(224 days)
Not Found
Yes
The device description explicitly mentions "Deep learning contouring" as one of its image processing functions.
No.
The device is described as software that assists in treatment planning by registering and segmenting medical images, not directly delivering therapy or having a therapeutic effect itself.
No
The device is used for treatment planning, evaluation, and adaptation in radiation oncology by registering and segmenting medical images. While it processes data from medical images, its primary output is for treatment-related activities rather than providing a diagnosis of a disease or condition.
Yes
The device description explicitly states that the proposed device, AccuContour™, is a "standalone software". It describes software functions for image processing and management without mentioning any accompanying hardware components that are 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 tests performed on samples taken from the human body, such as blood, urine, or tissue, to detect diseases, conditions, or infections. They are used to provide information for diagnosis, monitoring, or screening.
- Device Function: The described device, AccuContour™, is a software used for image processing (registration and segmentation) of medical images (CT, MRI, PET). Its purpose is to generate information for treatment planning, evaluation, and adaptation in radiation oncology.
- Lack of Biological Sample Analysis: The device does not analyze any biological samples from the patient. It operates solely on medical images.
- Intended Use: The intended use is clearly focused on supporting radiation therapy, not on diagnosing or monitoring conditions through the analysis of biological samples.
Therefore, AccuContour™ falls under the category of medical image processing software used for treatment planning and evaluation, which is distinct from an In Vitro Diagnostic device.
No
The input explicitly states "Control Plan Authorized (PCCP) and relevant text: Not Found", indicating no mention of an approved or cleared PCCP.
Intended Use / Indications for Use
It is used by radiation oncology department to register multimodality images and segment (non-contrast) CT images, to generate needed information for treatment planning, treatment evaluation and treatment adaptation.
Product codes
QKB
Device Description
The proposed device, AccuContour™, is a standalone software which is used by radiation oncology department to register multimodality images and segment (non-contrast) CT images, to generate needed information for treatment planning, treatment evaluation and treatment adaptation.
The product has two image process functions:
- (1) Deep learning contouring: it can automatically contour the organ-at-risk, including head and neck, thorax, abdomen and pelvis (for both male and female),
- (2) Automatic Registration, and
- (3) Manual Contour.
It also has the following general functions:
- Receive, add/edit/delete, transmit, input/export, medical images and DICOM data;
- A Patient management;
- Review of processed images;
- Open and Save of files.
Mentions image processing
Yes
Mentions AI, DNN, or ML
Deep learning
Input Imaging Modality
Multimodality images, (non-contrast) CT images. Compatible Modality for Segmentation: Non-Contrast CT. Compatible Modality for Registration: CT, MRI, PET.
Anatomical Site
head and neck, thorax, abdomen and pelvis (for both male and female)
Indicated Patient Age Range
Not Found
Intended User / Care Setting
radiation oncology department
Description of the training set, sample size, data source, and annotation protocol
Not Found
Description of the test set, sample size, data source, and annotation protocol
Segmentation performance test:
The segmentation performance test was performed on proposed device and predicate device to evaluate the automated segmentation accuracy. Two separate tests were performed. One test involved images generated in healthcare institutions in China using scanner models available in China covering three major vendors. The other involved images generated in healthcare institutions in US using scanner models available in US covering three major vendors. The three major vendors were GE, Siemens and Philips. For each body parts, all intended organs were included in images of the US and China. Ground truthing of each image was generated from the consensus of at least three licensed physicians.
Registration performance test:
The registration performance test was performed on proposed device and predicate device to evaluate the automated registration accuracy. Two separate tests were performed. One test involved images generated in healthcare institutions in China using scanner models available in China covering three major vendors. And the image registration feature is tested on multi-modality image sets from same patients. The other involved most images generated in healthcare institutions in U.S. All fixed image and most moving images came from U.S and a small amount of moving images adopted from online database were originally from non-US sources. All the scanner models covered three major vendors. And the image registration feature is only tested on multi-modality image sets from different patients. Both tests covered various modalities, including CT/CT, CT/MR and CT/PET.
Summary of Performance Studies (study type, sample size, AUC, MRMC, standalone performance, key results)
Segmentation performance test:
The segmentation performance test was performed on proposed device and predicate device to evaluate the automated segmentation accuracy. Two separate tests were performed. One test involved images generated in healthcare institutions in China using scanner models available in China covering three major vendors. The other involved images generated in healthcare institutions in US using scanner models available in US covering three major vendors. The three major vendors were GE, Siemens and Philips. For each body parts, all intended organs were included in images of the US and China. Ground truthing of each image was generated from the consensus of at least three licensed physicians. DICE similarity coefficients (DSC) was used for evaluation. DSC values were calculated on two sets of images for test group and control group, respectively. According to the results, it could be concluded that the DSC of proposed device was non-inferiority compared with that of the predicate device.
Registration performance test:
The registration performance test was performed on proposed device and predicate device to evaluate the automated registration accuracy. Two separate tests were performed. One test involved images generated in healthcare institutions in China using scanner models available in China covering three major vendors. And the image registration feature is tested on multi-modality image sets from same patients. The other involved most images generated in healthcare institutions in U.S. All fixed image and most moving images came from U.S and a small amount of moving images adopted from online database were originally from non-US sources. All the scanner models covered three major vendors. And the image registration feature is only tested on multi-modality image sets from different patients. Both tests covered various modalities, including CT/CT, CT/MR and CT/PET. The Normalized Mutual Information (NMI) was used for evaluation. NMI values were calculated on two sets of images for both the proposed device and predicate device, respectively. The NMI value of proposed device was compared with that of the predicate device. According to the results, it could be concluded that the NMI of proposed device was non-inferiority compared with that of the predicate device.
No clinical study is included in this submission.
Key Metrics (Sensitivity, Specificity, PPV, NPV, etc.)
DICE similarity coefficients (DSC), Normalized Mutual Information (NMI)
Predicate Device(s)
Reference Device(s)
Not Found
Predetermined Change Control Plan (PCCP) - All Relevant Information
Not Found
§ 892.2050 Medical image management and processing system.
(a)
Identification. A medical image management and processing system is a device that provides one or more capabilities relating to the review and digital processing of medical images for the purposes of interpretation by a trained practitioner of disease detection, diagnosis, or patient management. The software components may provide advanced or complex image processing functions for image manipulation, enhancement, or quantification that are intended for use in the interpretation and analysis of medical images. Advanced image manipulation functions may include image segmentation, multimodality image registration, or 3D visualization. Complex quantitative functions may include semi-automated measurements or time-series measurements.(b)
Classification. Class II (special controls; voluntary standards—Digital Imaging and Communications in Medicine (DICOM) Std., Joint Photographic Experts Group (JPEG) Std., Society of Motion Picture and Television Engineers (SMPTE) Test Pattern).
0
Image /page/0/Picture/0 description: The image shows the logo of the U.S. Food and Drug Administration (FDA). On the left is the Department of Health & Human Services logo. To the right of that is the FDA logo, with the letters "FDA" in a blue square. To the right of the square is the text "U.S. FOOD & DRUG ADMINISTRATION" in blue.
Xiamen Manteia Technology LTD. % Ms. Diana Hong General Manager Mid-Link Consulting Co. Ltd. P.O Box 120-119 Shanghai, 200120 CHINA
February 28, 2020
Re: K191928
Trade/Device Name: AccuContour™ Regulation Number: 21 CFR 892.2050 Regulation Name: Picture archiving and communications system Regulatory Class: Class II Product Code: QKB Dated: January 10, 2020 Received: January 17, 2020
Dear Ms. Hong:
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
1
801); medical device reporting of medical device-related adverse events) (21 CFR 803) 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,
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) K191928
Device Name AccuContour
Indications for Use (Describe)
It is used by radiation oncology department to register multimodality images and segment (non-contrast) CT images, to generate needed information for treatment planning, treatment evaluation and treatment adaptation.
Type of Use (Select one or both, as applicable)
X Prescription Use (Part 21 CFR 801 Subpart D)
| Over-The-Counter Use (21 CFR 801 Subpart C)
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3
510(k) Summary
This 510(k) Summary is being submitted in accordance with requirements of Title 21, CFR Section 807.92.
The assigned 510(k) Number: K191928
-
- Date of Preparation: 02/26/2020
-
- Sponsor Identification
Xiamen Manteia Technology LTD.
1903, B Tower, Zijin Plaza, No.1811 Huandao East Road, Xiamen, China
Establishment Registration Number: Not registered yet.
Contact Person: Lu Xie Position: Research Management Tel: +86 592-6100813 Email: xielu@manteiatech.com
-
- Designated Submission Correspondent
Ms. Diana Hong (Primary Contact Person) Mr. Lee Fu (Alternative Contact Person)
- Designated Submission Correspondent
Mid-Link Consulting Co., Ltd
P.O. Box 120-119, Shanghai, 200120, China
Tel: +86-21-22815850, Fax: +1-360-925-3199 Email: info@mid-link.net
4
4. Identification of Proposed Device
Trade Name: AccuContour™ Common Name: Medical Imaging Software
Regulatory Information
Classification Name: System, Imaging processing, Radiological Classification: II; Product Code: QKB Regulation Number: 21CFR 892.2050 Review Panel: Radiology;
Indication for Use Statement:
It is used by radiation oncology department to register multimodality images and segment (non-contrast) CT images, to generate needed information for treatment evaluation and treatment adaptation.
Device Description:
The proposed device, AccuContour™, is a standalone software which is used by radiation oncology department to register multimodality images and segment (non-contrast) CT images, to generate needed information for treatment planning, treatment evaluation and treatment adaptation.
The product has two image process functions:
- (1) Deep learning contouring: it can automatically contour the organ-at-risk, including head and neck, thorax, abdomen and pelvis (for both male and female),
- (2) Automatic Registration, and
- (3) Manual Contour.
It also has the following general functions:
-
Receive, add/edit/delete, transmit, input/export, medical images and DICOM data;
- A Patient management;
-
Review of processed images;
-
Open and Save of files.
5
-
ર. Identification of Predicate Device
510(k) Number: K182624 Product Name: MIM-MRT Dosimetry -
- Non-Clinical Test Conclusion
Segmentation performance test
The segmentation performance test was performed on proposed device and predicate device to evaluate the automated segmentation accuracy. Two separate tests were performed. One test involved images generated in healthcare institutions in China using scanner models available in China covering three major vendors. The other involved images generated in healthcare institutions in US using scanner models available in US covering three major vendors. The three major vendors were GE, Siemens and Philips. For each body parts, all intended organs were included in images of the US and China. Ground truthing of each image was generated from the consensus of at least three licensed physicians. DICE similarity coefficients (DSC) was used for evaluation. DSC values were calculated on two sets of images for test group and control group, respectively. According to the results, it could be concluded that the DSC of proposed device was non-inferiority compared with that of the predicate device.
Registration performance test
The registration performance test was performed on proposed device and predicate device to evaluate the automated registration accuracy. Two separate tests were performed. One test involved images generated in healthcare institutions in China using scanner models available in China covering three major vendors. And the image registration feature is tested on multi-modality image sets from same patients. The other involved most images generated in healthcare institutions in U.S. All fixed image and most moving images came from U.S and a small amount of moving images adopted from online database were originally from non-US sources. All the scanner models covered three major vendors. And the image registration feature is only tested on multi-modality image sets from different patients. Both tests covered various modalities, including CT/CT, CT/MR and CT/PET. The Normalized Mutual Information (NMI) was used for evaluation. NMI values were calculated on two sets of images for both the proposed device and predicate device, respectively. The NMI value of proposed device was compared with that of the predicate device. According to the results, it could be concluded that the NMI of proposed device was non-inferiority compared with that of the predicate device.
7. Clinical Test Conclusion
No clinical study is included in this submission.
6
8. Substantially Equivalent (SE) Comparison
| ITEM | Proposed Device | Predicate Device K182624 |
|---|---|---|
| Regulatory Information | ||
| Regulation No. | 21CFR 892.2050 | 21CFR 892.2050 |
| Product Code | QKB | LLZ |
| Class | II | II |
| Indication for Use | It is used by radiation oncology department to register multimodality images and segment (non-contrast) CT images, to generate needed information for treatment planning, treatment evaluation and treatment adaptation. | MIM software is used by trained medical professionals as a tool to aid in evaluation and information management of digital medical images. The medical image modalities include, but are not limited to, CT, MRI, CR, DX, MG, US, SPECT, PET and XA as supported by ACR/NEMA DICOM 3.0. MIM assists in the following indications: |
| • Receive, transmit, store, retrieve, display, print, and process medical images and DICOM objects. | ||
| • Create, display and print reports from medical images. | ||
| • Registration, fusion display, and review of medical images for diagnosis, treatment evaluation, and treatment planning. | ||
| • Evaluation of cardiac left ventricular function and perfusion, including left ventricular enddiastolic volume, end-systolic volume, and ejection fraction. | ||
| • Localization and definition of objects such as tumors and normal tissues in medical images. | ||
| • Creation, transformation, and modification of contours for applications including, but not limited to, quantitative analysis, aiding adaptive therapy, transferring contours to radiation therapy treatment planning systems and |
| | | | Table 1 Comparison of Technology Characteristics
J | |
| -- | -- | -- | ------------------------------------------------------- | -- |
|---|
7
| ITEM | Proposed Device | Predicate Device K182624 |
|---|---|---|
| archiving contours for patient follow-up | ||
| and management. | ||
| • Quantitative and statistical analysis of | ||
| PET/SPECT brain scans by comparing to | ||
| other registered PET/SPECT brain scans. | ||
| • Planning and evaluation of permanent | ||
| implant brachytherapy procedures (not | ||
| including radioactive microspheres). | ||
| • Calculating absorbed radiation dose as | ||
| a result of administering a radionuclide. | ||
| When using device clinically, the user | ||
| should only use FDA approved | ||
| radiopharmaceuticals. If using with | ||
| unapproved ones, this device should only | ||
| be used for research purposes. | ||
| Lossy compressed mammographic | ||
| images and digitized film screen images | ||
| must not be reviewed for primary image | ||
| interpretations. Images that are printed to | ||
| film must be printed using an | ||
| FDA-approved printer for the diagnosis | ||
| of digital mammography images. | ||
| Mammographic images must be viewed | ||
| on a display system that has been cleared | ||
| by the FDA for the diagnosis of digital | ||
| mammography images. The software is | ||
| not to be used for mammography CAD. | ||
| Label/labeling | Conform with 21CFR Part 801 | Conform with 21CFR Part 801 |
| Operating System | Windows | Windows and MAC system |
| Segmentation Features | ||
| Algorithm | Deep Learning | Atlas-based |
| Compatible | ||
| Modality | Non-Contrast CT | Non-Contrast CT |
| Compatible | ||
| Scanner Models | No Limitation on scanner model, | |
| DICOM 3.0 compliance required. | No Limitation on scanner model, | |
| DICOM3.0 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. | ||
| Contraindications | None | None |
| ITEM | Proposed Device | Predicate Device K182624 |
| Registration Features | ||
| Algorithm | Intensity Based | Intensity Based |
| Compatible | ||
| Modality | CT, MRI, PET | CT, MRI, CR, DX, MG, US, SPECT, |
| PET and XA | ||
| Compatible | ||
| Scanner Models | No Limitation on scanner model, | |
| DICOM 3.0 compliance required. | No Limitation on scanner model, | |
| DICOM3.0 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. |
8
-
- Substantially Equivalent (SE) Conclusion
Based on the comparison and analysis above, the proposed devices are determined to be Substantially Equivalent (SE) to the predicate devices.
- Substantially Equivalent (SE) Conclusion