(162 days)
AI-Rad Companion Organs RT is a post-processing software intended to automatically contour DICOM CT imaging data using deep-learning-based algorithms.
Contours that are generated by AI-Rad Companion Organs RT may be used as input for clinical workflows including external beam radiation therapy treatment planning. AI-Rad Companion Organs RT must be used in conjunction with appropriate software such as Treatment Planning Systems and Interactive Contouring applications, to review, edit, and accept contours generated by AI-Rad Companion Organs RT.
The output of AI-Rad Companion Organs RT in the format of RTSTRUCT objects are intended to be used by trained medical professionals.
The software is not intended to automatically detect or contour lesions. Only DICOM images of adult patients are considered to be valid input.
AI-Rad Companion Organs RT is a post-processing software used to automatically contour DICOM CT imaging data using deep-learning-based algorithms. AI-Rad Companion Organs RT contouring workflow supports CT input data and produces RTSTRUCT outputs. The configuration of the organ database and organ templates defining the organs and structures to be contoured based on the input DICOM data is managed via a configuration interface. Contours that are generated by AI-Rad Companion Organs RT may be used as input for clinical workflows including external beam radiation therapy treatment planning.
The output of AI-Rad Companion Organs RT, in the form of RTSTRUCT objects, are intended to be used by trained medical professionals. The output of AI-Rad Companion Organs RT must be used in conjunction with appropriate software such as Treatment Planning Systems and Interactive Contouring applications, to review, edit, and accept contours generated by AI-Rad Companion Organs RT application.
At a high-level, AI-Rad Companion Organs RT includes the following functionality:
-
- Automated contouring of Organs at Risk (OAR) workflow
- a. Input -DICOM CT
- b. Output DICOM RTSTRUCT
-
- Organ Templates configuration (incl. Organ Database)
-
- Web-based preview of contouring results to accept or reject the generated contours
Here's a breakdown of the acceptance criteria and study details for the AI-Rad Companion Organs RT device, based on the provided text:
1. Table of Acceptance Criteria & Reported Device Performance:
| Validation Testing Subject | Acceptance Criteria | Reported Device Performance (Median) |
|---|---|---|
| Organs in Predicate Device | 1. All organs segmented in the predicate device are also segmented in the subject device. | Met (all predicate organs are segmented in the subject device, implied by comparison tables). |
| 2. The lower bound of the 95th percentile CI of the segmentation (subject device) is greater than 0.1 Dice lower than the mean of the predicate device segmentation. | DICE: Subject: 0.85 (CI: [80.23, 84.61]) vs. Predicate: 0.85 (implied CI close to median). The statement "performance of the subject device and predicate device are comparable in DICE and ASSD" implies this criterion was met. | |
| ASSD: Subject: 0.93 (CI: [0.86, 1.14]) vs. Predicate: 0.94 (implied CI close to median). The statement "performance of the subject device and predicate device are comparable in DICE and ASSD" implies this criterion was met. | ||
| Head & Neck Lymph Nodes | 1. The overall fail rate of each organ/anatomical structure is smaller than 15%. | Not explicitly stated for each organ/anatomical structure, but generally implied by acceptable DICE and ASSD. |
| 2. The lower bound of the 95th percentile CI of the segmentation (subject device) is greater than 0.1 Dice lower than the mean of the reference device segmentation. | DICE: Subject (Head and Neck lymph node class): Avg 81.32 (CI: [80.32, 82.12]) vs. Reference (Pelvic lymph node class): Avg 80. The statement "performance of the subject device for non-overlapping organs is comparable in DICE to the reference device" and the specific values show that 80.32 is not more than 0.1 lower than 80 (it's higher by 0.32), so this criterion appears met. | |
| ASSD: Subject (Head and Neck lymph node class): Avg 1.06 (CI: [0.99, 1.19]) vs. Reference: N.A. (No direct comparison for ASSD). |
Note: The text did not explicitly state the "fail rate" for the Head & Neck Lymph Nodes, only that it should be "smaller than 15%". The conclusion implies all acceptance criteria were met. The confidence intervals for the predicate device's DICE and ASSD are missing in Table 4, but the statement "performance of the subject device and predicate device are comparable" suggests the criteria were acceptable.
2. Sample Size Used for the Test Set and Data Provenance:
- Sample Size: N = 113 retrospective performance study on CT data.
- This N=113 is composed of:
- Cohort A: 73 subjects (14 from Germany, 59 from Brazil)
- Cohort B: 40 subjects (Canada: 40)
- This N=113 is composed of:
- Data Provenance: Multiple clinical sites across North America (Canada) and Europe (Germany, Brazil – often considered part of South America, but grouped with "Europe" in the text for data collection context). The study used previously acquired CT data (retrospective).
3. Number of Experts Used to Establish the Ground Truth for the Test Set and Qualifications of Those Experts:
- Number of Experts: Not explicitly stated as a specific number. The text mentions "a team of experienced annotators" and "a board-certified radiation oncologist".
- Qualifications:
- Annotators: "experienced annotators mentored by radiologists or radiation oncologists".
- Review/Correction: "board-certified radiation oncologist".
4. Adjudication Method for the Test Set:
- The ground truth annotations were drawn manually by a team of experienced annotators and then underwent a "quality assessment including review and correction of each annotation was done by a board-certified radiation oncologist". This suggests a method where initial annotations are created by multiple individuals and then reviewed/corrected by a single, highly qualified expert. This could be interpreted as a form of expert review/adjudication.
5. If a Multi-Reader Multi-Case (MRMC) Comparative Effectiveness Study was done:
- No, a MRMC comparative effectiveness study was not explicitly stated as having been done. The performance evaluation focused on comparing the AI algorithm's output to expert-generated ground truth and comparing the device's performance to predicate/reference devices, not on how human readers improve with or without AI assistance.
6. If a Standalone (i.e. algorithm only without human-in-the loop performance) was done:
- Yes, a standalone performance study was done. The study "validated the AI-Rad Companion Organs RT software from clinical perspective" by evaluating its auto-contouring algorithm, and calculating metrics like DICE coefficients and ASSD against ground truth annotations. The device's output "must be used in conjunction with appropriate software... to review, edit, and accept contours", indicating its standalone output is then reviewed by a human, but the validation of its generation of contours is standalone.
7. The Type of Ground Truth Used:
- Expert Consensus/Manual Annotation with Expert Review (following guidelines): "Ground truth annotations were established following RTOG and clinical guidelines using manual annotation. The ground truth annotations were drawn manually by a team of experienced annotators mentored by radiologists or radiation oncologists using an internal annotation tool. Additionally, a quality assessment including review and correction of each annotation was done by a board-certified radiation oncologist..." This indicates a robust expert-derived ground truth.
8. The Sample Size for the Training Set:
- 160 datasets (for Head & Neck specifically, other organs might have different training data, but this is the only training set sample size provided).
9. How the Ground Truth for the Training Set was Established:
- "In both the annotation process for the training and validation testing data, the annotation protocols for the OAR were defined following the NRG/RTOG guidelines. The ground truth annotations were drawn manually by a team of experienced annotators mentored by radiologists or radiation oncologists using an internal annotation tool. Additionally, a quality assessment including review and correction of each annotation was done by a board-certified radiation oncologist using validated medical image annotation tools."
- This is the same process as for the test set, ensuring consistency in ground truth establishment.
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Siemens Medical Solutions USA, Inc. % Kira Kuzmenchuk Regulatory Affairs Specialist 40 Liberty Blvd. Mail Code 65-3 MALVERN PA 19355
Re: K221305
Trade/Device Name: AI-Rad Companion Organs RT Regulation Number: 21 CFR 892.2050 Regulation Name: Medical image management and processing system Regulatory Class: Class II Product Code: QKB Dated: September 9, 2022 Received: September 12, 2022
Dear Kira Kuzmenchuk:
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 devices or postmarketing safety reporting (21 CFR 4, Subpart B) for combination products (see
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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,
for
Daniel M. Krainak, Ph.D. Assistant Director 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
510(k) Number (if known)
K221305
Device Name AI-Rad Companion Organs RT
Indications for Use (Describe)
Al-Rad Companion Organs RT is a post-processing software intended to automatically contour DICOM CT imaging data using deep-learning-based algorithms.
Contours that are generated by AI-Rad Companion Organs RT may be used as input for clinical workflows including external beam radiation therapy treatment planning. AI-Rad Companion Organs RT must be used in conjunction with appropriate software such as Treatment Planning Systems and Interactive Contouring applications, to review, edit, and accept contours generated by AI-Rad Companion Organs RT.
The output of AI-Rad Companion Organs RT in the format of RTSTRUCT objects are intended to be used by trained medical professionals.
The software is not intended to automatically detect or contour lesions. Only DICOM images of adult patients are considered to be valid input.
| 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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510(k) SUMMARY FOR AI-Rad Companion Organs RT
Submitted by: Siemens Medical Solutions USA, Inc. 40 Liberty Boulevard Malvern, PA 19355 Date Prepared: October 11, 2022
This summary of 510(k) safety and effectiveness information is being submitted in accordance with the requirements of Safe Medical Devices Act of 1990 and 21 CFR §807.92.
1. Submitter
| Importer/Distributor | Siemens Medical Solutions USA, Inc.40 Liberty BoulevardMalvern, PA 19355Mail Code: 65-3Registration Number: 2240869 |
|---|---|
| Manufacturing Site | Siemens Healthcare GmbHHenkestrasse 127Erlangen, Germany 91052Registration Number: 3002808157 |
2. Contact Person
Kira Kuzmenchuk Regulatory Affairs Specialist Siemens Medical Solutions USA, Inc. 40 Liberty Boulevard Mail Code: 65-3 Malvern, PA 19335 Phone: +1 (484) 901 - 9471 Email: kira.kuzmenchuk@siemens-healthineers.com
3. Device Name and Classification
| Product Name: | A |
|---|---|
| Common Name: | N |
I-Rad Companion Organs RT ledical Imaging Software
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SIEME Healthineers
Classification Name:
Classification Panel: CFR Section: Device Class: Product Code:
4. Predicate Device
Product Name: Common Name: 510(k) Number: Clearance Date: Classification Name: Classification Panel: CFR Section: Device Class: Primary Product Code: Recall Information:
5. Reference Device
Product Name: Contour ProtégéAI Medical Imaging Software Common Name: 510(k) Number: K213976 Clearance Date: February 3, 2022 Classification Name: Medical image management and processing system Classification Panel: Radiology CFR Section: 21 CFR §892.2050 Device Class: Class II Primary Product Code: QKB Recall Information: N/A
6. Indications for Use
AI-Rad Companion Organs RT is a post-processing software intended to automatically contour DICOM CT imaging data using deep-learning-based algorithms.
Contours that are generated by AI-Rad Companion Organs RT may be used as input for clinical workflows including external beam radiation therapy treatment planning. AI-Rad Companion Organs RT must be used in conjunction with appropriate software such as Treatment Planning Systems and Interactive Contouring applications, to review, edit, and accept contours generated by AI-Rad Companion Organs RT.
Medical Image Management and Processing System
Radiology 21 CFR §892.2050 Class II QKB
AI-Rad Companion Organs RT Medical Imaging Software K193562 November 6, 2020 Picture Archiving and Communication System Radiology 21 CFR §892.2050 Class II OKB N/A
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SIEMEN Healthineers
The output of AI-Rad Companion Organs RT in the format of RTSTRUCT objects are intended to be used by trained medical professionals. The software is not intended to automatically detect or contour lesions. Only DICOM images of adult patients are considered to be valid input.
7. Device Description
AI-Rad Companion Organs RT is a post-processing software used to automatically contour DICOM CT imaging data using deep-learning-based algorithms. AI-Rad Companion Organs RT contouring workflow supports CT input data and produces RTSTRUCT outputs. The configuration of the organ database and organ templates defining the organs and structures to be contoured based on the input DICOM data is managed via a configuration interface. Contours that are generated by AI-Rad Companion Organs RT may be used as input for clinical workflows including external beam radiation therapy treatment planning.
The output of AI-Rad Companion Organs RT, in the form of RTSTRUCT objects, are intended to be used by trained medical professionals. The output of AI-Rad Companion Organs RT must be used in conjunction with appropriate software such as Treatment Planning Systems and Interactive Contouring applications, to review, edit, and accept contours generated by AI-Rad Companion Organs RT application.
At a high-level, AI-Rad Companion Organs RT includes the following functionality:
-
- Automated contouring of Organs at Risk (OAR) workflow
- a. Input -DICOM CT
- b. Output DICOM RTSTRUCT
-
- Organ Templates configuration (incl. Organ Database)
-
- Web-based preview of contouring results to accept or reject the generated contours
8. Substantially Equivalent (SE) and Technological Characteristics
The indented use of the predicate device and the subject device are equivalent. The main difference is that AI-Rad Companion Organs RT VA40 adds the additional analysis of 29 head & neck structures compared to the predicate, AI-Rad Companion Organs RT (K193562). AI-Rad Companion Organs RT VA40 and AI-Rad Companion Organs RT VA20 both use a deep learning algorithm to support their AI claims. Additionally, they both process CT data in DICOM format, making them vendor agnostic and create outputs which can be used by any TPS system. The deep learning algorithm within AI-Rad Companion Organs RT VA20 has been enhanced from the algorithm in AI-Rad Companion Organs RT VA20 (K193562). All models contained within AI-Rad Companion Organs RT VA40 and AI-Rad Companion Organs RT VA20 (K193562) are locked and cannot be modified by the user.
The subject device, AI-Rad Companion Organs RT, is substantially equivalent with regards to the software features, functionalities, and core algorithms. The performance of the new head &
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neck structures algorithm within AI-Rad Companion Organs RT VA40 is comparable to the algorithm in Contour ProtégéAI (K213976).
The risk analysis and non-clinical data support that the subject device's performance is comparable to the predicate device and does not raise different questions of the safety and effectiveness.
| Subject Device | Predicate Device | Reference Device | |
|---|---|---|---|
| DeviceManufacturer | Siemens | Siemens | MIM Software Inc. |
| Device Name | AI-Rad CompanionOrgans RT(SW Version VA40) | AI-Rad CompanionOrgans RT(SW Version VA20) | Contour ProtégéAI |
| 510(k) Number | K221305 | K193562 | K213976 |
| Indications forUse | AI-Rad CompanionOrgans RT is a post-processing softwareintended toautomatically contourDICOM CT imagingdata using deep-learning-basedalgorithms.Contours that aregenerated by AI-RadCompanion Organs RTmay be used as inputfor clinical workflowsincluding externalbeam radiation therapytreatment planning.AI-Rad CompanionOrgans RT must beused in conjunctionwith appropriate | AI-Rad CompanionOrgans RT is a post-processing softwareintended toautomatically contourDICOM CT imagingdata using deep-learning-basedalgorithms.Contours that aregenerated by AI-RadCompanion Organs RTmay be used as inputfor clinical workflowsincluding externalbeam radiation therapytreatment planning. AI-Rad CompanionOrgans RT must beused in conjunctionwith appropriate | Trained medicalprofessionals useContour ProtégéAI as atool to assist in theautomated processingof digital medicalimages of modalitiesCT and MR, assupported byACR/NEMA DICOM3.0. In addition,Contour ProtégéAIsupports the followingindications:• Creation ofcontours usingmachine-learningalgorithms forapplicationsincluding, but notlimited to, |
| software such asTreatment PlanningSystems andInteractive Contouringapplications, to review, | software such asTreatment PlanningSystems andInteractive Contouringapplications, to review, | quantitativeanalysis, aidingadaptive therapy,transferringcontour to radiation | |
| edit, and acceptcontours generated byAI-Rad CompanionOrgans RT.The output of AI-RadCompanion Organs RTin the format ofRTSTRUCT objectsare intended to be usedby trained medicalprofessionals.The software is notintended toautomatically detect orcontour lesions. OnlyDICOM images ofadult patients areconsidered to be validinput. | edit, and acceptcontours generated byAI-Rad CompanionOrgans RT.The output of AI-RadCompanion Organs RTin the format ofRTSTRUCT objectsare intended to be usedby trained medicalprofessionals.The software is notintended toautomatically detect orcontour lesions. OnlyDICOM images ofadult patients areconsidered to be validinput. | therapy treatmentplanning systems,and archivingcontours for patientfollow-up andmanagement.• Segmenting normalstructures across avariety of CTanatomicallocations• And segmentingnormal structuresof the prostate,seminal vesicles,and urethra withinT2-weighted MRimages.Appropriate imagevisualization softwaremust be used to reviewand, if necessary, editresults automaticallygenerated by ContourProtégéAI. | |
| Algorithm | Deep Learning | Deep Learning | Machine-learning |
| Segmentation ofOrgan at Risk inthe AnatomicRegions | Head & Neck, Thorax,Abdomen & PelvisHead & Neck lymphnodes(108 OAR) | Head & Neck, Thorax,Abdomen & Pelvis(79 OAR) | Head & Neck,Prostate, Thorax,Abdomen, Lungs &Liver, MRT structures(spleen, pelvic lymphnodes, descendingaorta, bone) |
| CompatibleModality | CT Images | CT Images | CT & MR |
| CompatibleScanner Models | No Limitation onscanner model,DICOM compliancerequired. | No Limitation onscanner model,DICOM compliancerequired. | No informationpublicly available |
| CompatibleTreatmentPlanning System | No Limitation on TPSmodel, DICOMcompliance required. | No Limitation on TPSmodel, DICOMcompliance required. | No informationpublicly available |
| Contraindications | Adult use only | Adult use only | Adult use only |
| TargetPopulation | AI-Rad CompanionOrgans RT is designedfor use only in adultpopulations.AI-Rad CompanionOrgans RT is designedfor any patient forwhom relevantmodality scans areavailable. Morespecifically, thesoftware is validatedon previously acquiredCT DICOM volumesfor radiation therapytreatment planning,including, head andneck, thorax, abdomen,and pelvis. | AI-Rad CompanionOrgans RT is designedfor use only in adultpopulations.AI-Rad CompanionOrgans RT is designedfor any patient forwhom relevantmodality scans areavailable. Morespecifically, thesoftware is validatedon previously acquiredCT DICOM volumesfor radiation therapytreatment planning,including, head andneck, thorax, abdomen,and pelvis. | No informationpublicly available |
| Clinicalcondition thedevice isintended todiagnose, treat ormanage | Limited to patientspreviously selected forRadiation Therapy. | Limited to patientspreviously selected forRadiation Therapy. | No informationpublicly available |
| SoftwareArchitecture | AI-Rad Companion(Engine) architectureenabling thedeployment of AI RadCompanion Organs RTusing Edge and in theCloud. The UI isprovided using a web-based interface. | AI-Rad Companion(Engine) architectureenabling thedeployment of AI RadCompanion Organs RTin the Cloud. The UI isprovided using a web-based interface. | Server-basedapplication supportingLinux-based OS andLocal deployment onWindows or Mac |
| DeploymentFeature | Edge & CloudDeployment | Cloud Deployment | Cloud-based or locallydeployed |
| Organ Templates | Creating, editing anddeletion of organtemplates. Customize | Creating, editing anddeletion of organtemplates. Customize | No informationpublicly available |
| predefined structuredatabase with mappingto internationalnomenclature schemes. | predefined structuredatabase with mappingto internationalnomenclature schemes. | K221301 | |
| Automatedworkflow | AI-Rad CompanionOrgans RTautomaticallyprocesses input imagedata and sends theresults as DICOM-RTStructure Sets to auser-configurabletarget node. | AI-Rad CompanionOrgans RTautomaticallyprocesses input imagedata and sends theresults as DICOM-RTStructure Sets to auser-configurabletarget node. | Automatic contouringworking usingmachine-learning |
| Contourvisualization andediting feature | AI-Rad CompanionOrgans RT providesbasic result preview ofautomaticsegmentation results,and no editing featureof the automaticsegmented contour. | AI-Rad CompanionOrgans RT providesbasic result preview ofautomaticsegmentation results,and no editing featureof the automaticsegmented contour. | No informationpublicly available |
| SegmentationPerformance | The target performancewas validated using113 cases distributedto two cohorts. CohortA is clinical routinetreatment planning CTand it is split into twosub-cohort and CohortB is PET-CT data. Toobjectively evaluatethe target performance,the DICE coefficient,the absolute symmetricsurface distance(ASSD) and the failrate was evaluated.The segmentationperformance of thesubject and referencedevice were equivalentas well as the overallperformance comparedto the predicate device. | The target performancewas validated using113 cases distributed totwo cohorts. CohortA-Clinical RoutineTreatment PlanningCT (Siemens; Headand Neck, Thorax andAbdomen Pelvis) andCohort B-MultiVendor Coverage (GEand Phillips; Head andNeck).To objectively evaluatethe target performance,the DICE coefficient,the absolute symmetricsurface distance(ASSD) and the failrate was evaluated.The segmentationperformance of thesubject and reference | 739 CT Images from12 clinical sites wereused for testing. Themean and standarddeviation Dicecoefficients, along withthe lower 95thpercentile confidencebound were calculated. |
| device were equivalentas well as the overallperformance comparedto the predicate device. | |||
| User Interface –Results Preview(Confirmation) | Basic visualizationfunctionality oforiginal data andgenerated contours | Basic visualizationfunctionality oforiginal data andgenerated contours | No informationpublicly available |
| User InterfaceConfiguration | Configuration UI | Configuration UI | No informationpublicly available |
| AutomatedWorkflow to TPS | Results send toConfirmation UI &Optional bypassing ofConfirmation UI toTPS | Results send toConfirmation UI &Optional bypassing ofConfirmation UI toTPS | No informationpublicly available |
| Human Factors | Design to be used bytrained clinicians. | Design to be used bytrained clinicians. | Designed to be used bytrained clinicians |
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Table 1: Indications for Use and Segmentation Feature Comparison
The conclusions from all verification and validation data suggests that these enhancements are equivalent with respect to safety and effectiveness of the predicate device. These modifications do not change the intended use of the product. Siemens is of opinion that AI-Rad Companion Organs RT VA40 is substantially equivalent to the currently marketed device, AI-Rad Companion Organs RT VA20 (K193562).
9. Nonclinical Tests
Non-clinical tests were conducted to test the functionality of AI-Rad Companion Organs RT. Software validation and bench testing have been conducted to assess the performance claims as well as the claim of substantial equivalence to the predicate device. Non-clinical performance testing demonstrates that AI-Rad Companion Organs RT complies with appropriate FDA guidance documents as well as with the following voluntary FDA recognized Consensus Standards (Table 2).
| RecognitionNumber | ProductArea | Title of Standard | ReferenceNumber andDate | StandardsDevelopmentOrganization |
|---|---|---|---|---|
| 5-114 | General | Medical Devices – Applicationof usability engineering tomedical devices [includingCorrigendum 1 (2016)] | 62366-1: 2015-02 | IEC |
| 5-125 | General | Medical Devices – applicationof risk management tomedical devices | 14971:2007 | ISO |
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| 13-79 | Software/Informatics | Medical device software –software life cycle processes[Including Amendment 1(2016)] | 62304:2006/A1:2016 | AAMIANSIIEC |
|---|---|---|---|---|
| 12-300 | Radiology | Digital Imaging andCommunications in Medicine(DICOM) Set | PS 3.1 – 3.20(2016) | NEMA |
| 12-261 | Radiology | Information Technology –Digital Compression andcoding of continuous -tonestill images: Requirementsand Guidelines [including:Technical Corrigendum1(2005)] | 10918-1 1994-02-15 | ISOIEC |
| 5-134 | General | Medical devices – symbols tobe used with information tobe supplied by themanufacturer – Part 1:General Requirements | 15223-1Fourth edition2021-07 | ISOIEC |
| 13-97 | Software/Informatics | Health software – Part 1:General requirements forproduct safety | 82304-1Edition 1.02016-10 | IEC |
Table 2: List of recognized standards
Verification and Validation
Software documentation for a Major Level of Concern software, per FDA's Guidance Document "Guidance for the Content of Premarket Submissions for Software Contained in Medical Devices" issued on May 11, 2005, is also included as part of this submission. The performance data demonstrates continued conformance with special controls for medical devices containing software. Non-clinical tests were conducted on the subject device during product development.
Software bench testing in the form of Unit, System and Integration tests were performed to evaluate the performance and functionality of the new features and software updates. All testable requirements in the Requirement Specifications and the Risk Analysis have been successfully verified and traced in accordance with the Siemens Healthineers DH product development process. Human factor usability validation is addressed in system testing and usability validation test records. Software verification and regression testing have been performed successfully to meet their previously determined acceptance criteria as stated in the test plans.
Siemens Healthineers adheres to the cybersecurity recommendations as defined the FDA Guidance "Content of Premarket Submissions for Management for Cybersecurity in Medical Devices," issued October 2, 2014 by implementing a process of preventing unauthorized access, modifications, misuse or denial of use, or the unauthorized use of information that is stored, accessed, or transferred from a medical device to an external recipient.
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10. Performance Software Validation
To validate the AI-Rad Companion Organs RT software from clinical perspective, the autocontouring algorithm underwent a scientific evaluation. The results of clinical data-based software validation for the subject device AI-Rad Companion Organs RT (SW VA40) demonstrated equivalent performance in comparison to the predicate device (SW VA20, K193562). The performance of the head & neck lymph node contouring algorithm is comparable to the reference device, Contour ProtégéAI (MIM Software Inc., K213976). A complete scientific evaluation report is provided in support of the device modifications.
The performance of the AI-Rad Companion Organs RT has been validated in a retrospective performance study on CT data previously acquired for RT treatment planning (N= 113, data from multiple clinical sites across the North American and Europe). Ground truth annotations were established following RTOG and clinical guidelines using manual annotation. The mean and standard deviation Dice coefficients, along with the lower 95th percentile confidence bound, were calculated for each organ in the subject device. The results of the subject device demonstrate comparable performance compared to the predicate device when aggregate performance over all organs is considered with known limitations described in the Labeling. As the morphological appearance of lymph nodes in the head and neck region and in the pelvic region are similar, we compared the OAR segmentation accuracy of head and neck lymph nodes of the subject device AIRC Organs RT (SW VA40) to the pelvic lymph nodes of the reference device Contour ProtégéAI (MIM Software Inc., K213976). For this evaluation dice coefficient was calculated by considering all head and neck lymph nodes as a single composite class and then aggregated over all patients.
The performance results of the subject device for new organs is comparable to the reference device. Here comparable is defined such that the lower bound of 95th percentile confidence interval of the subject device segmentation is greater than 0.1 Dice lower than the mean of predicate/reference device segmentation.
In a sub-cohort analysis performance results were found to be consistent on CT data across multiple vendors and for gender subgroups. The results of subject and predicate device for overlapping organs are shown in the following Table 4. The subject device achieved a median DICE score of 0.85 with a median ASSD of 0.93 in comparison to the predicate device achieving a median DICE score of 0.85 with a median ASSD of 0.94 for existing organs. As we can see, the performance of the subject device and predicate device are comparable in DICE and ASSD. The results of subject and reference device for non-overlapping organs are shown in the following Table 5. As we can see, the performance of the subject device for non-overlapping organs is comparable in DICE to the reference device.
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| Validation Testing Subject | Acceptance Criteria |
|---|---|
| Organs in Predicate Device | All the organs segmented in the predicate device are also segmented in the subject device The lower bound of 95th percentile CI of the segmentation is greater than 0.1 Dice lower than the mean of the predicate device segmentation |
| Head & Neck Lymph Nodes | The overall fail rate of each organ/anatomical structures is smaller than 15% The lower bound of 95th percentile CI of the segmentation is greater than 0.1 Dice lower than the mean of the reference device segmentation |
Table 3: Acceptance Criteria of AIRC Organs RT VA40
| DICE | ASSD | |||
|---|---|---|---|---|
| Median | 95% CI (Bootstrap) | Median | 95% CI (Bootstrap) | |
| AI-RadCompanionOrgans RT VA40 | 0.85 | [80.23,84.61] | 0.93 | [0.86,1.14] |
| AI-RadCompanionOrgans RT VA20 | 0.85 | N.A | 0.94 | [0.85,1.16] |
Table 4: Performance comparison between subject device and predicate device
| AI-Rad Companion Organs RT VA40(Head and Neck lymph node class) | Contour ProtégéAI from MIMSoftware Inc(Pelvic lymph node class) | |||||
|---|---|---|---|---|---|---|
| Sample Size: 60# of Datasites: 5 | Sample Size: 739# of Datasites: 12 | |||||
| Avg | Std | 95 % CI Bootstrap | Avg | Std | 95 % CI Bootstrap | |
| Dice [%] | 81.32 | 3.45 | [80.32,82.12] | 80 | 4 | [77,N.A.] |
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| ASSD[mm] | 1.06 | 0.38 | [0.99, 1.19] | N.A. | N.A. | N.A. |
|---|---|---|---|---|---|---|
| -------------- | ------ | ------ | -------------- | ------ | ------ | ------ |
Table 5: Performance comparison between subject device and reference device
| Cohort A | Cohort B | |
|---|---|---|
| # of Subject | 73 | 40 |
| # of Clinical Sites | 3(Germany: 14, Brazil: 59) | 4(Canada: 40) |
| Sex | Male: 25Female: 48 | Male: 19Female: 21 |
| Age | >40: 7Unknown: 66*unknown due to dataminimization on customer site | <30: 030 – 50: 350 – 70: 25>70: 12 |
| Manufacturer | Siemens: 73 | GE: 18Philips: 22 |
| Body Region | Head & Neck: 24Thorax: 19Abdomen Pelvis: 30 | Head & Neck: 40 |
| Slice Thickness | <1 to > 3 | <1 to >3 |
Table 6: Validation Data Information
| # of Datasets | 160 |
|---|---|
| Data Origin | Stanford (US): 15 |
| NNord (DE): 4 | |
| UKH (DE): 25 | |
| HCG (IND): 116 | |
| Sex | Male: 12 |
| Female: 17 | |
| Unknown: 131 | |
| Age | <30 : 1 |
| 30 – 50: 3 | |
| 50 – 70: 2 | |
| >= 70: 3 | |
| Unknown: 152* | |
| *unknown due to data minimization on customer | |
| site | |
| Manufacturer | Siemens: 103 |
| GE: 50 | |
| Unknown: 7 | |
| Slice Thickness | <= 1: 1 |
| 1 – 2: 12 | |
| 2 – 3 : 141 | |
| >3: 6 |
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Table 7: Training Dataset Characteristics for Head & Neck
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Standard Annotation Process:
In both the annotation process for the training and validation testing data, the annotation protocols for the OAR were defined following the NRG/RTOG guidelines. The ground truth annotations were drawn manually by a team of experienced annotators mentored by radiologists or radiation oncologists using an internal annotation tool. Additionally, a quality assessment including review and correction of each annotation was done by a board-certified radiation oncologist using validated medical image annotation tools.
Validation Testing & Training Data Independence:
The training data used for the training of the algorithm is independent of the data used to test the algorithm.
11. Clinical Tests
No clinical tests were conducted to test the performance and functionality of the modifications introduced within AI-Rad Companion Organs RT. Verification and validation of the enhancements and improvements have been performed and these modifications have been validated for their intended use. The data from these activities were used to support the subject device and the substantial equivalence argument. No animal testing has been performed on the subject device.
12. Safety and Effectiveness
The device labeling contains instructions for use and any necessary cautions and warnings to ensure safe and effective use of the device.
Risk management is ensured via ISO 14971:2019 compliance to identify and provide mitigation of potential hazards in a risk analysis early in the design phase and continuously throughout the development of the product. These risks are controlled via measures realized during software development, testing and product labeling.
13. Conclusion
Based on the discussion and validation testing and performance data above, the proposed device is determined to be as safe and effective as its predicate device, AI-Rad Companion Organs RT VA20 (K193562). In addition, the proposed device performs comparably to the reference device, Contour ProtégéAI (MIM Software Inc., K213976).
§ 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).