Who is the manufacturer of AutoContour Model RADAC V3?

Radformation, Inc. filed the 510(k) submission for AutoContour Model RADAC V3 (K230685).

What is the FDA product code for AutoContour Model RADAC V3?

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 V3?

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

Does AutoContour Model RADAC V3 use AI or machine learning?

Yes. AutoContour Model RADAC V3 (K230685) 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 V3?

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 V3 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 V3

Q: What are the predicate devices for AutoContour Model RADAC V3?

AutoContour Model RADAC V2 (K220598).

K230685 · Radformation, Inc. · QKB · Apr 14, 2023 · Radiology

Device Facts

Record ID	K230685
Device Name	AutoContour Model RADAC V3
Applicant	Radformation, Inc.
Product Code	QKB · Radiology
Decision Date	Apr 14, 2023
Decision	SESE
Submission Type	Special
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 is a software-based medical image management and processing system used in radiation therapy clinics. It accepts DICOM-compliant CT or MR image data as input. The device uses deep-learning-based machine learning models to automatically contour anatomical structures (head and neck, thorax, abdomen, pelvis). The system consists of a .NET client application for Windows, a local agent service for monitoring network storage, and a cloud-based automatic contouring service. Users review and modify the generated contours via the client application. The device outputs DICOM-compliant structure sets for import into radiation therapy treatment planning systems. By automating the initial contouring process, the device aims to improve efficiency for radiation treatment planners, who retain final authority to review and edit the output, ensuring clinical appropriateness for treatment planning.

Clinical Evidence

No clinical trials were performed. Non-clinical validation used independent datasets (10% of training size) and public datasets (TCIA). Performance was measured using Dice Similarity Coefficient (DSC) and qualitative clinical expert ratings (1-5 scale). CT models achieved mean DSCs of 0.88 (large), 0.88 (medium), and 0.75 (small). MR models achieved mean DSCs of 0.87 (medium) and 0.74 (small). Expert reviewers provided an average rating of 4.5/5 for CT and 4.4/5 for MR, indicating high clinical acceptability.

Technological Characteristics

Standalone software; Windows-based .NET client and agent; cloud-based contouring service. Uses deep-learning (CNN) models for automated image segmentation. Supports CT, MR, and PET/CT (registration only) modalities. Outputs DICOM RTSTRUCT. No patient contact; no sterilization, biocompatibility, or electrical safety testing required.

Indications for Use

Indicated for radiation treatment planners to assist in contouring and reviewing anatomical structures in CT and MR medical images for adult male and female patients undergoing 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 V2 (K220598)

Related Devices

K242729 — AutoContour (Model RADAC V4) · Radformation, Inc. · Dec 9, 2024
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}------------------------------------------------ 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. April 14, 2023 Radformation, Inc. % Kurt Sysock Co-founder/CEO 335 Madison Avenue, 4th floor NEW YORK NY 10017 Re: K230685 Trade/Device Name: AutoContour Model RADAC V3 Regulation Number: 21 CFR 892.2050 Regulation Name: Medical Image Management And Processing System Regulatory Class: Class II Product Code: QKB Dated: March 9, 2023 Received: March 13, 2023 Dear Kurt Sysock: 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). Sincerelv. Image /page/1/Picture/6 description: The image shows a digital signature. The signature is for Lora D. Weidner. The date of the signature is 2023.04.14, and the time is 10:50:08 -04'00'. 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 {2}------------------------------------------------ #### Indications for Use 510(k) Number (if known) K230685 Device Name AutoContour Model RADAC V3 Indications for Use (Describe) AutoContour is intended to assist radiation treatment planners in contouring structures within medical images in preparation for radiation therapy treatment planning. | 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) #### 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." {3}------------------------------------------------ # K230685 # AutoContour Software Radformation, Inc. # Special 510(k) Summary {4}------------------------------------------------ ### Table of Contents | 5.1. Submitter's Information | 3 | |------------------------------------|----| | 5.2. Device Information | 3 | | 5.3. Predicate Device Information | 4 | | 5.4. Device Description | 4 | | 5.5. Indications for Use | 4 | | 5.6. Technological Characteristics | 4 | | 5.7. Discussion of differences | 10 | | 5.9. Conclusion | 25 | {5}------------------------------------------------ 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: | 335 Madison Avenue, 4th Floor New York, NY 10017 | | Contact Person: | Alan Nelson Chief Science Officer, Radformation | | Phone: | 518-888-5727 | | Fax: | ---------- | | Email: | anelson@radformation.com | | Date of Summary Preparation | 03/09/2023 | #### 5.2. Device Information | Table 2 : Device Information | | |------------------------------|-----------------------------------------------------------------------------| | Trade Name: | AutoContour Model RADAC V3 | | 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 | {6}------------------------------------------------ #### 5.3. Predicate Device Information AutoContour Model RADAC V3 (Subject Device) makes use of its prior submissions -AutoContour Model RADAC V2 (K220598) - as the Predicate Device. #### 5.4. Device Description As with AutoContour Model RADAC V2, the AutoContour Model RADAC V3 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 V3 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 V3 makes use of AutoContour Model RADAC V2 (K220598) as the Predicate Device for substantial equivalence comparison. The functionality and technical components of this prior submission remain unchanged in AutoContour Model RADAC V3. This submission is intended to build on the technological characteristics of the 510(k) cleared AutoContour Model RADAC V2 pertaining to new structure models for both CT and MRI. {7}------------------------------------------------ #### 5.6.1. Updates vs. AutoContour (K220598) 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 V3 vs. AutoContour Model RADAC V2 (K220598) | | | |---------------------------------------------------------------------------------------------------------------|--------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------|--------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------| | Characteristic | Subject Device: AutoContour Model RADAC V3 | Predicate Device: AutoContour Model RADAC V2 (K220598) | | 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 for output | CT or MR input for contouring or registration/fusion. PET/CT input for registration/fusion only. DICOM RTSTRUCT for output | {8}------------------------------------------------ | 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 | CT Models: ● A_Aorta | | | ● A_Aorta_Asc | ● A_Aorta_Asc | | | ● A_Aorta_Dsc | ● A_Aorta_Dsc | | | ● A_LAD | ● A_LAD | | | ● A_Pulmonary | ● Bladder | | | ● Bladder | ● Bone_Illium_L | | | ● Bladder_F | ● Bone_Illium_R | | | ● Bone_Illium_L | ● Bone_Mandible | | | ● Bone_Illium_R | ● Bowel_Bag | | | ● Bone_Mandible | ● BrachialPlex_L | | | ● Bone_Pelvic | ● BrachialPlex_R | | | ● Bone_Skull | ● Brain | | | ● Bone_Sternum | ● Brainstem | | | ● Bowel | ● Breast_L | | | ● Bowel_Bag | ● Breast_R | | | ● Bowel_Large | ● Bronchus | | | ● Bowel_Small | ● Carina | | | ● BrachialPlex_L | ● CaudaEquina | | | ● BrachialPlex_R | ● Cavity_Oral | | | ● Brain | ● Cochlea_L | | | ● Brainstem | ● Cochlea_R | | | ● Breast_L | ● Ear_Internal_L | | | ● Breast_R | ● Ear_Internal_R | | | ● Bronchus | ● Esophagus | | | ● BuccalMucosa | ● External | | | ● Carina | ● Eye_L | | | ● CaudaEquina | ● Eye_R | | | ● Cavity_Oral | ● Femur_L | | | ● Cavity_Oral_Ext | ● Femur_R | | | ● Chestwall_L | ● Femur_RTOG_L | | | ● Chestwall_OAR | ● Femur_RTOG_R | | | ● Chestwall_R | ● Glnd_Lacrimal_L | | | ● Chestwall_RC_L | ● Glnd_Lacrimal_R | | | ● Chestwall_RC_R | ● Glnd_Submand_L | | | ● Cochlea_L | ● Glnd_Submand_R | | | ● Cochlea_R | ● Glnd_Thyroid | | | ● Colon_Sigmoid | ● HDR_Cylinder | | | ● Cornea_L | ● Heart | | | ● Cornea_R | ● Humerus_L | | | ● Duodenum | ● Humerus_R | | | ● Ear_Internal_L | ● Kidney_L | | | ● Ear_Internal_R | ● Kidney_R | | | ● Esophagus | ● Kidney_Outer_L | | | ● External | ● Kidney_Outer_R | | | ● Eye_L | ● Larynx | | | ● Eye_R | ● Lens_L | | Femur_Head_L Femur_Head_R Femur_L Femur_R Femur_RTOG_L Femur_RTOG_R GallBladder Genitals_F Genitals_M Glnd_Lacrimal_L Glnd_Lacrimal_R Glnd_Submand_L Glnd_Submand_R Glnd_Thyroid HDR_Cylinder Heart Hippocampus_L Hippocampus_R Humerus_L Humerus_R Kidney_L Kidney_R Kidney_Outer_L Kidney_Outer_R Larynx Larynx_Glottic Larynx_NRG Larynx_SG Lens_L Lens_R Lips Liver LN_Ax_L LN_Ax_L1_L LN_Ax_L1_R LN_Ax_L2_L LN_Ax_L2_L3_L LN_Ax_L2_L3_R LN_Ax_L2_R LN_Ax_L3_L LN_Ax_L3_R LN_Ax_R LN_IMN_L LN_IMN_R LN_IMN_RC_L LN_IMN_RC_R LN_Inguinofem_L LN_Inguinofem_R LN_Neck_IA LN_Neck_IB-V_L | Lens_R Lips LN_Ax_L LN_Ax_R LN_IMN_L LN_IMN_R LN_Neck_IA LN_Neck_IB-V_L LN_Neck_IB-V_R LN_Neck_II_L LN_Neck_II_R LN_Neck_II-IV_L LN_Neck_II-IV_R LN_Neck_III_L LN_Neck_III_R LN_Neck_IV_L LN_Neck_IV_R LN_Neck_VIA LN_Neck_VIIA_L LN_Neck_VIIA_R LN_Neck_VIIB_L LN_Neck_VIIB_R LN_Pelvics LN_Sclav_L LN_Sclav_R Liver Lung_L Lung_R Marrow_Ilium_L Marrow_Ilium_R Musc_Constrict OpticChiasm OpticNrv_L OpticNrv_R Parotid_L Parotid_R PenileBulb Pituitary Prostate Rectum Rib SeminalVes SpinalCanal SpinalCord Stomach Trachea V_Venacava_S | | | MR Models: OpticChiasm | | | {9}------------------------------------------------ {10}------------------------------------------------ | LN_Neck_VIIB_R | | |--------------------|---------------| | LN_Paraaortic | | | LN_Pelvics | | | LN_Pelvic_NRG | | | LN_Sclav_L | | | LN_Sclav_R | | | LN_Sclav_RADCOMP_L | | | LN_Sclav_RADCOMP_R | | | Lobe_Temporal_L | | | Lobe_Temporal_R | | | • | Rib_R | | • | SeminalVes | | • | SpinalCanal | | • | SpinalCord | | • | Spleen | | • | Stomach | | • | Trachea | | • | UteroCervix | | • | V_Venacava_I | | • | V_Venacava_S | | • | VB | | • | VB_C1 | | • | VB_C2 | | • | VB_C3 | | • | VB_C4 | | • | VB_C5 | | • | VB_C6 | | • | VB_C7 | | • | VB_L1 | | • | VB_L2 | | • | VB_L3 | | • | VB_L4 | | • | VB_L5 | | • | VB_T01 | | • | VB_T02 | | • | VB_T03 | | • | VB_T04 | | • | VB_T05 | | • | VB_T06 | | • | VB_T07 | | • | VB_T08 | | • | VB_T09 | | • | VB_T10 | | • | VB_T11 | | • | VB_T12 | | | MR Models: | | • | Brainstem | | • | Cerebellum | | • | Eye_L | | • | Eye_R | | • | Gind_Prostate | | • | Hippocampus_L | | • | Hippocampus_R | | • | Hypo_True | | • | Hypothalamus | | • | OpticChiasm | | • | OpticNrv_L | | • | OpticNrv_R | | • | OpticTract_L | | • | OpticTract_R | - ● Lung_L - Lung_R - Macula_L ● - Macula_R ● - Marrow_Ilium_L ● LN Neck II L LN_Neck_II_R LN_Neck_II-IV_L LN Neck II-IV R LN_Neck_II-V_L LN_Neck_II-V_R LN_Neck_III_L LN_Neck_III_R LN_Neck_IV_L LN_Neck_IV_R LN_Neck_V_L LN_Neck_V_R LN_Neck_VIA LN_Neck_VIIA_L LN_Neck_VIIA_R LN_Neck_VIIB_L ● . ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● - Marrow_Ilium_R ● - Musc_Constrict ● - Nipple_L ● - Nipple_R ● - OpticChiasm ● - OpticNrv_L - OpticNrv_R ● - Pancreas ● - Parotid_L ● - - Parotid_R ● - PenileBulb ● - Pericardium ● - Pituitary ● - Prostate ● - Rectum ● - Rectum_F - Retina_L ● - Retina_R ● - Rib ● - Rib_L ● - OpticNrv R . - Brainstem ● - Hippocampus_L - Hippocampus_R ● 510(k) Submission {11}------------------------------------------------ {12}------------------------------------------------ | | • Pituitary • Prostate • SeminalVes | | |-----------------------------------------------|---------------------------------------------------------------------------------------------------------------------------------------------------------------------|------------------------------------------------------------------------------------------------------------------------------------------------------------------| | Computer platform & Operating System | Windows based .NET front-end application that also serves as agent Uploader supporting Microsoft Windows 10 (64-bit) and Microsoft Windows Server 2016. | Windows based .NET front-end application that also serves as agent Uploader supporting Microsoft Windows 10 (64-bit) …