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510(k) Data Aggregation
(197 days)
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.
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.
Here's a breakdown of the acceptance criteria and the study that proves the device meets them, based on the provided text:
Acceptance Criteria and Device Performance Study for AI-Rad Companion Organs RT
1. Table of Acceptance Criteria and Reported Device Performance
The acceptance criteria for the AI-Rad Companion Organs RT device, particularly for the enhanced CT contouring algorithm, are based on comparing its performance to the predicate device and relevant literature/cleared devices. The primary metrics used are Dice coefficient and Absolute Symmetric Surface Distance (ASSD).
Table 3: Acceptance Criteria of AIRC Organs RT VA50
| Validation Testing Subject | Acceptance Criteria | Reported Device Performance (Summary) |
|---|---|---|
| Organs in Predicate Device | All organs segmented in the predicate device are also segmented in the subject device. | Confirmed. The device continued to segment all organs previously handled by the predicate. |
| The average (AVG) Dice score difference between the subject and predicate device is |
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(198 days)
AI-Rad Companion Organs RT is a post-processing software intended to automatically contour DICOM CT and MR predefined structures using deep-leaming-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 are intended to be used by trained medical professionals.
The software is not intended to automatically detect or contour lesions.
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 DICOM-RTSTRUCT 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 TPS, which is the standard location for the planning of radiation therapy).
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. The qualified medical professional 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.
Here's a summary of the acceptance criteria and the study that proves the device meets them, based on the provided FDA 510(k) summary for AI-Rad Companion Organs RT:
1. Table of Acceptance Criteria and Reported Device Performance
The acceptance criteria and reported performance are detailed for both MR and CT contouring algorithms.
MR Contouring Algorithm Performance
| Validation Testing Subject | Acceptance Criteria | Reported Device Performance (Average) |
|---|---|---|
| MR Contouring Organs | The average segmentation accuracy (Dice value) of all subject device organs should be equivalent or better than the overall segmentation accuracy of the predicate device. The overall fail rate for each organ/anatomical structure is smaller than 15%. | Dice [%]: 85.75% (95% CI: [82.85, 87.58]) |
| ASSD [mm]: 1.25 (95% CI: [0.95, 2.02]) | ||
| Fail [%]: 2.75% | ||
| (Compared to Reference Device MRCAT Pelvis (K182888)) | AI-Rad Companion Organs RT VA50 – all organs: 86% (83-88) | |
| AI-Rad Companion Organs RT VA50 – common organs: 82% (78-84) | ||
| MRCAT Pelvis (K182888) – all organs: 77% (75-79) |
CT Contouring Algorithm Performance
| Validation Testing Subject | Acceptance Criteria | Reported Device Performance (Average) |
|---|---|---|
| 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%. | (The document states "equivalent or had better performance than the predicate device" implicitly meeting this, but does not give a specific numerical difference.) |
| New Organs for Subject Device | Baseline value defined by subtracting the reference value using 5% error margin in case of Dice and 0.1 mm in case of ASSD. The subject device in the selected reference metric has a higher value than the defined baseline value. | Regional Averages: |
| Head & Neck: Dice 76.5% | ||
| Head & Neck lymph nodes: Dice 69.2% | ||
| Thorax: Dice 82.1% | ||
| Abdomen: Dice 88.3% | ||
| Pelvis: Dice 84.0% |
2. Sample Sizes Used for the Test Set and Data Provenance
- MR Contouring Algorithm Test Set:
- Sample Size: N = 66
- Data Provenance: Retrospective study, data from multiple clinical sites across North America & Europe. The document further breaks this down for different sequences:
- T1 Dixon W: 30 datasets (USA: 15, EU: 15)
- T2 W TSE: 36 datasets (USA: 25, EU: 11)
- Manufacturer: All Siemens Healthineers scanners.
- CT Contouring Algorithm Test Set:
- Sample Size: N = 414
- Data Provenance: Retrospective study, data from multiple clinical sites across North American, South American, Asia, Australia, and Europe. This dataset is distributed across three cohorts:
- Cohort A: 73 datasets (Germany: 14, Brazil: 59) - Siemens scanners only
- Cohort B: 40 datasets (Canada: 40) - GE: 18, Philips: 22 scanners
- Cohort C: 301 datasets (NA: 165, EU: 44, Asia: 33, SA: 19, Australia: 28, Unknown: 12) - Siemens: 53, GE: 59, Philips: 119, Varian: 44, Others: 26 scanners
3. Number of Experts Used to Establish Ground Truth for the Test Set and Qualifications
- The ground truth annotations were "drawn manually by a team of experienced annotators mentored by radiologists or radiation oncologists."
- "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."
- The exact number of individual annotators or experts is not specified beyond "a team" and "a board-certified radiation oncologist." Their specific experience level (e.g., "10 years of experience") is not given beyond "experienced" and "board-certified."
4. Adjudication Method for the Test Set
- The document implies a consensus/adjudication process: "a quality assessment including review and correction of each annotation was done by a board-certified radiation oncologist." This suggests that initial annotations by the "experienced annotators" were reviewed and potentially corrected by a higher-level expert. The specific number of reviewers for each case (e.g., 2+1, 3+1) is not explicitly stated, but it was at least a "team" providing initial annotations followed by a "board-certified radiation oncologist" for quality assessment/correction.
5. If a Multi-Reader Multi-Case (MRMC) Comparative Effectiveness Study was done
- No, the document does not describe a Multi-Reader Multi-Case (MRMC) comparative effectiveness study evaluating how much human readers improve with AI vs. without AI assistance. The validation studies focused on the standalone performance of the algorithm against expert-defined ground truth.
6. If a Standalone (i.e. algorithm only without human-in-the-loop performance) was done
- Yes, the performance validation described in section 10 ("Performance Software Validation") is a standalone (algorithm only) performance study. The metrics (Dice, ASSD, Fail Rate) compare the algorithm's output directly to the established ground truth. The device produces contours that must be reviewed and edited by trained medical professionals, but the validation tests the AI's direct output.
7. The Type of Ground Truth Used
- The ground truth used was expert consensus/manual annotation. It was established by "manual annotation" by "experienced annotators mentored by radiologists or radiation oncologists" and subsequently reviewed and corrected by a "board-certified radiation oncologist." Annotation protocols followed NRG/RTOG guidelines.
8. The Sample Size for the Training Set
- MR Contouring Algorithm Training Set:
- T1 VIBE/Dixon W: 219 datasets
- T2 W TSE: 225 datasets
- Prostate (T2W): 960 datasets
- CT Contouring Algorithm Training Set: The training dataset sizes vary per organ group:
- Cochlea: 215
- Thyroid: 293
- Constrictor Muscles: 335
- Chest Wall: 48
- LN Supraclavicular, Axilla Levels, Internal Mammaries: 228
- Duodenum, Bowels, Sigmoid: 332
- Stomach: 371
- Pancreas: 369
- Pulmonary Artery, Vena Cava, Trachea, Spinal Canal, Proximal Bronchus: 113
- Ventricles & Atriums: 706
- Descending Coronary Artery: 252
- Penile Bulb: 854
- Uterus: 381
9. How the Ground Truth for the Training Set Was Established
- For both training and validation data, the ground truth annotations were established using the "Standard Annotation Process." This involved:
- Annotation protocols defined following NRG/RTOG guidelines.
- Manual annotations drawn by a team of experienced annotators mentored by radiologists or radiation oncologists using an internal annotation tool.
- A quality assessment including review and correction of each annotation by a board-certified radiation oncologist using validated medical image annotation tools.
- The document explicitly states that the "training data used for the training of the algorithm is independent of the data used to test the algorithm."
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