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510(k) Data Aggregation
(78 days)
Aplio i900/i800/i700 Diagnostic Ultrasound System, V2.0
The Diaqnostic Ultrasound Systems Aplio i900 Model TUS-AI900, Aplio i800 Model TUS-AI800 and Aplio i700 Model TUS-AI700 are indicated for the visualization of structures, and dynamic processes with the human body usinq ultrasound and to provide image information for diaqnosis in the following clinical applications: fetal, abdominal, intra-operative (abdominal), pediatric, small orqans, trans-vaqinal, trans-rectal, neonatal cephalic, adult cephalic, cardiac (both adult and pediatric), peripheral vascular, transesophageal, musculo-skeletal (both conventional and superficial) and laparoscopic.
The Aplio i900 Model TUS-Al900, Aplio i800 Model TUS-Al800 and Aplio i700 Model TUS-Al700, V2.0 are mobile diagnostic ultrasound systems. These systems are Track 3 devices that employ a wide array of probes including flat linear array, convex linear array, and sector array with frequency ranges between approximately 2 MHz to 20 MHz.
This document is a 510(k) Pre-market Notification for a diagnostic ultrasound system (Aplio i900/i800/i700, V2.0). It focuses primarily on demonstrating substantial equivalence to a predicate device and safety, rather than providing detailed acceptance criteria and study results for specific performance metrics that a standalone AI/device would typically have.
Here's an analysis based on the provided text, addressing your points where possible:
1. Table of Acceptance Criteria and Reported Device Performance
The document does not explicitly present a table of quantitative acceptance criteria alongside corresponding test results. Instead, it describes performance in qualitative terms (e.g., "expected," "can be obtained," "acceptable," "improves workflow," "within specified range").
| Feature/Test | Acceptance Criteria (Implied/Qualitative) | Reported Device Performance (Qualitative) |
|---|---|---|
| Sensor 3D | Provide expected 3D images, measure accuracy, hardness information, and frequency dispersion of shear wave function. | Sensor 3D provides the expected 3D images of phantom structures, measurement accuracy, hardness information and the frequency dispersion of the target of the 3D image with the shear wave function. |
| Attenuation Imaging | Obtain accurate quantitative attenuation coefficient results and display a color map of spatial distribution. | Accurate quantitative attenuation coefficient results can be obtained using Attenuation Imaging and that a color map is displayed to show the spatial distribution of attenuation coefficient. (Confirmed in-vivo on volunteer livers with color map and numerical results). |
| Tissue Intensity Analysis (NLV) | Visualize distribution of homogeneous and heterogeneous areas and display an acceptable color map. | NLV can be used to visualize the distribution of homogeneous and heterogeneous areas of various phantoms by displaying a color map. (Confirmed on volunteer livers that NLV displays acceptable color map images and mean NLV values, in-vivo). |
| Fusion Auto Track | Enable automatic fusion of real-time ultrasound images to previously acquired CT or MR data sets using OmniTRAX. | Fusion Auto Track enables automatic fusion of real-time ultrasound images to previously acquired CT or MR data sets by using the OmniTRAX Active Patient Tracker. |
| i Auto Volume Measurement | Improve workflow and maintain measurement accuracy within a specified range compared to the predicate device. | i Auto Volume Measurement improves workflow using volume transducers compared with the predicate device and that the measurement accuracy is within the specified range. |
| CHI (Contrast Harmonic Imaging) | Visualize nonlinear signals (2nd harmonic) from contrast medium, quantify peak intensity, and time-to-peak. | CHI can visualize the nonlinear signal including 2nd harmonic from the Contrast medium, the quantification of the peak intensity, the time to reach the peak intensity. |
| Shear Wave Dispersion (SWD) | Visualize frequency dependency of shear wave speed (Phase velocity). | Shear Wave Dispersion (SWD) can visualize a frequency dependency of the shear wave speed (Phase velocity). |
| Shadow Glass | Display tissue and tissue with flow in a transparent manner. | Shadow Glass displays tissue and tissue with flow in a transparent manner. |
2. Sample Size Used for the Test Set and Data Provenance
The document does not specify exact sample sizes for the test sets. It mentions "various phantom studies" and "representative clinical images of volunteer livers" (plural, suggesting more than one, but no specific number).
The provenance for clinical data is described as "volunteer livers" (in-vivo), but no country of origin is specified. The studies appear to be prospective for the clinical images used for verification.
3. Number of Experts Used to Establish Ground Truth and Qualifications
The document does not explicitly state the number or qualifications of experts used to establish ground truth for the test set.
4. Adjudication Method
The document does not mention any adjudication method used for the test set.
5. Multi-Reader Multi-Case (MRMC) Comparative Effectiveness Study
No MRMC comparative effectiveness study is mentioned. The focus is on demonstrating the functionality and capabilities of the new features.
6. Standalone (Algorithm Only) Performance
The device is a diagnostic ultrasound system. The performance tests described (phantom studies, clinical images) inherently evaluate the algorithms as part of the overall system performance. There is no specific mention of "algorithm-only" performance metrics separate from the device's integrated operation. The new features incorporate advanced processing, and their performance is assessed directly through imaging and measurement tasks.
7. Type of Ground Truth Used
- Phantom studies: Physical phantoms with known properties (e.g., structures, hardness, attenuation coefficients) were used as ground truth.
- Clinical images: For the in-vivo evaluations, it is implied that the clinical images from "volunteer livers" served as the basis for assessing the qualitative aspects of the new features (e.g., whether the color maps were "acceptable," whether the expected features were visualized). However, there is no explicit mention of an external, independent ground truth (e.g., pathology, other imaging modalities) being used for these volunteer studies, beyond the visual and numerical output of the device itself being deemed acceptable by the evaluators.
8. Sample Size for the Training Set
The document describes pre-market notification for a diagnostic ultrasound system, not a device primarily driven by machine learning with distinct training and test sets in the AI sense. It discusses "new features" and "improvements to previously cleared features." Therefore, the concept of a "training set" in the context of AI models is not applicable or mentioned in this document. The system's development likely involved engineering, signal processing, and iterative refinement, not explicit AI model training.
9. How the Ground Truth for the Training Set Was Established
As explained in point 8, the concept of a "training set" for AI is not explicitly relevant to the descriptions in this document. Therefore, no information is provided on how ground truth for a training set was established.
In summary:
This 510(k) submission primarily focuses on establishing substantial equivalence for an ultrasound system with new and improved features. It describes functional performance verification through bench and clinical assessments, but it does not provide the detailed, quantitative acceptance criteria, explicit ground truth methodologies, or extensive statistical study results that would typically be expected for a standalone AI/ML-based medical device. The "acceptance criteria" are implied qualitative expectations for the performance of the new features, and the "study" is the verification and validation testing, predominantly bench testing with phantoms and limited clinical image acquisition in volunteers.
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