AATMA™ is a medical image processing library intended to produce derived data sets for use as input into radiation therapy treatment planning systems or other intermediate pre-treatment-planning applications. AATMA™ does not provide a user interface and is designed to be accessed through its application programming interface (API) by other devices. The data sets created by AATMA™ must be reviewed and validated by a qualified healthcare professional prior to clinical use.

Device Description

AATMA™ is an optional accessory to treatment planning systems and intermediate pre-treatment planning applications. The auto-segmentation algorithm in AATMA™ is based on machine-learning convolutional neural networks and includes pre-trained models that will be used to automatically segment image sets. The algorithm itself functions as a computational engine and does not store any input data, output data, or logs. The available models have been pre-trained on specific datasets that exhibit similar characteristics (e.g., body site and imaging modality).

As a medical image processing library, AATMA™ is designed to produce derived datasets in standard formats (e.g., DICOM) that can be utilized by other applications. AATMA™ does not have a user interface and, as such, calling applications must execute the auto-segmentation algorithms via AATMA™'s application programming interface (API).

AATMA™ must be used in conjunction with appropriate software to review and edit results generated automatically by the auto-segmentation algorithm. A pre-treatment planning system or treatment planning system must be used to facilitate the review and edit of contours generated by the auto-segmentation algorithm within AATMA™.

AI/ML Overview

The provided text describes the 510(k) premarket notification for Elekta Solutions AB's Advanced Algorithms for Treatment Applications (AATMA™). This device is a medical image processing library designed to produce derived datasets for radiation therapy treatment planning systems or intermediate pre-treatment planning applications, primarily through auto-segmentation using machine learning convolutional neural networks.

Here's an analysis of the acceptance criteria and the study that proves the device meets them, based solely on the provided text:

Acceptance Criteria and Reported Device Performance

The provided text implicitly defines acceptance criteria through the successful attainment of a stated DICE coefficient for model performance.

Criterion Type	Acceptance Criterion	Reported Device Performance
Software Validation	The device "meets the user needs and requirements" and is "substantially equivalent to those of the listed predicate device," demonstrating "compliance with the requirements of CFR 21 Part 820 and in adherence to the DICOM standard" and "does not introduce any new potential safety risks."	"The results of performance, functional and algorithmic testing demonstrate that AATMA™ meets the user needs and requirements of the device, which are demonstrated to be substantially equivalent to those of the listed predicate device." "Verification and Validation for AATMA™ has been carried out in compliance with the requirements of CFR 21 Part 820 and in adherence to the DICOM standard." "AATMA™ meets the requirements for safety and effectiveness as applicable to radiological image processing software and does not introduce any new potential safety risks."
Head & Neck Model	The average DICE coefficient over all structures must meet the defined acceptance criteria (specific numerical threshold not explicitly stated, but implied to be met).	For verification: "the average DICE coefficient over all structures was determined to be 0.84 which met the defined acceptance criteria." For validation: "A different set of 13 3D CT image sets were used for validation and these met the acceptance criteria as well."
Male Pelvis Model	The average DICE coefficient over all structures must meet the defined acceptance criteria (specific numerical threshold not explicitly stated, but implied to be met).	For verification: "the average DICE coefficient over all structures was determined to be 0.93 which met the defined acceptance criteria." For validation: "A different set of 20 3D CT image sets were used for validation and these met the acceptance criteria as well."
Clinical Use Requirement	The data sets created by AATMA™ must be reviewed and validated by a qualified healthcare professional prior to clinical use. (This is a constraint on use, rather than a performance metric of the device itself, but it's an important part of the acceptance for safe use).	The device's "Indications for Use" and "Intended Use" state this requirement: "The data sets created by AATMA™ must be reviewed and validated by a qualified healthcare professional prior to clinical use." Additionally, "AATMA™ must be used in conjunction with appropriate software to review and edit results generated automatically by the auto-segmentation algorithm."

Study Proving Device Meets Acceptance Criteria (Non-Clinical Performance Testing):

The document details non-clinical performance testing for two specific models: Head & Neck and Male Pelvis.

Sample sizes used for the test set and the data provenance:
- Head & Neck Model:
  - Verification Set: 6 unique patient 3D CT image sets.
  - Validation Set: 13 unique 3D CT image sets.
  - Data Provenance: The training data (from which these test sets are distinct but of similar characteristics) came "from a variety of institutions and equipment." The document does not specify the country of origin or whether the data was retrospective or prospective, but the nature of the training implies existing, likely retrospective, clinical data.
- Male Pelvis Model:
  - Verification Set: 5 unique patient CT image sets.
  - Validation Set: 20 unique 3D CT image sets.
  - Data Provenance: The training data (from which these test sets are distinct) came "from a global variety of institutions and equipment from patients undergoing RT." Again, the document does not specify the exact countries or whether it was retrospective/prospective, but implies existing clinical data.
Number of experts used to establish the ground truth for the test set and the qualifications of those experts:
- The text states that the verification sets for both models had "expert contours." However, it does not specify the number of experts or their qualifications (e.g., "radiologist with 10 years of experience").
Adjudication method (e.g., 2+1, 3+1, none) for the test set:
- The document mentions "expert contours" were used for the verification sets. It does not specify an adjudication method used if multiple experts were involved (e.g., 2+1, 3+1). If only one expert reviewed each, then no adjudication would be necessary.
If a multi-reader multi-case (MRMC) comparative effectiveness study was done, If so, what was the effect size of how much human readers improve with AI vs without AI assistance:
- No, an MRMC comparative effectiveness study was not done. The document explicitly states: "No animal or clinical tests were performed to establish substantial equivalence with the predicate device." The study focused on the algorithm's performance against expert contours, not on human reader improvement with AI assistance.
If a standalone (i.e. algorithm only without human-in-the-loop performance) was done:
- Yes, a standalone (algorithm only) performance assessment was done. The described testing ("average DICE coefficient over all structures was determined") measures the algorithm's output (auto-segmented contours) directly against the established ground truth (expert contours), without human intervention in the loop during the performance measurement itself. The device is designed as an API-only computational engine.
The type of ground truth used (expert consensus, pathology, outcomes data, etc.):
- The ground truth for the test (verification) sets was established using "expert contours." It is not specified if this was a single expert per case or expert consensus.
The sample size for the training set:
- Head & Neck Model: Trained on 66 unique clinical patient 3D CT image sets.
- Male Pelvis Model: Trained on 205 unique patient 3D CT image sets.
How the ground truth for the training set was established:
- The document states the models were "pre-trained on specific datasets." It does not explicitly describe how the ground truth within these training datasets was established. It is implied that these datasets contained "expert contours" (similar to the verification data), but this is not explicitly stated for the training data itself.

Summary

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Image /page/0/Picture/0 description: The image contains the logos of the Department of Health & Human Services and the Food and Drug Administration (FDA). The Department of Health & Human Services logo is on the left, and the FDA logo is on the right. The FDA logo includes the letters "FDA" in a blue square, followed by the words "U.S. Food & Drug Administration" in blue text.

Elekta Solutions AB % Anju Kurian, M.S., RAC Manager, Regulatory Affairs - Software 1450 Beale Street. Suite 205 SAINT CHARLES MO 63303

Re: K212218

Trade/Device Name: Advanced Algorithms for Treatment Applications (AATMA™) Regulation Number: 21 CFR 892.2050 Regulation Name: Medical Image Management And Processing System Regulatory Class: Class II Product Code: QKB, LLZ Dated: September 8, 2021 Received: September 16, 2021

Dear Anju Kurian:

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); medical device reporting of medical device-related adverse events) (21 CFR 803) for

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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.

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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Expiration Date: 06/30/2023

See PRA Statement below.

Form Approved: OMB No. 0910-0120

DEPARTMENT OF HEALTH AND HUMAN SERVICES Food and Drug Administration

Indications for Use

510(k) Number (if known)

K212218

Device Name Advanced Algorithms for Treatment Management Applications (AATMA™)

Indications for Use (Describe)

AATMA™ is a medical imaqe processing library intended to produce derived data sets for use as input into radiation therapy treatment planning systems or other intermediate pre-treatment-planning applications. AATMA™ does not provide a user interface and is designed to be accessed through its application programming interface (API) by other devices. The data sets created by AATMA™ must be reviewed and validated by a qualified healthcare professional prior to clinical use.

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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FORM FDA 3881 (6/20)

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Image /page/3/Picture/0 description: The image shows the logo for Elekta, a company that specializes in radiation therapy and neurosurgery solutions. The logo consists of a stylized circle with three smaller circles inside, followed by the word "Elekta" in a sans-serif font. The color of the logo is a teal blue.

TRADITIONAL 510(K) SUMMARY (21 CFR § 807.92)

I.	SUBMITTER	Elekta Solutions ABKungstensgatan 18 Box 7593Stockholm, Stockholms lan [SE-01] SE SE10393
	Contact:	Anju Kurian, M.S., RACManager, Regulatory Affairs - Software
	EstablishmentRegistration #:	3015232217
	510(k) Number:	K212218
	Date Prepared:	10/18/2021
II.	DEVICE
	Trade Name:	AATMA ™ (Advanced Algorithms for TreatmentManagement Applications)
	Release Version #:	Release 1.0
	Product Classification:	Class II
	Common Name:	Radiological Image Processing Software for RadiationTherapy
	Classification Name:	Medical Image Management and Processing System
	Regulation Number:	21 CFR § 892.2050
	Product Code:	QKB/LLZ

lll. PREDICATE DEVICE

Workflow Box by Mirada Medical (K181572)

IV. DEVICE DESCRIPTION

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execute the auto-segmentation algorithms via AATMA™'s application programming interface (API).

AATMA™ must be used in conjunction with appropriate software to review and edit results generated automatically by the auto-segmentation alqorithm. A pre-treatment planning system or treatment planning system must be used to facilitate the review and edit of contours generated by the auto-segmentation algorithm within AATMA™.

V. INTENDED USE

VI. INDICATIONS FOR USE

The data sets created by AATMA™ must be reviewed and validated by a qualified healthcare professional prior to clinical use.

VII. COMPARISON OF TECHNOLOGICAL CHARACTERISTICS WITH THE PREDICATE

Technological Characteristic	AATMA™(Subject Device)	Workflow BoxPredicate DeviceK181572
Automatic contouring of imaging data usingmachine learning based models	✓	✓
No Graphical User Interface	✓	✓
View manipulation and Volume rendering – NotApplicable	✓	✓
Image registration	N/A	✓
Reporting and Data Routing	N/A	✓
Supported modalities: Standard DICOM imagemodality support	✓Subject devicevalidated with CTimages for imageprocessing.	✓Predicate devicevalidated with CT,MR, DICOMRTSTRUCT forimage processing.
TCP/IP Networking and Communication	✓	✓

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VIII. SUMMARY OF PERFORMACE TESTING (NON-CLINICAL)

Software verification and validation testing were conducted, and documentation was provided as recommended by FDA's Guidance for Industry and FDA Staff, "Guidance for the Content of Premarket Submissions for Software Contained in Medical Devices."

AATMA™ is validated and verified against its user needs and intended use by the successful execution of planned performance, functional and algorithmic testing included in this submission. The results of performance, functional and algorithmic testing demonstrate that AATMA™ meets the user needs and requirements of the device, which are demonstrated to be substantially equivalent to those of the listed predicate device.

Verification and Validation for AATMA™ has been carried out in compliance with the requirements of CFR 21 Part 820 and in adherence to the DICOM standard.

Performance testing for two models – Head & Neck, Male Pelvis were conducted.

The Head & Neck model was trained on 66 unique clinical patient 3D CT image sets from a variety of institutions and equipment. A different set of six(6) patient CT image sets with expert contours were chosen for verification and the average DICE coefficient over all structures was determined to be 0.84 which met the defined acceptance criteria. A different set of 13 3D CT image sets were used for validation and these met the acceptance criteria as well.

The Male Pelvis model was trained on 205 unique patient 3D CT image sets from a global variety of institutions and equipment from patients undergoing RT. A different set of five (5) patient CT image sets with expert contours were chosen for verification and the average DICE coefficient over all structures was determined to be 0.93 which met the defined acceptance criteria. A different set of 20 3D CT image sets were used for validation and these met the acceptance criteria as well.

IX. SUMMARY OF PERFORMACE TESTING (CLINICAL)

No animal or clinical tests were performed to establish substantial equivalence with the predicate device. The performance data demonstrate that AATMA™ is as safe and effective and performs as well as the predicate device Workflow Box by Mirada Medical cleared under K181572.

SUBSTANTIAL EQUIVALENCE CONCLUSION X.

In conclusion, performance testing and device evaluations presented in this 510(k) demonstrates that AATMA™ is substantially equivalent to and performs at least as safely and effectively as the listed predicate device. AATMA™ meets the requirements for safety and effectiveness as applicable to radiological image processing software and does not introduce any new potential safety risks.

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).