(262 days)
Yes
The device description explicitly states that it uses a "machine learning-based approach" and mentions training "convolutional networks (CNNs)".
No.
The device is a software tool used in radiation therapy treatment planning to assist in segmentation and contouring, but it does not directly apply therapy or treat patients. It supports the treatment planning process, which is distinct from the therapeutic act itself.
No.
The device is intended to assist in the radiation therapy treatment planning process by segmenting structures, not to diagnose medical conditions. It explicitly states it is "not intended to replace a thorough review by qualified medical professionals" and is an "initial method to segment and contour study series," implying it generates data for further medical professional review and ultimate diagnostic/treatment decisions.
Yes
The device description explicitly states "INTContour is a software-only product". While it requires a workstation with a GPU and runs as a service accessed via a web interface, these are the necessary computing environment and access method for the software, not separate hardware components included as part of the medical device itself.
Based on the provided information, this device is NOT an IVD (In Vitro Diagnostic).
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, tissue) to provide information for the diagnosis, treatment, or prevention of disease. This examination is performed outside of the living body (in vitro).
-
INTContour's Function: INTContour processes medical images (CT and MRI) to automatically segment anatomical structures. It does not analyze biological specimens taken from the body. Its purpose is to assist in the radiation therapy treatment planning process by providing initial contours, which are then reviewed and potentially edited by medical professionals.
-
Intended Use: The intended use clearly states it supports the "radiation therapy treatment planning process" and is an "initial method to segment and contour study series." This is a function related to medical imaging analysis and treatment planning, not the analysis of biological samples.
Therefore, INTContour falls under the category of medical image processing software used for treatment planning, not an In Vitro Diagnostic device.
No
The letter does not mention that the FDA has reviewed and approved or cleared a PCCP for this specific device, nor does it reference PCCP in any context.
Intended Use / Indications for Use
INTContour provides a machine learning-based approach for the automatic structures including treatment targets and organs at risk to support the radiation therapy treatment planning process. INTContour is intended as an initial method to segment and contour study series; therefore, this software must be used in conjunction with an appropriate software to edit the segmentation results if necessary. It is not intended to replace a thorough review by qualified medical professionals. INTContour is developed for use by dosimetrists, and radiation oncologists. The currently supported anatomical regions for automatic segmentation are head and neck, thorax, and male pelvis.
Product codes (comma separated list FDA assigned to the subject device)
MUJ, QKB
Device Description
INTContour is a software-only product that uses a machine learning-based approach to perform automatic segmentation of structures in medical images, coupled with tools for visualizing the segmentation results. A library of previously contoured expert cases serves as inputs to train the machine learning algorithms, specifically, convolutional networks (CNNs), to perform automatic segmentation. The results of the automatic segmentation will be stored in the DICOM Radiotherapy Structure Set (RTSTRUCT) format, which can be sent to desired destinations via the DICOM protocol.
INTContour is intended to be used by dosimetrists, medical physicists, and radiation oncologists, and serves as an initial method to segment and contour study series. It must be used in conjunction with appropriate software to edit the segmentation results if necessary. The currently supported anatomical regions for automatic segmentation are head and neck, thorax, abdomen, and male pelvis. The supported structures for each region are shown below in section 10.
INTContour software is intended to be deployed within a hospital's private network on a workstation with an advanced graphics processing unit (GPU) and runs as a service. A webbased interface is used to access the service and manage the transfer of data, automatic segmentation, and visualization.
Mentions image processing
Not Found
Mentions AI, DNN, or ML
INTContour provides a machine learning-based approach for the automatic structures including treatment targets and organs at risk to support the radiation therapy treatment planning process.
INTContour is a software-only product that uses a machine learning-based approach to perform automatic segmentation of structures in medical images, coupled with tools for visualizing the segmentation results. A library of previously contoured expert cases serves as inputs to train the machine learning algorithms, specifically, convolutional networks (CNNs), to perform automatic segmentation.
INTContour provides a machine learning-based approach for automatic segmentation of structures including treatment targets and organs at risk to support the radiation therapy treatment planning process.
INTContour provides a machine learning-based approach for the automatic segmentation of structures including treatment targets and organs at risk to support the radiation therapy treatment planning process.
INTContour used a machine learning-based method to train CNNs from expert contours to perform segmentation on the target images to generate contours.
Input Imaging Modality
CT & MRI
Anatomical Site
head and neck, thorax, abdomen, and male pelvis.
Indicated Patient Age Range
18 – 76
Intended User / Care Setting
dosimetrists, medical physicists, and radiation oncologists.
hospital's private network
Description of the training set, sample size, data source, and annotation protocol
A library of previously contoured expert cases serves as inputs to train the machine learning algorithms, specifically, convolutional networks (CNNs), to perform automatic segmentation.
Testing data was acquired from multiple sources than the training data that covers head and neck, thorax, abdomen, and male pelvis regions.
Description of the test set, sample size, data source, and annotation protocol
Testing data was acquired from multiple sources than the training data that covers head and neck, thorax, abdomen, and male pelvis regions. The cases were selected from patients who went through radiation treatment with age 18 – 76, both male and various types of cancers. Ground truth was performed by at least two trained personnel including dosimetrist, medical physicist and/or radiation oncologist to minimize human bias in segmentation.
Summary of Performance Studies (study type, sample size, AUC, MRMC, standalone performance, key results)
The safety and performance of INTContour has been evaluated and verified in accordance with software specifications and applicable performance standards through software verification and validation testing. Non-clinical verification and validation test results, including model performance and software usability, established that the device meets its design requirements and intended use, that it is as safe and as effective as the predicate devices, and that no new issues of safety and effectiveness were raised.
The Dice metric and 95% Hausdorff Distance (HD95) were calculated for each organ. Statistical analysis on the test data was performed by comparing the calculated metrics of INTContour against the predicate/reference device. Dice metric was used for large organs and HD95 was used for small organs to define the acceptance criteria due to the large variations on Dice metric on small organs. By comparing the lower bound (Dice) or upper bound (HD95) of the performance differences between INTContour and the predicate/reference device with the threshold values, all organs have passed the acceptance criteria and demonstrated the noninferiority against the predicate/reference device.
Key Metrics (Sensitivity, Specificity, PPV, NPV, etc.)
Dice metric and 95% Hausdorff Distance (HD95)
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
§ 892.5050 Medical charged-particle radiation therapy system.
(a)
Identification. A medical charged-particle radiation therapy system is a device that produces by acceleration high energy charged particles (e.g., electrons and protons) intended for use in radiation therapy. This generic type of device may include signal analysis and display equipment, patient and equipment supports, treatment planning computer programs, component parts, and accessories.(b)
Classification. Class II. When intended for use as a quality control system, the film dosimetry system (film scanning system) included as an accessory to the device described in paragraph (a) of this section, is exempt from the premarket notification procedures in subpart E of part 807 of this chapter subject to the limitations in § 892.9.
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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, which is a blue square with the letters "FDA" in white. To the right of the FDA logo is the text "U.S. FOOD & DRUG ADMINISTRATION" in blue.
Carina Medical LLC % Xue Feng Chief Executive Officer 1233 Litchfield Ln Lexington, KY 40513
Re: K212274
Trade/Device Name: INT Contour Regulation Number: 21 CFR 892.5050 Regulation Name: Medical Charged-Particle Radiation Therapy System Regulatory Class: Class II Product Code: MUJ, QKB Dated: July 20, 2021 Received: July 20, 2021
Dear Xue Feng:
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 and Part 809); medical device reporting of medical device-related adverse events) (21 CFR
1
- for 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 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,
Julie Sullivan. Ph.D. Assistant Director Nuclear Medicine and Radiation Therapy 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) K212274
Device Name INT Contour
Indications for Use (Describe)
INTContour provides a machine learning-based approach for the automatic structures including treatment targets and organs at risk to support the radiation therapy treatment planning process. INTContour is intended as an initial method to segment and contour study series; therefore, this software must be used in conjunction with an appropriate software to edit the segmentation results if necessary. It is not intended to replace a thorough review by qualified medical professionals. INTContour is developed for use by dosimetrists, and radiation oncologists. The currently supported anatomical regions for automatic segmentation are head and neck, thorax, and male pelvis.
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/1 description: The image shows the word "CARINA" in a bold, sans-serif font. The letters "A" and "I" are colored red, while the remaining letters are black. The word is presented in a clean and modern style.
Summary 510(k)
1. Applicant:
- Carina Medical LLC
- 1233 Lichfield Ln
- Lexington, KY, 40513
- USA
- Contact Name: Xue Feng Chief Executive Officer
- Phone: 434-284-1073
- Fax: 855-615-2856
- E-mail: xfeng@carinaai.com
2. Device:
Trade Name: INTContour
Common Name: Intelligent Organ Contouring System
Model Number: v1.0
Product Code: MUJ
Regulation Description: Medical charged-particle radiation therapy system
Regulation Number: 21 CFR 892.5050
Device Class: II
3. Predicate Devices:
Trade Name: Smart Segmentation – Knowledge Based Contouring
+1 434-284-1073
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CARINA
Manufacturer: Varian Medical Systems, Inc.
Address: 3100 Hansen Way, Palo Alto, CA, 94303
Regulation Number: 21 CFR 892.5050
Regulation Name: Medical charged-particle radiation therapy system
Device Class: II
Product Code: MUJ
- 510(k) Number: K141248
- 510(k) Clearance Date: 09/05/2014
4. Reference Device:
Trade Name: AccuContour
Manufacturer: Xiamen Manteia Technology LTD.
Address: 1903, B Tower, Zijin Plaza, No.1811 Huandao East Road, Xiamen, China
Regulation Number: 21 CFR 892.2050
Regulation Name: Picture archiving and communications system
Device Class: II
Product Code: QKB
510(k) Number: K191928
510(k) Clearance Date: 02/28/2020
5. Device Description
INTContour is a software-only product that uses a machine learning-based approach to perform automatic segmentation of structures in medical images, coupled with tools for visualizing the seqmentation results. A library of previously contoured expert cases serves as inputs to train the machine learning algorithms, specifically, convolutional networks (CNNs), to perform
5
CARINA
automatic segmentation. The results of the automatic segmentation will be stored in the DICOM Radiotherapy Structure Set (RTSTRUCT) format, which can be sent to desired destinations via the DICOM protocol.
INTContour is intended to be used by dosimetrists, medical physicists, and radiation oncologists, and serves as an initial method to segment and contour study series. It must be used in conjunction with appropriate software to edit the segmentation results if necessary. The currently supported anatomical regions for automatic segmentation are head and neck, thorax, abdomen, and male pelvis. The supported structures for each region are shown below in section 10.
INTContour software is intended to be deployed within a hospital's private network on a workstation with an advanced graphics processing unit (GPU) and runs as a service. A webbased interface is used to access the service and manage the transfer of data, automatic segmentation, and visualization.
6. Intended Use Statement
INTContour provides a machine learning-based approach for automatic segmentation of structures including treatment targets and organs at risk to support the radiation therapy treatment planning process.
7. Indications for Use Statement
INTContour provides a machine learning-based approach for the automatic segmentation of structures including treatment targets and organs at risk to support the radiation therapy treatment planning process. INTContour is intended as an initial method to segment and contour study series; therefore, this software must be used in conjunction with an appropriate software to edit the segmentation results if necessary. It is not intended to replace a thorough review by qualified medical professionals. INTContour is developed for use by dosimetrists, medical physicists, and radiation oncologists. The currently supported anatomical regions for
6
automatic segmentation are head and neck, thorax, abdomen, and male pelvis. The supported structures for each region are shown below in Section 10.
8. Summary of Technological Characteristics Comparison
Table 1 shows the similarities and differences between the technological characteristics of the two products. The key difference is the detailed implementation of the automated segmentation algorithms. Testing demonstrates that the differences do not raise new questions of safety or effectiveness.
| Topic | Predicate Device | Subject Device |
|---|---|---|
| Physical | ||
| Characteristics | Software package that operates on | |
| off-the-shelf hardware | Software package that operates on a | |
| virtual machine within the off-the-shelf | ||
| hardware | ||
| Computer | PC Compatible | Same |
| Operating | ||
| System | Windows | Ubuntu (18.04 & 20.04) |
| DICOM Standard | ||
| Compliance | The software processes DICOM | |
| compliant image data, including | ||
| RTSTRUCT | Same | |
| Modalities | CT & MRI | Same |
| User Interface | The software is designed for use on a | |
| radiotherapy workstation with a native | ||
| user interface. | The software is designed for use on a | |
| radiotherapy workstation with a web- | ||
| based user interface | ||
| Segmentation | ||
| Structures | Organs at risk and target volumes | Same |
Table 1. Summary of Technological Characteristic Comparison
7
| Overall
Segmentation
Method | Model-based approach using a library
of expert contours | Same |
|------------------------------------------------------------|------------------------------------------------------------------------------------------------------------------------------------------------|------------------------------------------------------------------------------------------------------------------------------------------------------|
| Detailed
Implementation
of Segmentation
Algorithm | Smart Segmentation – knowledge-based contouring used an atlas-based method to deform expert contours to the target images to generate contours | INTContour used a machine learning-based method to train CNNs from expert contours to perform segmentation on the target images to generate contours |
| Support of
Manual Editing | Yes | No |
| Provide Expert
Case Library to
Users | Yes | No |
9. Performance Data
The safety and performance of INTContour has been evaluated and verified in accordance with software specifications and applicable performance standards through software verification and validation testing. Non-clinical verification and validation test results, including model performance and software usability, established that the device meets its design requirements and intended use, that it is as safe and as effective as the predicate devices, and that no new issues of safety and effectiveness were raised.
Testing data was acquired from multiple sources than the training data that covers head and neck, thorax, abdomen, and male pelvis regions. The cases were selected from patients who went through radiation treatment with age 18 – 76, both male and various types of cancers. Ground truth was performed by at least two trained personnel including dosimetrist, medical physicist and/or radiation oncologist to minimize human bias in segmentation.
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CARINA
Further, during the development, potential hazards were controlled by a risk management plan including risk analysis, risk mitigation, verification and validation.
10. Results
The Dice metric and 95% Hausdorff Distance (HD95) were calculated for each organ. Statistical analysis on the test data was performed by comparing the calculated metrics of INTContour against the predicate/reference device. Dice metric was used for large organs and HD95 was used for small organs to define the acceptance criteria due to the large variations on Dice metric on small organs. By comparing the lower bound (Dice) or upper bound (HD95) of the performance differences between INTContour and the predicate/reference device with the threshold values, all organs have passed the acceptance criteria and demonstrated the noninferiority against the predicate/reference device.
| Head and Neck | Thorax | Abdomen | Male Pelvis | |
|---|---|---|---|---|
| Brainstem | Pituitary | SpinalCanal | Spleen | Bladder |
| OpticChiasm | InnerEar_L | Lung_R | Kidney_R | Femur_Head_L |
| Bone_Mandible | InnerEar_R | Lung_L | Kidney_L | Femur_Head_R |
| OpticNrv_L | Lens_L | Heart | Gallbladder | PenileBulb |
| OpticNrv_R | Lens_R | Esophagus | Esophagus | Prostate |
| Parotid_L | Lobe_Temporal_L | Trachea | Liver | SeminalVesicle |
| Parotid_R | Lobe_Temporal_R | BronchialTree | Stomach | Rectum |
| Glnd_Submand_L | Larynx | SpinalCord | A_Aorta | |
| Glnd_Submand_R | Cavity_Oral | V_Venacava_I | ||
| Eye_L | Pharynx | PortalVein |
The table below shows the list of all supported organs:
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| Eye_R | Brain | Pancreas |
|---|---|---|
| MidEar_L | Cochlea_L | AdrenalGland_R |
| MidEar_R | Cochlea_R | AdrenalGland_L |
| Joint_TM_L | Glnd_Lacrimal_L | SplenicVein |
| Joint_TM_R | Glnd_Lacrimal_R | |
| BrachialPlex_L | BrachialPlex_R |
11. Substantial Equivalence Conclusion
INTContour is an automatic segmentation software to support the radiation treatment planning process which has similar intended use and indications for use statement as the predicate device. The two devices have similar technological characteristics: both algorithms rely on a library of expert contours and use model-based approach to generate contours on new images. This 510(k) submission includes information on the INTContour technological characteristics, as well as performance data and verification and validation activities demonstrating that INTContour is as safe and effective as the predicate, and does not raise different questions of safety and effectiveness.