrated that greater than 90% of orientations presented to the user are accurate within ± 30°. The results of this testing demonstrated that the SIS System version 5.1.0 has been fully verified and validated and the updated device performs as intended and is as safe and effective compared to the predicate.

Substantial Equivalence

Both the subject and predicate devices are applications 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. Both devices can be used in conjunction with other clinical methods as an aid in visualization of the target brain structures. In addition, typical users of both devices are medical professionals, including, but not limited to surgeons, neurologists and radiologists.

The subject device, like the predicate, operates on other computer platforms and uses a proprietary algorithm to generate 3D segmented anatomical models from patients' MRI scans. The subject device employs an updated version of the algorithm based on deep learning Convolutional Network Models, which were trained to identify the region of interest and individually predict the location and size of the anatomical structures of interest. Furthermore, the addition of the globus pallidus externa and interna (GPe/GPi) structures as well as the functionality to detect the orientation of the implanted directional lead, further facilitate the fundamental clinical purpose for which the predicate was cleared, namely assistance with visualization, surgical planning, image review and analysis. Validation testing demonstrated that the subject device is as safe and effective as the predicate device. The table below provides a summary comparison between the subject and predicate devices.

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	SIS System version 5.1.0(subject device)	SIS Software version 3.6.0(predicate device)
Intended Use /Indications for Use	SIS System is an application intended for use in theviewing, presentation anddocumentation of medicalimaging, including differentmodules for imageprocessing, image fusion,and intraoperative functionalplanning where the 3Doutput can be used withstereotactic image guidedsurgery or other devices forfurther processing andvisualization. The devicecan be used in conjunctionwith other clinical methodsas an aid in visualization ofthe subthalamic nuclei(STN) and globus pallidusexterna and interna (GPeand GPi, respectively).Typical users of the SISSystem are medicalprofessionals, including butnot limited to surgeons,neurologists andradiologists.	SIS Software is an application intended for use in theviewing, presentation anddocumentation of medicalimaging, including differentmodules for imageprocessing, image fusion, andintraoperative functionalplanning where the 3D outputcan be used with stereotacticimage guided surgery orother devices for furtherprocessing and visualization.The device can be used inconjunction with other clinicalmethods as an aid invisualization of thesubthalamic nuclei (STN).Typical users of SIS Softwareare medical professionals,including but not limited tosurgeons, neurologists, andradiologists.
User Population	Medical professionals,including but not limited tosurgeons, neurologists andradiologists.	Medical professionals,including but not limited tosurgeons, neurologists, andradiologists.
Allows for importing ofdigital imaging sets	Yes	Yes
Uses proprietarysoftware algorithm togenerate 3D segmentedanatomical models frompatient's MR scans	Yes	Yes
	SIS System version 5.1.0(subject device)	SIS Software version 3.6.0(predicate device)
Allows for review andanalysis of data in 2Dand 3D formats	Yes	Yes
Performs image fusionof datasets usingautomated or manualimage matchingtechnique	Yes	Yes
Segments structures inimages with manual andautomated tools andconverts them into 3Dobjects for display	Yes	Yes
Creates hybrid datasetsby filing in segmentedregions slice-by-sliceon anatomical datasets	Yes	Yes
Can be downloaded toplanning system	Yes	Yes
Segmentation of CTscan to identifystructures in relation tothose visualized on MR	Yes	Yes
Feature to Account forCT images with gantrytilt	Yes	Yes
Cross-registers imagesand creates 3D (fused)model	Yes	Yes
Uses registrationmethods (linear andnon-linear) by multipleregistration tools (ANTSand ELASTIX)	Yes	Yes

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Conclusion

The updated SIS System version 5.1.0 is as safe and effective as the predicate version previously cleared in K192304. The subject device has the same intended use and similar technological characteristics and principles of operation, with minor differences supported by performance validation testing demonstrating that the subject device is as safe and effective as the predicate device. Thus, the minor technological differences between SIS System version 5.1.0 and its predicate device raise no new issues of safety or effectiveness, and the updated SIS System version 5.1.0 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).