(147 days)
Contour+ (MVision Al Segmentation) is a software system for image analysis algorithms to be used in radiation therapy treatment planning workflows. The system includes processing tools for automatic contouring of CT and MR images using machine learning based algorithms. The produced segmentation templates for regions of interest must be transferred to appropriate image visualization systems as an initial template for a medical professional to visualize, review, modify and approve prior to further use in clinical workflows.
The system creates initial contours of pre-defined structures of common anatomical sites, i.e., Head and Neck, Brain, Breast, Lung and Abdomen, Male Pelvis, and Female Pelvis.
Contour+ (MVision Al Segmentation) is not intended to detect lesions or tumors. The device is not intended for use with real-time adaptive planning workflows.
Contour+ (MVision Al Segmentation) is a software-only medical device (software system) that can be used to accelerate region of interest (ROI) delineation in radiotherapy treatment planning by automatic contouring of predefined ROIs and the creation of segmentation templates on CT and MR images.
The Contour+ (MVision Al Segmentation) software system is integrated with a customer IT network and configured to receive DICOM CT and MR images, e.g., from a CT or MRI scanner or a treatment planning system (TPS). Automatic contouring of predefined ROIs is performed by pre-trained, locked, and static models that are based on machine learning using deep artificial neural networks. The models have been trained on several anatomical sites, including the brain, head and neck, bones, breast, lung and abdomen, male pelvis, and female pelvis using hundreds of scans from a diverse patient population. The user does not have to provide any contouring atlases. The resulting segmentation structure set is connected to the original DICOM images and can be transferred to an image visualization system (e.g., a TPS) as an initial template for a medical professional to visualize, modify and approve prior to further use in clinical workflows.
The provided text does not include a table of acceptance criteria and the reported device performance, nor does it specify the sample sizes used for the test set, the number of experts for ground truth, or details on comparative effectiveness studies (MRMC).
However, based on the available information, here is a description of the acceptance criteria and study details:
Acceptance Criteria and Study for Contour+ (MVision AI Segmentation)
The study evaluated the performance of automatic segmentation models by comparing them to ground truth segmentations using Dice Score (DSC) and Surface-Dice Score (S-DSC@2mm) as metrics. The acceptance criteria were based on a "set level of minimum agreement against ground truth segmentations determined through clinically relevant similarity metrics DSC and S-DSC@2mm." While specific numerical thresholds for these metrics are not provided, the submission states that the device fulfills "the same acceptance criteria" as the predicate device.
It's important to note that the provided document is an FDA 510(k) clearance letter and not the full study report. As such, it summarizes the findings and affirms the device's substantial equivalence without detailing every specific test result or acceptance threshold.
1. A table of acceptance criteria and the reported device performance
| Metric | Acceptance Criteria | Reported Device Performance |
|---|---|---|
| Dice Score (DSC) | Based on a "set level of minimum agreement against ground truth segmentations" (specific thresholds not provided). | "Performance verification and validation results for various subsets of the golden dataset show the generalizability and robustness of the device..." |
| Surface-Dice Score (S-DSC@2mm) | Based on a "set level of minimum agreement against ground truth segmentations" (specific thresholds not provided). | "...Contour+ (MVision AI Segmentation) fulfills the same acceptance criteria, provides the intended benefits, and it is as safe and as effective as the predicate software version." |
2. Sample sized used for the test set and the data provenance (e.g. country of origin of the data, retrospective or prospective)
- Sample Size for Test Set: The exact sample size for the test (golden) dataset is not specified, but it's referred to as "various subsets of the golden dataset" and chosen to "achieve high granularity in performance evaluation tests."
- Data Provenance: The datasets originate from "multiple EU and US clinical sites (with over 50% of data coming from US sites)." It is described as containing "hundreds of scans from a diverse patient population," ensuring representation of the "US population and medical practice." The text does not explicitly state if the data was retrospective or prospective, but the description of "hundreds of scans" from multiple sites suggests it is likely retrospective.
3. Number of experts used to establish the ground truth for the test set and the qualifications of those experts (e.g. radiologist with 10 years of experience)
The number of experts used to establish the ground truth for the test set is not specified in the provided text. The qualifications are vaguely mentioned as "radiotherapy experts" who performed "Performance validation of machine learning-based algorithms for automatic segmentation." No specific years of experience or board certifications are detailed.
4. Adjudication method (e.g. 2+1, 3+1, none) for the test set
The adjudication method for establishing ground truth on the test set is not specified in the provided text. The text only states that the auto-segmentations were compared to "ground truth segmentations."
5. 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
A multi-reader multi-case (MRMC) comparative effectiveness study focusing on the improvement of human readers with AI assistance versus without AI assistance is not explicitly described in the provided text.
The text states: "Performance validation of machine learning-based algorithms for automatic segmentation was also carried out by radiotherapy experts. The results show that Contour+ (MVision AI Segmentation) assists in reducing the upfront effort and time required for contouring CT and MR images, which can instead be devoted by clinicians on refining and reviewing the software-generated contours." This indicates that experts reviewed the output and perceived a benefit in efficiency, but it does not detail a formal MRMC study comparing accuracy or time, with a specific effect size.
6. If a standalone (i.e. algorithm only without human-in-the-loop performance) was done
Yes, a standalone performance evaluation of the algorithm was conducted. The primary performance metrics (DSC and S-DSC@2mm) were calculated by directly comparing the "produced auto-segmentations to ground truth segmentations," which is a standalone assessment of the algorithm's output. The statement "Performance verification and validation results for various subsets of the golden dataset show the generalizability and robustness of the device" further supports this.
7. The type of ground truth used (expert consensus, pathology, outcomes data, etc)
The ground truth used was expert consensus segmentations. The text repeatedly refers to comparing the device's output to "ground truth segmentations" established by "radiotherapy experts." There is no mention of pathology or outcomes data being used for ground truth.
8. The sample size for the training set
The exact sample size for the training set is not specified, but the models were "trained on several anatomical sites... using hundreds of scans from a diverse patient population."
9. How the ground truth for the training set was established
The text states that the machine learning models were "trained on several anatomical sites... using hundreds of scans from a diverse patient population." While it doesn't explicitly detail the process for establishing ground truth for the training set, it is implied to be through expert contouring/segmentation, as the validation uses "ground truth segmentations" which are established by "radiotherapy experts." Given the extensive training data required for machine learning, it's highly probable that these "hundreds of scans" also had expert-derived segmentations as their ground truth for training.
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October 18, 2024
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MVision AI Ov Kalpana Jha VP of Regulatory and Market Strategy Paciuksenkatu 29, 6th Floor Helsinki, 00270 Finland
Re: K241490
Trade/Device Name: Contour+ (MVision AI Segmentation) Regulation Number: 21 CFR 892.2050 Regulation Name: Medical Image Management And Processing System Regulatory Class: Class II Product Code: QKB Dated: June 25, 2024 Received: September 24, 2024
Dear Kalpana Jha:
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 (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/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.
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"
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(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 (OS) 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 Rule"). 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 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-regulatory
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assistance/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
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Indications for Use
Submission Number (if known)
Device Name
Contour+ (MVision Al Segmentation)
Indications for Use (Describe)
Contour+ (MVision Al Segmentation) is a software system for image analysis algorithms to be used in radiation therapy treatment planning workflows. The system includes processing tools for automatic contouring of CT and MR images using machine learning based algorithms. The produced seqmentation templates for reqions of interest must be transferred to appropriate image visualization systems as an initial template for a medical professional to visualize, review, modify and approve prior to further use in clinical workflows.
The system creates initial contours of pre-defined structures of common anatomical sites, i.e., Head and Neck, Brain, Breast, Lung and Abdomen, Male Pelvis, and Female Pelvis.
Contour+ (MVision Al Segmentation) is not intended to detect lesions or tumors. The device is not intended for use with real-time adaptive planning workflows.
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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The following information is provided as required by 21 CFR 807.92
| Date Prepared: | June 25, 2024 |
|---|---|
| ---------------- | --------------- |
Submitter's Information
| Company Name and Address: | MVision AI Oy |
|---|---|
| Paciuksenkatu 29, 6th floor | |
| 00270 Helsinki, Finland | |
| Tel: +358 (0) 40 721 3783 | |
| info@mvision.ai | |
| Establishment Registration Number: | 3022745617 |
| Contact Person: | Kalpana Jha |
| VP of Regulatory and Market Strategy | |
| kalpana.jha@mvision.ai | |
| Tel: +358 44 9214 354 |
Subject Device
| Device Trade Name: | Contour+ (MVision Al Segmentation) |
|---|---|
| Common Name: | Medical Image Segmentation Software |
| Device Classification Name: | Radiological Image Processing Software For Radiation Therapy |
| Product Code: | QKB |
| Device Class: | Class II |
| Review Panel: | Radiology |
| Regulation Description: | Medical Image Management And Processing System |
| Regulation Number: | 21 CFR §892.2050 |
Predicate Device
| Device Name: | MVision Al Segmentation |
|---|---|
| 510(k) Number: | K212915 |
| Manufacturer: | MVision AI Oy |
This predicate has not been subject to a design-related recall.
Device Description
Contour+ (MVision Al Segmentation) is a software-only medical device (software system) that can be used to accelerate region of interest (ROI) delineation in radiotherapy treatment planning by automatic contouring of predefined ROIs and the creation of segmentation templates on CT and MR images.
The Contour+ (MVision Al Segmentation) software system is integrated with a customer IT network and configured to receive DICOM CT and MR images, e.g., from a CT or MRI scanner or a treatment planning
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system (TPS). Automatic contouring of predefined ROIs is performed by pre-trained, locked, and static models that are based on machine learning using deep artificial neural networks. The models have been trained on several anatomical sites, including the brain, head and neck, bones, breast, lung and abdomen, male pelvis, and female pelvis using hundreds of scans from a diverse patient population. The user does not have to provide any contouring atlases. The resulting segmentation structure set is connected to the original DICOM images and can be transferred to an image visualization system (e.g., a TPS) as an initial template for a medical professional to visualize, modify and approve prior to further use in clinical workflows.
The following is a listing of supported anatomical sites and ROIs of the organs for which the device creates initial contours:
| CT Bones | CT Breast | CT Head and Neck |
|---|---|---|
| C1 | A_Aorta | A_Carotid_L/R |
| C2 | A_Aorta_v2 | Arytenoid_L/R |
| C3 | A_Carotid_L/R | Body |
| C4 | A_LAD | Bone_Mandible |
| C5 | A_Pulm | BrachialPlex_L/R |
| C6 | Atrium_L/R | Brain |
| C7 | Bag_Bowel | Brainstem |
| L1 | Body | Buccal_Mucosa_L/R |
| L2 | Br_1234_RTOG_L/R | Cavity_Oral |
| L3 | Br_234_RTOG_L/R | Cochlea_L/R |
| L4 | BrachialPlex_L/R | Cricophar_inlet |
| L5 | Breast_L/R | Esophagus_S |
| Rib01_L/R | Breast_RTOG_L/R | Eye_Ant_L/R |
| Rib02_L/R | BrTW_1234_RTOG_L/R | Eye_Post_L/R |
| Rib03_L/R | BrTW_234_RTOG_L/R | Eye_L/R |
| Rib04_L/R | BrTW_RTOG_L/R | Glnd_Lacrimal_L/R |
| Rib05_L/R | Esophagus | Glnd_Submand_L/R |
| Rib06_L/R | Glnd_Thyroid | Glnd_Thyroid |
| Rib07_L/R | Heart | Glottis |
| Rib08_L/R | Heart+A_Pulm | LN_Neck_IA |
| Rib09_L/R | Humerus_Head_L/R | LN_Neck_IB_L/R |
| Rib10_L/R | Humerus_L/R | LN_Neck_III_L/R |
| Rib11_L/R | Liver | LN_Neck_II_L/R |
| Rib12_L/R | LN_Axilla_RTOG_L/R | LN_Neck_IVA_L/R |
| T1 | LN_Axillary_L/R | LN_Neck_IVB_L/R |
| T10 | LN_B_RTOG_L1_L/R | LN_Neck_IX_L/R |
| T11 | LN_B_RTOG_L2_L/R | LN_Neck_VC_L/R |
| T12 | LN_B_RTOG_L3_L/R | LN_Neck_VIA |
| T2 | LN_B_RTOG_L4_L/R | LN_Neck_VIB |
| T3 | LN_B_RTOG_L5_L/R | LN_Neck_VIIA_L/R |
| T4 | LN_Breast_L1_L/R | LN_Neck_VIIB_L/R |
| T5 | LN_Breast_L2_L/R | LN_Neck_V_L/R |
| T6 | LN_Breast_L3_L/R | LN_Neck_XA_L/R |
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| LN_Breast_L4_L/R | LN_Neck_XB_L/R |
|---|---|
| LN_IMN_IC4_L/R | Larynx_SG |
| LN_IMN_L/R | Lens_L/R |
| LN_Intpect_L/R | Lips |
| LN_RTOG_IMN_L/R | Lung_L/R |
| Lung_L/R | Musc_Constrict |
| SpinalCanal | OpticChiasm |
| SpinalCord | OpticChiasm_cnv |
| Spleen | OpticNrv_L/R |
| Stomach | OpticNrv_cnv_L/R |
| Trachea | Parotid_L/R |
| V_Venacava_I | Pituitary |
| V_Venacava_S | SpinalCanal |
| Ventricle_L/R | SpinalCord |
| Wire | Trachea |
| CT Lung and Abdomen | CT Female Pelvis | CT Male Pelvis |
|---|---|---|
| A_Aorta | A_Aorta | A_Aorta |
| A_Aorta v2 | Bag_Bowel | Bag_Bowel |
| A_LAD | Bladder | Bladder |
| A_Pulm | Body | Body |
| Atrium L/R | Bone_Pelvic | Bone_Pelvic |
| Bag_Bowel | Bowel_Large | Bowel_Large |
| Body | Bowel_Small | Bowel_Small |
| Bones | CTV_Central | CaudaEquina |
| Bowel_Large | CTV_Param | Duodenum |
| Bowel_Small | CTV_Pelvis | Femur_Head_L/R |
| BrachialPlex L/R | Duodenum | Femur_Implant_L/R |
| Bronchus_Prox | Femur_Head_L/R | Femur_L/R |
| Chestwall L/R | Femur_Implant_L/R | Kidney_L/R |
| Duodenum | Femur_L/R | L4_VB |
| Esophagus | Kidney_L/R | L5_VB |
| Heart | L4_VB | Liver |
| Heart+A_Pulm | L5_VB | LN_Inguinal_L/R |
| Humerus_Head_L/R | Liver | LN_NRG |
| Humerus_L/R | LN_Gyn_RTOG | LN_Pivotal |
| Kidney_L/R | LN_Inguinal_L/R | LN_RTOG |
| Liver | LN_PAN | Markers |
| Lung_L/R | LN_PAN_Long | Musc_Coccygeus_L/R |
| Pancreas | LN_Pivotal | Musc_Iliacus_L/R |
| SpinalCanal | LN_RTOG | Musc_Obt_Int_L/R |
| SpinalCord | Musc_Coccygeus_L/R | Musc_Pirifor_L/R |
T7 T8 T9
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| Spleen | Musc_Iliacus_L/R | Musc_Psoas_Maj_L/R |
|---|---|---|
| Stomach | Musc_Obt_Int_L/R | Pancreas |
| Trachea | Musc_Pirifor_L/R | PenileBulb |
| Trachea_Prox | Musc_Psoas_Maj_L/R | Prostate |
| V_Venacava_I | Pancreas | RectoSigmoid |
| V_Venacava_S | RectoSigmoid | Rectum |
| Ventricle_L/R | Rectum | Sacrum |
| Sacrum | SeminalVes | |
| SpinalCanal | SpinalCanal | |
| SpinalCord | SpinalCord | |
| Spleen | Spleen | |
| Stomach | Stomach | |
| UteroCervix | V_Venacava_I | |
| V_Venacava_I | Vessels_L/R | |
| Vagina | Vessels_Long_L/R | |
| Vessels_L/R | ||
| Vessels_Long_L/R |
| CT Brain | MR Brain | MR T2 Male Pelvis |
|---|---|---|
| A_Carotid_L/R | Amygdala_L/R | Bladder |
| Body | Body | BladderTrigone |
| Brain | Brain | PenileBulb |
| Brainstem | Brainstem | Prostate |
| Cochlea_L/R | Cerebellum | Rectum |
| Eye_Ant_L/R | CorpusCallosum | SeminalVes |
| Eye_L/R | Eye_L/R | Spacer |
| Eye_Post_L/R | Glnd_Lacrimal_L/R | |
| Fossa_Pituitary | Hippocampus_L/R | |
| Glnd_Lacrimal_L/R | Hypothalamus | |
| Lens_L/R | MedullaOblongata | |
| OpticChiasm | Midbrain | |
| OpticChiasm_cnv | OpticChiasm | |
| OpticNrv_cnv_L/R | OpticChiasm_cnv | |
| OpticNrv_L/R | OpticNrv_cnv_L/R | |
| Parotid_L/R | OpticNrv_L/R | |
| Pituitary | OpticTract_cnv_L/R | |
| SpinalCanal | OpticTract_L/R | |
| SpinalCord | Pituitary | |
| Pons | ||
| Thalamus_L/R |
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The Contour+ (MVision Al Segmentation) software system has two deployment modes and is written in a way that allows running the same code in two different environments:
- On-premises (local): For the local Healthcare environment, DICOM image and structure set data ● is transferred via the DICOM TCP/IP protocol.
- Cloud: For the cloud environment, de-identified DICOM image and structure set data are ● transferred via secure HTTPS protocol. HTTPS data is protected by TLS encryption.
Indication for use / Intended use
Contour+ (MVision Al Segmentation) is a software system for image analysis algorithms to be used in radiation therapy treatment planning workflows. The system includes processing tools for automatic contouring of CT and MR images using machine learning based algorithms. The produced segmentation templates for regions of interest must be transferred to appropriate image visualization systems as an initial template for a medical professional to visualize, review, modify and approve prior to further use in clinical workflows.
The system creates initial contours of pre-defined structures of common anatomical sites, i.e., Head and Neck, Brain, Breast, Lung and Abdomen, Male Pelvis, and Female Pelvis.
Contour+ (MVision Al Segmentation) is not intended to detect lesions or tumors. The device is not intended for use with real-time adaptive planning workflows.
Comparison with the Predicate Device
Contour+ (MVision AI Segmentation) is an incremental minor version release with the same intended use, indications for use, and principles of operation as the predicate software version.
The following table outlines the similarities and differences between the two software versions:
| MVision AI Segmentation (K212915) | Contour+ (MVision AI Segmentation) | Comparison | |
|---|---|---|---|
| Indications for Use | MVision AI Segmentation is a softwaresystem for image analysis algorithms to beused in radiation therapy treatmentplanning workflows. The system includesprocessing tools for automatic contouringof CT images using machine learningbased algorithms. The producedsegmentation templates for regions ofinterest must be transferred to appropriateimage visualization systems as an initialtemplate for a medical professional tovisualize, review, modify and approveprior to further use in clinical workflows.The system creates initial contours of pre-defined structures of common anatomicalsites, i.e. Head and Neck, Brain, Breast,Lung and Abdomen, Male Pelvis, andFemale Pelvis in adult patients.MVision AI Segmentation is not intendedto detect lesions or tumors. The device isnot intended for use with real-timeadaptive planning workflows. | Contour+ (MVision AI Segmentation) isa software system for image analysisalgorithms to be used in radiation therapytreatment planning workflows. Thesystem includes processing tools forautomatic contouring of CT and MRimages using machine learning basedalgorithms. The produced segmentationtemplates for regions of interest must betransferred to appropriate imagevisualization systems as an initialtemplate for a medical professional tovisualize, review, modify and approveprior to further use in clinical workflows.The system creates initial contours ofpre-defined structures of commonanatomical sites, i.e., Head and Neck,Brain, Breast, Lung and Abdomen, MalePelvis, and Female Pelvis.Contour+ (MVision AI Segmentation) isnot intended to detect lesions or tumors.The device is not intended for use withreal-time adaptive planning workflows. | Same intended use.The only differenceis the inclusion ofprocessing tools forautomatic contouringof MR images, inaddition to CTimages — this is achange intechnologicalcharacteristics thatdoes not affect theintended use of thedevice. |
| MVision AI Segmentation (K212915) | Contour+ (MVision Al Segmentation) | Comparison | |
| IntendedUsers | Designed to be used by clinicians trainedin radiation therapy workflows. | Designed to be used by clinicians trainedin radiation therapy workflows. | Same. |
| Target PatientPopulation | Patients who have been prescribedradiation therapy. | Patients who have been prescribedradiation therapy. | Same |
| DeploymentEnvironment | • Cloud based software Application• Local (on customer premises)installation in healthcare provider's ITnetwork / server | • Cloud based software Application• Local (on customer premises)installation in healthcare provider's ITnetwork / server | Same |
| OperatingPlatform | • Ubuntu | • Ubuntu• Windows | Contour+ may alsorun in a Windowsoperating platform |
| Communications /Networking | DICOM image and structure set datatransfers• TCP/IP, SCP, and HTTP (Local)• TCP/IP, SCP, and HTTPS (Cloud) | DICOM image and structure set datatransfers• TCP/IP, SCP, and HTTP (Local)• TCP/IP, SCP, and HTTPS (Cloud) | Minor changes:Added API script tosend scans directlyfrom the VarianEclipse TreatmentPlanning System;Provided read-onlyImport/Export UI toupload/downloadDICOM data via aweb browser |
| Imagingmodalities | • CT | • CT• MR | Added support forautomatic contouringof MR images |
| Segmentationalgorithms | • Machine learning-based contouringusing deep learning-based models• Does not support segmentation basedon atlas-based techniques | • Machine learning-based contouringusing deep learning-based models• Does not support segmentation basedon atlas-based techniques | Same algorithms;Updated certainCT segmentationmodels; Added 1 CTand 2 MR models |
| Registration basedre-contouring | No | No | Same |
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The different technological characteristics of the two software versions do not raise different questions of safety and effectiveness.
- Both software versions are intended to accelerate region of interest delineation in radiological images . by automatic contouring and the creation of segmentation templates to be used by qualified healthcare professionals in radiation therapy treatment planning workflows.
- Both software versions are not intended to detect lesions or tumors, and they are not intended for use with real-time adaptive planning workflows.
- Both software versions are intended to create initial contours of predefined structures of common ● anatomical sites, such as head and neck, brain, breast, lung and abdomen, male pelvis, and female pelvis on scans that are appropriate for treatment planning. The addition of a model to segment specifically bone structures on CT images and the addition of models for the segmentation of MR images does not change the device intended use and indications for use.
- Both software versions are intended to produce segmentation templates for regions of interest that must ● be transferred to appropriate image visualization systems as an initial template for a medical professional to visualize, review, modify and approve prior to further use in clinical workflows.
- As radiotherapy treatment planning may be performed effectively using CT and/or MR images, 0 automatic contouring for the creation of segmentation templates of predefined structures of anatomical regions of interest in images from either modality offers the same benefits and presents the same risks.
- In both software versions, automatic contouring of images (segmentation of anatomical regions of ● interest) is performed by pre-trained, locked, and static models that are based on machine learning using the same deep artificial neural networks. The segmentation algorithms remain the same.
Performance data - Verification and Validation Testing
Verification and validation testing of this software release was also conducted as per FDA's Guidance for the "Content of Premarket Submissions for Device Software Functions (2023)," including compliance with recognized consensus standards (e.g., IEC 62304, IEC 62366-1, ISO 14971, DICOM) and FDA guidance for "Cybersecurity in Medical Devices: Quality System Considerations and Content of Premarket Submissions (2023)," and "Design Considerations and Pre-market Submission Recommendations for Interoperable Medical Devices (2017)."
Device performance with the updated CT models and the additional CT and MR models, i.e., the effectiveness of the automatic segmentation methods (machine learning-based models) in the modified software version, was evaluated using the same data management practices (including data sources, sample size, and model generalizability) and established test protocols as previously reviewed by the FDA.
The training and the test (golden) datasets were chosen to achieve high granularity in performance evaluation tests. The datasets originate from multiple EU and US clinical sites (with over 50% of data coming from US sites) with a broad diversity of patients and medical imaging technology that are deemed representative of the US population and medical practice. This also ensures that the evaluated model performance reflects the real clinical performance in any radiotherapy clinic following the segmentation consensus guidelines the models are trained to comply with.
The performance of both CT and MR automatic segmentation models was evaluated by comparing the produced auto-segmentations to ground truth segmentations and calculating scores DSC (Dice Score) and S-DSC@2mm (Surface-Dice Score) for all regions of interest (ROI). The ROI acceptance criteria are based on a set level of minimum agreement against ground truth segmentations determined through clinically relevant similarity metrics DSC and S-DSC(@2mm.
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Performance verification and validation results for various subsets of the golden dataset show the generalizability and robustness of the device for the US patient population and US medical practice.
Performance validation of machine learning-based algorithms for automatic segmentation was also carried out by radiotherapy experts. The results show that Contour+ (MVision AI Segmentation) assists in reducing the upfront effort and time required for contouring CT and MR images, which can instead be devoted by clinicians on refining and reviewing the software-generated contours.
The additional functional interoperability changes (the Varian Eclipse TPS, and the Import/Export UI) and the labeling change to support deployment of the software on a Windows operating platform do not have an impact on device safety or effectiveness. Successful software execution in a Windows operating environment and the functional interoperability changes was performed using the same established design verification test protocols as for the predicate software version per Product Life Cycle Procedure, based on FDA recognized consensus standards and design control regulation 21 CFR 820.30.
Conclusion
Contour+ (MVision AI Segmentation) is an incremental minor version release with the same intended use, indications for use, and principles of operation as the predicate software version. The different technological characteristics of the two software versions do not raise different questions of safety and effectiveness. Software verification and validation testing, including the performance of all CT and MR models using established protocols and recognized standards demonstrate that Contour+ (MVision AI Segmentation) fulfills the same acceptance criteria, provides the intended benefits, and it is as safe and as effective as the predicate software version (MVision AI Segmentation).
§ 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).