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510(k) Data Aggregation
(113 days)
Auto Segmentation generates a Radiotherapy Structure Set (RTSS) DICOM with segmented organs at risk which can be used by dosimetrists, medical physicists, and radiation oncologists as initial contours to accelerate workflow for radiation therapy planning. It is the responsibility of the user to verify the processed output contours and user-defined labels for each organ at risk and correct the contours/labels as needed. Auto Segmentation may be used with images acquired on CT scanners, in adult patients.
Auto Segmentation is a post-processing software designed to automatically generate contours of organ(s) at risk (OARs) from Computed Tomography (CT) images in the form of a DICOM Radiotherapy Structure Set (RTSS) series. The application is intended as a workflow tool for initial segmentation of OARs to streamline the process of organ at risk delineation. The Auto Segmentation is intended to be used by radiotherapy (RT) practitioners after review and editing, if necessary, and confirming the accuracy of the contours for use in radiation therapy planning.
Auto Segmentation uses deep learning algorithms to generate organ at risk contours for the head and neck, thorax, abdomen and pelvis regions from CT images across 40 organ subregion(s). The automatically generated organ at risk contours are networked to predefined DICOM destination(s), such as review workstations supporting RTSS format, for review and editing, as needed.
The organ at risk contours generated with the Auto Segmentation are designed to improve the contouring workflow by automatically creating contours for review by the intended users. The application is compatible with CT DICOM images with single energy acquisition modes and may be used with both GE and non-GE CT scanner acquired images (contrast), in adult patients.
Here's an analysis of the acceptance criteria and study detailed in the provided document for the GE Medical Systems Auto Segmentation device:
1. Table of Acceptance Criteria and Reported Device Performance
| OAR | Auto Segmentation (subject device) Dice Mean | Lower CI95 | Acceptance Criteria Type | Acceptance Criteria Dice Mean |
|---|---|---|---|---|
| Adrenal Left | 78.68% | 76.63% | Estimated | 68.0% |
| Adrenal Right | 72.48% | 69.78% | Estimated | 68.0% |
| Bladder | 81.50% | 78.33% | Deep learning | 80.0% |
| Body | 99.50% | 99.38% | Atlas-based | 98.1% |
| Brainstem | 87.69% | 87.15% | Deep learning | 88.4% |
| Chiasma | 43.81% | 41.03% | Atlas-based | 11.7% |
| Esophagus | 81.69% | 80.38% | Atlas-based | 45.8% |
| Eye Left | 91.32% | 89.77% | Deep learning | 90.1% |
| Eye Right | 90.25% | 88.23% | Deep learning | 89.9% |
| Femur Left | 97.65% | 97.18% | Atlas-based | 71.6% |
| Femur Right | 97.92% | 97.78% | Atlas-based | 70.8% |
| Kidney Left | 92.53% | 90.30% | Deep learning | 86.8% |
| Kidney Right | 94.82% | 93.48% | Deep learning | 85.6% |
| Lacrimal Gland Left | 59.79% | 57.65% | Deep learning | 50.0% |
| Lacrimal Gland Right | 58.09% | 55.81% | Deep learning | 50.0% |
| Lens Left | 76.86% | 74.80% | Deep learning | 73.3% |
| Lens Right | 79.09% | 77.40% | Deep learning | 75.6% |
| Liver | 94.28% | 92.27% | Deep learning | 91.1% |
| Lung Left | 97.70% | 97.38% | Deep learning | 97.4% |
| Lung Right | 97.99% | 97.81% | Deep learning | 97.8% |
| Mandible | 92.70% | 92.36% | Deep learning | 94.0% |
| Optic Nerve Left | 79.22% | 77.99% | Deep learning | 71.1% |
| Optic Nerve Right | 80.20% | 78.94% | Deep learning | 71.2% |
| Oral Cavity | 87.43% | 86.20% | Deep learning | 91.0% |
| Pancreas | 80.34% | 78.50% | Estimated | 73.0% |
| Parotid Left | 84.35% | 83.27% | Deep learning | 65.0% |
| Parotid Right | 85.55% | 84.48% | Deep learning | 65.0% |
| Proximal Bronchial Tree (PBtree) | 84.94% | 83.71% | Atlas-based | 54.8% |
| Inferior PCM (Pharyngeal Constrictor Muscle) | 70.51% | 68.72% | Estimated | 68.0% |
| Middle PCM | 67.09% | 65.21% | Estimated | 68.0% |
| Superior PCM | 59.57% | 57.85% | Estimated | 50.0% |
| Pericardium | 93.58% | 92.00% | Atlas-based | 84.4% |
| Pituitary | 75.62% | 74.12% | Deep learning | 78.0% |
| Prostate | 79.67% | 77.60% | Atlas-based | 52.1% |
| Spinal Cord | 88.55% | 87.43% | Deep learning | 87.0% |
| Submandibular Left | 86.85% | 85.95% | Deep learning | 77.0% |
| Submandibular Right | 85.70% | 84.79% | Deep learning | 78.0% |
| Thyroid | 85.37% | 84.27% | Deep learning | 83.0% |
| Trachea | 91.02% | 90.47% | Atlas-based | 69.2% |
| Whole Brain | 98.53% | 98.46% | Estimated | 93.0% |
Note: The reported device performance (Dice Mean and Lower CI95) for almost all organs meets or exceeds the specified acceptance criteria. The only exception where the device's Dice Mean is slightly below the acceptance criteria is for Mandible (92.70% vs 94.0%) and Oral Cavity (87.43% vs 91.0%) and Pituitary (75.62% vs 78.0%), however there is no further discussion or justification provided in the text for these specific instances. The document does state that "The evaluation of the Dice mean for the Auto Segmentation algorithms demonstrates that the algorithm performance is in line with the performance of the predicate, as well as state of the art, recently cleared similar automated contouring devices."
2. Sample Size Used for the Test Set and Data Provenance
- Sample Size for Test Set: 302 retrospective CT radiation therapy planning exams (generating 2552 contours).
- Data Provenance: Multiple clinical sites in North America, Asia, and Europe. The demographic distribution includes adults (18-89 years old) of various genders and ethnicities from 9 global sources (USA, EU, Asia). The data was acquired using a variety of CT scanners and scanner protocols from different manufacturers.
- Retrospective/Prospective: Retrospective.
3. Number of Experts Used to Establish Ground Truth for the Test Set and Their Qualifications
- Number of Experts: Three (3).
- Qualifications of Experts: Independent, qualified radiotherapy practitioners.
- Comment: The document states that the ground truth annotations were established following RTOG and DAHANCA clinical guidelines.
4. Adjudication Method for the Test Set
- The document implies a consensus-based approach guided by clinical guidelines, as "ground truth annotations were established (...) manually by three independent, qualified radiotherapy practitioners," but it does not specify an explicit adjudication method like "2+1" or "3+1" for resolving disagreements between the three experts. The phrase "established following RTOG and DAHANCA clinical guidelines" suggests that these guidelines were used to define the correct contours.
5. Multi-Reader Multi-Case (MRMC) Comparative Effectiveness Study
- The document describes a qualitative preference study that involved three qualified radiotherapy practitioners reviewing the contours generated by the Auto Segmentation application. They assessed the adequacy of the generated contours for radiotherapy planning using a Likert scale.
- However, this was NOT a comparative effectiveness study of human readers with and without AI assistance. It was a study to determine the adequacy of the AI-generated contours themselves for initial use. Therefore, no effect size of human readers improving with AI vs. without AI assistance can be reported from this document.
6. Standalone Performance Study (Algorithm Only)
- Yes, a standalone performance study was conducted. The "Performance testing to evaluate the device's performance in segmenting organs-at-risk was performed using a database of 302 retrospective CT radiation therapy planning exams." The Dice Similarity Coefficient (DSC) was used as the primary metric to compare the Auto Segmentation generated contours to ground truth contours. The reported Dice Mean values and their 95% confidence intervals are direct metrics of the algorithm's standalone performance.
7. Type of Ground Truth Used
- Expert Consensus/Manual Annotation: Ground truth annotations were "established following RTOG and DAHANCA clinical guidelines manually by three independent, qualified radiotherapy practitioners."
8. Sample Size for the Training Set
- 911 different CT exams.
9. How the Ground Truth for the Training Set Was Established
- The document states that "The Auto Segmentation algorithms were developed and trained using a dataset of 911 different CT exams from several clinical sites from multiple countries. The original development and training data was used for radiotherapy planning..."
- It does not explicitly detail the process for establishing ground truth for the training set, but given the context of the test set ground truth and the overall development, it is highly probable it involved manual annotation by experts for radiotherapy planning purposes.
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(176 days)
AdvantageSim™ MD is used to prepare geometric and anatomical data relating to a proposed external beam radiotherapy treatment prior to dosimetry planning. Anatomical volumes can be defined automatically or manually in three dimensions using a set of CT images acquired with the patient in the proposed treatment position. Definition of the anatomical volumes may be assisted by additional CT, MR or PET studies that have been co-registered with the planning CT scan. Additionally, CT & PET data from a respiratory tracked examination may be used to allow the user to define the target or treatment volume over a defined range of the respiratory cycle.
The geometric parameters of a proposed treatment field are selected to allow non-dosimetric, interactive optimization of field coverage. Defined anatomical structures and geometric treatments fields are displayed on transverse images, on reformatted sagittal, coronal or oblique images, on 3 D views created from the images, or on a beam eye's view display with or without the display of defined structures with or without the display of digitally reconstructed radiograph.
AdvantageSim™ MD is a CT/MR/PET oncology application used by clinicians (radiologist, radiation oncologist, medical oncologist nuclear medicine physicians and trained healthcare professional) to assist treatment planning.
AdvantageSim MD with MR pelvic organ at risk segmentation Option is used to provide MR based prostate and pelvic organs-at-risk segmentation. A suite of semi-automated MR based organ segmentation contouring allows generating complex structures around organs at risk. These contours overlay on the co-registered CT planning image.
The segmentation methods in the modified device are semi-automatic. The user has to place seed points to identify an inner point of the organ to contour.
The software offers a suite of manual contour editing tools enabling the user to edit, modify, or change contours generated from the MR segmentation tools to their desired configuration based on their medical and clinical knowledge and experience. The results provided by the software needs to be approved by the experienced clinician and can always be modified or corrected by him/her. It is up to the expert user to accept the result without any change, reject it completely and delineate manually, or modify the result and then save it. The software does not provide any auto-detection or auto-saving functionalities.
Same as the predicate devices, the clinician retains the ultimate responsibility for making the pertinent diagnosis and patient management decisions based on their standard practices and visual comparison of the individual images, regardless of the accuracy of the output generated by the software.
Here's an analysis of the provided text to fulfill your request:
Acceptance Criteria and Study for GE Healthcare AdvantageSim™ MD with MR pelvic organ at risk segmentation Option
1. Table of Acceptance Criteria and Reported Device Performance:
| Acceptance Criteria Category | Reported Device Performance |
|---|---|
| Accuracy of measurement | Not explicitly quantified, but reported to be "substantially equivalent to the predicate devices" and that the "new software device has the potential to reduce inter-operator variability". |
| Precision of the measurement | Not explicitly quantified, but reported to be "substantially equivalent to the predicate devices" and that the "new software device has the potential to reduce inter-operator variability". |
| Efficiency (time comparison) | Reported to provide "statistically significant and practically meaningful clinical efficiency improvements". |
| General user Qualitative feedback | Substantiated "the characteristics of this feature, among others, as easy to learn, useful, efficient and providing increased throughput." |
Important Note: The document focuses on demonstrating substantial equivalence to predicate devices rather than providing specific numerical acceptance criteria and performance metrics (e.g., Dice coefficients, Hausdorff distances, specific time savings). The reported performance is generally qualitative or comparative.
2. Sample Size Used for the Test Set and the Data Provenance:
- Sample Size for Test Set: Not explicitly stated. The document mentions "consented clinical images" but does not specify the number of cases.
- Data Provenance: "consented clinical images" - the country of origin is not specified, but the submission is from GE Hungary Kft. The study appears to be retrospective as it uses existing clinical images.
3. Number of Experts Used to Establish the Ground Truth for the Test Set and Qualifications:
- Number of Experts: Three.
- Qualifications of Experts: "board certified Radiation Oncologists who were considered experts."
4. Adjudication Method for the Test Set:
- The document does not explicitly state a formal adjudication method (e.g., 2+1, 3+1). It describes the experts assessing accuracy, precision, and efficiency, and providing qualitative feedback. It implies each expert evaluated the software's performance, but not how disagreements were resolved to establish a single ground truth.
5. Multi-Reader Multi-Case (MRMC) Comparative Effectiveness Study:
- The study described is a usability study that compared the new software against manual methods (implied through efficiency and inter-operator variability assessment). It involved "three board certified Radiation Oncologists". While it involved multiple readers, it is not explicitly labeled as an "MRMC comparative effectiveness study" in the sense of a formal statistical study with defined effect sizes of improvement with AI assistance.
- Effect Size of Human Reader Improvement: Not quantitatively reported. The document states it has "the potential to reduce inter-operator variability" and provides "statistically significant and practically meaningful clinical efficiency improvements," but no numerical effect size is given.
6. Standalone Performance Study:
- Yes, a standalone performance was performed. The device's segmentation methods are described as "semi-automatic" where "the user has to place seed points to identify an inner point of the organ to contour." The software then generates contours. The study assessed the software's output in terms of accuracy, precision, and efficiency, even though a clinician would typically review and edit the results. The clinicians evaluated the device's output and how it facilitated their workflow.
7. Type of Ground Truth Used:
- The ground truth for the test set was established by expert consensus/opinion among the three board-certified Radiation Oncologists. They likely compared the device's segmentations against their clinical knowledge and potentially manual segmentations, though this is not explicitly detailed.
8. Sample Size for the Training Set:
- Not specified. The document does not provide any information about the training data or its size.
9. How the Ground Truth for the Training Set Was Established:
- Not specified. As the training set size and details are absent, the method for establishing its ground truth is also not mentioned.
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