(139 days)
Yes
The device description explicitly states that "SIS Software uses machine learning and image processing to enhance standard clinical images". Additionally, the "Description of the training set" section details the use of "two separate commonly used outlier detection machine learning models".
No.
The device is used for viewing, presentation, image processing, and planning, which are aids for other clinical methods, but it does not directly treat or cure a disease or condition.
No
The device is described as an aid in visualization and for planning stereotactic surgical procedures, providing supplementary information rather than making a diagnosis.
Yes
The device description explicitly states "SIS Software is an application intended for use in the viewing, presentation and documentation of medical imaging...". The entire description focuses on the software's functionalities (image processing, fusion, planning, visualization, co-registration, segmentation) and its use with existing imaging modalities and surgical devices, without mentioning any accompanying hardware developed or provided by the submitter.
Based on the provided information, this device is not an In Vitro Diagnostic (IVD).
Here's why:
- IVDs are used to examine specimens derived from the human body. The intended use and device description clearly state that SIS Software is used for viewing, processing, and analyzing medical imaging (MRI, CT scans) of the brain. These are images of the body, not specimens taken from the body.
- The purpose of an IVD is to provide information about a physiological or pathological state, or to determine susceptibility to a disease or condition. While SIS Software aids in visualization and planning for surgical procedures related to a medical condition (neurological), it does not directly diagnose or provide information about a physiological or pathological state based on analysis of bodily fluids, tissues, or other specimens.
- The input modalities are imaging modalities (MRI, CT), not laboratory tests or analysis of biological samples.
The device falls under the category of medical imaging software, specifically for visualization and planning in neurosurgery. It processes and analyzes medical images, which is distinct from the function of an IVD.
No
The provided text explicitly states "Control Plan Authorized (PCCP): Not Found," which directly indicates no PCCP was authorized nor specifically approved or cleared.
Intended Use / Indications for Use
SIS Software 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 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).
Product codes
LLZ
Device Description
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 adjunctive information 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 neurosurgical procedures. The version of the software that is the subject of the current submission (Version 3.3.0) can also be employed to co-register a post-operative CT scan with the clinical scan of the same patient from before a surgery (on which the software has already visualized the STN) and to segment in the CT image (where needed), to further assist with visualization.
The software makes use of the fact that some structures in the brain are better visualized using high-resolution and high-contrast 7T MRI than via 1.5T or 3T clinical MRI. The methodology relies on a reference database of high-resolution brain images (7T MRI) and standard clinical brain images (1.5T or 3T MRI). The algorithm uses the 7T images from a database to find regions of interest within the brain (e.g., the STN) on a patient's clinical (1.5 or 3T MRI) image.
With regard to the updated functionality to process post-operative CT images, co-registration of the clinical MR and CT images allows alignment of the spatial positioning of the brains, and segmentation of objects (e.g., when an electrode is performed to ensure that the software accurately reflects their proper position.
STN visualization, image co-registration and the optional additional CT segmentation, are incorporated in the standard-of-care clinical workflow protocols. Use of the device does not require any additional visualization software or hardware platforms.
The subject and predicate devices rely on the same core technological principles. The only major differences between the two are that version 3.3.0 (the subject device) includes the added optional functionality to process post-operative CT images as well as incorporates a user interface. The user interface/labeling has also been enhanced to clarify this optional follow-on process for the clinician.
Mentions image processing
Yes
Mentions AI, DNN, or ML
Yes
Input Imaging Modality
MRI, CT
Anatomical Site
Brain, subthalamic nuclei (STN)
Indicated Patient Age Range
Not Found
Intended User / Care Setting
Typical users of the SIS Software are medical professionals, including but not limited to surgeons, neurologists and radiologists.
Description of the training set, sample size, data source, and annotation protocol
Not Found
Description of the test set, sample size, data source, and annotation protocol
STN Visualization: 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 superimposed): (1) Center of mass distance; (2) Surface distance; and (3) Dice coefficient values.
Co-Registration: 5 MR series and 1 CT series of a phantom brain. For each of the 5 MR series, 6 fiducial points were marked by an expert, resulting in 30 points of reference.
Segmentation: 26 post-surgical CT scans that contained leads with a total sample size of 45 electrodes. For each of the CT scans, ground truth segmentations were generated by 2 experts. To generate the ground truth data, the experts used the same set of 3D components (STL files) that are used by SIS Software version 3.3.0. First, the experts segmented the electrode(s) from each CT image. Second, the 3D components were aligned manually to the segmentation from step one (ground truth).
Summary of Performance Studies (study type, sample size, AUC, MRMC, standalone performance, key results)
STN Visualization: Pivotal validation testing. Sample Size: 68 STNs (from 34 subjects). Key Results: 90% of the center of mass distances and surface distances were below 1.66mm and 0.63mm, respectively. Specifically, 98.3% of the center of mass distances and 100% of the surface distances were not greater than 2.0mm. The Dice coefficient was 0.69.
Co-Registration: Testing performed to ensure 3D transformation to CT is accurate. Sample Size: 5 MR series and 1 CT series of a phantom brain (30 points of reference). Key Results: The average of all distances between fiducial points was 0.242 mm with a standard deviation of 0.062 mm. SIS statistics shows there is 95% confidence that the error will be below 0.454 mm 90% of the time.
Segmentation: Validation of the optional segmentation feature. Sample Size: 26 post-surgical CT scans containing 45 electrodes. Key Results:
- For the center of mass distance, there is a 95% chance that 90% of the cases will be lower than 0.491 mm from the center of mass of the real contact.
- For the difference in orientation, there is a 95% chance that 90% of the cases will be lower than 2.486 degrees from the real orientation of the lead.
Modified Anomaly Detection: Validation testing. Sample Size: 68 cases. Key Results:
- Version 1.0.0 Sensitivity: 0.00%, Specificity: 92.31%. Overall System Performance Success without AD: 95.59%, Success with AD: 95.24%.
- Version 3.3.0 Sensitivity: 50.00%, Specificity: 89.39%. Overall System Performance Success without AD: 97.06%, Success with AD: 98.33%.
STN Smoothing Functionality: Testing to validate smoothed STN visualizations. Key Results: Testing produced acceptable results based on Center of Mass (COM), Dice Coefficient (DC) and Surface Distance (SC). Results demonstrated significant correlation between the smoothed and non-smoothed STN objects.
Key Metrics (Sensitivity, Specificity, PPV, NPV, etc.)
STN Visualization:
- Center of mass distances: 98.3% not greater than 2.0mm (95% CI: 91 -100%).
- Surface distances: 100% not greater than 2.0mm (95% Cl: 94 100%).
- Dice coefficient: 0.69.
Anomaly Detection - Version 1.0.0:
- Sensitivity: 0.00%
- Specificity: 92.31%
Anomaly Detection - Version 3.3.0:
- Sensitivity: 50.00%
- Specificity: 89.39%
Predicate Device(s)
Reference Device(s)
Predetermined Change Control Plan (PCCP) - All Relevant Information
Not Found
§ 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).
0
Image /page/0/Picture/0 description: The image shows the logo of the U.S. Food and Drug Administration (FDA). The logo consists of two parts: the Department of Health & Human Services logo on the left and the FDA logo on the right. The FDA logo is a blue square with the letters "FDA" in white, followed by the words "U.S. FOOD & DRUG ADMINISTRATION" in blue.
Surgical Information Sciences, Inc. % Ms. Janice M. Hogan Regulatory Counsel Hogan Lovells US LLP 1735 Market Street, 23rd Floor PHILADELPHIA PA 19103
March 19, 2019
Re: K183019
Trade/Device Name: SIS Software version 3.3.0 Regulation Number: 21 CFR 892.2050 Regulation Name: Picture Archiving and communications system Regulatory Class: Class II Product Code: LLZ Dated: February 15, 2019 Received: February 15, 2019
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. 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
1
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/CombinationProducts/GuidanceRegulatoryInformation/ucm597488.htm); good manufacturing practice requirements as set forth in the quality systems (OS) 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 http://www.fda.gov/MedicalDevices/Safety/ReportaProblem/default.htm.
For comprehensive regulatory information about mediation-emitting products, including information about labeling regulations, please see Device Advice (https://www.fda.gov/MedicalDevices/DeviceRegulationandGuidance/) and CDRH Learn
(http://www.fda.gov/Training/CDRHLearn). 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 (http://www.fda.gov/DICE) for more information or contact DICE by email (DICE@fda.hhs.gov) or phone (1-800-638-2041 or 301-796-7100).
Sincerely,
Michael D. O'Hara
Thalia Mills, Ph.D. Director Division of Radiological Health Office of In Vitro Diagnostics and Radiological Health Center for Devices and Radiological Health
Enclosure
2
510(k) Number (if known)
Device Name
SIS Software (version 3.3.0) Indications for Use (Describe)
SIS Software 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 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).
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)
区 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
Sponsor's Name, Contact Information, and Date Prepared
Surgical Information Sciences, Inc. 50 South 6th Street, Suite 1310 Minneapolis, MN 55402 Contact Person: Ann Quinlan-Smith Phone: 612-325-0187 E-mail: ann.quinlan.smith@surgicalis.com
Date Prepared: February 15, 2019
Trade Name of Device: SIS Software version 3.3.0
Common or Usual Name/Classification Name: System, Image Processing, Radiological (Product Code: LLZ; 21 C.F.R. 892.2050)
Regulatory Class: Class II
Predicate and Reference Devices
Predicate device: Surgical Information Sciences SIS Software version 1.0 (K162830) Reference device: Merge Healthcare's Merge PACS™ (K173475)
Intended Use / Indications for Use
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 conjunction with other clinical methods as an aid in visualization of the subthalamic nuclei (STN).
Typical users of the SIS Software are medical professionals, including but not limited to surgeons, neurologists and radiologists.
Technological Characteristics
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 adjunctive information for use in visualization and planning stereotactic surgical procedures. SIS Software provides a patientspecific, 3D anatomical model of the patient's own brain structures that supplements other clinical information to facilitate visualization in neurosurgical procedures. The version of the software that is the subject of the current submission (Version 3.3.0) can also be employed to co-register a post-operative CT scan with the clinical scan of the same patient from before a surgery (on which
4
the software has already visualized the STN) and to segment in the CT image (where needed), to further assist with visualization.
The software makes use of the fact that some structures in the brain are better visualized using high-resolution and high-contrast 7T MRI than via 1.5T or 3T clinical MRI. The methodology relies on a reference database of high-resolution brain images (7T MRI) and standard clinical brain images (1.5T or 3T MRI). The algorithm uses the 7T images from a database to find regions of interest within the brain (e.g., the STN) on a patient's clinical (1.5 or 3T MRI) image.
With regard to the updated functionality to process post-operative CT images, co-registration of the clinical MR and CT images allows alignment of the spatial positioning of the brains, and segmentation of objects (e.g., when an electrode is performed to ensure that the software accurately reflects their proper position.
STN visualization, image co-registration and the optional additional CT segmentation, are incorporated in the standard-of-care clinical workflow protocols. Use of the device does not require any additional visualization software or hardware platforms.
The subject and predicate devices rely on the same core technological principles. The only major differences between the two are that version 3.3.0 (the subject device) includes the added optional functionality to process post-operative CT images as well as incorporates a user interface. The user interface/labeling has also been enhanced to clarify this optional follow-on process for the clinician.
Performance Data
STN Visualization
Pivotal validation testing of the subject device was completed to confirm performance with device modifications. 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 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 1.66mm and 0.63mm, respectively. Specifically, 98.3% 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 (98.3% of the center of mass distances not greater than 2.0mm) is significantly greater than the standard of care (p
2 mm | TP | TN | FP | FN | Sensitivity | Specificity |
|--------------|----------------|---------------------------------------|------------------------------------|----|----|----|----|-------------|-------------|
| 1.0.0 | 68 | 65 | 3 | 0 | 60 | 5 | 3 | 0.00% | 92.31% |
| 3.3.0 | 68 | 66 | 2 | 1 | 59 | 7 | 1 | 50.00% | 89.39% |
| Table 2: Overall System Performance | |
|---|---|
| -- | -------------------------------------- |
| Success without AD | Success with AD | |
|---|---|---|
| 1.0.0 | 95.59% | 95.24% |
| 3.3.0 | 97.06% | 98.33% |
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STN Smoothing Functionality
SIS validated the smoothed STN visualizations that were produced by the system, based on Center of Mass (COM), Dice Coefficient (DC) and Surface Distance (SC). Testing produced acceptable results.
In addition. SIS also analyzed the results of the difference between the smoothed STN visualization and the non-smoothed STN visualizations to compare the effect of this change at a unit level. The shapes of the visualized targets from the verification accuracy testing were compared using COM, SD and DC. The results demonstrated significant correlation between the smoothed and non-smoothed STN objects. These results, in addition to the overall system accuracy, demonstrate that the overall system performance remains in line with the verification criteria for the predicate device.
Substantial Equivalence
Both the subject and predicate versions of the SIS Software are applications used for visualization, presentation and documentation of medical imaging, including different modules for image processing, image fusion, and intraoperative functional planning where the 2D or 3D output can be used with stereotactic image quided 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 subject device additionally segments post-operative CT scans (when needed) of a patient whose pre-operative MR has already been processed by the software, and enables coregistration of the two images. These additional functionalities serve the same fundamental purpose as those carried over from the predicate - to assist the clinician in surgical case management. Finally, the new features of version 3.3.0 as compared to the version 1.0 predicate device are supported by other cleared PACS systems, which perform image registration/fusion including CT and MR, such as the reference device (K173475), as well as validation testing. The table below provides a summary comparison between the SIS Software and the predicate and reference devices.
| | SIS Software
version 3.3.0
(subject) | SIS Software
version 1.0
(K162830) | Merge PACS
(K173475) |
|-----------------------------------------------------------------------------------------------|--------------------------------------------|------------------------------------------|-------------------------|
| Allows for importing of digital
imaging sets | Yes | Yes | Yes |
| Uses proprietary software
algorithm for 3D image
processing | Yes | Yes | Yes |
| Allows for review and
analysis of data in various
2D and 3D presentation
formats | Yes | Yes | Yes |
| Performs image fusion of
datasets using automated or
manual image matching
technique | Yes | Yes | Yes |
| Segments structures in | Yes | Yes | Unclear from publicly |
| SIS Software Technological Characteristics Comparison Table | ||||
|---|---|---|---|---|
| -- | -- | ------------------------------------------------------------- | -- | -- |
9
| | SIS Software
version 3.3.0
(subject) | SIS Software
version 1.0
(K162830) | Merge PACS
(K173475) |
|--------------------------------------------------------------------------------------------------------|---------------------------------------------------------------------------------------------------------------------------------|------------------------------------------|--------------------------------------------------------------------------------------------|
| images with manual and
automated tools and
converts them into 3D
objects for display | | | available information; but
these features are
already supported by the
predicate. |
| Creates hybrid datasets by
filling in segmented regions
slice-by-slice on anatomical
datasets | Yes | Yes | |
| Results can be uploaded to
planning system | Yes | Yes | Yes |
| Segmentation of CT scan to
identify structures in relation
to those visualized on MR | Yes | No | Processes images to
enable cross-registration
or cross-referencing. |
| Cross-registration of two
multi-modality images and
creation of 3D (fused) model | Yes | No | Yes |
| Uploading and viewing
images via web-based portal
or directly via separately
cleared PACS | Yes | No | Yes |
| Anomaly Detection | Yes | Yes | No |
| STN Smoothing
Functionality | Yes; supported by
testing
demonstrating new
feature does not
alter device output
compared to
predicate device | No | No |
Conclusions
The updated SIS Software (version 3.3.0) is as safe and effective as the version previously cleared in K162830 (predicate device). The subject device has the same intended use and indications for use as the predicate, and very similar technological characteristics and principles of operation, with minor differences supported by clearance of the reference device (K173475), as well as performance validation testing demonstrating that the subject device is as safe and effective as the predicate device and performs as intended. Thus, the minor technological differences between SIS Software (version 3.3.0) and its predicate device raise no new issues of safety or effectiveness, and the updated SIS Software (version 3.3.0) is substantially equivalent.