Automatic Anatomy Recognition (AAR) is a software-only medical device intended for use by technicians and trained physicians to derive contours of anatomical structures from computed tomography studies for input to a radiation treatment planning system. It is only intended to work for anatomical structures in the head & neck and thoracic body regions. It is not for use on patients below 18 years of age and it relies on third party treatment planning systems to display and edit the contours.

Device Description

Automatic Anatomy Recognition product for radiation therapy planning (AAR) is a software-only medical device and is deployed on a cloud-based platform. AAR is intended to be used on adults undergoing treatment that requires the identification of anatomical structures in the body considered to be "organs at risk" (OAR). AAR is intended to be used in the head and thoracic body regions. AAR automatically processes computed tomography (CT) studies and produces contours with no human intervention. AAR does not provide the capability to modify contours. If adjustments are required, they must be performed on another system.

AI/ML Overview

Here's a breakdown of the acceptance criteria and study information based on the provided text, focusing on the Automatic Anatomy Recognition (AAR) device.

1. Table of Acceptance Criteria and Reported Device Performance

The document mentions "segmentation accuracy non-inferiority using DICE similarity coefficients" and "mean 95% Hausdorff Distance (HD)" calculations as performance testing methods. However, it does not explicitly state specific numerical acceptance criteria (e.g., "DICE score > 0.8" or "HD < 2mm") for each anatomical structure. It only indicates that these metrics were used to evaluate performance against the predicate.

Therefore, the table below reflects what is stated in the document and highlights the missing specific numerical criteria.

Metric / Anatomical Structure	Acceptance Criteria (from document)	Reported Device Performance (from document)	Notes
DICE Similarity Coefficient	Non-inferiority to predicate device	Evaluated automated segmentation accuracy.	Specific numerical values for DICE scores for each structure are not provided in this document.
Mean 95% Hausdorff Distance (HD)	Non-inferiority to predicate device	These tests were further supported by additional tests using a mean 95% Hausdorff Distance (HD) calculation.	Specific numerical values for HD for each structure are not provided in this document.

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

Test Set Sample Size: Not explicitly stated in the provided text. The document mentions "segmentation performance tests" and "additional tests," but no specific number of cases or images for the test set is given.
Data Provenance: Not explicitly stated. The document does not mention the country of origin of the data or whether it was retrospective or prospective.

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

Number of Experts: Not explicitly stated.
Qualifications of Experts: Not explicitly stated. The document refers to "ground truth" (implicitly through DICE and HD calculations which require a reference standard), but does not detail how this ground truth was established or by whom.

4. Adjudication Method for the Test Set

Adjudication Method: Not explicitly stated. Given that the number of experts and their roles are not detailed, the adjudication method (e.g., 2+1, 3+1, none) for establishing ground truth is not described in this document.

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

MRMC Study Done: No, the document does not indicate that a Multi-Reader Multi-Case (MRMC) comparative effectiveness study was done. The study described focuses on standalone algorithm performance comparison against a predicate device's method using quantitative metrics. Human readers' improvement with or without AI assistance is not discussed.

6. Standalone (Algorithm Only) Performance Study

Standalone Study Done: Yes. The document refers to "automated segmentation accuracy" and states that "AAR automatically processes computed tomography (CT) studies and produces contours with no human intervention." This confirms that a standalone algorithm performance without human-in-the-loop was performed. The evaluation used DICE similarity coefficients and Mean 95% Hausdorff Distance (HD) calculations.

7. Type of Ground Truth Used

Type of Ground Truth: Expert consensus (implied). While not explicitly stated as "expert consensus," the use of metrics like DICE similarity coefficient and Hausdorff Distance for evaluating segmentation accuracy heavily implies the existence of a meticulously hand-segmented gold standard, which is typically created by medical experts (e.g., radiologists, radiation oncologists, or dosimetrists) who define the "true" boundaries of anatomical structures. The term "ground truth" itself is used in the context of performance testing, and for contouring, this usually refers to expert-drawn contours.

8. Sample Size for the Training Set

Training Set Sample Size: Not explicitly stated. The document mentions the use of "Deep Learning" and that the algorithm is "trained with clinical and/or artificial radiological images," but provides no details on the size of the training dataset.

9. How Ground Truth for the Training Set Was Established

Ground Truth for Training Set: Not explicitly stated. Given that the device uses "Deep Learning contouring," it necessitates a large dataset of images with corresponding accurate ground truth contours for training. However, the document does not detail the specific process or individuals involved in establishing these ground truth contours for the training data.

Summary

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Image /page/0/Picture/10 description: The image shows the logo of the U.S. Food and Drug Administration (FDA). The logo consists of two parts: a seal on the left and the FDA acronym with the full name of the agency on the right. The seal features an eagle with its wings spread, surrounded by text that reads "DEPARTMENT OF HEALTH & HUMAN SERVICES-USA". The right side of the logo displays the acronym "FDA" in blue, followed by the words "U.S. FOOD & DRUG ADMINISTRATION" in a larger, bolder font, also in blue.

Quantitative Radiology Solutions, LLC % Mary Vater 510(k) Consultant Medical Device Academy 345 Lincoln Hill Rd. SHREWSBURY, VT 05738

Re: K203610

Trade/Device Name: Automatic Anatomy Recognition (AAR) Regulation Number: 21 CFR 892.2050 Regulation Name: Picture Archiving And Communications System Regulatory Class: Class II Product Code: QKB Dated: March 22, 2021 Received: March 23, 2021

Dear Mary Vater:

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

April 20, 2021

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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 mediation-emitting products, including information about labeling regulations, please see Device Advice (https://www.fda.gov/medicaldevices/device-advice-comprehensive-regulatory-assistance) and CDRH Learn (https://www.fda.gov/training-and-continuing-education/cdrh-learn). Additionally, you may contact the Division of Industry and Consumer Education (DICE) to ask a question about a specific regulatory topic. See the DICE website (https://www.fda.gov/medical-device-advice-comprehensive-regulatoryassistance/contact-us-division-industry-and-consumer-education-dice) for more information or contact DICE by email (DICE@fda.hhs.gov) or phone (1-800-638-2041 or 301-796-7100).

Sincerely,

For

Thalia T. Mills, Ph.D. Director Division of Radiological Health OHT7: Office of In Vitro Diagnostics and Radiological Health Office of Product Evaluation and Quality Center for Devices and Radiological Health

Enclosure

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Indications for Use

510(k) Number (if known) K203610

Device Name Automatic Anatomy Recognition System

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 summary of 510(k) safety and effectiveness information is submitted in accordance with the requirements of 21 CFR §807.92:

SUBMITTER Quantitative Radiology Solutions, LLC 3675 Market Street, Suite 200 Philadelphia, PA 19104 | USA Tel: +1.973.590.8574

Contact Person: Steve Owens Date Prepared: December 9, 2020

II. DEVICE
Name of Device:	Automatic Anatomy Recognition
Classification Name:	Picture Archiving And Communications System
Regulation:	21 CFR §892.2050
Regulatory Class:	Class II
Product Classification Code:	QKB

Ⅲ. PREDICATE DEVICE

Predicate Manufacturer:	Xiamen Manteia Technology LTD.
Predicate Trade Name:	AccuContour™
Predicate 510(k):	K191928

No reference devices were used in this submission.

IV. DEVICE DESCRIPTION

...

AAR operates independently from the treatment plan that is subsequently created based on AARgenerated contours. Therefore, AAR is agnostic to the method of radiation treatment delivery such as photons, protons, or other, to the modality of radiation treatment such as three-dimensional conformal radiation therapy (3D-CRT), intensity modulated radiation therapy (IMRT), or other, and to the intent of radiation treatment such as definitive (curative), neoadjuvant, adjuvant, or palliative.

AAR is also agnostic to the disease process being treated in the head and neck or thoracic body regions, For example, the identification of OARs is required during the treatment of head and neck cancers such as squamous cell carcinoma, brain cancer, and lymphoma. The identification of OARs is also required during the treatment of thoracic cancers such as lung cancer, breast cancer, esophageal cancer, lymphoma, and thymoma, just to name a few.

AAR automatically processes computed tomography (CT) studies and produces contours with no human intervention. AAR does not provide the capability to modify contours. If adjustments are required, they must be performed on another system.

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V. INDICATIONS FOR USE

COMPARISON OF TECHNOLOGICAL CHARACTERISTICS WITH THE VI. PREDICATE DEVCE

The following characteristics were compared between the subject device and the predicate device in order to demonstrate substantial equivalence:

Indications for Use The predicate and subject device have identical indications for use. .
. Materials - The predicate and subject device are both software-only medical devices.
Design The predicate and subject device both utilize deep learning contouring to . automatically contour the organ-at-risk for the head, neck, and thorax.
. Energy Source - The predicate and subject device both run within the users' existing computer system.
Other Design Features The predicate device has additional features such as patient . management, review of processed images, automatic image registration, manual contouring functionality, and segmentation in the abdomen and pelvic regions. This additional functionality is not required to achieve the intended use for the subject device.
. Performance Testing - The predicate and subject device conducted segmentation performance tests to evaluate the automated segmentation accuracy using DICE similarity coefficients. These tests were further supported by additional tests using a mean 95% Hausdorff Distance (HD) calculation.

Table 1: Proposed Predicate Device
	Subject Device	Proposed Predicate Device	Rationale for SE
Device Name	Automatic Anatomy Recognition(AAR)	AccuContourTM	N/A
Applicant	Quantitative Radiology Solutions	Xiamen Manteia Technology LTD.	N/A
510(k) Number	TBD	K191928	N/A
Decision Date	TBD	02/28/2020	N/A
RegulationNumber	892.2050	892.2050	Same
Regulation Name	Picture archiving andcommunication system	Picture archiving and communicationsystem	Same
Device	Radiological Image ProcessingSoftware for Radiation Therapy	Radiological Image ProcessingSoftware for Radiation Therapy	Same
RegulatoryDefinition	To provide semi-automatic-or fully-automated radiological imageprocess and analysis tools forradiation therapy. Softwareimplementing artificial intelligence(AI) including non-adaptivemachine learning algorithms trainedwith clinical and/or artificialradiological images. In thesedevices, the algorithm trainingimages typically impact device.	To provide semi-automatic or fully-automated radiological imageprocess and analysis tools forradiation therapy. Softwareimplementing artificial intelligence(AI) including non-adaptive machinelearning algorithms trained withclinical and/or artificial radiologicalimages. In these devices, thealgorithm training images typicallyimpact device performance. AI based	Both the subjectdevice and thepredicate fall underthe regulatorydefinition for892.2050, productcode QKB.
Product Code
	performance. AI based radiologicalimage processing software isintended to be used in the workflowof radiation therapy. Adaptive AIalgorithms are not within the scopeof this product code. Primaryradiation dose calculation or planoptimization for treatment planningare not within scope of the productcode.	radiological image processingsoftware is intended to be used in theworkflow of radiation therapy.Adaptive AI algorithms are notwithin the scope of this product code.Primary radiation dose calculation orplan optimization for treatmentplanning are not within scope of theproduct code.
Product Code	QKB	QKB	Same
Classification	Class II	Class II	Same
510(k) ReviewPanel	Radiology	Radiology	Same
CombinationProduct?	No	No	Same
Rx or OTC?	RX	RX	Same
Intended Use /Indications for Use	Automatic Anatomy Recognition(AAR) is a software-only medicaldevice intended for use bytechnicians and trained physiciansto derive contours of anatomicalstructures from computedtomography studies for input to aradiation treatment planning system.It is only intended to work foranatomical structures in the head &neck and thoracic body regions. It isnot for use on patients below 18years of age and it relies on thirdparty treatment planning systems todisplay and edit the contours.	It is used by radiation oncologydepartment to register multimodalityimages and segment (non-contrast)CT images, to generate neededinformation for treating planning,treatment evaluation and treatmentadaptation.	Same
Image processfunctions	1) Deep learning contouring: itcan automatically contouranatomical structures, includinghead and neck, thorax (for bothmale and female).	1) Deep learning contouring: it canautomatically contour the organ-at-risk, including head and neck,thorax, abdomen and pelvis (forboth male and female);2) Automatic Registration, and3) Manual Contour	Same. AAR containsonly Deep Learningcontouring
GeneralFunctionalities	Receive, add/edit/delete,transmit, input/export, medicalimages and DICOM data	Receive, add/edit/delete,transmit, input/export, medicalimages and DICOM data;Patient management;Review of processed images;Open and Save of files.	Same. AAR onlyreceives,adds/edits/deletes,transmits,inputs/exportsmedical images andDICOM data
Operating Systems	Linux	Windows	QRS utilizes Linux asthis OS is moresecure as comparedto Windows
Segmentation Features
Algorithm	Deep Learning	Deep Learning	Same
CompatibleModality	Non-Contrast CT	Non-Contrast CT	Same
CompatibleScanner Models	No limitation on scanner model,DICOM 3.0 compliance required	No limitation on scanner model,DICOM 3.0 compliance required	Same
CompatibleTreatmentPlanning System	No limitation on TPS model,DICOM 3.0 compliance required.	No limitation on TPS model, DICOM3.0 compliance required.	Same
Contraindications	AAR is not intended for use onpatients below 18 years of age;	None	AAR is intended foruse in adults
Segmentation Features
PerformanceTesting	Segmentation Performance TestEvaluated automatedsegmentation accuracy non-inferiority using DICEsimilarity coefficients. Software Verification andValidation testing	Segmentation Performance TestEvaluated automatedsegmentation accuracy non-inferiority using DICE similaritycoefficients. Registration Performance Test	SegmentationPerformance Testingis equivalent.Registrationperformance test isN/A because thesubject device doesnot do registration.

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VII. PERFORMANCE DATA

The following performance data were provided in support of the substantial equivalence determination.

Sterilization & Shelf-life Testing

Not Applicable (Standalone Software)

Biocompatibility Testing

Not Applicable (Standalone Software)

Electrical safety and electromagnetic compatibility (EMC)

Not Applicable (Passive Device)

Software Verification and Validation Testing

Software Verification and Validation Testing included testing at the unit, integration, and system level per IEC 62304 standard.

Mechanical and acoustic Testing

Not Applicable (Standalone Software)

Animal Study

Animal performance testing was not required to demonstrate safety and effectiveness of the device.

Human Clinical Performance Testing

Clinical testing was not required to demonstrate the safety and effectiveness of the device.

VIII. CONCLUSIONS

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).