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.

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.

AI/ML Overview

Here's a breakdown of the acceptance criteria and study information for the AccuContour™ device, based on the provided text:

1. Table of Acceptance Criteria and Reported Device Performance

The document does not explicitly state numerical acceptance criteria for DICE Similarity Coefficients (DSC) for segmentation or Normalized Mutual Information (NMI) for registration. Instead, it states the acceptance criterion is non-inferiority compared to the predicate device.

Performance Metric	Acceptance Criteria	Reported Device Performance
Segmentation (DSC)	Non-inferiority to predicate device (K182624)	DSC of proposed device was non-inferior compared to predicate device K182624
Registration (NMI)	Non-inferiority to predicate device (K182624)	NMI of proposed device was non-inferior compared to predicate device K182624

2. Sample Size Used for the Test Set and Data Provenance

Segmentation Performance Test:
- Test Set Description: Two separate tests were performed.
  - One test involved images generated in healthcare institutions in China using scanner models from GE, Siemens, and Philips.
  - The other test involved images generated in healthcare institutions in the US using scanner models from GE, Siemens, and Philips.
  - For each body part, all intended organs were included in images from both US and China datasets.
- Sample Size: The exact number of images or cases in each test set is not specified.
- Data Provenance: Retrospective, from healthcare institutions in China and the US.
Registration Performance Test:
- Test Set Description: Two separate tests were performed.
  - One test involved images generated in healthcare institutions in China using scanner models from GE, Siemens, and Philips, tested on multi-modality image sets from the same patients.
  - The other test involved most images generated in healthcare institutions in the US, with a small amount of moving images adopted from online databases (originally from non-US sources). This test was on multi-modality image sets from different patients.
  - Both tests covered various modalities (CT/CT, CT/MR, CT/PET).
- Sample Size: The exact number of images or cases in each test set is not specified.
- Data Provenance: Retrospective, from healthcare institutions in China and the US, with some online database images (non-US origin) for the US registration test.

3. Number of Experts Used to Establish the Ground Truth for the Test Set and Their Qualifications

Number of Experts: At least three licensed physicians.
Qualifications of Experts: Licensed physicians. (Further sub-specialty or years of experience are not specified, but licensure implies a professional medical qualification.)

4. Adjudication Method for the Test Set

The ground truth was generated from the consensus of at least three licensed physicians. This implies an adjudication method where all experts agree, or a majority agreement based on the "consensus" phrasing, but the specific process (e.g., voting, discussion to reach full agreement) is not detailed.

5. Multi-Reader Multi-Case (MRMC) Comparative Effectiveness Study

No, a multi-reader multi-case (MRMC) comparative effectiveness study was not performed or reported in this summary. The comparison was algorithm-to-algorithm (proposed device vs. predicate device), not involving human readers' performance with and without AI assistance.

6. Standalone Performance Study

Yes, a standalone (algorithm only without human-in-the-loop performance) study was performed. The segmentation and registration accuracies (DICE and NMI respectively) were calculated for the proposed device's algorithm and compared to the predicate device's algorithm.

7. Type of Ground Truth Used

The ground truth used was expert consensus. Specifically, for both segmentation and registration, ground truthing of each image was generated from the consensus of at least three licensed physicians.

8. Sample Size for the Training Set

The document does not specify the sample size used for the training set. It only describes the test sets.

9. How the Ground Truth for the Training Set was Established

The document does not provide information on how the ground truth for the training set was established. It only details the ground truth establishment for the test sets.

Summary

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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, 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

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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

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Indications for Use

510(k) Number (if known) K191928

Device Name AccuContour

Indications for Use (Describe)

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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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

1. Date of Preparation: 02/26/2020
1. 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

1. Designated Submission Correspondent
  Ms. Diana Hong (Primary Contact Person) Mr. Lee Fu (Alternative Contact Person)

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

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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 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.

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ર. Identification of Predicate Device
510(k) Number: K182624 Product Name: MIM-MRT Dosimetry
1. 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.

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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 CharacteristicsJ
--	--	--	-------------------------------------------------------	--

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ITEM	Proposed Device	Predicate Device K182624
		archiving contours for patient follow-upand management.
		• Quantitative and statistical analysis ofPET/SPECT brain scans by comparing toother registered PET/SPECT brain scans.
		• Planning and evaluation of permanentimplant brachytherapy procedures (notincluding radioactive microspheres).
		• Calculating absorbed radiation dose asa result of administering a radionuclide.
		When using device clinically, the usershould only use FDA approvedradiopharmaceuticals. If using withunapproved ones, this device should onlybe used for research purposes.
		Lossy compressed mammographicimages and digitized film screen imagesmust not be reviewed for primary imageinterpretations. Images that are printed tofilm must be printed using anFDA-approved printer for the diagnosisof digital mammography images.Mammographic images must be viewedon a display system that has been clearedby the FDA for the diagnosis of digitalmammography images. The software isnot 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
CompatibleModality	Non-Contrast CT	Non-Contrast CT
CompatibleScanner Models	No Limitation on scanner model,DICOM 3.0 compliance required.	No Limitation on scanner model,DICOM3.0 compliance required.
CompatibleTreatmentPlanning System	No Limitation on TPS model, DICOM3.0 compliance required.	No Limitation on TPS model, DICOM3.0 compliance required.
Contraindications	None	None
ITEM	Proposed Device	Predicate Device K182624
Registration Features
Algorithm	Intensity Based	Intensity Based
CompatibleModality	CT, MRI, PET	CT, MRI, CR, DX, MG, US, SPECT,PET and XA
CompatibleScanner Models	No Limitation on scanner model,DICOM 3.0 compliance required.	No Limitation on scanner model,DICOM3.0 compliance required.
CompatibleTreatmentPlanning System	No Limitation on TPS model, DICOM3.0 compliance required.	No Limitation on TPS model, DICOM3.0 compliance required.

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1. 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.

Regulation Number and Section

§ 892.2050 Medical image management and processing system.

(a)
Identification. A medical image management and processing system is a device that provides one or more capabilities relating to the review and digital processing of medical images for the purposes of interpretation by a trained practitioner of disease detection, diagnosis, or patient management. The software components may provide advanced or complex image processing functions for image manipulation, enhancement, or quantification that are intended for use in the interpretation and analysis of medical images. Advanced image manipulation functions may include image segmentation, multimodality image registration, or 3D visualization. Complex quantitative functions may include semi-automated measurements or time-series measurements.(b)
Classification. Class II (special controls; voluntary standards—Digital Imaging and Communications in Medicine (DICOM) Std., Joint Photographic Experts Group (JPEG) Std., Society of Motion Picture and Television Engineers (SMPTE) Test Pattern).