(197 days)
Yes
The intended use and device description explicitly state the use of "deep-learning-based algorithms" and "AI algorithms" for automatic contouring.
No
The device is described as post-processing software for contouring medical images to be used as input for clinical workflows, specifically external beam radiation therapy treatment planning. It is not intended for standalone diagnosis or clinical decision-making. Its function is to assist in planning, not to directly treat or diagnose.
No
The "Device Description" explicitly states, "AI-Rad Companion Organs RT is not intended to be used as a standalone diagnostic device and is not a clinical decision-making software." Its purpose is post-processing for radiation therapy planning, not diagnosis.
Yes
The device is described as "post-processing software" and its function is to automatically contour structures from existing medical images (CT and MR). It explicitly states it is not a standalone diagnostic device and must be used in conjunction with other software. There is no mention of any accompanying hardware component being part of the device itself.
Based on the provided information, this device is not an In Vitro Diagnostic (IVD).
Here's why:
- Intended Use: The intended use clearly states that the software is for "post-processing software intended to automatically contour DICOM CT and MR pre-defined structures using deep-learning-based algorithms." It is used as input for clinical workflows like radiation therapy treatment planning. It is explicitly stated that it is "not intended to automatically detect or contour lesions" and "not intended to be used as a standalone diagnostic device and is not a clinical decision-making software."
- Device Description: The description reinforces that the software provides "automatic segmentation of pre-defined structures such as Organs-at-risk (OAR) from CT or MR medical series, prior to dosimetry planning in radiation therapy." It also reiterates that it is "not intended to be used as a standalone diagnostic device and is not a clinical decision-making software."
- Nature of Input and Output: The input is medical imaging data (CT and MR), and the output is contour data in DICOMRTSTRUCT format. This is image processing and segmentation, not the analysis of biological samples (blood, urine, tissue, etc.) which is characteristic of IVDs.
- Role in Clinical Workflow: The software's role is to assist in the planning of radiation therapy by providing initial contours that are then reviewed, edited, and accepted by trained medical professionals. It is a tool for image processing and workflow efficiency, not for diagnosing a disease or condition based on in vitro analysis.
IVDs are devices used to examine specimens derived from the human body to provide information for the diagnosis, prevention, monitoring, treatment, or alleviation of disease. This device does not perform such analysis on biological specimens. Its function is to process medical images for treatment planning purposes.
No
The provided text does not explicitly state that the FDA has reviewed and approved or cleared a Predetermined Change Control Plan (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 and MR pre-defined structures 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 outputs of AI-Rad Companion Organs RT are intended to be used by trained medical professionals.
The software is not intended to automatically detect or contour lesions.
Product codes
OKB
Device Description
AI-Rad Companion Organs RT provides automatic segmentation of pre-defined structures such as Organs-at-risk (OAR) from CT or MR medical series, prior to dosimetry planning in radiation therapy. AI-Rad Companion Organs RT is not intended to be used as a standalone diagnostic device and is not a clinical decision-making software.
CT or MR series of images serve as input for AI-Rad Companion Organs RT and are acquired as part of a typical scanner acquisition. Once processed by the AI algorithms, generated contours in DICOMRTSTRUCT format are reviewed in a confirmation window, allowing clinical user to confirm or reject the contours before sending to the target system. Optionally, the user may select to directly transfer the contours to a configurable DICOM node (e.g., the Treatment Planning System (TPS), which is the standard location for the planning of radiation therapy).
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 the automatically generated contours. Then the output of AI-Rad Companion Organs RT must be reviewed and, where necessary, edited with appropriate software before accepting generated contours as input to treatment planning steps. The output of AI-Rad Companion Organs RT is intended to be used by qualified medical professionals, who can perform a complementary manual editing of the contours or add any new contours in the TPS (or any other interactive contouring application supporting DICOM-RT objects) as part of the routine clinical workflow.
Mentions image processing
Yes
Mentions AI, DNN, or ML
Yes
Input Imaging Modality
CT, MR
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 / Not Found
Description of the training set, sample size, data source, and annotation protocol
The training data used for the training of the algorithm is independent of the data used to test the algorithm.
Organ Group (No. of Training):
Lacrimal Glands Left (247)
Lacrimal Glands Right (Not Found)
Pituitary Gland (247)
Humeral Head Left (207)
Humeral Head Right (Not Found)
Bowel Bag (544)
Pelvic Bone Left (160)
Pelvic Bone Right (Not Found)
Sacrum (160)
Mediastinal LN I Left (Not Found)
Mediastinal LN 1 Right (136)
Mediastinal LN II Left (Not Found)
Mediastinal LN II Right (Not Found)
Mediastinal LN III Anterior (Not Found)
Mediastinal LN III Posterior (Not Found)
Mediastinal LN IV Left (Not Found)
Mediastinal LN IV Right (Not Found)
Mediastinal LN V (Not Found)
Mediastinal LN VI (Not Found)
Mediastinal LN VII (Not Found)
Mediastinal LN VIII (Not Found)
Mediastinal LN IX Left (Not Found)
Mediastinal LN IX Right (Not Found)
Mediastinal LN X Left (Not Found)
Mediastinal LN X Right (Not Found)
Femoral Head Left (160)
Femoral Head Right (Not Found)
Brainstem (247)
Esophagus (247)
Breast Left (172)
Breast Right (Not Found)
Supraglottic Larynx (247)
Glottis (Not Found)
Annotation protocol: In both the annotation process for the training and validation testing data, the annotation protocols for the OAR were defined following the applicable 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
Validation Testing Data Information for new OARs:
Patients: 244
(Data origin): South/North America: 163, EU: 70, Asia: 6, Australia: 3, Unknown: 2
Gender: F: 111, M: 100, Unknown: 33
Manufacturer: GE: 29, Philips: 33, Siemens: 158, Other/Unknown: 24
Slice Thickness: 3: 4
Validation Testing Data Information based on Cohort:
Cohort A.1:
Patients: 73
Of clinical sites (Data origin): 3 (Germany: 14, Brazil: 59)
Body Region: Head & Neck: 24, Thorax & Abdomen: 20, Pelvis: 29
Cohort A.2:
Patients: 40
Of clinical sites (Data origin): 4 (Canada: 40)
Body Region: Head & Neck: 40
Cohort A.3:
Patients: 301
Of clinical sites (Data origin): 12+ (South/North America: 184, EU: 44, Asia: 33, Australia: 28, Unknown: 12)
Body Region: Head & Neck: 50, Thorax: 81, Abdomen: 115, Pelvis: 55
Cohort B:
Patients: 165
Of clinical sites (Data origin): 20+ (South/North America: 100, EU: 51, Asia: 6, Australia: 3, Unknown: 5)
Body Region: Head & Neck: 40, Thorax: 69, Abdomen: 25, Pelvis: 31
Organ Group (No. of Validation):
Lacrimal Glands Left (62)
Lacrimal Glands Right (Not Found)
Pituitary Gland (62)
Humeral Head Left (52)
Humeral Head Right (Not Found)
Bowel Bag (25)
Pelvic Bone Left (40)
Pelvic Bone Right (Not Found)
Sacrum (40)
Mediastinal LN I Left (Not Found)
Mediastinal LN 1 Right (34)
Mediastinal LN II Left (Not Found)
Mediastinal LN II Right (Not Found)
Mediastinal LN III Anterior (Not Found)
Mediastinal LN III Posterior (Not Found)
Mediastinal LN IV Left (Not Found)
Mediastinal LN IV Right (Not Found)
Mediastinal LN V (Not Found)
Mediastinal LN VI (Not Found)
Mediastinal LN VII (Not Found)
Mediastinal LN VIII (Not Found)
Mediastinal LN IX Left (Not Found)
Mediastinal LN IX Right (Not Found)
Mediastinal LN X Left (Not Found)
Mediastinal LN X Right (Not Found)
Femoral Head Left (40)
Femoral Head Right (Not Found)
Brainstem (62)
Esophagus (62)
Breast Left (44)
Breast Right (Not Found)
Supraglottic Larynx (62)
Glottis (Not Found)
Annotation protocol: In both the annotation process for the training and validation testing data, the annotation protocols for the OAR were defined following the applicable 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.
Summary of Performance Studies (study type, sample size, AUC, MRMC, standalone performance, key results)
Study type: Retrospective performance study.
Sample size: N=579 (CT data).
Key results: The results of subject device were equivalent or had better performance than the predicate device. The performance results of the subject device for the new CT organs are comparable to the reference literature & cleared devices. For existing organs, the average (AVG) Dice score difference between the subject device and predicate device is smaller than 3%.
MR Contouring algorithm: Identical to the predicate device AI-Rad Companion Organs RT VA50 (K232899).
Key Metrics (Sensitivity, Specificity, PPV, NPV, etc.)
Dice (%) for Head & Neck: Avg 76.1, Std 14.3, 95% CI [75.1, 77.2]
Dice (%) for Head & Neck lymph nodes: Avg 69.3, Std 13.9, 95% CI [68.7, 70.0]
Dice (%) for Thorax: Avg 76.9, Std 15.8, 95% CI [76.2, 77.6]
Dice (%) for Abdomen: Avg 87.3, Std 10.1, 95% CI [86.3, 88.2]
Dice (%) for Pelvis: Avg 85.7, Std 9.6, 95% CI [85.0, 86.5]
Dice (%) for Cardiac: Avg 75.6, Std 15.1, 95% CI [74.1, 77.1]
Detailed Performance evaluation of the new organs in the subject device, with Dice (%) and ASSD (mm) metrics including AVG, STD, MED, and 95% CI:
Left Breast: Dice AVG 90.4, STD 3.8, MED 91, 95%CI [89, 91.8]; ASSD AVG 2.4, STD 2.2, MED 1.8, 95%CI [1.5, 3.2]
Right Breast: Dice AVG 90.2, STD 3.7, MED 90.8, 95%CI [88.8, 91.5]; ASSD AVG 1.9, STD 0.7, MED 1.8, 95%CI [1.7, 2.2]
Bowel Bag: Dice AVG 95, STD 3.6, MED 96.5, 95%CI [93.7, 96.3]; ASSD AVG 1.9, STD 1.5, MED 1.4, 95%CI [1.4, 2.5]
Left Pelvic Bone: Dice AVG 93.4, STD 1.6, MED 93.8, 95%CI [92.8, 94]; ASSD AVG 0.6, STD 0.2, MED 0.5, 95%CI [0.5, 0.6]
Right Pelvic Bone: Dice AVG 93.9, STD 1.3, MED 94, 95%CI [93.4, 94.4]; ASSD AVG 0.5, STD 0.1, MED 0.5, 95%CI [0.4, 0.6]
Pituitary: Dice AVG 75.8, STD 7.4, MED 77, 95%CI [73.1, 78.6]; ASSD AVG 0.7, STD 0.3, MED 0.6, 95%CI [0.5, 0.8]
Sacrum: Dice AVG 90.7, STD 4, MED 91.9, 95%CI [89.2, 92.2]; ASSD AVG 0.8, STD 0.6, MED 0.6, 95%CI [0.5, 1]
Brainstem: Dice AVG 88.4, STD 2.5, MED 88.8, 95%CI [87.5, 89.3]; ASSD AVG 1, STD 0.3, MED 0.9, 95%CI [0.9, 1.1]
Glottis: Dice AVG 68, STD 8.7, MED 70.9, 95%CI [64.8, 71.3]; ASSD AVG 1.2, STD 0.4, MED 1.1, 95%CI [1.1, 1.4]
Supraglottic Larynx: Dice AVG 80, STD 5.9, MED 81, 95%CI [77.8, 82.20]; ASSD AVG 0.8, STD 0.3, MED 0.7, 95%CI [0.7, 0.9]
Esophagus: Dice AVG 85.6, STD 4.2, MED 86, 95%CI [84, 87.2]; ASSD AVG 0.6, STD 0.3, MED 0.6, 95%CI [0.5, 0.7]
Left Lacrimal Gland: Dice AVG 72.1, STD 7.3, MED 71.3, 95%CI [69.4, 74.9]; ASSD AVG 0.8, STD 0.3, MED 0.8, 95%CI [0.7, 0.9]
Right Lacrimal Gland: Dice AVG 69.9, STD 12.4, MED 73.6, 95%CI [65.3, 74.6]; ASSD AVG 0.9, STD 0.7, MED 0.8, 95%CI [0.7, 1.2]
Left Femur Head: Dice AVG 95.2, STD 1.1, MED 95.2, 95%CI [94.8, 95.6]; ASSD AVG 0.5, STD 0.2, MED 0.5, 95%CI [0.5, 0.6]
Right Femur Head: Dice AVG 94.9, STD 1.3, MED 95.1, 95%CI [94.5, 95.4]; ASSD AVG 0.6, STD 0.2, MED 0.5, 95%CI [0.5, 0.6]
Left Humeral Head: Dice AVG 94.2, STD 2.1, MED 94.8, 95%CI [93.4, 94.9]; ASSD AVG 0.7, STD 0.3, MED 0.6, 95%CI [0.6, 0.8]
Right Humeral Head: Dice AVG 94.6, STD 3.2, MED 95.4, 95%CI [93.4, 95.8]; ASSD AVG 0.7, STD 0.5, MED 0.5, 95%CI [0.5, 0.9]
MEDIASTINAL LN 10L: Dice AVG 52.9, STD 9.7, MED 55.1, 95%CI [49.4, 56.5]; ASSD AVG 1.2, STD 0.6, MED 1.1, 95%CI [1, 1.5]
MEDIASTINAL LN 10R: Dice AVG 50.2, STD 9.5, MED 50.6, 95%CI [46.7, 53.7]; ASSD AVG 1.4, STD 0.6, MED 1.2, 95%CI [1.1, 1.6]
MEDIASTINAL LN 1L: Dice AVG 70.7, STD 10, MED 73, 95%CI [67, 74.4]; ASSD AVG 2.4, STD 1.2, MED 1.9, 95%CI [1.9, 2.8]
MEDIASTINAL LN 1R: Dice AVG 66, STD 12.8, MED 67.9, 95%CI [61.3, 70.7]; ASSD AVG 2.4, STD 0.9, MED 2.3, 95%CI [2.1, 2.8]
MEDIASTINAL LN 2L: Dice AVG 67.6, STD 7.6, MED 68.6, 95%CI [64.8, 70.5]; ASSD AVG 1.4, STD 0.4, MED 1.3, 95%CI [1.2, 1.5]
MEDIASTINAL LN 2R: Dice AVG 55.5, STD 13.6, MED 56.9, 95%CI [50.6, 60.5]; ASSD AVG 2.3, STD 1.5, MED 2, 95%CI [1.7, 2.8]
MEDIASTINAL LN 3A: Dice AVG 75.6, STD 6.3, MED 77.6, 95%CI [73.3, 77.9]; ASSD AVG 1.6, STD 0.4, MED 1.5, 95%CI [1.5, 1.8]
MEDIASTINAL LN 3P: Dice AVG 65.9, STD 6, MED 66.5, 95%CI [63.7, 68.1]; ASSD AVG 1.6, STD 0.6, MED 1.5, 95%CI [1.4, 1.8]
MEDIASTINAL LN 4L: Dice AVG 65.7, STD 8.4, MED 67.2, 95%CI [62.6, 68.8]; ASSD AVG 1.4, STD 0.7, MED 1.1, 95%CI [1.1, 1.7]
MEDIASTINAL LN 4R: Dice AVG 72.8, STD 8, MED 74.6, 95%CI [69.9, 75.7]; ASSD AVG 1.7, STD 0.7, MED 1.6, 95%CI [1.4, 1.9]
MEDIASTINAL LN 5: Dice AVG 61, STD 14.8, MED 65.7, 95%CI [55.5, 66.4]; ASSD AVG 1.8, STD 0.8, MED 1.6, 95%CI [1.5, 2.1]
MEDIASTINAL LN 6: Dice AVG 58.8, STD 14.8, MED 62.5, 95%CI [53.4, 64.2]; ASSD AVG 2, STD 1.1, MED 1.6, 95%CI [1.6, 2.4]
MEDIASTINAL LN 7: Dice AVG 59, STD 7.6, MED 61.3, 95%CI [56.2, 61.8]; ASSD AVG 1.9, STD 0.8, MED 1.7, 95%CI [1.6, 2.2]
MEDIASTINAL LN 8: Dice AVG 65.8, STD 8, MED 67.9, 95%CI [62.9, 68.8]; ASSD AVG 2, STD 0.9, MED 1.7, 95%CI [1.7, 2.4]
MEDIASTINAL LN 9L: Dice AVG 38.3, STD 21.1, MED 42.9, 95%CI [30.6, 46.1]; ASSD AVG 5.3, STD 4.4, MED 3.7, 95%CI [3.7, 6.9]
MEDIASTINAL LN 9R: Dice AVG 38.8, STD 15.6, MED 38.5, 95%CI [32.9, 44.7]; ASSD AVG 3.7, STD 2, MED 3.5, 95%CI [2.9, 4.4]
Predicate Device(s)
Reference Device(s)
Predetermined Change Control Plan (PCCP) - All Relevant Information
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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March 27, 2025
Siemens Healthcare GmbH Kira Morales Regulatory Affairs Manager Henkestrasse 127 Erlangen, 91052 Germany
Re: K242745
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: OKB Dated: September 9, 2024 Received: September 11, 2024
Dear Kira Morales:
We have reviewed your section 510(k) premarket notification of intent to market the device referenced above and have determined the device is substantially equivalent (for the indications for use stated in the enclosure) to legally marketed predicate devices marketed in interstate commerce prior to May 28, 1976, the enactment date of the Medical Device Amendments, or to devices that have been reclassified in accordance with the provisions of the Federal Food, Drug, and Cosmetic Act (the Act) that do not require approval of a premarket approval application (PMA). You may, therefore, market the device, subject to the general controls provisions of the Act. Although this letter refers to your product as a device, please be aware that some cleared products may instead be combination products. The 510(k) Premarket Notification Database available at https://www.accessdata.fda.gov/scripts/cdrb/cfdocs/cfpmn/pmn.cfm identifies combination product submissions. The general controls provisions of the Act include requirements for annual registration, listing of devices, good manufacturing practice, labeling, and prohibitions against misbranding and adulteration. Please note: CDRH does not evaluate information related to contract liability warranties. We remind you, however, that device labeling must be truthful and not misleading.
If your device is classified (see above) into either class II (Special Controls) or class III (PMA), it may be subject to additional controls. Existing major regulations affecting your device can be found in the Code of Federal Regulations, Title 21, Parts 800 to 898. In addition, FDA may publish further announcements concerning your device in the Federal Register.
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Additional information about changes that may require a new premarket notification are provided in the FDA guidance documents entitled "Deciding When to Submit a 510(k) for a Change to an Existing Device" (https://www.fda.gov/media/99812/download) and "Deciding When to Submit a 510(k) for a Software Change to an Existing Device" (https://www.fda.gov/media/99785/download).
Your device is also subject to, among other requirements, the Quality System (QS) regulation (21 CFR Part 820), which includes, but is not limited to, 21 CFR 820.30, Design controls; 21 CFR 820.90, Nonconforming product; and 21 CFR 820.100, Corrective and preventive action. Please note that regardless of whether a change requires premarket review, the QS regulation requires device manufacturers to review and approve changes to device design and production (21 CFR 820.30 and 21 CFR 820.70) and document changes and approvals in the device master record (21 CFR 820.181).
Please be advised that FDA's issuance of a substantial equivalence determination does not mean that FDA has made a determination that your device complies with other requirements of the Act or any Federal statutes and regulations administered by other Federal agencies. You must comply with all the Act's requirements, including, but not limited to: registration and listing (21 CFR Part 807); labeling (21 CFR Part 801); medical device reporting of medical device-related adverse events) (21 CFR Part 803) for devices or postmarketing safety reporting (21 CFR Part 4, Subpart B) for combination products (see https://www.fda.gov/combination-products/guidance-regulatory-information/postmarketing-safety-reportingcombination-products); good manufacturing practice requirements as set forth in the quality systems (QS) regulation (21 CFR Part 820) for devices or current good manufacturing practices (21 CFR Part 4, Subpart A) for combination products; and, if applicable, the electronic product radiation control provisions (Sections 531-542 of the Act); 21 CFR Parts 1000-1050.
All medical devices, including Class I and unclassified devices and combination product device constituent parts are required to be in compliance with the final Unique Device Identification System rule ("UDI Re"). The UDI Rule requires, among other things, that a device bear a unique device identifier (UDI) on its label and package (21 CFR 801.20(a)) unless an exception or alternative applies (21 CFR 801.20(b)) and that the dates on the device label be formatted in accordance with 21 CFR 801.18. The UDI Rule (21 CFR 830.300(a) and 830.320(b)) also requires that certain information be submitted to the Global Unique Device Identification Database (GUDID) (21 CFR Part 830 Subpart E). For additional information on these requirements, please see the UDI System webpage at https://www.fda.gov/medical-device-advicecomprehensive-regulatory-assistance/unique-device-identification-system-udi-system.
Also, please note the regulation entitled, "Misbranding by reference to premarket notification" (21 CFR 807.97). For questions regarding the reporting of adverse events under the MDR regulation (21 CFR Part 803), please go to https://www.fda.gov/medical-device-safety/medical-device-reportingmdr-how-report-medical-device-problems.
For comprehensive regulatory information about mediation-emitting products, including information about labeling regulations, please see Device Advice (https://www.fda.gov/medicaldevices/device-advice-comprehensive-regulatory-assistance) and CDRH Learn (https://www.fda.gov/training-and-continuing-education/cdrh-learn). Additionally, you may contact the Division of Industry and Consumer Education (DICE) to ask a question about a specific regulatory topic. See
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the DICE website (https://www.fda.gov/medical-device-advice-comprehensive-regulatoryassistance/contact-us-division-industry-and-consumer-education-dice) for more information or contact DICE by email (DICE@fda.hhs.gov) or phone (1-800-638-2041 or 301-796-7100).
Sincerely,
Locon Weidner
Lora D. Weidner, Ph.D. Assistant Director Radiation Therapy Team DHT8C: Division of Radiological Imaging and Radiation Therapy Devices OHT8: Office of Radiological Health Office of Product Evaluation and Quality Center for Devices and Radiological Health
Enclosure
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Indications for Use
Submission Number (if known)
Device Name
Al-Rad Companion Organs RT
Indications for Use (Describe)
Al-Rad Companion Organs RT is a post-processing software intended to automatically contour DICOM CT and MR pre-defined structures using deep-learning-based algorithms.
Contours that are generated by Al-Rad Companion Organs RT may be used as input for clinical workflows including external beam radiation therapy treatment planning. Al-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 bv Al-Rad Companion Organs RT.
The outputs of Al-Rad Companion Orqans RT are intended to be used by trained medical professionals.
The software is not intended to automatically detect or contour lesions.
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 K242745
Submitted by: Siemens Medical Solutions USA, Inc. 40 Liberty Boulevard Malvern, PA 19355 Date Prepared: September 9, 2024
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
Registration Number: 2240869 |
|----------------------|------------------------------------------------------------------------------------------------------------------|
| Manufacturing Site | Siemens Healthcare GmbH
Henkestrasse 127
Erlangen, Germany 91052
Registration Number: 3002808157 |
2. Contact Person
Kira Morales Senior Regulatory Affairs Specialist Siemens Medical Solutions USA, Inc. 40 Liberty Boulevard Malvern, PA 19335 Phone: +1 (484) 901 - 9471 Email: kira.morales(@siemens-healthineers.com
3. Device Name and Classification
| Product Name: | AI-Rad Companion Organs RT |
|---|---|
| Common Name: | Medical Imaging Software |
| Classification Name: | Medical Image Management and Processing System |
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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 Devices
Product Name: Common Name: 510(k) Number: Clearance Date: Classification Name: Classification Panel: CFR Section: Device Class: Primary Product Code:
Product Name:
Common Name: 510(k) Number: Clearance Date: Classification Name: Classification Panel: CFR Section: Device Class: Primary Product Code:
Product Name:
Common Name: 510(k) Number: Clearance Date:
Radiology 21 CFR §892.2050 Class II OKB
AI-Rad Companion Organs RT Medical Imaging Software K232899 April 3, 2024 Medical Image Management and Processing System Radiology 21 CFR §892.2050 Class II QKB N/A
Contour Protégé AI
Medical Imaging Software K231765 November 8, 2023 Medical image management and processing system Radiology 21 CFR §892.2050 Class II QKB
Contour Protégé AI
Medical Imaging Software K223774 April 6, 2023 Medical image management and processing system Radiology 21 CFR §892.2050 Class II QKB
AI Segmentation
Medical Image Segmentation Software K211881 September 2, 2021
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Classification Name: Classification Panel: CFR Section: Device Class: Primary Product Code:
Medical charged-particle radiation therapy system Radiology 21 CFR §892.2050 Class II MUJ
6. Indications for Use
AI-Rad Companion Organs RT is a post-processing software intended to automatically contour DICOM CT and MR pre-defined structures 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 outputs of AI-Rad Companion Organs RT are intended to be used by trained medical professionals.
The software is not intended to automatically detect or contour lesions.
7. Device Description
AI-Rad Companion Organs RT provides automatic segmentation of pre-defined structures such as Organs-at-risk (OAR) from CT or MR medical series, prior to dosimetry planning in radiation therapy. AI-Rad Companion Organs RT is not intended to be used as a standalone diagnostic device and is not a clinical decision-making software.
CT or MR series of images serve as input for AI-Rad Companion Organs RT and are acquired as part of a typical scanner acquisition. Once processed by the AI algorithms, generated contours in DICOMRTSTRUCT format are reviewed in a confirmation window, allowing clinical user to confirm or reject the contours before sending to the target system. Optionally, the user may select to directly transfer the contours to a configurable DICOM node (e.g., the Treatment Planning System (TPS), which is the standard location for the planning of radiation therapy).
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 the automatically generated contours. Then the output of AI-Rad Companion Organs RT must be reviewed and, where necessary, edited with appropriate software before accepting generated contours as input to treatment planning steps. The output of AI-Rad Companion Organs RT is intended to be used by qualified medical professionals, who can perform a complementary manual editing of the contours or add any new contours in the TPS (or any other interactive contouring application supporting DICOM-RT objects) as part of the routine clinical workflow.
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Image /page/7/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.
8. Substantially Equivalent (SE) and Technological Characteristics
The intended use of the subject device is unchanged from the predicate device. The following modifications have been made in the subject device, compared to the predicate, AI-Rad Companion Organs RT (K232899).
- Enhanced CT contouring algorithm include 37 new organs & structures
- Multi Guideline Support and corresponding update to UI ●
AI-Rad Companion Organs RT VA60 and AI-Rad Companion Organs RT VA50 both use a deep learning algorithm to support their AI claims. Additionally, they both process CT and MR data in DICOM format, making them vendor agnostic and create outputs which can be used by any TPS system. The deep learning CT algorithm within AI-Rad Companion Organs RT VA60 has been enhanced from the algorithm in AI-Rad Companion Organs RT VA50 (K232899). All models contained within AI-Rad Companion Organs RT VA60 and AI-Rad Companion Organs RT VA50 (K232899) 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 enhanced CT contouring algorithm has been validated against FDA/CE cleared devices or from literature.
| Subject Device | Predicate Device | |
|---|---|---|
| Device | ||
| Manufacturer | Siemens | Siemens |
| Device Name | AI-Rad Companion Organs RT | AI-Rad Companion Organs RT |
| 510(k) Number | K242745 | K232899 |
| Indications for | ||
| Use | AI-Rad Companion Organs RT is | |
| a post-processing software | ||
| intended to automatically contour | ||
| DICOM CT and MR pre-defined | ||
| structures 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 | AI-Rad Companion Organs RT is | |
| a post-processing software | ||
| intended to automatically contour | ||
| DICOM CT and MR pre-defined | ||
| structures 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 | planning. AI-Rad Companion | |
| Organs RT must be used in | Organs RT must be used in | |
| conjunction with appropriate | conjunction with appropriate | |
| software such as Treatment | software such as Treatment | |
| Planning Systems and Interactive | Planning Systems and Interactive | |
| Contouring applications, to | Contouring applications, to | |
| review, edit, and accept contours | review, edit, and accept contours | |
| generated by AI-Rad Companion | generated by AI-Rad Companion | |
| Organs RT. | Organs RT. | |
| The outputs of AI-Rad Companion | The outputs of AI-Rad Companion | |
| Organs RT are intended to be used | Organs RT are intended to be used | |
| by trained medical professionals. | by trained medical professionals. | |
| The software is not intended to | The software is not intended to | |
| automatically detect or contour | automatically detect or contour | |
| lesions | lesions | |
| Algorithm | Deep Learning | Deep Learning |
| Segmentation of | CT: Head & Neck, Thorax, | CT: Head & Neck, Thorax, |
| Organ at Risk in the | Abdomen & Pelvis | Abdomen & Pelvis |
| Anatomic Regions | Head & Neck lymph nodes | Head & Neck lymph nodes |
| (203 OAR) | (166 OAR) | |
| MR: Pelvis (9 OAR) | MR: Pelvis (9 OAR) | |
| Compatible | ||
| Modality | CT & MR Images | CT & MR Images |
| Compatible | ||
| Scanner Models | No Limitation on scanner model | |
| for CT. Siemens Healthineers' | ||
| data only for MR. DICOM | ||
| compliance required. | No Limitation on scanner model | |
| for CT. Siemens Healthineers' | ||
| data only for MR. DICOM | ||
| compliance required. | ||
| Compatible | ||
| Treatment Planning | ||
| System | No Limitation on TPS model, | |
| DICOM compliance required. | No Limitation on TPS model, | |
| DICOM compliance required. | ||
| Contraindications | 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. | 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. | ||
| 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. | ||
| 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 using Edge and in the | ||
| Cloud. The UI is provided using a | ||
| web-based interface. | ||
| Deployment | ||
| Feature | Edge & Cloud Deployment | Edge & Cloud Deployment |
| Organ Templates | Creating, editing and deletion of | |
| organ templates. Customize | ||
| predefined structure database with | ||
| mapping to international | ||
| nomenclature schemes. | Creating, editing and deletion of | |
| organ templates. Customize | ||
| predefined structure database with | ||
| mapping to international | ||
| nomenclature schemes. | ||
| 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. | ||
| 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. | ||
| Segmentation | ||
| Performance | MR: The algorithm is unchanged | |
| from the predicate and is separate | ||
| from the CT algorithm therefore | ||
| the performance is unchanged | ||
| from the predicate. |
CT: The target performance was | MR: The target performance was
validated using 66 cases to
validate the overall performance of
the MR contouring.
CT: The target performance was
validated using 414 cases |
| | validated using 579 cases
distributed to four cohorts. | distributed to three cohorts. |
| | Both: To objectively evaluate the
target performance, the DICE
coefficient, the absolute symmetric
surface distance (ASSD) and the
fail rate was evaluated. The | Both: 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 |
| | segmentation performance of the
subject was equivalent to the
overall performance compared to
the predicate, reference device and
comparable literature & devices. | subject was equivalent to the
overall performance compared to
the predicate, reference device and
comparable literature & devices. |
| User Interface -
Results Preview
(Confirmation) | Basic visualization functionality of
original data and generated
contours | Basic visualization functionality of
original data and generated
contours |
| User Interface
Configuration | Configuration UI | Configuration UI |
| 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 |
| Human Factors | Design to be used by trained
clinicians. | Design to be used by trained
clinicians. |
The risk analysis and non-clinical data support that both devices perform equivalently and do not raise different questions of the safety and effectiveness.
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Image /page/9/Picture/0 description: The image shows 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.
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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 VA60 is substantially equivalent to the currently marketed device, AI-Rad Companion Organs RT (K232899).
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.
AI-Rad Companion has been tested to meet the requirements of conformity to multiple industry standards. Non-clinical performance testing demonstrates that AI-Rad Companion Organs RT complies with the FDA guidance document, "Guidance for the Content of Premarket Submissions for Device Software Functions" (June 2023) as well as with the following voluntary FDA recognized Consensus Standards listed in Table 2.
| Recognition
Number | Product
Area | Title of Standard | Reference
Number and
Date | Standards
Development
Organization |
|-----------------------|-----------------|------------------------------------------------------------------------------------|---------------------------------|------------------------------------------|
| 5-129 | General | Medical Devices –
Application of usability
engineering to medical
devices | 62366-1 Ed
1.1 2020-06
CV | IEC |
| 5-125 | General | Medical Devices –
application of risk | 14971:2019-
12 | ISO |
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| | | management to medical
devices | | |
|--------|--------------------------|-------------------------------------------------------------------------------------------------------------------------------------|--------------------------------------|---------------------|
| 13-79 | Software/
Informatics | Medical device software –
software life cycle
processes [Including
Amendment 1 (2016)] | 62304 Ed 1.1
2015-06 CV | AAMI
ANSI
IEC |
| 12-352 | Radiology | Digital Imaging and
Communications in
Medicine (DICOM) Set | PS 3.1 – 3.20
2023e | NEMA |
| 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 |
| 13-122 | Software/
Informatics | Health software and Health
IT system safety
effectiveness and security | 81001-5-1
Edition 1.0
2021-12 | IEC |
| 5-135 | General | Medical devices –
Information to be supplied
by the manufacturer | 20417 First
edition 2021-
04 | ISO |
Table 2: List of recognized standards
Verification and Validation
Software documentation level, per FDA's Guidance Document "Guidance for the Content of Premarket Submissions for Software Contained in Medical Devices" issued on June 14, 2023, 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 "Cybersecurity in Medical Devices: Ouality System Considerations and Content of Premarket Submissions" (September 2023) 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 algorithms underwent a scientific evaluation. The results of clinical data-based software validation for the subject device AI-Rad Companion Organs RT (SW VA60A) demonstrated equivalent performance in comparison to the predicate device (SW VA50A. K232899). The performance of the enhanced CT organ contouring algorithm is comparable to the predicate device and comparable reference literature and cleared devices. A complete scientific evaluation report is provided in support of the device modifications.
The MR Contouring algorithm contained within AI-Rad Companion Organs RT VA60 is identical to the predicate device AI-Rad Companion Organs RT VA50 (K232899).
CT Contouring Algorithm Performance
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= 579, data from multiple clinical sites across the North American, South American, Asia, Australia 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 subject device were equivalent or had better performance than the predicate device. To encountered for different datasets, variation, we first calculate the average of multiple references or the average of anatomical region for the specific organ or anatomical region. We then define the baseline value by subtracting the reference value using 5% error margin in case of Dice and 0.1 mm in case of ASSD.
The performance results of the subject device for the new CT organs are comparable to the reference literature & cleared devices. Here equivalence for the new organs is defined such that the selected reference metric has a higher value than the defined baseline. For existing organs, the average (AVG) Dice score difference between the subject device and predicate device is smaller than 3%.
| 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 average (AVG) Dice score difference between the subject and predicate device is smaller than 3% |
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| New Organs for Subject Device | • The subject device in the selected reference
metric has a higher value than the defined
baseline value |
| ------------------------------- | ---------------------------------------------------------------------------------------------------------------- |
|---|
Table 3: Acceptance Criteria of AIRC Organs RT VA50
| Dice (%) | |||
|---|---|---|---|
| Avg | Std | 95% CI | |
| Head & Neck | 76.1 | 14.3 | [75.1, 77.2] |
| Head & Neck lymph | |||
| nodes | 69.3 | 13.9 | [68.7, 70.0] |
| Thorax | 76.9 | 15.8 | [76.2, 77.6] |
| Abdomen | 87.3 | 10.1 | [86.3, 88.2] |
| Pelvis | 85.7 | 9.6 | [85.0, 86.5] |
| Cardiac | 75.6 | 15.1 | [74.1, 77.1] |
Table 3: Performance summary of the subject device CT contouring
| Organ Name | NO. | Dice (%) | ASSD (mm) | ||||||
|---|---|---|---|---|---|---|---|---|---|
| AVG | STD | MED | 95%CI | AVG | STD | MED | 95%CI | ||
| Left Breast | 30 | 90.4 | 3.8 | 91 | [89, 91.8] | 2.4 | 2.2 | 1.8 | [1.5, 3.2] |
| Right Breast | 30 | 90.2 | 3.7 | 90.8 | [88.8, 91.5] | 1.9 | 0.7 | 1.8 | [1.7, 2.2] |
| Bowel Bag | 33 | 95 | 3.6 | 96.5 | [93.7, 96.3] | 1.9 | 1.5 | 1.4 | [1.4, 2.5] |
| Left Pelvic Bone | 30 | 93.4 | 1.6 | 93.8 | [92.8, 94] | 0.6 | 0.2 | 0.5 | [0.5, 0.6] |
| Right Pelvic Bone | 30 | 93.9 | 1.3 | 94 | [93.4, 94.4] | 0.5 | 0.1 | 0.5 | [0.4, 0.6] |
| Pituitary | 30 | 75.8 | 7.4 | 77 | [73.1, 78.6] | 0.7 | 0.3 | 0.6 | [0.5, 0.8] |
| Sacrum | 30 | 90.7 | 4 | 91.9 | [89.2, 92.2] | 0.8 | 0.6 | 0.6 | [0.5, 1] |
| Brainstem | 30 | 88.4 | 2.5 | 88.8 | [87.5, 89.3] | 1 | 0.3 | 0.9 | [0.9, 1.1] |
| Glottis | 30 | 68 | 8.7 | 70.9 | [64.8, 71.3] | 1.2 | 0.4 | 1.1 | [1.1, 1.4] |
| Supraglottic Larynx | 30 | 80 | 5.9 | 81 | [77.8, 82.20] | 0.8 | 0.3 | 0.7 | [0.7, 0.9] |
| Esophagus | 30 | 85.6 | 4.2 | 86 | [84, 87.2] | 0.6 | 0.3 | 0.6 | [0.5, 0.7] |
| Left Lacrimal Gland | 30 | 72.1 | 7.3 | 71.3 | [69.4, 74.9] | 0.8 | 0.3 | 0.8 | [0.7, 0.9] |
| Right Lacrimal Gland | 30 | 69.9 | 12.4 | 73.6 | [65.3, 74.6] | 0.9 | 0.7 | 0.8 | [0.7, 1.2] |
| Left Femur Head | 30 | 95.2 | 1.1 | 95.2 | [94.8, 95.6] | 0.5 | 0.2 | 0.5 | [0.5, 0.6] |
14
Hea
| Right Femur Head | 30 | 94.9 | 1.3 | 95.1 | [94.5, 95.4] | 0.6 | 0.2 | 0.5 | [0.5, 0.6] |
|---|---|---|---|---|---|---|---|---|---|
| Left Humeral Head | 30 | 94.2 | 2.1 | 94.8 | [93.4, 94.9] | 0.7 | 0.3 | 0.6 | [0.6, 0.8] |
| Right Humeral Head | 30 | 94.6 | 3.2 | 95.4 | [93.4, 95.8] | 0.7 | 0.5 | 0.5 | [0.5, 0.9] |
| MEDIASTINAL LN 10L | 31 | 52.9 | 9.7 | 55.1 | [49.4, 56.5] | 1.2 | 0.6 | 1.1 | [1, 1.5] |
| MEDIASTINAL LN 10R | 31 | 50.2 | 9.5 | 50.6 | [46.7, 53.7] | 1.4 | 0.6 | 1.2 | [1.1, 1.6] |
| MEDIASTINAL LN 1L | 31 | 70.7 | 10 | 73 | [67, 74.4] | 2.4 | 1.2 | 1.9 | [1.9, 2.8] |
| MEDIASTINAL LN 1R | 31 | 66 | 12.8 | 67.9 | [61.3, 70.7] | 2.4 | 0.9 | 2.3 | [2.1, 2.8] |
| MEDIASTINAL LN 2L | 31 | 67.6 | 7.6 | 68.6 | [64.8, 70.5] | 1.4 | 0.4 | 1.3 | [1.2, 1.5] |
| MEDIASTINAL LN 2R | 31 | 55.5 | 13.6 | 56.9 | [50.6, 60.5] | 2.3 | 1.5 | 2 | [1.7, 2.8] |
| MEDIASTINAL LN 3A | 31 | 75.6 | 6.3 | 77.6 | [73.3, 77.9] | 1.6 | 0.4 | 1.5 | [1.5, 1.8] |
| MEDIASTINAL LN 3P | 31 | 65.9 | 6 | 66.5 | [63.7, 68.1] | 1.6 | 0.6 | 1.5 | [1.4, 1.8] |
| MEDIASTINAL LN 4L | 31 | 65.7 | 8.4 | 67.2 | [62.6, 68.8] | 1.4 | 0.7 | 1.1 | [1.1, 1.7] |
| MEDIASTINAL LN 4R | 31 | 72.8 | 8 | 74.6 | [69.9, 75.7] | 1.7 | 0.7 | 1.6 | [1.4, 1.9] |
| MEDIASTINAL LN 5 | 31 | 61 | 14.8 | 65.7 | [55.5, 66.4] | 1.8 | 0.8 | 1.6 | [1.5, 2.1] |
| MEDIASTINAL LN 6 | 31 | 58.8 | 14.8 | 62.5 | [53.4, 64.2] | 2 | 1.1 | 1.6 | [1.6, 2.4] |
| MEDIASTINAL LN 7 | 31 | 59 | 7.6 | 61.3 | [56.2, 61.8] | 1.9 | 0.8 | 1.7 | [1.6, 2.2] |
| MEDIASTINAL LN 8 | 31 | 65.8 | 8 | 67.9 | [62.9, 68.8] | 2 | 0.9 | 1.7 | [1.7, 2.4] |
| MEDIASTINAL LN 9L | 31 | 38.3 | 21.1 | 42.9 | [30.6, 46.1] | 5.3 | 4.4 | 3.7 | [3.7, 6.9] |
| MEDIASTINAL LN 9R | 29 | 38.8 | 15.6 | 38.5 | [32.9, 44.7] | 3.7 | 2 | 3.5 | [2.9, 4.4] |
Table 4: Detailed Performance evaluation of the new organs in the subject device
| Test Cohort | |
|---|---|
| # Patients | 244 |
| (Data origin) | South/North America: 163 |
| EU: 70 | |
| Asia: 6 | |
| Australia: 3 | |
| Unknown: 2 | |
| Gender | F: 111 |
| M: 100 | |
| Unknown: 33 | |
| Manufacturer | GE: 29 |
| Philips: 33 | |
| Siemens: 158 | |
| Other/Unknown: 24 | |
| Slice Thickness | 3: 4 |
Table 5: Validation Testing Data Information for new OARs
| Cohort | A | B | ||
|---|---|---|---|---|
| A.1 | A.2 | A.3 | ||
| # Patients | 73 | 40 | 301 | 165 |
| # Of clinical | ||||
| sites | ||||
| (Data origin) | 3 | 4 | 12+ | 20+ |
| Germany: 14 | ||||
| Brazil: 59 | Canada: 40 | South/North America: 184 | ||
| EU: 44 | ||||
| Asia: 33 | ||||
| Australia: 28 | ||||
| Unknown: 12 | South/North America: 100 | |||
| EU: 51 | ||||
| Asia: 6 | ||||
| Australia: 3 | ||||
| Unknown: 5 | ||||
| Body Region | Head & Neck: 24 | |||
| Thorax & Abdomen: 20 | ||||
| Pelvis: 29 | Head & Neck: 40 | Head & Neck: 50 | ||
| Thorax: 81 | ||||
| Abdomen: 115 | ||||
| Pelvis: 55 | Head & Neck: 40 | |||
| Thorax: 69 | ||||
| Abdomen: 25 | ||||
| Pelvis: 31 |
Table 5: Validation Testing Data Information based on Cohort
| Organ Group | No. of Training | No. of Validation |
|---|---|---|
| Lacrimal Glands Left | 247 | 62 |
| Lacrimal Glands Right | ||
| Pituitary Gland | 247 | 62 |
| Humeral Head Left | 207 | 52 |
| Humeral Head Right | ||
| Bowel Bag | 544 | 25 |
| Pelvic Bone Left | 160 | 40 |
| Pelvic Bone Right | ||
| Sacrum | 160 | 40 |
| Mediastinal LN I Left | ||
| Mediastinal LN 1 Right | 136 | 34 |
| Mediastinal LN II Left |
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| Mediastinal LN II Right | ||
|---|---|---|
| Mediastinal LN III Anterior | ||
| Mediastinal LN III Posterior | ||
| Mediastinal LN IV Left | ||
| Mediastinal LN IV Right | ||
| Mediastinal LN V | ||
| Mediastinal LN VI | ||
| Mediastinal LN VII | ||
| Mediastinal LN VIII | ||
| Mediastinal LN IX Left | ||
| Mediastinal LN IX Right | ||
| Mediastinal LN X Left | ||
| Mediastinal LN X Right | ||
| Femoral Head Left | 160 | 40 |
| Femoral Head Right | ||
| Brainstem | 247 | 62 |
| Esophagus | 247 | 62 |
| Breast Left | 172 | 44 |
| Breast Right | ||
| Supraglottic Larynx | 247 | 62 |
| Glottis |
Table 6: Training Dataset Characteristics
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 applicable 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
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The device labeling contains instructions for use and any necessary cautions and warnings to ensure safe and effective use of the device as compared to the predicate.
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 VA50 (K232899).