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510(k) Data Aggregation
(14 days)
AEGIS NAVIGATION ANDA EGIS PELVIC APPLICATION
This device provides two and three-dimensional image review, manipulation, and analysis tools to assist users in screening, diagnosis, planning and performing image-guided interventional procedures. Supported imaging modalities include Magnetic Resonance (MR), Ultrasound (US), Single Photon Emission Computed Tomography (SPECT), Computed Tomography (CT), Positron Emission Tomography (PET), Fluoroscopy and Endoscopy. Images and data are received from various imaging systems and other sources such as calibrated spatial positioning devices.
This device provides the capability to overlay annotations on 2D or 3D medical image displays. These annotations may represent the position of instruments including but not limited to biopsy needles, guidance wires, imaging probes or other tracked devices.
This device is intended to assist skilled medical professionals in clinical screening and interventions, for anatomical structures where imaging is currently used for visualizing such structures, including head and neck, breast, thoracic, and abdominal applications (including pelvis).
Lossy compressed mammographic images and digitized film screen images must not be reviewed for primary image interpretations. Mammographic images may only be interpreted using a FDA approved monitor that offers at least 5 MPixel resolution and meets other technical specifications reviewed and accepted by the FDA.
Aegis Navigation and Aegis Pelvic Application products are line-extensions to Sentinelle's medical image software product Aegis. These are also referred to below as (Aegis) plug-ins.
The products are designed to assist with multi-modality (including MRI, US, SPECT, CT, PET, Fluoroscopy, Endoscopy and others) imaging and guidance of screening and interventional procedures for anatomical structures such as head and neck, thoracic, breast, abdominal and pelvis(including prostate).
Aegis Pelvic Application is a software tool intended for analyzing multi-modality images. This plug-in also identifies where and how deeply, a biopsy or localization needle should be inserted into a region of interest (including prostate and surrounding structures) to strike a target or region of interest. Registration of target anatomy with an interventional device can be performed manually or automatically based on fiducial markers. This in turn provides a calculation of the location and depth of the targeted region of interest.
Similarly, Aegis Navigation is a software tool intended for analyzing multi-modality images. This plug-in also identifies where an imaging device (e.g. an ultrasound transducer), interventional tool (e.g. a biopsy or localization needle) or other tracked instrument should be placed in order to visualize or strike a target or region of interest. In addition, Aegis Navigation provides the user with the option to view previously acquired DICOM data (e.g. MRI data) alongside of a separate imaging modality such as Ultrasound (US) or a fluoroscopic device. In the context of US, a 3-D tracking system provides positional data regarding the transducer, which is used to guide the real-time reformatting of DICOM data to match the current US image. Interventional and imaging device operation and manipulation remains under the manual control of the trained medical professional at all times.
Additional applications of Aegis Navigation include:
- The ability to display the position of a tracked probe or instrument, overlaid on DICOM . (e.g.MR or CT) data
- The ability to track the position of an US transducer as it collects data, then retrospectively display reformatted DICOM (e.g. MR/CT) data slices oriented to match the previously acquired ultrasound images.
Here's a breakdown of the acceptance criteria and the study information for the Sentinelle Aegis Navigation and Pelvic Application, based on the provided document:
1. Table of Acceptance Criteria and Reported Device Performance
The document does not explicitly state numerical acceptance criteria in a table format. Instead, it refers to fulfilling functional requirements and meeting or exceeding specified accuracy criteria. The performance is gauged by comparison to the predicate device.
Acceptance Criteria (Inferred from Text) | Reported Device Performance |
---|---|
Functional requirements met | All functional requirements have been met. |
Core functions execute as expected | Core functions execute as expected. |
Registration accuracy (MR and ultrasound) meets or exceeds specified criteria | Registration and correspondence between MR and ultrasound meets or exceeds specified criteria. |
Needle registration and targeting results meet or exceed specified criteria | Needle registration and targeting results met or exceeded specified criteria. |
Accuracy within specification and performs as well as, or better than Aegis predicate device | System accuracy is within specification and performs as well as, or better than Aegis, the legally marketed predicate device. |
2. Sample Size and Data Provenance
- Test Set Sample Size: Not explicitly stated. The document mentions "targeting locations within a phantom" and "volunteers," but does not quantify the number of phantoms, volunteers, or the specific cases used in the accuracy and verification tests.
- Data Provenance: The tests were conducted "in-house by trained personnel in a simulated work-environment using phantoms or volunteers." This implies prospective data collection specifically for these tests. The country of origin of the data is Canada (where Sentinelle Medical Inc. is located).
3. Number of Experts and Their Qualifications for Ground Truth
- Number of Experts: Not specified.
- Qualifications of Experts: Not specified. The document states "trained personnel" conducted the testing but does not elaborate on their clinical or expert qualifications for establishing ground truth for the device's accuracy.
4. Adjudication Method for the Test Set
- Adjudication Method: Not specified. Ground truth establishment appears to be based on the direct observation and measurement of the device's output against the known properties of the phantoms or volunteer setups, rather than an expert consensus process requiring adjudication.
5. Multi-Reader Multi-Case (MRMC) Comparative Effectiveness Study
- Was an MRMC study done? No. The document does not mention any MRMC study comparing human readers with and without AI assistance. The testing focuses on the device's standalone accuracy and functionality.
- Effect Size of Human Reader Improvement: Not applicable, as no MRMC study was performed.
6. Standalone (Algorithm Only) Performance Study
- Was a standalone study done? Yes. The entire testing described in the document (registration accuracy, needle verification, and functional tests) describes the performance of the Aegis Navigation and Aegis Pelvic Application software itself, independent of a human operator, to verify its accuracy and functionality.
7. Type of Ground Truth Used
- Type of Ground Truth: The ground truth for the accuracy tests was established using phantoms or volunteers with known anatomical structures and target locations. This provides a measurable, objective ground truth against which the device's calculations and guidance were compared. Specifically, "targeting locations within a phantom and confirming that the selected target location/needle location based on the registration calculation is within the same tolerance range or better than the Aegis predicate device."
8. Sample Size for the Training Set
- Training Set Sample Size: Not applicable. The document describes a software plug-in whose functionality is inherited from an approved device (Sentinelle Aegis) and combines features of several predicate devices. There is no mention of a machine learning or AI model being trained, thus no explicit training set. The performance evaluation is based on verifying its compliance with specifications and equivalence to predicate devices, not on a new learning algorithm.
9. How Ground Truth for the Training Set Was Established
- Ground Truth for Training Set: Not applicable, as there is no mention of a training set for a machine learning model.
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