(260 days)
K203195 Simpleware ScanIP Medical
Not Found
Yes
The document explicitly states that the segmentation aspect uses a "Convolutional Networks model trained with arthritic CT scans" and refers to the "Hermes Segment Knee Neural Network Task," which are clear indicators of machine learning technology.
No
The device is described as a software interface and image segmentation system used for diagnostic and surgical planning, and for the fabrication of physical replicas for diagnostic purposes. It is explicitly stated that "The product does not currently make any claims of individual implant fits", indicating it's not directly involved in treatment.
Yes
The "Intended Use / Indications for Use" section explicitly states that the device is "intended as preoperative software for diagnostic and surgical planning" and that "The physical replicas can be used for diagnostic purposes in the field of orthopedic applications." It also mentions that "The software is intended to be used in conjunction with other diagnostic tools."
Yes
The device is described as a software interface and image segmentation system that takes DICOM CT scans as input and produces output files for diagnostic and surgical planning. It explicitly states it is "cloud-based python code" and that a custom UI is "considered to be software outside of the medical device". There is no mention of any accompanying hardware that is part of the regulated medical device.
Based on the provided information, this device is NOT an IVD (In Vitro Diagnostic).
Here's why:
-
IVD Definition: An In Vitro Diagnostic (IVD) device is defined as a medical device that is intended for use in vitro for the examination of specimens, including blood, tissue, and urine, derived from the human body, solely or principally for the purpose of providing information concerning a physiological or pathological state, or concerning a congenital abnormality, or to determine the safety and compatibility with potential recipients, or to monitor therapeutic measures.
-
Hermes' Intended Use: The intended use of Hermes is described as:
- A software interface and image segmentation system for transferring medical imaging information to an output file.
- Preoperative software for diagnostic and surgical planning.
- Generating output files for fabricating physical replicas for diagnostic purposes in orthopedic applications.
- Used in conjunction with other diagnostic tools and expert clinical judgment.
-
Lack of Specimen Examination: Hermes does not examine specimens derived from the human body (like blood, tissue, or urine) in vitro. Its input is medical imaging data (DICOM CT scans).
-
Focus on Imaging and Planning: The core function of Hermes is processing medical images (CT scans) to perform segmentation, landmarking, and generate data for surgical planning and creating physical models. While these outputs can be used for diagnostic purposes (e.g., the physical replicas), the device itself is not performing the in vitro diagnostic test on a specimen.
In summary, Hermes is a medical device that processes medical imaging data for diagnostic and surgical planning purposes, but it does not meet the definition of an In Vitro Diagnostic device because it does not examine human specimens in vitro.
No
The letter explicitly states 'Control Plan Authorized (PCCP) and relevant text: Not Found', indicating the device is not a PCCP authorized device.
Intended Use / Indications for Use
Hermes is intended for use as a software interface and image segmentation system for the transfer of medical imaging information to an output file. It is also intended as preoperative software for diagnostic and surgical planning.
For these purposes, output files can also be used for the fabrication of physical replicas using traditional or additive manufacturing methods. The physical replicas can be used for diagnostic purposes in the field of orthopedic applications.
The software is intended to be used in conjunction with other diagnostic tools and expert clinical judgment.
Product codes
OIH, LLZ
Device Description
Vent Hermes takes DICOM based CT scans from the scanning machine and plots the data generated by the machine in the form of slices of data, similar to all commercially available predicates.
The intensity data (Hounsfield Unit data) inside these is displayed to ensure that all intensity data is visible to the users. The point clouds from these data points are used to find landmarks in discrete locations, unlike the predicates that use bone outlines from automatic or semi automatic or manual sources to generate points for landmarks. This approach allows for more accurate predictions, as validated in previous studies by radiology experts. Published literature is used to define the 3 cut planes where proximal tibia and distal and posterior femoral cuts are calculated.
The product does not currently make any claims of individual implant fits but planes shared by all surgeon and implant manufacturers as the first step. The python code has been history file with design terations recorded on all major changes to the system. The segmentation aspect is a Convolutional Networks model trained with arthritic CT scans from 120 de-identified patients with varying arthritic states, demographics, and centers partnering with surgeon champions.
The seqmentation and landmarking system was validated aqainst manual, Materialize Mimics 24 autosegmentaiton, and Synopsys Simpleware knee auto segmentation models, and results were reviewed with the Chief of Radiology of Hospital for Special Surgery in New York, NY. The results were published in the International Society for Technology in Arthroplasty 2022 annual conference. Vent Hermes is a cloud-based python code that allows users to upload the de-identified DICOM files to their encrypted Google Cloud Platform folder (a business agreement certified partner with HIPAA and 21 CFR 11 certifications available).
Once the files are uploaded, the cloud server de-identifies the folder again for redundant safety, then passes it to Vent Creativity's internal server for the system to automatically run the scripts to generate the bone models, landmarks, and the final cut planes and returns these files to the user file same file number. Once the point clouds, meshes, and landmarks are calculated, any number of CAD or visualization tools can be used to review the data.
The system uses a custom Ul qenerated on the OHF medical imaging platform with system version controls and validation documents on file. This portion is considered to be software outside of the medical device as no calculation is allowed outside the python code. The visualizations are final and are for user review and approval only.
Mentions image processing
Vent Hermes takes DICOM based CT scans from the scanning machine and plots the data generated by the machine in the form of slices of data, similar to all commercially available predicates.
The intensity data (Hounsfield Unit data) inside these is displayed to ensure that all intensity data is visible to the users. The point clouds from these data points are used to find landmarks in discrete locations, unlike the predicates that use bone outlines from automatic or semi automatic or manual sources to generate points for landmarks. This approach allows for more accurate predictions, as validated in previous studies by radiology experts. Published literature is used to define the 3 cut planes where proximal tibia and distal and posterior femoral cuts are calculated.
The segmentation aspect is a Convolutional Networks model trained with arthritic CT scans from 120 de-identified patients with varying arthritic states, demographics, and centers partnering with surgeon champions.
The seqmentation and landmarking system was validated aqainst manual, Materialize Mimics 24 autosegmentaiton, and Synopsys Simpleware knee auto segmentation models, and results were reviewed with the Chief of Radiology of Hospital for Special Surgery in New York, NY. The results were published in the International Society for Technology in Arthroplasty 2022 annual conference. Vent Hermes is a cloud-based python code that allows users to upload the de-identified DICOM files to their encrypted Google Cloud Platform folder (a business agreement certified partner with HIPAA and 21 CFR 11 certifications available).
Once the files are uploaded, the cloud server de-identifies the folder again for redundant safety, then passes it to Vent Creativity's internal server for the system to automatically run the scripts to generate the bone models, landmarks, and the final cut planes and returns these files to the user file same file number. Once the point clouds, meshes, and landmarks are calculated, any number of CAD or visualization tools can be used to review the data.
The system uses a custom Ul qenerated on the OHF medical imaging platform with system version controls and validation documents on file. This portion is considered to be software outside of the medical device as no calculation is allowed outside the python code. The visualizations are final and are for user review and approval only.
Mentions AI, DNN, or ML
The segmentation aspect is a Convolutional Networks model trained with arthritic CT scans from 120 de-identified patients with varying arthritic states, demographics, and centers partnering with surgeon champions.
Input Imaging Modality
DICOM based CT scans
Anatomical Site
Not Found
Indicated Patient Age Range
Not Found
Intended User / Care Setting
Not Found
Description of the training set, sample size, data source, and annotation protocol
The segmentation aspect is a Convolutional Networks model trained with arthritic CT scans from 120 de-identified patients with varying arthritic states, demographics, and centers partnering with surgeon champions.
Description of the test set, sample size, data source, and annotation protocol
The seqmentation and landmarking system was validated aqainst manual, Materialize Mimics 24 autosegmentaiton, and Synopsys Simpleware knee auto segmentation models, and results were reviewed with the Chief of Radiology of Hospital for Special Surgery in New York, NY. The results were published in the International Society for Technology in Arthroplasty 2022 annual conference.
Summary of Performance Studies (study type, sample size, AUC, MRMC, standalone performance, key results)
Substantial equivalence was evaluated in software validation reports pertaining to accuracy of the segmentation and subsequent DICE score comparison to previously marketed devices under the same regulation. See V&V section for DICE Validation Reports.
The nonclinical and clinical tests performed as part of the Segmentation Analysis and DICE Score Study have provided a comprehensive assessment of the Hernes Segment Knee Network Task. The study compared the segmentation accuracy of Hermes with two predicate seqmentation tools, Synopsys and Mimics, using the Dice Similarity Coefficient (Dice score) as the primary metric.
The Hermes Segment Knee Neural Network Task was evaluated against stringent design and software requirements, including the ability to accurately identify and seqment relevant bones (femur, fibula, tibia, and patella) from DVCOM CT scan files that Hermes met these requirements without introducing unintended bone segmentations.
The nonclinical and clinical tests conducted for the Hermes Segment Knee Neural Network Task demonstrated that it meets essential safety and effectiveness criteria, effectively identifying and segmenting relevant bones from DICOM CT scans without unintended segmentations. When compared to the two predicate tools, Synopsys and Mimics, Hermes demonstrates substantial equivalence.
Key Metrics (Sensitivity, Specificity, PPV, NPV, etc.)
Dice Similarity Coefficient (Dice score)
Predicate Device(s)
K203195 Simpleware ScanIP Medical
Reference Device(s)
Not Found
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). On the left is the Department of Health & Human Services logo. To the right of that is the FDA logo in blue, with the words "U.S. FOOD & DRUG" stacked on top of the word "ADMINISTRATION".
Vent Creativity Craig Vittorio Director of Product / QARA 101 6th Ave 3rd Floor New York, New York 10013
March 20, 2025
Re: K241961
Trade/Device Name: Vent Creativity Knee v1.0 (Hermes) Regulation Number: 21 CFR 892.2050 Regulation Name: Medical Image Management And Processing System Regulatory Class: Class II Product Code: OIH Dated: February 18, 2025 Received: February 18, 2025
Dear Craig Vittorio:
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 (the 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 available 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.
1
Additional information about changes that may require a new premarket notification are provided in the FDA guidance documents entitled "Deciding When to Submit a 510(k) for a Change to an Existing Device" (https://www.fda.gov/media/99812/download) and "Deciding When to Submit a 510(k) for a Software Change to an Existing Device" (https://www.fda.gov/media/99785/download).
Your device is also subject to, among other requirements, the Quality System (QS) regulation (21 CFR Part 820), which includes, but is not limited to, 21 CFR 820.30, Design controls; 21 CFR 820.90, Nonconforming product; and 21 CFR 820.100, Corrective and preventive action. Please note that regardless of whether a change requires premarket review. the OS regulation requires device manufacturers to review and approve changes to device design and production (21 CFR 820.30 and 21 CFR 820.70) and document changes and approvals in the device master record (21 CFR 820.181).
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 Part 803) for devices or postmarketing safety reporting (21 CFR Part 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 Part 4, Subpart A) for combination products; and, if applicable, the electronic product radiation control provisions (Sections 531-542 of the Act); 21 CFR Parts 1000-1050.
All medical devices, including Class I and unclassified devices and combination product device constituent parts are required to be in compliance with the final Unique Device Identification System rule ("UDI Re"). The UDI Rule requires, among other things, that a device bear a unique device identifier (UDI) on its label and package (21 CFR 801.20(a)) unless an exception or alternative applies (21 CFR 801.20(b)) and that the dates on the device label be formatted in accordance with 21 CFR 801.18. The UDI Rule (21 CFR 830.300(a) and 830.320(b)) also requires that certain information be submitted to the Global Unique Device Identification Database (GUDID) (21 CFR Part 830 Subpart E). For additional information on these requirements, please see the UDI System webpage at https://www.fda.gov/medical-devices/device-advicecomprehensive-regulatory-assistance/unique-device-identification-system-udi-system.
Also, please note the regulation entitled, "Misbranding by reference to premarket notification" (21 CFR 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
2
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,
Jessica Lamb
Jessica Lamb, PhD Assistant Director Imaging Software Team DHT8B: Division of Radiological Imaging Devices and Electronic Products OHT8: Office of Radiological Health Office of Product Evaluation and Quality Center for Devices and Radiological Health
Enclosure
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Indications for Use
Submission Number (if known)
Device Name
Vent Creativity Knee v1.0 (Hermes)
Indications for Use (Describe)
Hermes is intended for use as a software interface and image segmentation system for the transfer of medical imaging information to an output file. It is also intended as preoperative software for diagnostic and surgical planning.
For these purposes, output files can also be used for the fabrication of physical replicas using traditional or additive manufacturing methods. The physical replicas can be used for diagnostic purposes in the field of orthopedic applications.
The software is intended to be used in conjunction with other diagnostic tools and expert clinical judgment.
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) #: | K241961 |
|---|---|
| 510(k) Summary | |
| Prepared on: 2025-03-13 | |
| Contact Details | |
| 21 CFR 807.92(a)(1) | |
| Applicant Name | Vent Creativity |
| Applicant Address | 101 6th Ave 3rd Floor New York NY 10013 United States |
| Applicant Contact Telephone | 203-814-7954 |
| Applicant Contact | Mr. Craig Vittorio |
| Applicant Contact Email | cvittorio@ventcreativity.com |
| Device Name | |
| 21 CFR 807.92(a)(2) | |
| Device Trade Name | Vent Creativity Knee v1.0 (Hermes) |
| Common Name | Hermes |
| Classification Name | Automated Radiological Image Processing Software |
| Regulation Number | 892.2050 |
| Product Code(s) | QIH, LLZ |
| Legally Marketed Predicate Devices | |
| 21 CFR 807.92(a)(3) | |
| Predicate # | Predicate Trade Name (Primary Predicate is listed first) |
| K203195 | Simpleware ScanIP Medical |
| Product Code | |
| LLZ | |
| Device Description Summary | |
| 21 CFR 807.92(a)(4) |
5
Vent Hermes takes DICOM based CT scans from the scanning machine and plots the data generated by the machine in the form of slices of data, similar to all commercially available predicates.
The intensity data (Hounsfield Unit data) inside these is displayed to ensure that all intensity data is visible to the users. The point clouds from these data points are used to find landmarks in discrete locations, unlike the predicates that use bone outlines from automatic or semi automatic or manual sources to generate points for landmarks. This approach allows for more accurate predictions, as validated in previous studies by radiology experts. Published literature is used to define the 3 cut planes where proximal tibia and distal and posterior femoral cuts are calculated.
The product does not currently make any claims of individual implant fits but planes shared by all surgeon and implant manufacturers as the first step. The python code has been history file with design terations recorded on all major changes to the system. The segmentation aspect is a Convolutional Networks model trained with arthritic CT scans from 120 de-identified patients with varying arthritic states, demographics, and centers partnering with surgeon champions.
The seqmentation and landmarking system was validated aqainst manual, Materialize Mimics 24 autosegmentaiton, and Synopsys Simpleware knee auto segmentation models, and results were reviewed with the Chief of Radiology of Hospital for Special Surgery in New York, NY. The results were published in the International Society for Technology in Arthroplasty 2022 annual conference. Vent Hermes is a cloud-based python code that allows users to upload the de-identified DICOM files to their encrypted Google Cloud Platform folder (a business agreement certified partner with HIPAA and 21 CFR 11 certifications available).
Once the files are uploaded, the cloud server de-identifies the folder again for redundant safety, then passes it to Vent Creativity's internal server for the system to automatically run the scripts to generate the bone models, landmarks, and the final cut planes and returns these files to the user file same file number. Once the point clouds, meshes, and landmarks are calculated, any number of CAD or visualization tools can be used to review the data.
The system uses a custom Ul qenerated on the OHF medical imaging platform with system version controls and validation documents on file. This portion is considered to be software outside of the medical device as no calculation is allowed outside the python code. The visualizations are final and are for user review and approval only.
Intended Use/Indications for Use
21 CFR 807.92(a)(5)
Hermes is intended for use as a software interface and image segmentation system for the transfer of medical imaging information to an output file. It is also intended as preoperative software for diagnostic and surgical planning.
For these purposes, output files can also be the fabrication of physical replicas using traditional or additive manufacturing methods. The physical replicas can be used for diagnostic purposes in the field of orthopedic applications.
The software is intended to be used in conjunction with other diagnostic tools and expert clinical judgment.
Indications for Use Comparison
Simpleware ScanlP Medical is intended for use as a software interface and image segmentation system for the transfer of medical imaging information to an output file. It is also intended as preoperative software for diagnostic and surgical planning.
For these purposes, output files can also be the fabrication of physical replicas using traditional or additive manufacturing methods. The physical replicas can be used for diagnostic purposes in the field of orthopedic, maxillofacial and cardiovascular applications.
The software is intended to be used in conjunction with other diagnostic tools and expert clinical judgment.
21 CFR 807 92(a)(5)
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Technological Comparison
21 CFR 807.92(a)(6)
ScanlP operates using foundational technologies to handle image processing and the creation of models. In contrast. Vent Hermes incorporates state-of-the-art algorithmic techniques to bolster both precision and Validation stages of its development, comprehensive tests were carried out on Vent Hermes to ascertain that the DICOM file interpretations were as consistent and precise as those produced by the predicate, ScanlP.
Non-Clinical and/or Clinical Tests Summary & Conclusions 21 CFR 807.92(b)
Substantial equivalence was evaluated in software validation reports pertaining to accuracy of the segmentation and subsequent DICE score comparison to previously marketed devices under the same regulation. See V&V section for DICE Validation Reports.
N/A
Conclusions from the Nonclinical and Clinical Tests
The nonclinical and clinical tests performed as part of the Segmentation Analysis and DICE Score Study have provided a comprehensive assessment of the Hernes Segment Knee Network Task. The study compared the segmentation accuracy of Hermes with two predicate seqmentation tools, Synopsys and Mimics, using the Dice Similarity Coefficient (Dice score) as the primary metric.
Safety and Effectiveness
The Hermes Segment Knee Neural Network Task was evaluated against stringent design and software requirements, including the ability to accurately identify and seqment relevant bones (femur, fibula, tibia, and patella) from DVCOM CT scan files that Hermes met these requirements without introducing unintended bone segmentations.
The nonclinical and clinical tests conducted for the Hermes Segment Knee Neural Network Task demonstrated that it meets essential safety and effectiveness criteria, effectively identifying and segmenting relevant bones from DICOM CT scans without unintended segmentations. When compared to the two predicate tools, Synopsys and Mimics, Hermes demonstrates substantial equivalence.