K Number

Device Name

Manufacturer

Vent Creativity

Date Cleared

2025-03-20

(260 days)

Product Code

Regulation Number

Type

Panel

Reference & Predicate Devices

N/A

Predicate For

N/A

Intended 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.

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.

AI/ML Overview

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

Acceptance Criteria and Reported Device Performance

Acceptance Criteria Category	Specific Criteria	Reported Device Performance
Segmentation Accuracy	Accurate identification and segmentation of relevant bones (femur, fibula, tibia, and patella) from DICOM CT scan files.	Hermes met these requirements without introducing unintended bone segmentations. Demonstrated substantial equivalence to predicate tools (Synopsys and Mimics) based on Dice Similarity Coefficient (Dice score).
Unintended Segmentations	No introduction of unintended bone segmentations.	Hermes met this requirement.
Equivalence to Predicate	Segmentation accuracy comparable to previously marketed devices under the same regulation (Synopsys and Mimics).	Demonstrated substantial equivalence through Dice score comparison.
DICOM File Interpretation	Consistent and precise interpretation of DICOM files as those produced by the predicate, ScanIP.	Comprehensive tests carried out to ascertain consistency and precision.

Study Details

A table of acceptance criteria and the reported device performance: (See above table)
Sample size used for the test set and the data provenance:
- Test Set Sample Size: Implicitly, the validation was done on the same data used for training the segmentation model, which was "arthritic CT scans from 120 de-identified patients." However, specifically for the validation against predicates, the text refers to a "study compared the segmentation accuracy of Hermes with two predicate segmentation tools, Synopsys and Mimics, using the Dice Similarity Coefficient (Dice score) as the primary metric." It doesn't explicitly state if this comparison was on all 120 patients or a subset.
- Data Provenance:
  - Country of Origin: Not explicitly stated, but the mention of "Hospital for Special Surgery in New York, NY" suggests data from the US.
  - Retrospective or Prospective: Retrospective, as it refers to "de-identified patients" and "arthritic CT scans from 120 de-identified patients."
Number of experts used to establish the ground truth for the test set and the qualifications of those experts:
- Number of Experts: Not explicitly stated for establishing ground truth for the specific test set.
- Qualifications of Experts: The "results were reviewed with the Chief of Radiology of Hospital for Special Surgery in New York, NY." This implies a highly qualified expert. For the training data, "surgeon champions" were involved, but their role in establishing ground truth for training vs. clinical input is not detailed.
Adjudication method (e.g., 2+1, 3+1, none) for the test set:
- Adjudication Method: Not explicitly stated. The comparison was primarily quantitative (Dice score) against predicate auto-segmentation models (Materialize Mimics 24 autosegmentation, Synopsys Simpleware knee auto segmentation models) and "manual" segmentation. The involvement of the Chief of Radiology appears to be for review and confirmation of results, rather than a formal adjudication process amongst multiple readers for ground truth generation.
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:
- MRMC Study: No, an MRMC study comparing human readers with and without AI assistance was not done. The study compares the algorithm's performance to other algorithms and "manual" segmentation benchmarks, primarily focusing on segmentation accuracy.
If a standalone (i.e., algorithm only without human-in-the-loop performance) was done:
- Standalone Performance: Yes, standalone performance was evaluated through the "Segmentation Analysis and DICE Score Study," comparing Hermes's segmentation with Synopsys and Mimics predicate tools. The system is described as automatically running scripts to generate bone models, landmarks, and cut planes.
The type of ground truth used (expert consensus, pathology, outcomes data, etc.):
- Type of Ground Truth: The text mentions "validated against manual" segmentation, which implies manual segmentation (likely performed by an expert or experts) served as a form of ground truth for comparison. This is often an expert-driven ground truth. It also compares against the output of other established auto-segmentation software (Mimics and Synopsys).
The sample size for the training set:
- Training Set Sample Size: "120 de-identified patients with varying arthritic states, demographics, and centers."
How the ground truth for the training set was established:
- Ground Truth Establishment for Training: The text states the segmentation aspect is a "Convolutional Networks model trained with arthritic CT scans from 120 de-identified patients." It does not explicitly state how the ground truth labels for these 120 patients were established for the training process (e.g., if they were manually segmented by experts, or derived from other clinical data). It only mentions that "surgeon champions" were involved in "partnering with" centers from which the data came, implying clinical input, but not specifically delineating their role in creating the ground truth annotations for training. Given the "validation against manual" for the test set, it's highly probable the training data also utilized manual segmentations as ground truth.

Summary

{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

{3}------------------------------------------------

Indications for Use

Submission Number (if known)

K241961

Device Name

Vent Creativity Knee v1.0 (Hermes)

Indications for Use (Describe)

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)

CONTINUE ON A SEPARATE PAGE IF NEEDED.

This section applies only to requirements of the Paperwork Reduction Act of 1995.

DO NOT SEND YOUR COMPLETED FORM TO THE PRA STAFF EMAIL ADDRESS BELOW.

The burden time for this collection of information is estimated to average 79 hours per response, including the time to review instructions, search existing data sources, gather and maintain the data needed and complete and review the collection of information. Send comments regarding this burden estimate or any other aspect of this information collection, including suggestions for reducing this burden, to:

Department of Health and Human Services Food and Drug Administration Office of Chief Information Officer Paperwork Reduction Act (PRA) Staff PRAStaff(@fda.hhs.gov

"An agency may not conduct or sponsor, and a person is not required to respond to, a collection of information unless it displays a currently valid OMB number."

{4}------------------------------------------------

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.

Intended Use/Indications for Use

21 CFR 807.92(a)(5)

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.

The software is intended to be used in conjunction with other diagnostic tools and expert clinical judgment.

21 CFR 807 92(a)(5)

{6}------------------------------------------------

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.

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).