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510(k) Data Aggregation
(231 days)
InferOperate Suite is medical imaging software that is intended to provide trained medical professionals with tools to aid them in reading, interpreting, reporting, and treatment planning for patients, including both preoperative surgical planning and intraoperative image display. InferOperate Suite accepts DICOM compliant medical images acquired from a variety of imaging devices.
This product is not intended for use with or for the primary diagnostic interpretation of Mammography images.
It provides several categories of tools. It includes basic imaging tools for general images, including 2D viewing, volume rendering and 3D volume viewing, orthogonal Multi-Planar Reconstructions (MPR), surface rendering, measurements, surgical planning, reporting, storing, general image management and administration tools, etc.
It includes a basic image processing workflow and a custom UI to segment anatomical structures. The processing may include the generation of preliminary segmentations of anatomy using software that employs machine learning and other computer vision algorithms, as well as interactive segmentation tools, etc.
InferOperate Suite is designed for use by trained professionals and is intended to assist the clinician who is responsible for making all final patient management decisions.
InferOperate Suite utilizes machine learning-based algorithms for adult patients undergoing CT chest, abdominal, or pelvic scans. For image data of other anatomical regions or modalities, patients under 21 years of age, or patients with unknown age, we provide non-ML software functions, such as STL viewer.
InferOperate Suite is medical imaging software that is intended to provide trained medical professionals with tools to aid them in reading, interpreting, reporting, and treatment planning for patients, including both preoperative surgical planning and intraoperative image display.
InferOperate Suite receives medical images in DICOM standard format and utilizes machine learning (ML) and other medical image processing techniques, along with interactive segmentation tools, to segment anatomical structures and target ROIs. InferOperate Suite performs 3D reconstruction and visualization, and provides several tools for surgical planning. The server receives DICOM images, analyzes the images, and provides 3D visualization of the anatomical structures. The system can be deployed on a dedicated on-premise server or a cloud server.
InferOperate Suite provides several categories of tools. It includes basic imaging tools for general image, including 2D viewing, volume rendering and 3D volume viewing, Multi-Planar Reconstructions (MPR), surface rendering, measurements, surgical planning, reporting, storing, general image management and administration tools
Here's an analysis of the acceptance criteria and the study proving the device meets them, based on the provided FDA 510(k) clearance letter for the InferOperate Suite.
1. Table of Acceptance Criteria and Reported Device Performance
The acceptance criteria for segmentation performance are explicitly stated as "Target" values for the Dice coefficient (DSC) and 95% Hausdorff Distance (HD95). The reported device performance is presented as the Mean and 95% Confidence Interval (CI) for these metrics.
| No. | Model | Metric | Mean Reported Performance | 95% CI Reported Performance | Target Acceptance Criteria | Meets Criteria? |
|---|---|---|---|---|---|---|
| 1 | Bronchus | Dice | 0.87 | 0.85-0.88 | 0.79 | Yes |
| HD95 | 2.33 | 2.07-2.59 | 3.5 | Yes | ||
| 2 | Pulmonary artery | Dice | 0.87 | 0.86-0.88 | 0.76 | Yes |
| HD95 | 3.35 | 2.79-3.90 | 5.55 | Yes | ||
| Pulmonary vein | Dice | 0.85 | 0.84-0.86 | 0.77 | Yes | |
| HD95 | 3.19 | 2.96-3.42 | 5.55 | Yes | ||
| 3 | Pulmonary lobe | Dice | 0.98 | 0.97-0.98 | 0.88 | Yes |
| HD95 | 2.63 | 2.34-2.91 | 4.15 | Yes | ||
| Pulmonary segment | Dice | 0.88 | 0.88-0.89 | 0.79 | Yes | |
| HD95 | 3.42 | 3.13-3.70 | 4.15 | Yes | ||
| 4 | Liver | Dice | 0.98 | 0.98-0.98 | 0.87 | Yes |
| HD95 | 2.15 | 2.09-2.22 | 4.95 | Yes | ||
| 5 | Hepatic segment (Couinaud's method) | Dice | 0.91 | 0.89-0.94 | 0.80 | Yes |
| HD95 | 2.54 | 2.18-2.89 | 4.95 | Yes | ||
| Hepatic segment (Vascular method) | Dice | 0.91 | 0.89-0.94 | 0.80 | Yes | |
| HD95 | 3.52 | 2.90-4.14 | 4.95 | Yes | ||
| 6 | Hepatic artery | Dice | 0.89 | 0.88-0.91 | 0.80 | Yes |
| HD95 | 2.36 | 1.98-2.74 | 5.55 | Yes | ||
| 7 | Hepatic vein | Dice | 0.91 | 0.90-0.91 | 0.80 | Yes |
| HD95 | 1.86 | 1.75-1.98 | 5.55 | Yes | ||
| Portal vein | Dice | 0.86 | 0.85-0.86 | 0.80 | Yes | |
| HD95 | 2.24 | 1.65-2.82 | 5.55 | Yes | ||
| 8 | Portal vein segment | Dice | 0.85 | 0.83-0.86 | 0.75 | Yes |
| HD95 | 3.46 | 2.74-4.18 | 5.55 | Yes | ||
| 9 | Gallbladder | Dice | 0.94 | 0.93-0.96 | 0.78 | Yes |
| HD95 | 2.19 | 1.74-2.63 | 3.5 | Yes | ||
| 10 | Common hepatic-bile duct | Dice | 0.83 | 0.79-0.88 | 0.73 | Yes |
| HD95 | 3.54 | 2.05-5.03 | 5.55 | Yes | ||
| 11 | Pancreas | Dice | 0.97 | 0.95-0.98 | 0.7 | Yes |
| HD95 | 2.49 | 1.23-3.75 | 10.63 | Yes | ||
| 12 | Spleen | Dice | 0.97 | 0.96-0.97 | 0.84 | Yes |
| HD95 | 2.79 | 1.64-3.94 | 4.94 | Yes | ||
| 13 | Kidney | Dice | 0.98 | 0.98-0.98 | 0.85 | Yes |
| HD95 | 1.79 | 1.63-2.09 | 4.86 | Yes | ||
| Bladder | Dice | 0.98 | 0.97-0.99 | 0.80 | Yes | |
| HD95 | 2.33 | 0.00-5.33 | 6.22 | Yes | ||
| 14 | Renal vein | Dice | 0.86 | 0.85-0.87 | 0.80 | Yes |
| HD95 | 3.03 | 2.01-4.13 | 5.55 | Yes | ||
| 15 | Renal artery | Dice | 0.85 | 0.85-0.86 | 0.80 | Yes |
| HD95 | 2.24 | 2.08-2.76 | 5.55 | Yes | ||
| 16 | Upper urinary tract | Dice | 0.84 | 0.82-0.85 | 0.70 | Yes |
| HD95 | 2.81 | 2.39-3.53 | 5.55 | Yes | ||
| 17 | Adrenal gland | Dice | 0.85 | 0.82-0.87 | 0.70 | Yes |
| HD95 | 2.69 | 1.98-3.70 | 10.63 | Yes | ||
| 18 | Bone | Dice | 0.97 | 0.97-0.98 | 0.80 | Yes |
| HD95 | 0.83 | 0.69-0.97 | 5.75 | Yes | ||
| 19 | Skin | Dice | 0.97 | 0.97-0.98 | 0.90 | Yes |
| HD95 | 0.40 | 0.32-0.48 | 10.00 | Yes |
All reported device performance metrics (Mean Dice and Mean HD95) meet or exceed their respective target acceptance criteria.
2. Sample Size Used for the Test Set and Data Provenance
- Sample Size: A total of 188 cases were used for algorithm performance testing, broken down as:
- 70 cases of the chest
- 61 cases of the abdomen
- 57 cases of the pelvis
- Some individual segmentations (e.g., Gallbladder, Bladder, Spleen, Bone, Skin) had slightly fewer cases than the overall anatomical region, indicating not all structures were present or analyzed in every case (e.g., 56 for Gallbladder out of 61 abdominal cases).
- Data Provenance: The dataset was composed of predominantly U.S. subjects. The letter specifies that the data for performance validation was independent of the training set, with no overlap in data sources. The imaging devices mainly included Siemens, GE, Philips, and Toshiba. The cases included a mix of contrast-enhanced and non-contrast enhanced CT scans for chest. All abdominal and pelvic cases were contrast-enhanced CT. The study design is retrospective, as cases were "collected" and analyzed, and the ground truth established post-acquisition.
3. Number of Experts Used to Establish the Ground Truth for the Test Set and Qualifications
- Number of Experts: Three experts were used.
- Two Chinese radiologists: Their specific qualifications (e.g., years of experience, subspecialty) are not explicitly stated beyond "radiologists."
- One American board-certified radiologist: This expert served as an arbitrator. Their specific years of experience or subspecialty are not explicitly stated, but "board-certified" indicates a recognized standard of expertise in the U.S.
4. Adjudication Method for the Test Set
The adjudication method used was a 2+1 consensus with arbitration.
- Two Chinese radiologists independently annotated the organs and anatomical structures.
- An American board-certified radiologist served as an arbitrator.
- If there were disagreements between the two initial radiologists' annotations, the arbitrator was responsible for resolving the discrepancies by either selecting the more accurate segmentation as the final ground truth or making any necessary modification.
5. Multi-Reader Multi-Case (MRMC) Comparative Effectiveness Study
No, a Multi-Reader Multi-Case (MRMC) comparative effectiveness study was not explicitly detailed in the provided document. The performance evaluation focused solely on the standalone performance of the AI algorithm in segmenting anatomical structures against expert-established ground truth. There is no information provided regarding human readers improving with AI assistance vs. without AI assistance.
6. Standalone Performance
Yes, a standalone (algorithm only without human-in-the-loop performance) study was done. The entire "Performance testing" section quantifies the segmentation accuracy of the InferOperate Suite's machine learning algorithms directly against the established ground truth, using Dice coefficient and Hausdorff distance. This specifically measures the intrinsic performance of the algorithm.
7. Type of Ground Truth Used
The ground truth used was expert consensus with arbitration. This means the ground truth was established by human experts (radiologists) with a formal process for resolving disagreements.
8. Sample Size for the Training Set
The document explicitly states that the dataset for performance validation was "independent of the training set" and had "no overlap in data sources." However, the sample size for the training set is not provided in the given text.
9. How the Ground Truth for the Training Set Was Established
The document mentions that the ground truth for the test set was established by annotators independent of the algorithm development annotators, implying that ground truth was also established for the training set by human annotators. However, the specifics of how the ground truth for the training set was established (e.g., number of experts, qualifications, adjudication method) are not provided in the given text.
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