Who is the manufacturer of AutoContour (Model RADAC V4)?

Radformation, Inc. filed the 510(k) submission for AutoContour (Model RADAC V4) (K242729).

What is the FDA product code for AutoContour (Model RADAC V4)?

QKB. It is classified under 21 CFR 892.2050 as a Class 2 device in the Radiology panel.

What is the regulatory pathway for AutoContour (Model RADAC V4)?

510(k) clearance (submission type: Traditional).

Does AutoContour (Model RADAC V4) use AI or machine learning?

Yes. AutoContour (Model RADAC V4) (K242729) is flagged as an AI/ML-enabled medical device in the FDA's AI/ML device list.

Who can help prepare an FDA 510(k) submission like AutoContour (Model RADAC V4)?

Innolitics — a medical-device software consultancy — provides regulatory and engineering support for FDA 510(k) submissions. See https://innolitics.com/services/510ks/ or contact https://innolitics.com/contact.

Can an AI/ML device like AutoContour (Model RADAC V4) get a guaranteed FDA 510(k) clearance?

Yes. Innolitics offers a guaranteed FDA 510(k) clearance engagement for AI/ML Software as a Medical Device — combining engineering and regulatory execution under one agreement with a guaranteed clearance outcome. Eligibility: the device must be an AI/ML Software as a Medical Device (SaMD), and its performance must meet the criteria Innolitics determines during an initial regulatory strategy engagement to be necessary for FDA clearance. See https://innolitics.com/services/end-to-end-samd/ or contact https://innolitics.com/contact.

AutoContour (Model RADAC V4)

Q: What are the predicate devices for AutoContour (Model RADAC V4)?

AutoContour Model RADAC V3 (K230685).

K242729 · Radformation, Inc. · QKB · Dec 9, 2024 · Radiology

Device Facts

Record ID	K242729
Device Name	AutoContour (Model RADAC V4)
Applicant	Radformation, Inc.
Product Code	QKB · Radiology
Decision Date	Dec 9, 2024
Decision	SESE
Submission Type	Traditional
Regulation	21 CFR 892.2050
Device Class	Class 2
Attributes	AI/ML, Software as a Medical Device

Intended Use

AutoContour is intended to assist radiation treatment planners in contouring and reviewing structures within medical images in preparation for radiation therapy treatment planning.

Device Story

AutoContour (Model RADAC V4) is a software-based medical image management and processing system used by radiation treatment planners in clinical settings. It accepts DICOM-compliant CT or MR image data as input. The device utilizes deep-learning-based machine learning models to automatically contour anatomical structures (head and neck, thorax, abdomen, pelvis). The system comprises a .NET Windows client for image loading and review, a local agent service for monitoring network storage, and a cloud-based server for automatic contouring. Users review and modify the generated contours via the client application. The device outputs DICOM-compliant structure sets, registration, and dose files for import into radiation therapy treatment planning systems. By automating the contouring process, the device assists planners in preparing treatment plans, potentially improving efficiency and consistency in radiation therapy workflows.

Clinical Evidence

No clinical trials were performed. Non-clinical bench testing validated the machine learning models using independent datasets sequestered from training data. Accuracy was measured using Mean Dice Similarity Coefficient (DSC) against manual ground truth generated by experts. CT models (large, medium, small) achieved mean DSCs of 0.92, 0.85, and 0.81 respectively. MR models achieved mean DSCs of 0.96 (large), 0.84 (medium), and 0.74 (small). External clinical expert review rated the clinical appropriateness of generated contours on a 1-5 scale, with average ratings of 4.57 (CT) and 4.6 (MR), indicating high clinical utility with only minor edits required.

Technological Characteristics

Software-based medical image management and processing system. Operates on Windows-based .NET client and cloud-based server. Utilizes deep-learning (CNN) models for automated contouring. Supports DICOM RTSTRUCT, REGISTRATION, and DOSE file formats. Connectivity includes local network monitoring and cloud-based processing. No patient-contacting materials; no sterilization or biocompatibility requirements. Software is non-sterile, standalone.

Indications for Use

Indicated for radiation treatment planners to assist in contouring and reviewing structures within CT or MR medical images for adult male and female patients in preparation for radiation therapy treatment planning.

Regulatory Classification

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.

Special Controls

*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).

Predicate Devices

AutoContour Model RADAC V3 (K230685)

Related Devices

K230685 — AutoContour Model RADAC V3 · Radformation, Inc. · Apr 14, 2023
K220598 — AutoContour Model RADAC V2 · Radformation, Inc. · Aug 24, 2022
K200323 — AutoContour · Radformation, Inc. · Oct 30, 2020
K260509 — AutoContour (RADAC V5) · Radformation, Inc. · Mar 19, 2026
K232928 — DeepContour (V1.0) · Wisdom Technologies., Inc. · May 7, 2024

Submission Summary (Full Text)

{0}------------------------------------------------ December 9, 2024 Image /page/0/Picture/1 description: The image contains 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 consists of the letters "FDA" in a blue square. To the right of the blue square is the text "U.S. FOOD & DRUG ADMINISTRATION" in blue. Radformation, Inc. Jennifer Wampler Regulatory Affairs Specialist 261 Madison Avenue 9th Floor New York, New York 10016 Re: K242729 Trade/Device Name: AutoContour (Model RADAC V4) Regulation Number: 21 CFR 892.2050 Regulation Name: Medical Image Management And Processing System Regulatory Class: Class II Product Code: OKB Dated: September 5, 2024 Received: September 10, 2024 Dear Jennifer Wampler: 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 (the 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 available at https://www.accessdata.fda.gov/scripts/cdrb/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. {1}------------------------------------------------ Additional information about changes that may require a new premarket notification are provided in the FDA guidance documents entitled "Deciding When to Submit a 510(k) for a Change to an Existing Device" (https://www.fda.gov/media/99812/download) and "Deciding When to Submit a 510(k) for a Software Change to an Existing Device" (https://www.fda.gov/media/99785/download). Your device is also subject to, among other requirements, the Quality System (QS) regulation (21 CFR Part 820), which includes, but is not limited to, 21 CFR 820.30, Design controls; 21 CFR 820.90, Nonconforming product; and 21 CFR 820.100, Corrective and preventive action. Please note that regardless of whether a change requires premarket review, the QS regulation requires device manufacturers to review and approve changes to device design and production (21 CFR 820.30 and 21 CFR 820.70) and document changes and approvals in the device master record (21 CFR 820.181). 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 Part 803) for devices or postmarketing safety reporting (21 CFR Part 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 Part 4, Subpart A) for combination products; and, if applicable, the electronic product radiation control provisions (Sections 531-542 of the Act); 21 CFR Parts 1000-1050. All medical devices, including Class I and unclassified devices and combination product device constituent parts are required to be in compliance with the final Unique Device Identification System rule ("UDI Re"). The UDI Rule requires, among other things, that a device bear a unique device identifier (UDI) on its label and package (21 CFR 801.20(a)) unless an exception or alternative applies (21 CFR 801.20(b)) and that the dates on the device label be formatted in accordance with 21 CFR 801.18. The UDI Rule (21 CFR 830.300(a) and 830.320(b)) also requires that certain information be submitted to the Global Unique Device Identification Database (GUDID) (21 CFR Part 830 Subpart E). For additional information on these requirements, please see the UDI System webpage at https://www.fda.gov/medical-device-advicecomprehensive-regulatory-assistance/unique-device-identification-system-udi-system. Also, please note the regulation entitled, "Misbranding by reference to premarket notification" (21 CFR 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 {2}------------------------------------------------ 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, Locon Weidner Lora D. Weidner, Ph.D. Assistant Director Radiation Therapy Team DHT8C: Division of Radiological Imaging and Radiation Therapy Devices OHT8: Office of Radiological Health Office of Product Evaluation and Quality Center for Devices and Radiological Health Enclosure {3}------------------------------------------------ # Indications for Use Submission Number (if known) K242729 Device Name AutoContour (Model RADAC V4) Indications for Use (Describe) AutoContour is intended to assist radiation treatment planners in contouring and reviewing structures within medical images in preparation for radiation therapy treatment planning. 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) #### CONTINUE ON A SEPARATE PAGE IF NEEDED. This section applies only to requirements of the Paperwork Reduction Act of 1995. #### *DO NOT SEND YOUR COMPLETED FORM TO THE PRA STAFF EMAIL ADDRESS BELOW." The burden time for this collection of information is estimated to average 79 hours per response, including the time to review instructions, search existing data sources, gather and maintain the data needed and complete and review the collection of information. Send comments regarding this burden estimate or any other aspect of this information collection, including suggestions for reducing this burden, to: > Department of Health and Human Services Food and Drug Administration Office of Chief Information Officer Paperwork Reduction Act (PRA) Staff PRAStaff(@fda.hhs.gov "An agency may not conduct or sponsor, and a person is not required to respond to, a collection of information unless it displays a currently valid OMB number." {4}------------------------------------------------ This 510(k) Summary has been created per the requirements of the Safe Medical Device Act (SMDA) of 1990, and the content is provided in conformance with 21 CFR Part 807.92. # 5.1. Submitter's Information | Table 1 : Submitter's Information | | |-----------------------------------|-------------------------------------------------------| | Submitter's Name: | Kurt Sysock | | Company: | Radformation, Inc. | | Address: | 261 Madison Avenue, 9th Floor New York, NY 10016 | | Contact Person: | Alan Nelson Chief Technology Officer, Radformation | | Phone: | 518-888-5727 | | Fax: | ---------- | | Email: | anelson@radformation.com | | Date of Summary Preparation | 09/05/2024 | # 5.2. Device Information | Table 2 : Device Information | | |------------------------------|-----------------------------------------------------------------------------| | Trade Name: | AutoContour Model RADAC V4 | | Common Name: | AutoContour, AutoContouring, AutoContour Agent, AutoContour Cloud Server | | Classification Name: | Class II | | Classification: | Medical image management and processing system | | Regulation Number: | 892.2050 | | Product Code: | QKB | | Classification Panel: | Radiology | {5}------------------------------------------------ # 5.3. Predicate Device Information AutoContour Model RADAC V4 (Subject Device) makes use of its prior submissions -AutoContour Model RADAC V3 (K230685) - as the Predicate Device. ### 5.4. Device Description As with AutoContour Model RADAC V3, the AutoContour Model RADAC V4 device is software that uses DICOM-compliant image data (CT or MR) as input to: (1) automatically contour various structures of interest for radiation therapy treatment planning using machine learning based contouring. The deep-learning based structure models are trained using imaging datasets consisting of anatomical organs of the head and neck, thorax, abdomen and pelvis for adult male and female patients, (2) allow the user to review and modify the resulting contours, and (3) generate DICOM-compliant structure set data the can be imported into a radiation therapy treatment planning system. AutoContour Model RADAC V4 consists of 3 main components: - 1. A .NET client application designed to run on the Windows Operating System allowing the user to load image and structure sets for upload to the cloud-based server for automatic contouring, perform registration with other image sets, as well as review, edit, and export the structure set. - 2. A local "agent" service designed to run on the Windows Operating System that is configured by the user to monitor a network storage location for new CT and MR datasets that are to be automatically contoured. - 3. A cloud-based automatic contouring service that produces initial contours based on image sets sent by the user from the .NET client application. # 5.5. Indications for Use AutoContour is intended to assist radiation treatment planners in contouring and reviewing structures within medical images in preparation for radiation therapy treatment planning. # 5.6. Technological Characteristics The Subject Device, AutoContour Model RADAC V4 makes use of AutoContour Model RADAC V3 (K230685) as the Predicate Device for substantial equivalence comparison. The functionality and technical components of this prior submission remain unchanged in AutoContour Model RADAC V4. This submission is intended to build on the technological characteristics of the 510(k) cleared AutoContour Model RADAC V3 pertaining to new structure models for both CT and MRI. {6}------------------------------------------------ # 5.6.1. Updates vs. AutoContour (K230685) The updated submission expands the use of machine-learning based contouring to include additional organs and volumes of Interest found in MR and CT image types. | Table 3: Technological Characteristics AutoContour Model RADAC V4 vs. AutoContour Model RADAC V3 (K230685) | | | |---------------------------------------------------------------------------------------------------------------|--------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------|--------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------| | Characteristic | Subject Device: AutoContour Model RADAC V4 | Predicate Device: AutoContour Model RADAC V3 (K230685) | | Indications for Use | AutoContour is intended to assist radiation treatment planners in contouring and reviewing structures within medical images in preparation for radiation therapy treatment planning. | AutoContour is intended to assist radiation treatment planners in contouring and reviewing structures within medical images in preparation for radiation therapy treatment planning. | | Design: Image registration | Manual and Automatic Rigid registration. Automatic Deformable Registration | Manual and Automatic Rigid registration. Automatic Deformable Registration | | Design: Supported modalities | CT or MR input for contouring or registration/fusion. PET/CT input for registration/fusion only. DICOM RTSTRUCT and REGISTRATION for input. (Minor differences) | CT or MR input for contouring or registration/fusion. PET/CT input for registration/fusion only. DICOM RTSTRUCT and REGISTRATION for input. | | Design: Reporting and data routing | No built-in reporting, supports exporting DICOM RTSTRUCT, REGISTRATION and DOSE files. (Minor differences) | No built-in reporting, supports exporting DICOM RTSTRUCT file. (Substantially Equivalent) | {7}------------------------------------------------ | Regions and Volumes of interest (ROI) | CT or MR input for contouring of anatomical regions: Head and Neck, Thorax, Abdomen and Pelvis. | | |---------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------|-----------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------|-----------------------------------------------------------------------------| | CT or MR input for contouring of anatomical regions: Head and Neck, Thorax, Abdomen and Pelvis. CT Models: A_Aorta A_Aorta_Asc A_Aorta_Dsc A_Brachiocephis A_Carotid_L A_Carotid_R A_Coronary A_LAD A_Pulmonary A_Subclavian_L A_Subclavian_R Atrium_L Atrium_R Bladder Bladder_F Bone_Hyoid Bone_Illium_L Bone_Illium_R Bone_Mandible Bone_Pelvic Bone_Skull Bone_Sternum Bone_Teeth Bowel Bowel_Bag Bowel_Large Bowel_Small BrachialPlex_L BrachialPlex_R Brain Brainstem Breast_L Breast_R Breast_Prone Bronchus BuccalMucosa Carina CaudaEquina Cavity_Oral Cavity_Oral_Ext Chestwall_L Chestwall_OAR Chestwall_R Chestwall_RC_L Chestwall_RC_R | CT or MR input for contouring of anatomical regions: Head and Neck, Thorax, Abdomen and Pelvis. CT Models: A_Aorta A_Aorta_Asc A_Aorta_Dsc A_LAD A_Pulmonary Bladder Bladder_F Bone_Illium_L Bone_Illium_R Bone_Mandible Bone_Pelvic Bone_Skull Bone_Sternum Bowel Bowel_Bag Bowel_Large Bowel_Small BrachialPlex_L BrachialPlex_R Brain Brainstem Breast_L Breast_R Bronchus BuccalMucosa Carina CaudaEquina Cavity_Oral Cavity_Oral_Ext Chestwall_L Chestwall_OAR Chestwall_R Chestwall_RC_L Chestwall_RC_R Cochlea_L Cochlea_R Colon_Sigmoid Cornea_L Cornea_R Duodenum Ear_Internal_L Ear_Internal_R Esophagus External Eye_L | | | | | | | Clavicle_R ● | Femur_Head_L ● | | | Cochlea_L ● | Femur_Head_R ● | | | Cochlea_R ● | Femur_L ● | | | Colon_Sigmoid ● | Femur_R ● | | | Cornea_L ● | Femur_RTOG_L ● | | | Cornea_R ● | Femur_RTOG_R ● | | | Dental_Artifact ● | GallBladder ● | | | Duodenum ● | Genitals_F ● | | | Ear_Internal_L ● | Genitals_M ● | | | Ear_Internal_R ● | Glnd_Lacrimal_L ● | | | Esophagus ● | Glnd_Lacrimal_R ● | | | External ● | Glnd_Submand_L ● | | | Eye_L ● | Glnd_Submand_R ● | | | Eye_R ● | Glnd_Thyroid ● | | | Femur_Head_L ● | HDR_Cylinder ● | | | Femur_Head_R ● | Heart ● | | | Femur_L ● | Hippocampus_L ● | | | Femur_R ● | Hippocampus_R ● | | | Femur_RTOG_L ● | Humerus_L ● | | | Femur_RTOG_R ● | Humerus_R ● | | | Foley_Balloon ● | Kidney_L ● | | | GallBladder ● | Kidney_R ● | | | Genitals_F ● | Kidney_Outer_L ● | | | Genitals_M ● | Kidney_Outer_R ● | | | Glnd_Lacrimal_L ● | Larynx ● | | | Glnd_Lacrimal_R ● | Larynx_Glottic ● | | | Glnd_Submand_L ● | Larynx_NRG ● | | | Glnd_Submand_R ● | Larynx_SG ● | | | Glnd_Thyroid ● | Lens_L ● | | | HDR_Bladder ● | Lens_R ● | | | HDR_Bowel ● | Lips ● | | | HDR_Cylinder ● | Liver ● | | | HDR_Rectum ● | LN_Ax_L ● | | | Heart ● | LN_Ax_L1_L ● | | | Heart_Prone ● | LN_Ax_L1_R ● | | | Hippocampus_L ● | LN_Ax_L2_L ● | | | Hippocampus_R ● | LN_Ax_L2_L3_L ● | | | Humerus_L ● | LN_Ax_L2_L3_R ● | | | Humerus_R ● | LN_Ax_L2_R ● | | | Iliac_Int_L ● | LN_Ax_L3_L ● | | | Iliac_Int_R ● | LN_Ax_L3_R ● | | | Iliac_L ● | LN_Ax_R ● | | | Iliac_R ● | LN_IMN_L ●…

AutoContour (Model RADAC V4)

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Device Story

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Technological Characteristics

Indications for Use

Regulatory Classification

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Submission Summary (Full Text)

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AutoContour (Model RADAC V4)

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Intended Use

Device Story

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Technological Characteristics

Indications for Use

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Special Controls

Predicate Devices

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Submission Summary (Full Text)

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