(162 days)
Yes
The device description explicitly states that it uses "deep-learning-based algorithms" for automatic contouring, which is a form of machine learning.
No
This device is a post-processing software that automatically contours DICOM CT imaging data for use in clinical workflows like external beam radiation therapy treatment planning. It aids in planning but does not directly treat or diagnose.
No
This device is post-processing software that automatically contours DICOM CT imaging data for use in clinical workflows like radiation therapy treatment planning. It explicitly states it is "not intended to automatically detect or contour lesions," which would be a diagnostic function. Its purpose is to facilitate treatment planning, not to provide a diagnosis.
Yes
The device is explicitly described as "post-processing software" and its functionality is limited to processing existing DICOM CT data and producing RTSTRUCT outputs. There is no mention of any associated hardware components included with the device.
Based on the provided information, this device is not an IVD (In Vitro Diagnostic).
Here's why:
- IVD Definition: In Vitro Diagnostics are medical devices used to perform tests on samples taken from the human body (like blood, urine, tissue) to provide information about a person's health.
- Device Function: The AI-Rad Companion Organs RT software processes medical images (CT scans) to automatically contour organs. It does not analyze biological samples.
- Intended Use: The intended use is to provide contours for clinical workflows like radiation therapy treatment planning. This is a post-processing step for image data, not a diagnostic test performed on a biological sample.
Therefore, while it is a medical device used in a clinical setting, it does not fit the definition of an In Vitro Diagnostic.
No
The letter does not explicitly state that the FDA has reviewed and approved or cleared a PCCP for this specific device.
Intended Use / 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.
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.
Product codes (comma separated list FDA assigned to the subject device)
QKB
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
Mentions image processing
Yes
Mentions AI, DNN, or ML
Yes
Input Imaging Modality
CT Images
Anatomical Site
Head & Neck, Thorax, Abdomen & Pelvis, Head & Neck lymph nodes
Indicated Patient Age Range
Adult use only
Intended User / Care Setting
Trained medical professionals / Limited to patients previously selected for Radiation Therapy.
Description of the training set, sample size, data source, and annotation protocol
The training data characteristics for Head & Neck are:
-
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: = 70: 3, Unknown: 152* (*unknown due to data minimization on customer site)
- Manufacturer: Siemens: 103, GE: 50, Unknown: 7
- Slice Thickness: 3: 6
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.
Description of the test set, sample size, data source, and annotation protocol
The AI-Rad Companion Organs RT software was validated 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.
Validation Testing Data Information:
Cohort A:
-
of Subject: 73
-
of Clinical Sites: 3 (Germany: 14, Brazil: 59)
- Sex: Male: 25, Female: 48
- Age: >40: 7, Unknown: 66 (*unknown due to data minimization on customer site)
- Manufacturer: Siemens: 73
- Body Region: Head & Neck: 24, Thorax: 19, Abdomen Pelvis: 30
- Slice Thickness: 3
Cohort B:
-
of Subject: 40
-
of Clinical Sites: 4 (Canada: 40)
- Sex: Male: 19, Female: 21
- Age: 70: 12
- Manufacturer: GE: 18, Philips: 22
- Body Region: Head & Neck: 40
- Slice Thickness: 3
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.
Summary of Performance Studies (study type, sample size, AUC, MRMC, standalone performance, key results)
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).
Performance Study Type: Retrospective performance study on CT data previously acquired for RT treatment planning.
Sample Size: N=113 cases (data from multiple clinical sites across the North American and Europe).
Key Results:
- The segmentation performance of the subject and reference device were equivalent as well as the overall performance compared to the predicate device.
- For overlapping organs (subject vs predicate): The subject device achieved a median DICE score of 0.85 with a median ASSD of 0.93. The predicate device achieved a median DICE score of 0.85 with a median ASSD of 0.94. The performance is comparable.
- For non-overlapping organs (subject vs reference for head & neck lymph node class vs pelvic lymph node class):
- Subject device (Head and Neck lymph node class): Sample Size: 60, # of Datasites: 5. Avg Dice [%]: 81.32, Std: 3.45, 95% CI Bootstrap: [80.32,82.12]. ASSD [mm]: 1.06, Std: 0.38, 95% CI: [0.99, 1.19].
- Reference device (Pelvic lymph node class): Sample Size: 739, # of Datasites: 12. Avg Dice [%]: 80, Std: 4, 95% CI Bootstrap: [77,N.A.]. ASSD [mm]: N.A., Std: N.A., 95% CI: N.A.
- The performance of the subject device for non-overlapping organs is comparable in DICE to the reference device, defined as 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.
Key Metrics (Sensitivity, Specificity, PPV, NPV, etc.)
DICE coefficient, Absolute Symmetric Surface Distance (ASSD), Fail Rate, 95th percentile confidence bound.
Predicate Device(s): If the device was cleared using the 510(k) pathway, identify the Predicate Device(s) K/DEN number used to claim substantial equivalence and list them here in a comma separated list exactly as they appear in the text. List the primary predicate first in the list.
Reference Device(s): Identify the Reference Device(s) K/DEN number and list them here in a comma separated list exactly as they appear in the text.
Predetermined Change Control Plan (PCCP) - All Relevant Information for the subject device only (e.g. presence / absence, what scope was granted / cleared under the PCCP, any restrictions, etc).
Not Found
§ 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).
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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
1
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
2
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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Image /page/3/Picture/0 description: The image shows the Siemens Healthineers logo. The word "SIEMENS" is in teal, and the word "Healthineers" is in orange. To the right of the words is a graphic of orange dots arranged in a triangular shape.
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 Boulevard
Malvern, PA 19355
Mail Code: 65-3
Registration Number: 2240869 |
|----------------------|-------------------------------------------------------------------------------------------------------------------------------------|
| Manufacturing Site | Siemens Healthcare GmbH
Henkestrasse 127
Erlangen, Germany 91052
Registration 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
4
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
5
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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Image /page/6/Picture/0 description: The image contains the logo for Siemens Healthineers. The word "SIEMENS" is in teal, and the word "Healthineers" is in orange. To the right of the text is a graphic of orange dots.
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 | |
|---|---|---|---|
| Device | |||
| Manufacturer | Siemens | Siemens | MIM Software Inc. |
| Device Name | AI-Rad Companion | ||
| Organs RT | |||
| (SW Version VA40) | AI-Rad Companion | ||
| Organs RT | |||
| (SW Version VA20) | Contour ProtégéAI | ||
| 510(k) Number | K221305 | K193562 | K213976 |
| 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 | 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 | Trained medical | ||
| professionals use | |||
| Contour ProtégéAI as a | |||
| tool to assist in the | |||
| automated processing | |||
| of digital medical | |||
| images of modalities | |||
| CT and MR, as | |||
| supported by | |||
| ACR/NEMA DICOM | |||
| 3.0. In addition, | |||
| Contour ProtégéAI | |||
| supports the following | |||
| indications: | |||
| • Creation of | |||
| contours using | |||
| machine-learning | |||
| algorithms for | |||
| applications | |||
| including, but not | |||
| limited to, | |||
| software such as | |||
| Treatment Planning | |||
| Systems and | |||
| Interactive Contouring | |||
| applications, to review, | software such as | ||
| Treatment Planning | |||
| Systems and | |||
| Interactive Contouring | |||
| applications, to review, | quantitative | ||
| analysis, aiding | |||
| adaptive therapy, | |||
| transferring | |||
| contour to radiation | |||
| 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. | 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. | therapy treatment | ||
| planning systems, | |||
| and archiving | |||
| contours for patient | |||
| follow-up and | |||
| management. | |||
| • Segmenting normal | |||
| structures across a | |||
| variety of CT | |||
| anatomical | |||
| locations | |||
| • And segmenting | |||
| normal structures | |||
| of the prostate, | |||
| seminal vesicles, | |||
| and urethra within | |||
| T2-weighted MR | |||
| images. | |||
| Appropriate image | |||
| visualization software | |||
| must be used to review | |||
| and, if necessary, edit | |||
| results automatically | |||
| generated by Contour | |||
| ProtégéAI. | |||
| Algorithm | Deep Learning | Deep Learning | Machine-learning |
| Segmentation of | |||
| Organ at Risk in | |||
| the Anatomic | |||
| Regions | Head & Neck, Thorax, | ||
| Abdomen & Pelvis | |||
| Head & Neck lymph | |||
| nodes | |||
| (108 OAR) | Head & Neck, Thorax, | ||
| Abdomen & Pelvis | |||
| (79 OAR) | Head & Neck, | ||
| Prostate, Thorax, | |||
| Abdomen, Lungs & | |||
| Liver, MRT structures | |||
| (spleen, pelvic lymph | |||
| nodes, descending | |||
| aorta, bone) | |||
| Compatible | |||
| Modality | CT Images | CT Images | CT & MR |
| Compatible | |||
| Scanner Models | No Limitation on | ||
| scanner model, | |||
| DICOM compliance | |||
| required. | No Limitation on | ||
| scanner model, | |||
| DICOM compliance | |||
| required. | No information | ||
| publicly available | |||
| Compatible | |||
| Treatment | |||
| Planning System | No Limitation on TPS | ||
| model, DICOM | |||
| compliance required. | No Limitation on TPS | ||
| model, DICOM | |||
| compliance required. | No information | ||
| publicly available | |||
| Contraindications | Adult use only | Adult use only | Adult use only |
| Target | |||
| Population | AI-Rad Companion | ||
| Organs RT is designed | |||
| for use only in adult | |||
| populations. | |||
| AI-Rad Companion | |||
| Organs RT is designed | |||
| for any patient for | |||
| whom relevant | |||
| modality scans are | |||
| available. More | |||
| specifically, the | |||
| software is validated | |||
| on previously acquired | |||
| CT DICOM volumes | |||
| for radiation therapy | |||
| treatment planning, | |||
| including, head and | |||
| neck, thorax, abdomen, | |||
| and pelvis. | AI-Rad Companion | ||
| Organs RT is designed | |||
| for use only in adult | |||
| populations. | |||
| AI-Rad Companion | |||
| Organs RT is designed | |||
| for any patient for | |||
| whom relevant | |||
| modality scans are | |||
| available. More | |||
| specifically, the | |||
| software is validated | |||
| on previously acquired | |||
| CT DICOM volumes | |||
| for radiation therapy | |||
| treatment planning, | |||
| including, head and | |||
| neck, thorax, abdomen, | |||
| and pelvis. | No information | ||
| publicly available | |||
| Clinical | |||
| condition the | |||
| device is | |||
| intended to | |||
| diagnose, treat or | |||
| manage | Limited to patients | ||
| previously selected for | |||
| Radiation Therapy. | Limited to patients | ||
| previously selected for | |||
| Radiation Therapy. | No information | ||
| publicly available | |||
| Software | |||
| Architecture | AI-Rad Companion | ||
| (Engine) architecture | |||
| enabling the | |||
| deployment of AI Rad | |||
| Companion Organs RT | |||
| using Edge and in the | |||
| Cloud. The UI is | |||
| provided using a web- | |||
| based interface. | AI-Rad Companion | ||
| (Engine) architecture | |||
| enabling the | |||
| deployment of AI Rad | |||
| Companion Organs RT | |||
| in the Cloud. The UI is | |||
| provided using a web- | |||
| based interface. | Server-based | ||
| application supporting | |||
| Linux-based OS and | |||
| Local deployment on | |||
| Windows or Mac | |||
| Deployment | |||
| Feature | Edge & Cloud | ||
| Deployment | Cloud Deployment | Cloud-based or locally | |
| deployed | |||
| Organ Templates | Creating, editing and | ||
| deletion of organ | |||
| templates. Customize | Creating, editing and | ||
| deletion of organ | |||
| templates. Customize | No information | ||
| publicly available | |||
| predefined structure | |||
| database with mapping | |||
| to international | |||
| nomenclature schemes. | predefined structure | ||
| database with mapping | |||
| to international | |||
| nomenclature schemes. | K221301 | ||
| Automated | |||
| workflow | AI-Rad Companion | ||
| Organs RT | |||
| automatically | |||
| processes input image | |||
| data and sends the | |||
| results as DICOM-RT | |||
| Structure Sets to a | |||
| user-configurable | |||
| target node. | AI-Rad Companion | ||
| Organs RT | |||
| automatically | |||
| processes input image | |||
| data and sends the | |||
| results as DICOM-RT | |||
| Structure Sets to a | |||
| user-configurable | |||
| target node. | Automatic contouring | ||
| working using | |||
| machine-learning | |||
| Contour | |||
| visualization and | |||
| editing feature | AI-Rad Companion | ||
| Organs RT provides | |||
| basic result preview of | |||
| automatic | |||
| segmentation results, | |||
| and no editing feature | |||
| of the automatic | |||
| segmented contour. | AI-Rad Companion | ||
| Organs RT provides | |||
| basic result preview of | |||
| automatic | |||
| segmentation results, | |||
| and no editing feature | |||
| of the automatic | |||
| segmented contour. | No information | ||
| publicly available | |||
| Segmentation | |||
| Performance | The target performance | ||
| was validated using | |||
| 113 cases distributed | |||
| to two cohorts. Cohort | |||
| A is clinical routine | |||
| treatment planning CT | |||
| and it is split into two | |||
| sub-cohort and Cohort | |||
| B is PET-CT data. To | |||
| objectively evaluate | |||
| the target performance, | |||
| the DICE coefficient, | |||
| the absolute symmetric | |||
| surface distance | |||
| (ASSD) and the fail | |||
| rate was evaluated. | |||
| The segmentation | |||
| performance of the | |||
| subject and reference | |||
| device were equivalent | |||
| as well as the overall | |||
| performance compared | |||
| to the predicate device. | The target performance | ||
| was validated using | |||
| 113 cases distributed to | |||
| two cohorts. Cohort | |||
| A-Clinical Routine | |||
| Treatment Planning | |||
| CT (Siemens; Head | |||
| and Neck, Thorax and | |||
| Abdomen Pelvis) and | |||
| Cohort B-Multi | |||
| Vendor Coverage (GE | |||
| and Phillips; Head and | |||
| Neck). | |||
| To objectively evaluate | |||
| the target performance, | |||
| the DICE coefficient, | |||
| the absolute symmetric | |||
| surface distance | |||
| (ASSD) and the fail | |||
| rate was evaluated. | |||
| The segmentation | |||
| performance of the | |||
| subject and reference | 739 CT Images from | ||
| 12 clinical sites were | |||
| used for testing. The | |||
| mean and standard | |||
| deviation Dice | |||
| coefficients, along with | |||
| the lower 95th | |||
| percentile confidence | |||
| bound were calculated. | |||
| device were equivalent | |||
| as well as the overall | |||
| performance compared | |||
| to the predicate device. | |||
| User Interface – | |||
| Results Preview | |||
| (Confirmation) | Basic visualization | ||
| functionality of | |||
| original data and | |||
| generated contours | Basic visualization | ||
| functionality of | |||
| original data and | |||
| generated contours | No information | ||
| publicly available | |||
| User Interface | |||
| Configuration | Configuration UI | Configuration UI | No information |
| publicly available | |||
| Automated | |||
| Workflow to TPS | Results send to | ||
| Confirmation UI & | |||
| Optional bypassing of | |||
| Confirmation UI to | |||
| TPS | Results send to | ||
| Confirmation UI & | |||
| Optional bypassing of | |||
| Confirmation UI to | |||
| TPS | No information | ||
| publicly available | |||
| Human Factors | Design to be used by | ||
| trained clinicians. | Design to be used by | ||
| trained clinicians. | Designed to be used by | ||
| trained clinicians |
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Image /page/7/Picture/0 description: The image contains the logo for Siemens Healthineers. The word "SIEMENS" is in teal, and the word "Healthineers" is in orange. To the right of the words is a graphic of orange dots.
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Image /page/8/Picture/0 description: The image contains the logo for Siemens Healthineers. The word "SIEMENS" is in teal, and the word "Healthineers" is in orange. To the right of the words is a graphic of orange dots.
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Image /page/9/Picture/0 description: The image contains the logo for Siemens Healthineers. The word "SIEMENS" is in teal, and the word "Healthineers" is in orange. To the right of the words is a graphic of orange dots.
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Image /page/10/Picture/0 description: The image contains the logo for Siemens Healthineers. The word "SIEMENS" is written in teal, and the word "Healthineers" is written in orange below it. To the right of the words is a graphic of orange dots arranged in a circular pattern.
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).
| Recognition
Number | Product
Area | Title of Standard | Reference
Number and
Date | Standards
Development
Organization |
|-----------------------|-----------------|---------------------------------------------------------------------------------------------------------------------|---------------------------------|------------------------------------------|
| 5-114 | General | Medical Devices – Application
of usability engineering to
medical devices [including
Corrigendum 1 (2016)] | 62366-1: 2015-
02 | IEC |
| 5-125 | General | Medical Devices – application
of risk management to
medical devices | 14971:2007 | ISO |
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Image /page/11/Picture/0 description: The image shows the Siemens Healthineers logo. The word "SIEMENS" is in teal, and the word "Healthineers" is in orange. To the right of the words is a pattern of orange dots arranged in a circular shape.
| 13-79 | Software/
Informatics | Medical device software –
software life cycle processes
[Including Amendment 1
(2016)] | 62304:
2006/A1:2016 | AAMI
ANSI
IEC |
|--------|--------------------------|------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------|--------------------------------------|---------------------|
| 12-300 | Radiology | Digital Imaging and
Communications in Medicine
(DICOM) Set | PS 3.1 – 3.20
(2016) | NEMA |
| 12-261 | Radiology | Information Technology –
Digital Compression and
coding of continuous -tone
still images: Requirements
and Guidelines [including:
Technical Corrigendum
1(2005)] | 10918-1 1994-
02-15 | ISO
IEC |
| 5-134 | General | Medical devices – symbols to
be used with information to
be supplied by the
manufacturer – Part 1:
General Requirements | 15223-1
Fourth edition
2021-07 | ISO
IEC |
| 13-97 | Software/
Informatics | Health software – Part 1:
General requirements for
product safety | 82304-1
Edition 1.0
2016-10 | 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.
12
SIEME Healthineer
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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Image /page/13/Picture/0 description: The image shows the logo for Siemens Healthineers. The word "SIEMENS" is in teal, and the word "Healthineers" is in orange. To the right of the words is a graphic of orange dots arranged in a circular pattern.
| 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-Rad | ||||
| Companion | ||||
| Organs RT VA40 | 0.85 | [80.23,84.61] | 0.93 | [0.86,1.14] |
| AI-Rad | ||||
| Companion | ||||
| Organs 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 MIM
Software 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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Image /page/14/Picture/0 description: The image contains the logo for Siemens Healthineers. The word "SIEMENS" is written in teal, and the word "Healthineers" is written in orange below it. To the right of the words is a graphic of orange dots.
| 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: 25 | |
| Female: 48 | Male: 19 | |
| Female: 21 | ||
| Age | >40: 7 | |
| Unknown: 66 | ||
| *unknown due to data | ||
| minimization on customer site | 70: 12 | |
| Manufacturer | Siemens: 73 | GE: 18 |
| Philips: 22 | ||
| Body Region | Head & Neck: 24 | |
| Thorax: 19 | ||
| Abdomen Pelvis: 30 | Head & Neck: 40 | |
| Slice Thickness | 3 | 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 | = 70: 3 |
| Unknown: 152* | |
| *unknown due to data minimization on customer | |
| site | |
| Manufacturer | Siemens: 103 |
| GE: 50 | |
| Unknown: 7 | |
| Slice Thickness | 3: 6 |
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Image /page/15/Picture/0 description: The image contains the logo for Siemens Healthineers. The word "SIEMENS" is in teal, and the word "Healthineers" is in orange. To the right of the word "Healthineers" is a graphic of several orange dots arranged in a circular pattern.
Table 7: Training Dataset Characteristics for Head & Neck
16
Healthineer
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).