(230 days)
Yes
The device description explicitly states "DeepContour is a deep learning based medical imaging software" and the intended use mentions "Creation of contours using deep-learning algorithms". The training and test set descriptions detail the use of deep learning models for auto-segmentation.
No
The device is described as a medical imaging software that processes CT images for contour creation, analysis of anatomical structures, registration, and 3D reconstruction. Its intended use focuses on providing tools for trained healthcare professionals to assist with tasks normally undertaken in medical imaging. It does not directly provide a therapeutic effect or intervene in the treatment of a condition. Therefore, it is not a therapeutic device.
No
The device is described as a medical imaging software to automatically process CT images for contour creation, quantitative analysis, and 3D reconstruction, among other visual tools. It aids in the workflow of trained healthcare professionals and does not provide a medical diagnosis or treatment decision.
Yes
The device is explicitly described as "deep learning based medical imaging software" and its function is to process CT images and produce RTSTRUCT outputs. There is no mention of any associated hardware component being part of the device itself.
Based on the provided information, DeepContour is not an In Vitro Diagnostic (IVD) device.
Here's why:
- IVD Definition: In Vitro Diagnostics are devices intended for use in the collection, preparation, and examination of specimens taken from the human body (such as blood, urine, or tissue) to provide information for the diagnosis, treatment, or prevention of disease.
- DeepContour's Function: DeepContour processes medical images (CT scans) to automatically generate contours of anatomical structures. It is a tool for trained healthcare professionals to assist in tasks like quantitative analysis, treatment planning (transferring contours to TPS), and patient management.
- Lack of Specimen Analysis: DeepContour does not analyze biological specimens. Its input is image data, not samples from the human body.
While DeepContour is a medical device that provides information relevant to patient care, its function falls under the category of medical imaging software and image processing, not in vitro diagnostic testing.
No
The letter does not state that the FDA has reviewed and approved or cleared a PCCP for this specific device.
Intended Use / Indications for Use
DeepContour is a deep learning based medical imaging software that allows trained healthcare professionals to use DeepContour as a tool to automatically process CT images. In addition, DeepCoutour is suitable for the following conditions:
- Creation of contours using deep-learning algorithms, support quantitative analysis, organ HU distribution statistics, transfer contour files to TPS, and create management archives for patients.
- Analyze the anatomical structure at different anatomical positions.
- Rigid and elastic registration based on CT.
- 3D reconstruction, editing and other visual tools based on organ contours
Product codes (comma separated list FDA assigned to the subject device)
QKB
Device Description
DeepContour is a deep learning based medical imaging software that allows trained healthcare professionals to use DeepContour as a tool to automatically process CT images. DeepContour contouring workflow supports CT input data and produces RTSTRUCT outputs. The organ segmentation can also be combined into templates, which can be customized by different hospitals according to their needs. DeepContour provides an interactive contouring application to edit and review the contours automatically generated by DeepContour.
Mentions image processing
Yes
Mentions AI, DNN, or ML
Yes
Input Imaging Modality
CT Images
Anatomical Site
Head & Neck, Thorax, Abdomen & Pelvis (82 OARs)
Indicated Patient Age Range
DeepContour is designed for use only in adult populations.
Intended User / Care Setting
Trained healthcare professionals / professional environment
Description of the training set, sample size, data source, and annotation protocol
For DeepContour, the deep learning models were trained on a pool of training data that did not include any patients from the same institution as the test subjects. The data was collected from 35 hospitals across China over a period of about 3 years. The data collection time is random, including patients with different ages, races, size, et al. Data collected in a hospital tends to be a collection of consecutive cases. Due to the different time periods applicable to different hospitals, the time for collecting data varies among different hospitals. The specific patient subgroups were not the special consideration when collecting the patients' data.
The training data included 800 CT images (200 for each region of head and neck region, chest region, abdomen region and pelvic region) at various ages, no ethnicities or genders were excluded from training. Within 200 cases collected in each region, 160 cases were used for training and 40 cases were used for validation to establish the auto-segmentation models. The auto-segmentation process includes two steps: first step to distinguish the four different regions (head and neck region, chest region, abdomen region and pelvic region), second step to call the model of this region to realize auto-segmentation. The initial segmentations were then reviewed and corrected by two radiation oncologists for model training, with a third qualified internal staff member available to adjudicate if needed.
Description of the test set, sample size, data source, and annotation protocol
The performance of the DeepContour were verified by a total of 203 CT images with 2 datasets:
(1) clinical datasets retrospectively collected from multiple clinical regions across the Peking Union Medical College Hospital (N=100), consisting of four selected parts based on their location, 25 cases for head and neck, 25 cases for chest, 25 cases for abdomen and 25 cases for pelvic; These clinical datasets are not used in the training datasets at all.
(2) American public datasets (N=103);
(a)The 2017 lung CT segmentation challenge (LCTSC), which contains 60 thoracic CT scan patients with five segmented organs (left lung, right lung, heart, spinal cord, and esophagus),
(b) Pancreas-CT (PCT), which contains 43 abdominal contrast enhanced CT scan patients with seven segmented organs (the spleen, left kidney, esophagus, liver, stomach, pancreas, and duodenum). The American public datasets were annotated by the American doctors. The article was published in the journal (Medical Physics), doi: 10.1002/mp.14131. (Please refer to more information about the two publicly available datasets: https://wiki.cancerimagingarchive.net/pages/viewpage.action?pageId=24284539, https://wiki.cancerimagingarchive.net/display/Public/Pancreas-CT)
The auto-segmentation process is the same with what was applied to the training dataset with two steps: first step to distinguish the four different regions (head and neck region, chest region, abdomen region and pelvic region), second step to call the model of this region to realize auto-segmentation. The ground truth annotations for the 100 cases in China were established by two different radiation oncologists with more than 10 years of clinical practice (See Appendix 2 for their detailed CVs) following RTOG and clinical guidelines using manual annotation. A third qualified internal staff member is also available to adjudicate if needed. American public datasets (N=103) were annotated by the American doctors.
Summary of Performance Studies (study type, sample size, AUC, MRMC, standalone performance, key results)
Study Type: Software verification and validation, performance comparison (Dice coefficients, MAE, RMSE, PSNR, SSIM, ASSD).
Sample Size: 100 clinical cases from Peking Union Medical College Hospital, 103 American public datasets (60 LCTSC, 43 Pancreas-CT).
Key Results:
The results of the subject device demonstrate equivalent or better performance compared to the predicate device when aggregate performance over all organs is considered with known limitations described in the labeling. Equivalence is defined such that the lower bound of 95th percentile confidence interval of the subject device segmentation is greater than 0.1 Dice lower than the mean of predicate device segmentation.
Rigid and deformable registration (rpCT-CBCT vs. dpCT1-CBCT vs. dpCT2-CBCT):
- MAE(HU): 51.23±13.67 vs. 43.98±10.74 vs. 46.71±12.71
- RMSE: 121.09±30.23 vs. 117.58±28.22 vs. 127.96±30.76
- PSNR: 20.01±2.74 vs. 22.23±2.61 vs. 20.00±3.77
- SSIM: 0.623±0.084 vs. 0.680±0.050 vs. 0.685±0.055
Segmentation Performance (Dice Coefficients):
- Clinical performance comparison (Peking Union Medical College Hospital): Detailed Dice coefficients for 69 different anatomical structures (OARs) were provided, comparing DeepContour against AI-Rad CAI-Rad Companion Organs RT (K221305) and Contour ProtégéAI (K223774). DeepContour generally showed comparable or higher Dice coefficients across many structures.
- Clinical performance comparison (LCTSC American public datasets): Dice coefficients for 5 anatomical structures were compared. DeepContour showed comparable or higher Dice coefficients for Spinal Cord, Lung L, Lung R, Heart, and Esophagus.
- Clinical performance comparison (Pancreas-CT American public datasets): Dice coefficients for 7 anatomical structures were compared. DeepContour showed comparable or higher Dice coefficients for Spleen, Pancreas, Kidney_L, Esophagus, Liver, Stomach, and Duodenum.
Average Symmetric Surface Distance (ASSD):
- Subject device (DeepContour) median ASSD: 0.95 (95% CI: [0.85, 1.13])
- Predicate device (AI-Rad CAI-Rad Companion Organs RT (K221305)) median ASSD: 0.96 (95% CI: [0.84, 1.15])
- Reference device (Contour ProtégéAI (K223774)) median ASSD: 0.95 (95% CI: [0.86, 1.17])
The subject device achieved a median ASSD of 0.95 in comparison to the predicate device achieving a median ASSD of 0.96 for existing organs.
Key Metrics (Sensitivity, Specificity, PPV, NPV, etc.)
Dice coefficients, mean absolute error (MAE), root mean square error (RMSE), peak signal-to-noise ratio (PSNR), structural similarity (SSIM), and median Average Symmetric Surface Distance (ASSD).
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).
0
May 7, 2024
Image /page/0/Picture/1 description: The image contains the logo of the U.S. Food and Drug Administration (FDA). On the left, there is a seal with an abstract design and the text "DEPARTMENT OF HEALTH & HUMAN SERVICES-USA" arranged around it. To the right, there is a blue square with the letters "FDA" in white, followed by the words "U.S. FOOD & DRUG" in blue, and "ADMINISTRATION" in a smaller font size below it.
Wisdom Technologies., Inc. % Wei Wang Regulatory Consultant 11 Longstreet IRVINE, CA 92620
Re: K232928
Trade/Device Name: DeepContour (V1.0) Regulation Number: 21 CFR 892.2050 Regulation Name: Medical Image Management And Processing System Regulatory Class: Class II Product Code: QKB Dated: April 5, 2024 Received: April 5, 2024
Dear Wei Wang:
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.
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
1
product; and 21 CFR 820.100, Corrective and preventive action. Please note that regardless of whether a change requires premarket review, the QS 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.
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 medical devices and radiation-emitting products, including information about labeling regulations, please see Device Advice (https://www.fda.gov/medicaldevices/device-advice-comprehensive-regulatory-assistance) and CDRH Learn (https://www.fda.gov/training-and-continuing-education/cdrh-learn). Additionally, you may contact the Division of Industry and Consumer Education (DICE) to ask a question about a specific regulatory topic. See the DICE website (https://www.fda.gov/medical-device-advice-comprehensive-regulatoryassistance/contact-us-division-industry-and-consumer-education-dice) for more information or contact DICE by email (DICE@fda.hhs.gov) or phone (1-800-638-2041 or 301-796-7100).
Sincerely,
Loran Werchner
Lora D. Weidner, Ph.D. Assistant Director Radiation Therapy Team DHT8C: Division of Radiological Imaging and Radiation Therapy Devices OHT8: Office of Radiological Health Office of Product Evaluation and Quality Center for Devices and Radiological Health
2
Indications for Use
Submission Number (if known)
Device Name
DeepContour (V1.0)
Indications for Use (Describe)
DeepContour is a deep learning based medical imaging software that allows trained healthcare professionals to use DeepContour as a tool to automatically process CT images. In addition, DeepCoutour is suitable for the following conditions:
-
Creation of contours using deep-learning algorithms , support quantitative analysis, organ HU distribution statistics, transfer contour files to TPS, and create management archives for patients.
-
Analvze the anatomical structure at different anatomical positions.
-
Rigid and elastic registration based on CT.
-
3D reconstruction, editing and other visual tools based on organ contours
Type of Use (Select one or both, as applicable)
Prescription Use (Part 21 CFR 801 Subpart D)
he-Counter Use (21 CFR 801 Subpart C)
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3
K232928
Image /page/3/Picture/1 description: The image shows a logo with a stylized human figure in the center, surrounded by radiating lines. The figure is placed within a blue, semi-circular shape. To the right of the figure, there are Chinese characters followed by the words "WISDOM TECH" in blue font. The logo appears to represent a technology company, possibly focused on human-centered solutions.
510(k) Summary
The following information is provided as required by 21 CFR 807.92
1. SUBMITTER
Name: Wisdom Technologies., Inc.
Address: 4th Floor, Building F2, Phase II, Innovation Industrial Park, Hefei, Anhui, China 230088
Phone: +86-0551-65116387
Email: registration(@wisdom-tech.online
Contact Person: Wei Wang, Consultant, Regulatory Affairs Phone: 949-7849283
Date Prepared: August 24, 2023
2. DEVICE
Subject Device Name: DeepContour v1.0 Common/Trade Name: DeepContour Product Code and Classification: Medical Image Management And Processing System 21 CFR 892.2050 | QKB | Class II
3. PREDICATE DEVICE
Primary: AI-Rad CAI-Rad Companion Organs RT (K221305) Siemens Reference: Contour ProtégéAI (K223774) MIM Software
4. DEVICE DESCRIPTION
DeepContour is a deep learning based medical imaging software that allows trained healthcare professionals to use DeepContour as a tool to automatically process CT images. DeepContour contouring workflow supports CT input data and produces RTSTRUCT outputs. The organ segmentation can also be combined into templates, which can be customized by different hospitals according to their needs. DeepContour provides an interactive contouring application to edit and review the contours automatically generated by DeepContour.
5. INDICATIONS FOR USE
4
DeepContour is a deep learning based medical imaging software that allows trained healthcare professionals to use DeepContour as a tool to automatically process CT images. In addition, DeepCoutour is suitable for the following conditions:
1). Creation of contours using deep-learning algorithms , support quantitative analysis, organ HU distribution statistics, transfer contour files to TPS, and create management archives for patients.
2). Analyze the anatomical structure at different anatomical positions.
- 3). Rigid and elastic registration based on CT.
- 4). 3D reconstruction, editing and other visual tools based on organ contours
6. COMPARISON OF TECHNOLOGICAL CHARACTERISTICS WITH PREDICATE DEVICE
The primary technological components of DeepContour and its predicate device are to achieve the deep learning based medical imaging software functions that allows trained healthcare professionals to automatically process CT images. Both are software devices that receive inputs related to radiological images; Both generate contours as output that may be used as input for radiation Treatment Planning Systems and interactive contouring applications to review and edit; Both are software devices for prescription use in a professional environment with no patient contact.
There are no known differences in technological characteristics between the subject device and the predicate device that raise any questions of safety or effectiveness. The technological characteristics of the subject device are believed to be substantially equivalent to the predicate device.
| Area of
Comparison | Subject Device-
DeepContour | Primary-AI-Rad CAI-
Rad Companion Organs
RT (K221305) Siemens | Reference-Contour
ProtégéAI(K223774)
MIM Software |
|------------------------------------------------------------------------------------|------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------|--------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------|-------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------|
| Regulation
Number/code | 21 CFR 892.2050 QKB | 22 CFR 892.2050 QKB | 21 CFR 892.2050 QKB |
| Regulation Name | Medical Image Management
And Processing System | Medical Image Management
And Processing System | Medical Image
Management And
Processing System |
| Indications for
Use | DeepContour is a deep
learning based medical
imaging software that allows
trained healthcare
professionals to use
DeepContour as a tool to
automatically process CT
images. In addition,
DeepCoutour is suitable for
the following conditions:
- Creation of contours using
deep-learning algorithms ,
support quantitative analysis,
organ HU distribution
statistics, transfer contour files
to TPS, and create
management archives for
patients. - Analyze the anatomical
structure at different
anatomical positions. - Rigid and elastic registration
based on CT. - 3D reconstruction, editing
and other visual tools based on
organ contours | AI-Rad Companion Organs
RT is a post-processing
software intended to
automatically contour
DICOM CT imaging data
using deep-learning-based
algorithms.
Contours that are generated
by AI-Rad Companion
Organs RT may be used as
input for clinical workflows
including external beam
radiation therapy treatment
planning. AI-Rad
Companion Organs RT must
be used in conjunction with
appropriate software such as
Treatment Planning Systems
and Interactive Contouring
applications, to review, edit,
and accept contours
generated by AI-Rad
Companion Organs RT.
The output of AI-Rad
Companion Organs RT in
the format of RTSTRUCT
objects are intended to be
used by trained medical
professionals.
The software is not intended
to automatically detect or
contour lesions. Only
DICOM images of adult
patients are considered to be
valid input. | Trained medical
professionals use Contour
ProtégéAI as a tool to
assist in the automated
processing of digital
medical images of
modalities CT and MR, as
supported by ACR/NEMA
DICOM 3.0. In addition,
Contour ProtégéAI
supports the following
indications:
• Creation of contours
using machine-learning
algorithms for applications
including, but not limited
to, quantitative
analysis, aiding adaptive
therapy, transferring
contours to radiation
therapy treatment planning
systems, and archiving
contours for patient
follow-up and
management.
• Segmenting anatomical
structures across a variety
of CT anatomic locations.
• And segmenting the
prostate, the seminal
vesicles, and the urethra
within T2-weighted MR
images.
Appropriate image
visualization software
must be used to review
and, if necessary, edit |
| | | | results automatically
generated by Contour
ProtégéAI. |
| Device
description | DeepContour is a deep
learning based medical
imaging software that allows
trained healthcare
professionals to use
DeepContour as a tool to
automatically process CT
images. DeepContour
contouring workflow supports
CT input data and produces
RTSTRUCT outputs. The
organ segmentation can also
be combined into templates,
which can be customized by
different hospitals according to
their needs.
DeepContour provides an
interactive contouring
application to edit and review
the contours automatically
generated by DeepContour. | AI-Rad Companion Organs
RT is a post-processing
software used to
automatically contour
DICOM CT imaging data
using deep-learning-based
algorithms. AI-Rad
Companion Organs RT
contouring workflow
supports CT input data and
produces RTSTRUCT
outputs. The
configuration of the organ
database and organ
templates defining the
organs and structures to be
contoured based on the input
DICOM data is managed via
a configuration interface.
Contours that are generated
by AI-Rad Companion
Organs RT may be used as
input for clinical workflows
including external beam
radiation therapy treatment
planning.
The output of AI-Rad
Companion Organs RT, in
the form of RTSTRUCT
objects, are intended to be
used by trained medical
professionals. The output of
AI-Rad Companion Organs
RT must be used in
conjunction with appropriate
software such as Treatment
Planning Systems and
Interactive Contouring
applications, to review, edit,
and accept contours
generated by AI-Rad
Companion Organs RT
application.
At a high-level, AI-Rad
Companion Organs RT
includes the following
functionality: 1. Automated
contouring of Organs at Risk
(OAR) workflow
a. Input -DICOM CT
b. Output - DICOM | Contour ProtégéAI is an
accessory to MIM
software that automatically
creates contours on
medical images through
the use of machine-
learning algorithms. It is
designed for use in the
processing of medical
images and operates on
Windows, Mac, and Linux
computer systems.
Contour ProtégéAI is
deployed on a remote
server using the MIMcloud
service for data
management and transfer;
or locally on the
workstation or server
running MIM software. |
| | | RTSTRUCT - Organ Templates
configuration (incl. Organ
Database) - Web-based preview of
contouring results to accept
or reject the generated
contours. | |
| Algorithm | Deep Learning | Deep Learning | Machine-learning |
| Segmentation of
Organ at Risk in
the Anatomic
Regions | Head & Neck, Thorax,
Abdomen & Pelvis
(82 OARs) | Head & Neck, Thorax,
Abdomen & Pelvis
Head & Neck lymph
nodes
(108 OARs) | Head & Neck, Prostate,
Thorax, Abdomen, Lungs
& Liver, MRT structures
(spleen, pelvic lymph
nodes, descending
aorta, bone) |
| Compatible
Modality | CT Images | CT Images | CT & MR |
| Compatible
Scanner Models | No Limitation on scanner
model,
DICOM compliance required. | No Limitation on scanner
model,
DICOM compliance
required. | No Limitation on scanner
model,
DICOM compliance
required. |
| Compatible
Treatment
Planning System | No Limitation on TPS model,
DICOM
compliance required. | No Limitation on TPS
model, DICOM
compliance required. | No Limitation on TPS
model, DICOM
compliance required. |
| Contraindications | Adult use only | Adult use only | Adult use only |
| Target
Population | DeepContour is designed for
use only in adult populations
for whom relevant modality
scans , including head and
neck, thorax, abdomen, and
pelvis, are available . | AI-Rad Companion Organs
RT is designed for use only
in adult populations. AI-Rad
Companion Organs RT is
designed for any patient for
whom relevant modality
scans are available. More
specifically, the software is
validated on previously
acquired CT DICOM
volumes for radiation
therapy treatment planning,
including, head and neck, | No public record found |
| Clinical condition
the device is
intended to
diagnose, treat or
manage | Limited to patients previously
selected for Radiation
Therapy. | Limited to patients
previously selected for
Radiation Therapy. | Limited to patients
previously selected for
Radiation Therapy. |
| Software
Architecture | Server-based application
supporting
Windows and Local
deployment on Windows. | AI-Rad Companion
(Engine) architecture
enabling the deployment of
AI Rad Companion Organs
RT using Edge and in the
Cloud. The UI is provided
using a webbased interface. | Server-based application
supporting
Linux-based OS and Local
deployment on Windows
or Mac |
| Deployment
Feature | locally deployed or Cloud-
based | Edge & Cloud Deployment | Cloud-based or locally
deployed |
| Organ Templates | Creating, editing and deletion
of organ templates. Customize
predefined structure database
with mapping to international
nomenclature schemes. | Creating, editing and
deletion of organ templates.
Customize predefined
structure database with
mapping to international
nomenclature schemes. | No public record found |
| Automated
workflow | DeepContour automatically
processes input image data and
sends the results as DICOM-
RT Structure Sets to a user-
configurable target node. | AI-Rad Companion Organs
RT automatically processes
input image data and sends
the results as DICOM-RT
Structure Sets to a user-
configurable target node. | Automatic contouring
working using machine-
learning |
| Contour
visualization and
editing feature | DeepContour provides basic
result preview of automatic
segmentation results, and
editing feature of the
automatic segmented contour. | AI-Rad Companion Organs
RT provides basic result
preview of automatic
segmentation results, and no
editing feature of the
automatic segmented
contour. | No public record found |
Table 1. Substantial Equivalence Comparison
5
6
7
8
9
| Segmentation
Performance | The target performance was
validated using 100 cases. The
mean and standard deviation
Dice coefficients, along with
the lower 95th percentile
confidence bound were
calculated. | The target performance was
validated using 113 cases
distributed to two cohorts.
Cohort A is clinical routine
treatment planning CT and it
is split into two sub-cohort
and Cohort B is PET-CT
data. To objectively evaluate
the target performance, the
DICE coefficient, the
absolute symmetric surface
distance (ASSD) and the fail
rate was evaluated. The
segmentation performance
of the subject and reference
device were equivalent as
well as the overall
performance compared to
the predicate device. | 739 CT Images from 12
clinical sites were used for
testing. The mean and
standard deviation Dice
coefficients, along with the
lower 95th percentile
confidence bound were
calculated. |
|-------------------------------------------------------|---------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------|------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------|------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------|
| User Interface -
Results Preview
(Confirmation) | Basic visualization
functionality of original data
and generated contours | Basic visualization
functionality of original data
and generated contours | Basic visualization
functionality of original
data and generated
contours |
| User Interface
Configuration | Configuration UI | Configuration UI | Configuration UI |
| Automated
Workflow to TPS | Results send to Confirmation
UI & Optional bypassing of
Confirmation UI to TPS | Results send to
Confirmation UI & Optional
bypassing of Confirmation
UI to TPS | Results send to
Confirmation UI &
Optional bypassing of
Confirmation UI to TPS |
| Human Factors | Design to be used by
trained clinicians. | Design to be used by
trained clinicians. | Design to be used by
trained clinicians. |
| Patient Contact | None | None | None |
7. PERFORMANCE AND NONCLINICAL TESTS
Software verification and validation were conducted, and the process was documented per FDA's Guidance for Industry and FDA Staff, "Guidance for the Content of Premarket Submissions for Software Contained in Medical Devices."
Verification test results demonstrate conformance to applicable requirements and specifications. Testing against the predicate device demonstrates good agreement, proving that tested device can be used as equivalent contouring software for clinical purposes. No animal studies or clinical tests were required for validation of the software.
10
7.1 Training Datasets
For DeepContour, the deep learning models were trained on a pool of training data that did not include any patients from the same institution as the test subjects. The data was collected from 35 hospitals across China over a period of about 3 years. The data collection time is random, including patients with different ages, races, size, et al. Data collected in a hospital tends to be a collection of consecutive cases. Due to the different time periods applicable to different hospitals, the time for collecting data varies among different hospitals. The specific patient subgroups were not the special consideration when collecting the patients' data.
The training data included 800 CT images (200 for each region of head and neck region, chest region, abdomen region and pelvic region) at various ages, no ethnicities or genders were excluded from training. Within 200 cases collected in each region, 160 cases were used for training and 40 cases were used for validation to establish the auto-segmentation models. The auto-segmentation process includes two steps: first step to distinguish the four different regions (head and neck region, chest region, abdomen region and pelvic region), second step to call the model of this region to realize auto-segmentation. The initial segmentations were then reviewed and corrected by two radiation oncologists for model training, with a third qualified internal staff member available to adjudicate if needed.
| # of Datasets | 800 |
|---|---|
| Data Origin | 35 hospitals across China |
| Sex | Male:372 |
| Female:428 | |
| Age | 70:70 |
| Unknown:26 (unknown due to data minimization on customer site) | |
| Body Region | Head and neck region: 200 |
| chest region: 200 | |
| abdomen region: 200 | |
| pelvic region: 200 | |
| CT Scanner | Philips: 301 |
| GE: 226 | |
| Simens: 128 | |
| Unknown:145 (unknown due to data minimization on customer site) |
|--|
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7.2 Verification datasets
The performance of the DeepContour were verified by a total of 203 CT images with 2 datasets:
(1) clinical datasets retrospectively collected from multiple clinical regions across the Peking Union Medical College Hospital (N=100), consisting of four selected parts based on their location, 25 cases for head and neck, 25 cases for chest, 25 cases for abdomen and 25 cases for pelvic; These clinical datasets are not used in the training datasets at all. (2) American public datasets (N=103);
(a)The 2017 lung CT segmentation challenge (LCTSC), which contains 60 thoracic CT scan patients with five segmented organs (left lung, right lung, heart, spinal cord, and esophagus), (b) Pancreas-CT (PCT), which contains 43 abdominal contrast enhanced CT scan patients with seven segmented organs (the spleen, left kidney, esophagus, liver, stomach, pancreas, and duodenum). The American public datasets were annotated by the American doctors. The article was published in the journal (Medical Physics), doi: 10.1002/mp.14131. (Please refer to more information about the two publicly available datasets: https://wiki.cancerimagingarchive.net/pages/viewpage.action?pageId=24284539, https://wiki.cancerimagingarchive.net/display/Public/Pancreas-CT)
The auto-segmentation process is the same with what was applied to the training dataset with two steps: first step to distinguish the four different regions (head and neck region, chest region, abdomen region and pelvic region), second step to call the model of this region to realize auto-segmentation. The ground truth annotations for the 100 cases in China were established by two different radiation oncologists with more than 10 years of clinical practice (See Appendix 2 for their detailed CVs) following RTOG and clinical guidelines using manual annotation. A third qualified internal staff member is also available to adjudicate if needed. American public datasets (N=103) were annotated by the American doctors.
The CT images in the 100 test cases were curated for suitability and to avoid bias through rich pre-processing and post-processing methods. Due to the differences between patients and the differences in CT machine image data, the data augmentation was used during training to simulate possible data situations in clinical practice, thereby increasing the model's generalization ability. The strategy of first localization and then segmentation is also used to improve the accuracy of the model.
| # of Datasets | 100 | 103 |
|---|---|---|
| Data Origin | Peking Union Medical | |
| College Hospital | The 2017 lung CT segmentation | |
| challenge (LCTSC): 60 | ||
| Pancreas-CT (PCT): 43 | ||
| Sex | Male: 43 | |
| Female: 57 | Unknow:103 (unknown due to data | |
| minimization on customer site) |
Table 3: Verification Data Information
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| Age | 70: 23 | |
| Body Region | Head and neck region: 25 | 60 thoracic CT scan: 5 segmented
organs (left lung, right lung, heart, spinal
cord, and esophagus) |
| | chest region: 25 | |
| | abdomen region: 25 | 43 abdominal CT scan: 7 segmented
organs (the spleen, left kidney, esophagus,
liver, stomach, pancreas, and duodenum) |
| | pelvic region: 25 | |
| CT Scanner | Philips: 34 | Unknown 103 (unknown due to data
minimization on customer site) |
| | GE: 36 | |
| | Simens: 30 | |
7.3 Rigid and deformable registration
The rigid and deformable registration methods are mainly used for contour mapping in the delineation of target areas and organs at risk. The following Table showed the results between planning CT images (moving image) and CBCT images (Fixed images), rpCT represents rigid planning CT, dpCT1 represents deformed planning CT with our algorithms, and dpCT2 represents deformed planning CT with predicate device. The metrics include the mean absolute error (MAE), root mean square error (RMSE), peak signal-to-noise ratio (PSNR), and structural similarity (SSIM).
| rpCT-CBCT | dpCT1- CBCT | dpCT2- CBCT | |
|---|---|---|---|
| MAE(HU) | 51.23±13.67 | 43.98±10.74 | 46.71±12.71 |
| RMSE | 121.09±30.23 | 117.58±28.22 | 127.96±30.76 |
| PSNR | 20.01±2.74 | 22.23±2.61 | 20.00±3.77 |
| SSIM | 0.623±0.084 | 0.680±0.050 | 0.685±0.055 |
Table 4: Rigid and deformable registration
7.4 Performance comparison
The mean and standard deviation of Dice coefficients was calculated for each organ in the subject device compared with the predicate device. The results of the subject device demonstrate equivalent or better performance compared to the predicate device when aggregate performance over all organs is considered with known limitations described in the labeling. Here equivalence is defined such that the lower bound of 95th percentile confidence interval of the subject device segmentation is greater than 0.1 Dice lower than the mean of predicate device segmentation.
13
Dice coefficients of 100 clinical datasets collected from multiple clinical regions across the Peking Union Medical College Hospital compared to the predicate device are presented here:
| Structure: | DeepContour | AI-Rad CAI-Rad
Companion Organs RT
(K221305) | Contour ProtégéAI
(K223774) |
|--------------------------|---------------------|----------------------------------------------------|--------------------------------|
| Brain | 0.98±0.01(0.97) | 0.93±0.11 | 0.98 ± 0.01 |
| BrainStem | 0.91±0.03(0.89) | 0.90±0.02 | 0.82 ± 0.09 |
| Cochlea_L | 0.86±0.03(0.84) | 0.84±0.03 | 0.27 ± 0.17 |
| Cochlea_R | 0.85±0.01(0.84) | 0.86±0.07 | 0.29 ± 0.18 |
| Eye_L | 0.89±0.02(0.88) | 0.81±0.06 | 0.87 ± 0.06 |
| Eye_R | 0.88±0.03(0.86) | 0.89±0.13 | 0.87 ± 0.06 |
| Lens_L | 0.89±0.02(0.88) | 0.85±0.05 | 0.61±0.17 |
| Lens_R | 0.88±0.02(0.85) | 0.81±0.12 | 0.63 ± 0.15 |
| Larynx | 0.88±0.03(0.83) | 0.84±0.08 | 0.50 ± 0.16 |
| Larynx_extend | 0.92±0.02(0.91) | 0.90±0.11 | None |
| Mandible | 0.95±0.02(0.91) | 0.91±0.07 | 0.85 ± 0.07 |
| Optic_Chiasm | 0.88±0.03(0.82) | 0.63±0.11 | 0.12±0.11 |
| OpticalNerve_L | 0.86±0.04(0.79) | 0.66±0.06 | 0.53 ± 0.13 |
| OpticalNerve_R | 0.89±0.02(0.81) | 0.59±0.10 | 0.52 ± 0.12 |
| OralCavity | 0.92±0.03(0.89) | 0.82±0.09 | 0.77 ± 0.12 |
| OralCavity_WithGum | 0.91±0.06(0.89) | 0.71±0.06 | None |
| Parotid_L | 0.88±0.05(0.82) | 0.80±0.15 | 0.80 ± 0.10 |
| Parotid_R | 0.86±0.03(0.81) | 0.81±0.04 | 0.80 ± 0.06 |
| Pituitary | 0.78±0.04(0.69) | 0.68±0.14 | 0.49±0.15 |
| Temporal_Lobe_L | 0.92±0.02(0.90) | 0.82±0.09 | 0.68 ± 0.17 |
| Temporal_Lobe_R | 0.91±0.11(0.86) | 0.81±0.07 | 0.79 ± 0.18 |
| TMJ_L | 0.85±0.02(0.83) | 0.84±0.02 | 0.84± 0.06 |
| TMJ_R | | | |
| | 0.86±0.15(0.81) | 0.85±0.15 | 0.83 ± 0.06 |
| InternalAcousticCanal_L | 0.76±0.02(0.71) | 0.73±0.12 | 0.71 ± 0.17 |
| InternalAcousticCanal_R | 0.79±0.02(0.75) | 0.77±0.05 | 0.73±0.15 |
| MiddleEar_L | 0.82±0.12(0.78) | 0.72±0.02 | 0.70±0.16 |
| MiddleEar_R | 0.85±0.03(0.81) | 0.80±0.13 | 0.77 ± 0.17 |
| TemporalLobe_withHippo_L | 0.89±0.08(0.85) | 0.87±0.14 | 0.85 ± 0.18 |
| TemporalLobe_withHippo_R | 0.90±0.05(0.86) | 0.88±0.09 | 0.87 ± 0.06 |
| Submandibular_L | 0.91±0.06(0.88) | 0.81±0.11 | 0.75 ± 0.10 |
| Submandibular_R | 0.92±0.03(0.89) | 0.72±0.16 | 0.74±0.09 |
| PharyngealConstrictors_U | 0.82±0.12(0.76) | 0.77±0.13 | None |
| PharyngealConstrictors_M | 0.86±0.14(0.83) | 0.76±0.11 | None |
| PharyngealConstrictors_L | 0.84±0.17(0.80) | 0.74±0.07 | None |
| BrachialPlexus_L | 0.81±0.13(0.79) | 0.71±0.08 | 0.37±0.13 |
| BrachialPlexus_R | 0.82±0.11(0.69) | 0.52±0.06 | 0.36±0.16 |
| Hippocampus_L | 0.76±0.03(0.73) | 0.75 ± 0.12 | 0.75 ± 0.02 |
| Hippocampus_R | 0.79±0.02(0.76) | 0.73 ± 0.09 | 0.76 ± 0.02 |
| EustachianTubeBone_L | 0.85±0.05(0.84) | 0.81±0.05 | 0.88 ± 0.07 |
| EustachianTubeBone_R | 0.87±0.07(0.80) | 0.77±0.09 | 0.72 ± 0.17 |
| TympanicCavity_L | 0.84±0.03(0.83) | 0.80±0.13 | 0.75 ± 0.15 |
| TympanicCavity_R | 0.86±0.09(0.81) | 0.76±0.05 | 0.74 ± 0.17 |
| Vestibule_L | 0.85±0.06(0.82) | 0.80±0.16 | 0.79 ± 0.10 |
| Vestibule_R | 0.87±0.02(0.86) | 0.77±0.10 | 0.74±0.10 |
| InnerEar_L | 0.87±0.10(0.83) | 0.83±0.11 | 0.82 ± 0.07 |
| InnerEar_L | 0.86±0.13(0.83) | 0.87±0.03 | 0.91 ± 0.07 |
| Lung_L | 0.98±0.05(0.96) | 0.92±0.16 | 0.96 ± 0.02 |
| Lung_R | 0.99±0.03(0.98) | 0.95±0.08 | 0.96 ± 0.02 |
| Lung_All | 0.98±0.14(0.97) | 0.91±0.04 | None |
| Heart | 0.93±0.16(0.90) | 0.91±0.06 | 0.90 ± 0.07 |
| Trachea | 0.89±0.03(0.88) | 0.89±0.03 | 0.73 ± 0.17 |
| Esophagus | 0.88±0.11(0.85) | 0.78±0.07 | 0.70 ± 0.15 |
| Breast_L | 0.92±0.08(0.86) | 0.82±0.05 | 0.74 ± 0.17 |
| Breast_R | 0.93±0.01(0.92) | 0.83±0.04 | 0.77 ± 0.10 |
| Aorta | 0.89±0.09(0.87) | 0.70 ± 0.08 | 0.74 ± 0.10 |
| Liver | 0.96±0.07(0.95) | 0.86±0.17 | 0.93 ± 0.07 |
| Kidney_L | 0.92±0.03(0.91) | 0.82±0.13 | 0.92 ± 0.05 |
| Kidney_R | 0.93±0.04(0.91) | 0.88±0.07 | 0.91 ± 0.06 |
| Duodenum | 0.88±0.16(0.83) | 0.81±0.012 | None |
| Pancreas | 0.86±0.01(0.86) | 0.87±0.03 | 0.45 ± 0.22 |
| Smallintestine | 0.89±0.12(0.85) | 0.88±0.08 | None |
| Bowelbag | 0.93±0.16(0.88) | 0.83 ± 0.08 | 0.68 ± 0.08 |
| Bladder | 0.95±0.15(0.93) | 0.87±0.15 | 0.52 ± 0.19 |
| Stomach | 0.86±0.01(0.86) | 0.79 ± 0.21 | 0.81 ± 0.11 |
| Femur_Head_L | 0.92±0.12(0.89) | 0.90±0.16 | 0.93 ± 0.05 |
| Femur_Head_R | 0.91±0.14(0.86) | 0.90±0.09 | 0.93 ± 0.04 |
| Pelvis | 0.87±0.01(0.87) | 0.88±0.08 | 0.93 ± 0.11 |
| Marrow | 0.85±0.13(0.84) | 0.81±0.03 | None |
| Sigmoid | 0.82±0.02(0.81) | 0.70 ± 0.17 | 0.60 ± 0.26 |
| Rectum | 0.87±0.15(0.83) | 0.73 ± 0.18 | 0.83 ± 0.11 |
| Spleen | 0.91±0.01(0.90) | 0.92±0.07 | 0.95 ± 0.03 |
| SeminalVesicle | 0.86±0.02(0.85) | 0.78 ± 0.27 | 0.68 ± 0.15 |
| Testis | 0.87±0.03(0.84) | 0.79 ± 0.16 | 0.63 ± 0.16 |
| Prostate | 0.87±0.02(0.85) | 0.74 ± 0.12 | 0.85 ± 0.06 |
| Ovid_L | $0.85\pm0.03(0.82)$ | $0.65\pm0.03$ | $0.39 \pm 0.17$ |
| Ovid_R | $0.86\pm0.01(0.85)$ | $0.66\pm0.01$ | $0.43 \pm 0.15$ |
| Bladder-Brt | $0.86\pm0.02(0.84)$ | $0.82 \pm 0.23$ | $0.91 \pm 0.12$ |
| SmallIntestine-Brt | $0.87\pm0.04(0.85)$ | $0.76 \pm 0.14$ | $0.73 \pm 0.11$ |
| Rectum-Brt | $0.86\pm0.03(0.84)$ | $0.85 \pm 0.18$ | $0.83 \pm 0.11$ |
| Sigmoid-Brt | $0.79\pm0.02(0.78)$ | $0.70 \pm 0.17$ | $0.60 \pm 0.26$ |
| SpinalCord | $0.93\pm0.01(0.92)$ | $0.66 \pm 0.14$ | $0.63\pm0.16$ |
| Body | $0.98\pm0.05(0.96)$ | $0.97\pm0.03$ | None |
Table 5: Clinical performance comparison (Peking Union Medical College Hospital)
14
15
16
Data format: Mean ± Std Dice coefficient (lower 95th percentile confidence bound based on normal distribution in parentheses)
Dice coefficients of LCTSC American public datasets compared to the predicate device are presented here:
| Structure: | DeepContour | AI-Rad CAI-Rad
Companion Organs RT
(K221305) | Contour
ProtégéAI
(K223774) |
|------------|-----------------|----------------------------------------------------|-----------------------------------|
| SpinalCord | 0.92±0.02(0.91) | 0.64±0.13 | 0.62 ± 0.21 |
| Lung L | 0.97±0.15(0.96) | 0.90±0.13 | 0.95 ± 0.05 |
| Lung R | 0.98±0.06(0.98) | 0.93±0.11 | 0.94 ± 0.08 |
| Heart | 0.92±0.11(0.90) | 0.91±0.04 | 0.90 ± 0.04 |
| Esophagus | 0.89±0.13(0.86) | 0.75±0.13 | 0.68 ± 0.19 |
Table 6: Clinical performance comparison (LCTSC American public datasets)
Data format: Mean ± Std Dice coefficient (lower 95th percentile confidence bound based on normal distribution in parentheses)
Dice coefficients of Pancreas-CT American public datasets compared to the predicate device are presented here:
Table 7: Clinical performance comparison (Pancreas-CT American public datasets)
17
| Structure: | DeepContour | AI-Rad CAI-Rad
Companion Organs RT
(K221305) | Contour
ProtégéAI
(K223774) |
|------------|-----------------|----------------------------------------------------|-----------------------------------|
| Spleen | 0.90±0.05(0.88) | 0.91±0.12 | 0.89 ± 0.08 |
| Pancreas | 0.85±0.03(0.83) | 0.84±0.02 | 0.43 ± 0.25 |
| Kidney_L | 0.93±0.02(0.91) | 0.84±0.03 | 0.92 ± 0.17 |
| Esophagus | 0.88±0.02(0.87) | 0.80±0.06 | 0.70 ± 0.06 |
| Liver | 0.97±0.03(0.97) | 0.85±0.13 | 0.92 ± 0.06 |
| Stomach | 0.85±0.02(0.84) | 0.80±0.05 | 0.81 ± 0.17 |
| Duodenum | 0.86±0.02(0.85) | 0.82±0.12 | None |
Data format: Mean ± Std Dice coefficient (lower 95th percentile confidence bound based on normal distribution in parentheses)
A comparison of the median Average Symmetric Surface Distance (ASSD) between the subject device and the predicate device is also performed and the results are comparable as shown in the following table. The subject device achieved a median ASSD of 0.95 in comparison to the predicate device achieving a median ASSD of 0.96 for existing organs.
Table 8: Rigid and deformable registration
| | DeepContour | | AI-Rad CAI-Rad
Companion Organs RT
(K221305) | | Contour ProtégéAI
(K223774) | |
|------|-------------|-----------------------|----------------------------------------------------|-----------------------|--------------------------------|-----------------------|
| | Median | 95% CI
(Bootstrap) | Median | 95% CI
(Bootstrap) | Median | 95% CI
(Bootstrap) |
| ASSD | 0.95 | [0.85,1.13] | 0.96 | [0.84,1.15] | 0.95 | [0.86,1.17] |
8 CONCLUSION
DeepContour is believed to be substantially equivalent to predicate device in terms of its indications for use, technical characteristics, and overall performance. The information provided in this submission indicates the subject device is as safe, is as effective, and performs as well as predicate device. Therefore, it is in the opinion of Wisdom Technologies, Inc. that the medical device, DeepContour, is substantially equivalent to predicate device.