SIS System is 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, visualization and localization. The device can be used in conjunction with other clinical methods as an aid in visualization and location of the subthalamic nuclei (STN) and globus pallidus externa and interna (GPe and GPi, respectively) in neurological procedures. The system is indicated for surgical procedures in which anatomical structure locations are identified in images, including Deep Brain Stimulation Lead Placement. Typical users of the SIS Software are medical professionals, including but not limited to surgeons, neurologists and radiologists.

Device Description

The SIS System version 5.6.0 is a software only device based on machine learning and image processing. The device 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 surqical procedures and planning of lead output. The SIS System version 5.6.0 provides a patient-specific, 3D anatomical model of specific brain structures based on the patient's own clinical MR image using pre-trained deep learning neural network models. As discussed in more detail below, the 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. 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 System version 5.1.0 that was cleared under K210071. The primary change is an update to the indications for use statement to clarify that deep brain stimulation (DBS) lead placement is a type of procedure that may be assisted by the information generated by the SIS System. The technological characteristics of the proposed device are fundamentally the same with minor updates to the backend of the software. The core algorithm that processes patient images has not changed since the prior clearance.

AI/ML Overview

The provided text is a 510(k) summary for the SIS System (version 5.6.0). It primarily focuses on demonstrating substantial equivalence to a predicate device (SIS System version 5.1.0) rather than providing a detailed study report with specific acceptance criteria and performance data in the format requested. While it mentions performance data, it doesn't provide the detailed metrics or the specific study setup to prove the device meets acceptance criteria.

However, based on the available information, I can infer and summarize some aspects and state what information is not present to answer your questions fully.

Key Information from the Document:

Device: SIS System (version 5.6.0)
Intended Use: Viewing, presentation, and documentation of medical imaging; image processing, fusion, and intraoperative functional planning; aid in visualization and location of STN, GPe, and GPi in neurological procedures; indicated for surgical procedures where anatomical structure locations are identified (including Deep Brain Stimulation Lead Placement).
Technological Characteristics: Software-only device based on machine learning and image processing. Enhances standard clinical images for visualization of basal ganglia structures (STN, GPe/GPi). Uses pre-trained deep learning neural network models based on ultra-high resolution 7T MR images to determine ground truth for training. Applies these models to patient's clinical MR images to predict shape and position of brain structures. Can locate and identify implanted leads in post-operative CT images.
Changes from Predicate (v5.1.0): Primary change is an update to the indications for use statement to clarify DBS lead placement. Core algorithm unchanged. Minor backend updates.
Performance Data Mentioned: "software verification testing was repeated to validate that the software functions as specified and performs similarly to the predicate device using the same test methods and acceptance criteria for the previously cleared predicate device. Visualization accuracy testing was repeated to validation of the STN and GPi/GPe structures. In addition, the company repeated the MRI to CT registration to ensure that 3D transformation remains accurate. The company also repeated the testing for image processing of CT images to validate the lead segmentation, as well as testing for electrode orientation to validate the lead detection functionality."

Addressing Your Specific Questions based on the Provided Text:

1. A table of acceptance criteria and the reported device performance

Based on the provided text, a detailed table with specific acceptance criteria and reported numerical performance metrics is not available. The document generally states that the device "performs similarly to the predicate device" and "performs as intended and is as safe and effective." It does not quantify the "visualization accuracy" or present the results of the "MRI to CT registration" or "lead segmentation/detection validation" in a tabulated format with acceptance thresholds.

2. Sample size used for the test set and the data provenance (e.g. country of origin of the data, retrospective or prospective)

The document does not specify the sample size for the test set or the provenance of the data (e.g., retrospective/prospective, country of origin). It only refers to a "test set" without explicit details.

3. Number of experts used to establish the ground truth for the test set and the qualifications of those experts (e.g. radiologist with 10 years of experience)

The document does not provide this information for the test set. It mentions ultra-high resolution 7T (7 Tesla) Magnetic Resonance images were used to "determine ground truth for the training data set," but this detail is specifically for the training data, not the test set, and it doesn't specify experts for even that.

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

The document does not describe any adjudication method for the test set.

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

The document does not mention a multi-reader multi-case (MRMC) comparative effectiveness study or any data on how human readers improve with AI assistance. The study described focuses on technical performance of the device itself and its similarity to the predicate, not human-in-the-loop performance.

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

Yes, the information implies that a standalone performance evaluation was done. The "software verification testing" and "visualization accuracy testing," alongside validation of "MRI to CT registration" and "lead segmentation," are inherent evaluations of the algorithm's performance without human interaction during the measurement of these specific metrics.

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

The document states: "the 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." This suggests that high-resolution imaging was considered the ground truth for anatomical structure definition. It doesn't explicitly state whether expert consensus or pathology was additionally involved in establishing this ground truth from the 7T images for either training or testing.

8. The sample size for the training set

The document does not specify the sample size for the training set. It only mentions that the deep learning models were trained using 7T MR images for ground truth.

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." The document implies that these images themselves, due to their high resolution, served as the basis for defining the ground truth for the specific brain structures (STN, GPe, GPi) used to train the deep learning models. It doesn't explicitly detail a human outlining or consensus process on these 7T images, although such a process is commonly implicit when using anatomical imaging as ground truth for segmentation tasks.

Summary

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November 21, 2022

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

Re: K223032

Trade/Device Name: SIS System (Version 5.6.0) Regulation Number: 21 CFR 892.2050 Regulation Name: Medical image management and processing system Regulatory Class: Class II Product Code: QIH, LLZ Dated: September 29, 2022 Received: September 29, 2022

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

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801); medical device reporting of medical device-related adverse events) (21 CFR 803) for 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 medical devices and radiation-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,

D.G.K.

Daniel M. Krainak, Ph.D. Assistant Director Magnetic Resonance and Nuclear Medicine Team DHT8C: Division of Radiological Imaging and Radiation Therapy Devices OHT8: Office of 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)

K223032

Device Name

SIS System (version 5.6.0)

Indications for Use (Describe)

SIS System is 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 guided surgery or other processing, visualization and localization. The device can be used in conjunction with other clinical methods as an aid in visualization and location of the subthalamic nuclei (STN) and globus pallidus externa and interna (GPe and GPi, respectively) in neurological procedures. The system is indicated for surgical procedures in which anatomical structure locations are identified in images, including Deep Brain Stimulation Lead Placement.

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)

☑ Requisition Use (Part 21 CFR 801 Subpart D)
☐ Over-The-Counter Use (21 CFR 801 Subpart G)

区 Prescription Use (Part 21 CFR 801 Subpart D)

_ Over-The-Counter Use (21 CFR 801 Subpart C)

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K223032

510(k) SUMMARY Surgical Information Sciences, Inc.'s SIS System (version 5.6.0)

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

Surgical Information Sciences, Inc. 4602 141st Ln NE Ham Lake, MN 55304 Contact Person: Ann Quinlan-Smith Phone: 612-325-0187 E-mail: ann.quinlan.smith@surqicalis.com

Date Prepared: September 29, 2022

Trade Name of Device: SIS System version 5.6.0

Common or Usual Name/Classification Name

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

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

Regulatory Class: Class II

Predicate Devices

Predicate: Surgical Information Sciences SIS Software version 5.1.0 (K210071)

Reference: Medtronic Navigation, Inc. StealthStation System with StealthStation Cranial Software (K153660)

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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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 surqical procedures and planning of lead output.

The SIS System version 5.6.0 provides a patient-specific, 3D anatomical model of specific brain structures based on the patient's own clinical MR image using pre-trained deep learning neural network models. As discussed in more detail below, the 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. 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 System version 5.1.0 that was cleared under K210071. The primary change is an update to the indications for use statement to clarify that deep brain stimulation (DBS) lead placement is a type of procedure that may be assisted by the information generated by the SIS System. The technological characteristics of the proposed device are fundamentally the same with minor updates to the backend of the software. The core algorithm that processes patient images has not changed since the prior clearance.

Performance Data

Following the modifications, the software verification testing was repeated to validate that the software functions as specified and performs similarly to the predicate device using the same test methods and acceptance criteria for the previously cleared predicate device. Visualization accuracy testing was repeated to validation of the STN and GPi/GPe structures. In addition, the company repeated the MRI to CT registration to ensure that 3D transformation remains accurate. The company also repeated the testing for image processing of CT images to validate the lead segmentation, as well as testing for electrode orientation to validate the lead detection functionality. The results of this testing demonstrated that the SIS System version 5.6.0 has been fully verified and the updated device performs as intended and is as safe and effective compared to the predicate.

Substantial Equivalence

The SIS System version 5.6.0 is as safe and effective as the SIS System version 5.1.0. The SIS System version 5.6.0 has the same intended uses and similar indications, technological characteristics, and principles of operation as its predicate device. The minor differences in the indications for use do not raise new questions of safety or effectiveness. Performance data demonstrate that the SIS System version 5.6.0 is as safe and effective as the predicate device. Thus, the SIS System version 5.6.0 is substantially equivalent.

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	SIS System version5.6.0(subject device)	SIS System version5.1.0 (K210071)(predicate device)	Comparison
Intended Use /Indications for Use	SIS System is intendedfor use in the viewing,presentation anddocumentation ofmedical imaging,including differentmodules for imageprocessing, imagefusion, andintraoperative functionalplanning where the 3Doutput can be used withstereotactic imageguided surgery or otherdevices for furtherprocessing,visualization andlocalization. The devicecan be used inconjunction with otherclinical methods as anaid in visualization andlocation of thesubthalamic nuclei(STN) and globuspallidus externa andinterna (GPe and GPi,respectively) inneurologicalprocedures. The systemis indicated for surgicalprocedures in whichanatomical structurelocations are identifiedin images, includingDeep Brain StimulationLead Placement.Typical users of the SISSoftware are medicalprofessionals, includingbut not limited tosurgeons, neurologistsand radiologists.	SIS System is anapplication intended foruse in the viewing,presentation anddocumentation ofmedical imaging,including differentmodules for imageprocessing, imagefusion, andintraoperative functionalplanning where the 3Doutput can be used withstereotactic imageguided surgery or otherdevices for furtherprocessing andvisualization. Thedevice can be used inconjunction with otherclinical methods as anaid in visualization ofthe subthalamic nuclei(STN) and globuspallidus externa andinterna (GPe and GPi,respectively).Typical users of the SISSystem are medicalprofessionals, includingbut not limited tosurgeons, neurologistsand radiologists.	Similar. Addition ofclarifying statement(about use in medicalprocedures in whichanatomical structurelocations such as STN,GPe and GPi areidentified in images,including deep brainstimulation leadplacement) does notraise different questionsof safety oreffectiveness becausepredicate was alreadyintended for use in suchprocedures and otherreference devices (e.g.,StealthStation withCranial Software,K153660) with similarfunctions include thislanguage.
User Population	Medical professionals,including but not limitedto surgeons,neurologists andradiologists.	Medical professionals,including but not limitedto surgeons,neurologists andradiologists.	Same
Allows for importingof digital imaging sets	Yes	Yes	Same
	SIS System version5.6.0(subject device)	SIS System version5.1.0 (K210071)(predicate device)	Comparison
Uses proprietarysoftware algorithm togenerate 3Dsegmented anatomicalmodels from patient'sMR scans	Yes	Yes	Same
Allows for review andanalysis of data in 2Dand 3D formats	Yes	Yes	Same
Performs image fusionof datasets usingautomated or manualimage matchingtechnique	Yes	Yes	Same
Segments structuresin images with manualand automated toolsand converts theminto 3D objects fordisplay	Yes	Yes	Same
Creates hybriddatasets by filing insegmented regionsslice-by-slice onanatomical datasets	Yes	Yes	Same
Can be downloaded toplanning system	Yes	Yes	Same
Segmentation of CTscan to identifystructures in relationto those visualized onMR	Yes	Yes	Same
Feature to Account forCT images with gantrytilt	Yes	Yes	Same
Cross-registersimages and creates 3D(fused) model	Yes	Yes	Same
Uses registrationmethods (linear andnon-linear) by multipleregistration tools(ANTS and ELASTIX)	Yes	Yes	Same

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Conclusions

The SIS System version 5.6.0 is as safe and effective as the predicate version previously cleared in K210071.

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).