(185 days)
Yes
The summary explicitly states that the device uses "machine learning-based algorithms" and supports "deep learning processing" for automatic delineation of organs-at-risk.
No
The device aids in the preparation for radiation therapy by contouring organs, but it does not directly administer treatment or affect the body in a therapeutic way itself. It is a tool for image processing that facilitates clinical workflows.
No
The device, DV.Target, automatically delineates organs-at-risk (OARs) for radiation therapy treatment planning. While it processes medical images, its primary function is to facilitate treatment planning by providing contours, not to diagnose a patient's condition or disease directly. The output is an input to clinical workflows for treatment planning, which is a therapeutic rather than diagnostic step.
Yes
The device is described as "standalone software" and its function is to process medical images and generate contours. While it requires a server for installation and other software (like a TPS) for visualization and editing, the core medical device functionality is entirely within the software itself.
Based on the provided information, DV.Target is not an In Vitro Diagnostic (IVD) device.
Here's why:
- Definition of IVD: An IVD device is a medical device that is used to examine specimens taken from the human body, such as blood, urine, or tissue, to provide information for diagnosis, monitoring, or screening.
- DV.Target's Function: DV.Target processes medical images (CT scans) of the human body in situ (while still within the body). It does not analyze specimens taken from the body.
- Intended Use: The intended use is to automatically delineate organs-at-risk on CT images to facilitate radiation therapy workflows. This is an image processing and analysis function, not a diagnostic test performed on a biological sample.
- Input Data: The input data is DICOM-compliant CT images, not biological specimens.
Therefore, DV.Target falls under the category of medical image processing software, not an In Vitro Diagnostic device.
No
The letter does not explicitly state that the FDA has reviewed, approved, or cleared a Predetermined Change Control Plan (PCCP) for this specific device.
Intended Use / Indications for Use
DV.Target is a software application that enables the routing of DICOM-compliant data (CT Images) to automatic image processing workflows, using machine learning-based algorithms to automatically delineate organs-at-risk (OARs). Contours generated by DV.Target may be used as an input to clinical workflows for treatment planning in radiation therapy.
DV.Target is intended to be used by trained medical professionals including radiologists, radiation oncologists, dosimetrists, and physicists.
DV.Target does not provide a user interface for data visualization. Image data uploaded, auto-contouring results, and other functionalities are managed via an administration interface. Thus, it is required that DV.Target be used in conjunction with appropriate software, such as a treatment planning system (TPS), to review, edit, and approve for all contours generated by DV.Target.
DV.Target is only intended for normal organ contouring, not for tumor or clinical target volume contouring.
Product codes (comma separated list FDA assigned to the subject device)
QKB
Device Description
The proposed device, DV.Target, is a standalone software that is designed to be used by trained medical professionals to automatically delineate organs-at-risk (OARs) on CT images. This OARs delineation function, often referred as auto-contouring, is intended to facilitate radiation therapy workflows. Supported image modalities include CT and RTSTURCT.
DV.Target can automatically delineate major OARs in three anatomical sites --- Head & Neck, Thorax, and Abdomen & Pelvis. It receives CT images in DICOM format as input and automatically generates the contours of OARs, which are stored in DICOM format and in RTSTRUCT modality.
The deployment environment of the proposed device is recommended to be a local network with an existing hospital-grade IT system in place. DV.Target should be installed on a specialized server supporting deep learning processing. After installation, users can login to the DV.Target administration interface via browsers from their local computers. All activities, including autocontouring, are operated by users through the administration interface.
In addition to auto-contouring, DV.Target also has the following auxiliary functions:
- User interface for receiving, updating and transmitting medical images in DICOM format;
- User management;
- Processed image management and output (RTSTRUCT) file management.
Once data is routed to DV.Target auto-contouring workflows, no user interaction is required, nor provided. The image data, auto-contouring results, and other functionalities can be managed by DV.Target users via an administration user interface. Third-party image visualization and editing software, such as a treatment planning system (TPS), must be used to facilitate the review and editing of contours generated by DV.Target.
Mentions image processing
DV.Target is a software application that enables the routing of DICOM-compliant data (CT Images) to automatic image processing workflows, using machine learning-based algorithms to automatically delineate organs-at-risk (OARs).
Mentions AI, DNN, or ML
machine learning-based algorithms
Input Imaging Modality
CT Images, CT, RTSTRUCT
Anatomical Site
Head & Neck, Thorax, Abdomen & Pelvis
Indicated Patient Age Range
Any patient type for whom relevant modality scan data is available.
Intended User / Care Setting
DV.Target is intended to be used by trained medical professionals including radiologists, radiation oncologists, dosimetrists, and physicists.
Clinical/Hospital environment
Description of the training set, sample size, data source, and annotation protocol
All validation data described above were invisible in model training.
Description of the test set, sample size, data source, and annotation protocol
The validation data used in these studies consists of two independent dataset collected from a large medical images archive --- TCIA and b) a clinical in-house dataset collected retrospectively from the City of Hope (our primary validation site). A comprehensive characteristic analysis of validation data to demonstrate the representativeness of the intended patient population is presented and related backgrounds are introduced. The ground truth OARs contours on the public validation data were generated from the consensus of three board-certified physicians. The ground truth contours on the in-house clinical data (collected retrospectively) were based on actual clinical contouring results.
Summary of Performance Studies (study type, sample size, AUC, MRMC, standalone performance, key results)
Comparison Study 1: Conducted between the proposed and predicate devices on a public validation dataset (64% are from the US) to evaluate the auto-contouring accuracy of 19 overlapping OARs (OARs delineated by both devices).
Comparison Study 2: Conducted between the proposed and predicate devices on an in-house clinical dataset to evaluate the auto-contouring accuracy of the overlapping OARs.
Comparison Study 3: Conducted between the proposed device and a reference device (MIM -MRT Dosimetry 510(k) Number K182624) on the public validation data (64% are from the US) to evaluate the auto-contouring accuracy of 30 non-overlapping OARs (OARs delineated by the proposed device, but not by the predicate).
The Dice-Sørensen coefficients (DICE score) were calculated and used to evaluate contouring accuracies by comparing devicegenerated contours with ground truth contours. A systematic statistical methodology, including data presentation (histogram, Box and Whisker plot, and Bland-Altman plot) and statistical inferences (i.e. the non-inferiority tests), is established as the guidelines for data analysis in the three Comparison Studies.
In each of the studies, we observed the following results:
a) The DICE scores from the proposed and the predicate/reference devices both have a central tendency (DICE Score differences can be approximated by the normal distribution) and have a good agreement with each other (from histograms and Bland-Altman plots).
b) The DICE scores of the proposed device are generally higher than those of the predicate/reference device (from the Box and Whisker plots).
c) The confidence interval of performance differences between the proposed and the predicate/reference devices are within the non-inferiority margin for all compared OARs. Hence the statement that the proposed device is noninferior to the predicate/reference device is established. Additionally, we demonstrate that the performance of the proposed device on the non-overlapping OARs is similar to its performance on the overlapping OARs (Comparison Study 3b).
Conclusions from these studies:
- DV.Target is non-inferior to the predicate device Mirada on all 19 overlapping OARs. This conclusion is supported by Comparison Studies 1&2, based on validation data from different sites and with independent annotations.
- DV.Target is non-inferior to the reference device MIM on the 30 non-overlapping OARs. The performance of DV.Target on the non-overlapping OARs is similar to its performance on the overlapping OARs. This is supported by Comparison Studies 3a & 3b.
Key Metrics (Sensitivity, Specificity, PPV, NPV, etc.)
Dice-Sørensen coefficients (DICE score)
Predicate Device(s): If the device was cleared using the 510(k) pathway, identify the Predicate Device(s) K/DEN number used to claim substantial equivalence and list them here in a comma separated list exactly as they appear in the text. List the primary predicate first in the list.
Reference Device(s): Identify the Reference Device(s) K/DEN number and list them here in a comma separated list exactly as they appear in the text.
Predetermined Change Control Plan (PCCP) - All Relevant Information for the subject device only (e.g. presence / absence, what scope was granted / cleared under the PCCP, any restrictions, etc).
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).
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April 2, 2021
Image /page/0/Picture/1 description: The image shows the logo of the U.S. Food and Drug Administration (FDA). The logo consists of two parts: a symbol on the left and the FDA acronym with the agency's name on the right. The symbol on the left is a stylized representation of the Department of Health and Human Services emblem. To the right of the symbol is a blue square containing the acronym "FDA" in white, followed by the words "U.S. FOOD & DRUG ADMINISTRATION" in blue.
Deepvoxel, Inc. % Albert Rego, Ph.D. Consultant Albert Rego, Ph.D., Inc. 27001 La Paz Road, Suite #314 MISSION VIEJO CA 92691
Re: K202928
Trade/Device Name: DV.Target Regulation Number: 21 CFR 892.2050 Regulation Name: Picture archiving and communications system Regulatory Class: Class II Product Code: QKB Dated: February 19, 2021 Received: March 2, 2021
Dear Dr. Rego:
We have reviewed your Section 510(k) premarket notification of intent to market the device referenced above and have determined the device is substantially equivalent (for the indications for use stated in the enclosure) to legally marketed predicate devices marketed in interstate commerce prior to May 28, 1976, the enactment date of the Medical Device Amendments, or to devices that have been reclassified in accordance with the provisions of the Federal Food, Drug, and Cosmetic Act (Act) that do not require approval of a premarket approval application (PMA). You may, therefore, market the device, subject to the general controls provisions of the Act. Although this letter refers to your product as a device, please be aware that some cleared products may instead be combination products. The 510(k) Premarket Notification Database located at https://www.accessdata.fda.gov/scripts/cdrh/cfdocs/cfpmn/pmn.cfm identifies combination product submissions. The general controls provisions of the Act include requirements for annual registration, listing of devices, good manufacturing practice, labeling, and prohibitions against misbranding and adulteration. Please note: CDRH does not evaluate information related to contract liability warranties. We remind you, however, that device labeling must be truthful and not misleading.
If your device is classified (see above) into either class II (Special Controls) or class III (PMA), it may be subject to additional controls. Existing major regulations affecting your device can be found in the Code of Federal Regulations, Title 21, Parts 800 to 898. In addition, FDA may publish further announcements concerning your device in the Federal Register.
Please be advised that FDA's issuance of a substantial equivalence determination does not mean that FDA has made a determination that your device complies with other requirements of the Act or any Federal statutes and regulations administered by other Federal agencies. You must comply with all the Act's requirements, including, but not limited to: registration and listing (21 CFR Part 807); labeling (21 CFR Part 801); medical device reporting of medical device-related adverse events) (21 CFR 803) for
1
devices or postmarketing safety reporting (21 CFR 4, Subpart B) for combination products (see https://www.fda.gov/combination-products/guidance-regulatory-information/postmarketing-safety-reportingcombination-products); good manufacturing practice requirements as set forth in the quality systems (QS) regulation (21 CFR Part 820) for devices or current good manufacturing practices (21 CFR 4, Subpart A) for combination products; and, if applicable, the electronic product radiation control provisions (Sections 531-542 of the Act); 21 CFR 1000-1050.
Also, please note the regulation entitled, "Misbranding by reference to premarket notification" (21 CFR Part 807.97). For questions regarding the reporting of adverse events under the MDR regulation (21 CFR Part 803), please go to https://www.fda.gov/medical-device-safety/medical-device-reportingmdr-how-report-medical-device-problems.
For comprehensive regulatory information about mediation-emitting products, including information about labeling regulations, please see Device Advice (https://www.fda.gov/medicaldevices/device-advice-comprehensive-regulatory-assistance) and CDRH Learn (https://www.fda.gov/training-and-continuing-education/cdrh-learn). Additionally, you may contact the Division of Industry and Consumer Education (DICE) to ask a question about a specific regulatory topic. See the DICE website (https://www.fda.gov/medical-device-advice-comprehensive-regulatoryassistance/contact-us-division-industry-and-consumer-education-dice) for more information or contact DICE by email (DICE@fda.hhs.gov) or phone (1-800-638-2041 or 301-796-7100).
Sincerely.
For
Thalia T. Mills, Ph.D. Director Division of Radiological Health OHT7: Office of In Vitro Diagnostics and Radiological Health Office of Product Evaluation and Quality Center for Devices and Radiological Health
Enclosure
2
Indications for Use
510(k) Number (if known) K202928
Device Name DV.Target
Indications for Use (Describe)
DV.Target is a software application that enables the routing of DICOM-compliant data (CT Images) to automatic image processing workflows, using machine learning-based algorithms to automatically delineate organs-at-risk (OARs). Contours generated by DV.Target may be used as an input to clinical workflows for treatment planning in radiation therapy.
DV.Target is intended to be used by trained medical professionals including radiologists, radiation oncologists, dosimetrists, and physicists.
DV.Target does not provide a user interface for data visualization. Image data uploaded, auto-contouring results, and other functionalities are managed via an administration interface. Thus, it is required that DV.Target be used in conjunction with appropriate software, such as a treatment planning system (TPS), to review, edit, and approve for all contours generated by DV.Target.
DV.Target is only intended for normal organ contouring, not for tumor or clinical target volume contouring.
| 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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Image /page/3/Picture/0 description: The image contains the logo for Deep Voxel. The logo consists of a blue square with a smaller, light blue cube extending from one of its corners. To the right of the logo, the words "DEEP VOXEL" are written in a blue, sans-serif font.
510(k) Summary of Safety and Effectiveness
The assigned 510(k) Number: K202928
1. Submitter
| Applicant Information: | DeepVoxel Inc.
22 Talisman
Irvine, CA 92620 |
|------------------------|-----------------------------------------------------------------------------------|
| Phone:
Email: | 858-281-8029
support@deep-voxel.com |
| Contact Person: | Dr. Albert Rego
27001 La Paz Road, Suite #314
Mission Viejo, CA, 92691, USA |
| Date Prepared: | April 1, 2021 |
2. Device Name
| Trade Name: | DV.Target |
|---|---|
| Device Common Name: | Radiological Image Processing Software For Radiation Therapy |
| Regulation Number: | 21 CFR 892.2050 |
| Product Code: | QKB |
| Classification Name: | Picture archiving and communications system |
| Regulation Class: | Class II |
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Image /page/4/Picture/0 description: The image contains the logo for Deep Voxel. The logo consists of a blue square with a smaller, light blue square attached to one of its corners, creating a 3D effect. To the right of the square is the text "DEEP VOXEL" in a sans-serif font, with both words in the same blue color as the square.
3. Identification of Predicate Device
Predicate Device
Table 1. Identification of Predicate Device.
| Device trade
name | 510(k)
number | Date of
clearance | Classification
name | Product
code | Regulation | Class | Classification
panel | Submitter's
name |
|------------------------------------------------------------------------------------------------|------------------|----------------------|-------------------------------------------------------|-----------------|-------------------|----------|-------------------------|-----------------------|
| Workflow
BoxTM
(including
DLCExpert™,
Embrace:CT™,
Embrace:MR™,
Re:Contour™) | K181572 | July 10,
2018 | Picture
Archiving and
Communicatio
ns System | LLZ | 21CFR
892.2050 | Class II | Radiology | Mirada
Medical Ltd |
Reference Device
MIM - MRT Dosimetry, K182624
4. Indications for use
DV.Target is a software application that enables the routing of DICOM-compliant data (CT Images) to automatic image processing workflows, using machine learning-based algorithms to automatically delineate organs-at-risk (OARs). Contours generated by DV.Target may be used as an input to clinical workflows for treatment planning in radiation therapy.
DV.Target is intended to be used by trained medical professionals including radiologists, radiation oncologists, dosimetrists, and physicists.
DV.Target does not provide a user interface for data visualization. Image data uploaded, autocontouring results, and other functionalities are managed via an administration interface. Thus, it is required that DV.Target be used in conjunction with appropriate software, such as a treatment planning system (TPS), to review, edit, and approve for all contours generated by DV.Target.
DV.Target is only intended for normal organ contouring, not for tumor or clinical target volume contouring.
5. Device Description
The proposed device, DV.Target, is a standalone software that is designed to be used by trained medical professionals to automatically delineate organs-at-risk (OARs) on CT images. This OARs delineation function, often referred as auto-contouring, is intended to facilitate radiation therapy workflows. Supported image modalities include CT and RTSTURCT.
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Image /page/5/Picture/0 description: The image contains the logo for Deep Voxel. The logo consists of a blue square with a smaller, light blue square attached to the bottom right corner. To the right of the logo, the words "DEEP VOXEL" are written in a blue, sans-serif font.
DV.Target can automatically delineate major OARs in three anatomical sites --- Head & Neck, Thorax, and Abdomen & Pelvis. It receives CT images in DICOM format as input and automatically generates the contours of OARs, which are stored in DICOM format and in RTSTRUCT modality.
The deployment environment of the proposed device is recommended to be a local network with an existing hospital-grade IT system in place. DV.Target should be installed on a specialized server supporting deep learning processing. After installation, users can login to the DV.Target administration interface via browsers from their local computers. All activities, including autocontouring, are operated by users through the administration interface.
In addition to auto-contouring, DV.Target also has the following auxiliary functions:
- User interface for receiving, updating and transmitting medical images in DICOM format;
- User management;
- Processed image management and output (RTSTRUCT) file management.
Once data is routed to DV.Target auto-contouring workflows, no user interaction is required, nor provided. The image data, auto-contouring results, and other functionalities can be managed by DV.Target users via an administration user interface. Third-party image visualization and editing software, such as a treatment planning system (TPS), must be used to facilitate the review and editing of contours generated by DV.Target.
DV.Target can delineate the following 49 OARs distributed across three anatomic sites (Table 2):
| Anatomic Site | OARs | No. of OARs |
|---|---|---|
| Head & Neck | Brachial plexus, Brain Stem, Constrictor naris, Ear Left, Ear Right, Eye Left, | |
| Eye Right, Hypophysis, Larynx, Lens Left, Lens Right, Mandible, Optic | ||
| chiasm, Optic nerve Left, Optic nerve Right, Oral cavity, Parotid Left, | ||
| Parotid Right, Sublingual gland, Submandibular gland Left, | ||
| Submandibular gland Right, Spinal Cord, Temporal Lobe Left, Temporal | ||
| Lobe Right, Temporomandibular joint Left, Temporomandibular joint | ||
| Right, Thyroid, Trachea | 28 | |
| Thorax | Esophagus, Heart, Lung Left, Lung Right, Spinal Cord, Trachea | 6 |
| Abdomen & | ||
| Pelvis | Bladder, Duodenum, Gallbladder, Femur Left, Femur Right, Kidney Left, | |
| Kidney Right, Large Bowel, Liver, Pancreas, Rectum, Small Bowel, Spleen, | ||
| Spinal Cord, Stomach | 15 |
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Image /page/6/Picture/0 description: The image shows the logo for Deep Voxel. The logo consists of a blue square with a smaller, light blue cube attached to one of its corners. To the right of the logo is the text "DEEP VOXEL" in a sans-serif font, also in blue.
6. Comparison of Indications for Use with Predicate Device
Both the proposed and predicate devices are software applications designed to be used by trained medical professionals within a hospital environment, and are indicated for the creation of contours for use in clinical workflows for the purpose of radiation therapy treatment planning.
Both the predicate and proposed devices support contouring based on machine learning techniques, whereas the predicate device additionally supports atlas-based contouring and registration-based re-contouring.
Both the proposed and predicate devices are designed to interoperate via DICOM objects and to network with other DICOM capable devices such as PACS and Radiation Treatment Planning Systems.
Both the proposed and predicate devices do not facilitate the display or visualization of the data by users. Reviewing and editing of contouring results cannot be performed within both devices.
Both the proposed and predicate devices require users to confirm and review generated contours in a separate image visualization system.
In summary, DeepVoxel Inc. believes the intended use of the proposed device is substantially equivalent to the predicate device, excepting the registration-based features in the predicate device, which are not applicable to the proposed device.
| Table 3. Comparison of Indications for Use with Predicate Device. | ||
|---|---|---|
| Proposed Device | Predicate Device | |
| Indications | ||
| for use | DV.Target is a software application that enables the routing of image data (CT Images) to automatic image processing workflows, using machine learning learning-based algorithms to automatically delineate OARs (Organs-at-risk). Contours generated by DV.Target may be used as an input to clinical workflows for treatment planning in radiation therapy. DV.Target is intended to be used by trained medical professionals including radiologists, radiation oncologists, dosimetrists, and physicists. | Workflow Box is a software system designed to allow users to route DICOM-compliant data to and from automated processing components. Supported modalities include CT, MR, RTSTRUCT. Workflow Box includes processing components for automatically contouring imaging data using deformable image registration to support atlas-based contouring, re-contouring of the same patient and machine learning based contouring. Workflow Box is a data routing and image processing tool which automatically applies contours to data which is sent to one or more of the included image processing workflows. |
3. Comparison of Indications for Use with Predicate Device
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Image /page/7/Picture/0 description: The image contains the logo for Deep Voxel. The logo consists of a blue square with a white square cut out of the center. To the right of the square is the text "DEEP VOXEL" in a sans-serif font.
| • DV.Target does not provide a user interface for data visualization. The
image data uploaded, auto-contouring results and other
functionalities are managed via an administration interface. Thus, it is
required that DV.Target be used in conjunction with appropriate
software, such as a treatment planning system (TPS), to review, edit
and approve for all contours generated by DV.Target. | Contours generated by Workflow Box may be used as an input to clinical
workflows including, but not limited to, radiation therapy treatment planning.
• Workflow Box must be used in conjunction with appropriate software
to review and edit results generated automatically by Workflow
Box components, for example image visualization software must be used to
facilitate the review and edit of contours generated by Workflow Box component
applications. |
|--------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------|-----------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------|
| • DV.Target is only intended for normal organ contouring, not for tumor or
clinical target volume contouring. | • Workflow Box is intended to be used by trained medical professionals.
• Workflow Box is not intended to automatically detect lesions. |
7. Comparison of Technological Characteristics with Predicate Device
Table 4. Comparison of Technological Characteristics with Predicate Device.
| Characteristic | Proposed Device | Predicate Device |
|---|---|---|
| Device Name | DV.Target | Workflow Box (K181572) |
| Regulation No. | No. 21CFR 892.2050 | No. 21CFR 892.2050 |
| Classification Name | Picture archiving and | |
| communications system | Picture archiving and | |
| communications system | ||
| Product Code | QKB | LLZ |
| Class | II | II |
| Target Population | Any patient type for whom | |
| relevant modality scan data is | ||
| available. | Any patient type for whom | |
| relevant modality scan data is | ||
| available. | ||
| Where Used | Clinical/Hospital environment | Clinical/Hospital environment |
| Target Users | Designed to be used by trained | |
| clinicians | Designed to be used by trained | |
| clinicians | ||
| Energy Used and/or | ||
| Delivered | None - software only | |
| application. The software | ||
| application does not deliver or | ||
| depend on energy delivered to or | ||
| from patients | None - software only application. | |
| The software application does not | ||
| deliver or depend on energy | ||
| delivered to or from patients |
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Image /page/8/Picture/0 description: The image shows the logo for Deep Voxel. The logo consists of a blue square with a smaller, light blue cube extending from the bottom right corner. To the right of the logo are the words "DEEP VOXEL" in a blue sans-serif font. The logo is simple and modern, and the colors are calming and professional.
22 Talisman, Irvine, CA, 92620
858-281-8029
| Data Visualization | None - the proposed device has
no data visualization
functionality. All data processing
is automated and does not
require user interaction. A
control interface is provided for
system administration and
configuration only. | None - the predicate device has
no data visualization functionality.
All data processing is automated
and does not require user
interaction. A control interface is
provided for system
administration and configuration
only. |
|------------------------------------------|----------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------|-----------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------|
| Regions and Volumes
of Interest (ROI) | Machine learning-based
contouring | Atlas-based contouring,
registration-based re-contouring,
machine learning-based
contouring |
| ROI measurements
and quantification | Not applicable | Not applicable |
| Image Registration | Not applicable | Registration for the purposes of
re-planning/re-contouring and
atlas based contouring. |
| Label/labeling | Conform with 21CFR Part 801 | Conform with 21CFR Part 801 |
| Operating System | Ubuntu, Windows | Windows |
| Algorithm | Machine learning-based | Machine learning-based and Atlas-
based |
| Supported Modalities | CT, RTSTRUCT | CT, MR, RTSTRUCT |
| Reporting and data
routing | Supports automatic routing
images to processing workflow.
No customized options for the
user | Supports routing and distribution
of images to other DICOM nodes
including to custom executables
determined by the user |
| Communications/
Networking | TCP/IP and SCP | TCP/IP and SCP |
| Compatible Scanner
Models | No Limitation on scanner model,
DICOM 3.0 compliance required | No Limitation on scanner model,
DICOM 3.0 compliance required |
| Compatible Treatment
Planning System | No Limitation on TPS model,
DICOM 3.0 compliance required | No Limitation on TPS model,
DICOM 3.0 compliance required |
The predicate device and the proposed device are both standalone software applications for medical image processing. Both devices process DICOM image data and include design features to enable automatic delineation of contours on input image data.
Both the proposed and predicate devices utilize algorithms to automatically generate regions of interest structures/contours. The predicate device also utilizes image registration for atlas-based contouring and re-contouring.
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Image /page/9/Picture/0 description: The image contains the logo for Deep Voxel. The logo consists of a blue square with a white square cut out of the center. There is a small blue cube in the bottom right corner of the square. To the right of the square is the text "DEEP VOXEL" in blue.
Both devices are compatible with the same use environments and utilize the same networking technology. The predicate device operates on Microsoft Windows operating systems, while the proposed device can operate on both Microsoft Windows and Ubuntu (a Linux distribution) operating systems.
19 OARs (hereinafter referred to as "overlapping OARs") are delineated by both the proposed and predicate devices. However, there are additional 30 OARs only delineated by the proposed device, and 16 OARs only delineated by the predicate device.
The proposed device offers a subset of the image processing technical features presented by the predicate device. The shared features are substantially equivalent to the predicate device and do not present any additional or new risks when compared to the predicate device.
8. Non-Clinical Test Conclusion
In summary, we have conducted three Comparison Studies to evaluate the performance of the proposed device:
- Comparison Study 1: Conducted between the proposed and predicate devices on a public validation dataset (64% are from the US) to evaluate the auto-contouring accuracy of 19 overlapping OARs (OARs delineated by both devices).
- Comparison Study 2: Conducted between the proposed and predicate devices on an in-house clinical dataset to evaluate the auto-contouring accuracy of the overlapping OARs.
- Comparison Study 3: Conducted between the proposed device and a reference device (MIM -MRT Dosimetry 510(k) Number K182624) on the public validation data (64% are from the US) to evaluate the auto-contouring accuracy of 30 non-overlapping OARs (OARs delineated by the proposed device, but not by the predicate).
The validation data used in these studies consists of two independent dataset collected from a large medical images archive --- TCIA and b) a clinical in-house dataset collected retrospectively from the City of Hope (our primary validation site). A comprehensive characteristic analysis of validation data to demonstrate the representativeness of the intended patient population is presented and related backgrounds are introduced. The ground truth OARs contours on the public validation data were generated from the consensus of three board-certified physicians. The ground truth contours on the in-house clinical data (collected retrospectively) were based on actual clinical contouring results.
All validation data described above were invisible in model training. The Dice-Sørensen coefficients (DICE score) were calculated and used to evaluate contouring accuracies by comparing devicegenerated contours with ground truth contours. A systematic statistical methodology, including data presentation (histogram, Box and Whisker plot, and Bland-Altman plot) and statistical inferences
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(i.e. the non-inferiority tests), is established as the guidelines for data analysis in the three Comparison Studies.
We have presented detailed results from the three Comparison Studies. In each of the studies, we observed the following results: a) The DICE scores from the proposed and the predicate/reference devices both have a central tendency (DICE Score differences can be approximated by the normal distribution) and have a good agreement with each other (from histograms and Bland-Altman plots). b) The DICE scores of the proposed device are generally higher than those of the predicate/reference device (from the Box and Whisker plots). c) The confidence interval of performance differences between the proposed and the predicate/reference devices are within the non-inferiority margin for all compared OARs. Hence the statement that the proposed device is noninferior to the predicate/reference device is established. Additionally, we demonstrate that the performance of the proposed device on the non-overlapping OARs is similar to its performance on the overlapping OARs (Comparison Study 3b).
We draw the following conclusions from these studies:
- DV.Target is non-inferior to the predicate device Mirada on all 19 overlapping OARs. This conclusion is supported by Comparison Studies 1&2, based on validation data from different sites and with independent annotations.
- . DV.Target is non-inferior to the reference device MIM on the 30 non-overlapping OARs. The performance of DV.Target on the non-overlapping OARs is similar to its performance on the overlapping OARs. This is supported by Comparison Studies 3a & 3b.
According to these results, we conclude that the performance of the proposed device is substantially equivalent to the performance of the predicate device.
9. Clinical Test Conclusion
No clinical study is included in this submission.
10. Substantially Equivalent (SE) Conclusion
Based on the comparison and analysis above, DeepVoxel Inc. believes that the proposed device can be determined to be Substantially Equivalent (SE) to the predicate device.