(122 days)
The Clear Guide Scenergy is a stereotaxic accessory for the fusion of images from Computed Tomography (CT) and Ultrasound (US) modalities.
The Clear Guide Scenergy utilizes the Clear Guide SuperPROBE platform to display images of the target regions and the projected path of the interventional instrument, while taking into account patient movement and deformation. Instrumentation used with the Clear Guide Scenergy might include an interventional needle or needle-like rigid device, such as a biopsy needle, an aspiration needle. The device is intended to be used in any interventional or diagnostic procedure where the combination of these modalities is used for visualization, except for procedures on the brain. The device is intended for use in a clinical setting.
The Clear Guide SCENERGY guidance system is intended to be an accessory to existing ultrasound imaging systems, to provide image fusion, instrument tracking, and image/instrument guidance functionality to operators during image-guided medical interventions that utilize data from ultrasound and CT modalities. The Clear Guide SCENERGY uses optical detection technology to identify and track objects in the field of view. By pairing this information with the aforementioned imaging data, the Clear Guide SCENERGY executes proprietary software algorithms to display fused images in real-time to the clinician. These segmentation and registration algorithms are automated, and the user cannot modify either result. Segmentation results are deterministic, meaning that new inputs (e.g., a new CT) would be required to change the segmentation output. Registration can be reset by the user at any time during use.
Here's a breakdown of the acceptance criteria and study information for the Clear Guide SCENERGY, based on the provided text:
1. Table of Acceptance Criteria and Reported Device Performance
The document does not explicitly state numerical acceptance criteria for each test. Instead, it generally states that tests yielded "passing results" or were "within acceptable limits/criteria." However, it does describe the metrics used and confirms successful performance.
| Test Category | Metric Used | Reported Device Performance | Acceptance Criteria (Implicitly Met) |
|---|---|---|---|
| Segmentation Testing | Segmentation error, Detection rate | Passing results | Software outputs match manual selection within acceptable error and detection rates. |
| Registration Testing | Fiducial Registration Error (FRE) | FRE within acceptable limits; no instances of misregistration. | FRE within acceptable limits, misregistration instances are zero. |
| Guidance (Tip-to-Target) Testing | Tip-to-target distance | Passing test result | End user's ability to hit a desired target within acceptable distance. |
| Fusion Testing | Tissue Registration Error (TRE) | Passing test results | TRE within acceptable limits. |
| Systematic Error (Tip-to-Tip) Testing | Cumulative "tip-to-tip" distance | Within test acceptance criteria (passing test result) | Cumulative error (segmentation, registration, fusion, guidance) within acceptable limits. |
| Deformation Testing | Estimated recovery (percent) | Positive effect compared to no deformation simulation. | Demonstrate a positive and effective compensation for compression error. |
2. Sample Size Used for the Test Set and Data Provenance
The document states that phantom, animal, and human datasets were used for various tests (Segmentation, Registration, Fusion, Deformation). However, it does not specify the exact sample sizes for each of these categories (e.g., number of CT scans, number of animals, number of human subjects).
The data provenance is generally described as:
- Phantom datasets: Used for Segmentation, Registration, Fusion, Systematic Error testing.
- Animal datasets (in-vivo porcine): Used for Segmentation, Registration, Fusion, Deformation testing.
- Human datasets: Used for Segmentation, Registration, Fusion testing.
The country of origin is not explicitly stated, nor is whether the data was retrospective or prospective. Given the nature of a 510(k) submission and the use of animal and human datasets, it's likely a mix of prospective (as part of the validation study) and potentially some retrospective data, but this is not confirmed.
3. Number of Experts Used to Establish the Ground Truth for the Test Set and Qualifications of Those Experts
The document mentions "manual selection" as the ground truth for segmentation testing but does not specify the number of experts who performed this manual selection, nor their qualifications (e.g., radiologist with X years of experience).
4. Adjudication Method for the Test Set
The document does not describe any specific adjudication method (e.g., 2+1, 3+1) for establishing ground truth or resolving discrepancies, beyond stating that software outputs were compared to "manual selection" for segmentation.
5. If a Multi-Reader Multi-Case (MRMC) Comparative Effectiveness Study was Done
The document does not report a multi-reader multi-case (MRMC) comparative effectiveness study or any effect size of how human readers improve with AI vs. without AI assistance. The performance data focuses on the device's accuracy and functionality in performing its tasks.
6. If a Standalone (i.e. algorithm only without human-in-the-loop performance) was done
Yes, the testing described appears to be largely standalone algorithm performance from the perspective of accuracy of segmentation, registration, guidance, and fusion. For example, "Segmentation error and detection rate were analyzed, with passing results (per acceptability criteria)" directly assesses algorithms' outputs against ground truth. The "Guidance (Tip-to-Target) Testing" also assesses the device's ability to facilitate hitting a desired target. The entire section on "Performance Data" describes how the device itself (its software algorithms) performs.
7. The Type of Ground Truth Used
The types of ground truth used include:
- Manual Selection: Mentioned for Segmentation Testing (comparing software outputs to manual selection in phantom, animal, and human datasets).
- CT: Implied as the reference for "tip-to-tip" distance in Systematic Error Testing ("needle point seen by ground truth CT").
- Identifiable landmarks seen on ultrasound and CT: Used for Fusion Testing (Tissue Registration Error (TRE)).
It does not explicitly mention pathology or outcomes data as ground truth for these specific performance tests.
8. The Sample Size for the Training Set
The document does not provide any information regarding the sample size used for the training set for the Clear Guide SCENERGY's algorithms.
9. How the Ground Truth for the Training Set was Established
The document does not provide any information on how the ground truth for the training set was established. It only discusses the ground truth used for the validation/test sets.
§ 892.1750 Computed tomography x-ray system.
(a)
Identification. A computed tomography x-ray system is a diagnostic x-ray system intended to produce cross-sectional images of the body by computer reconstruction of x-ray transmission data from the same axial plane taken at different angles. This generic type of device may include signal analysis and display equipment, patient and equipment supports, component parts, and accessories.(b)
Classification. Class II.