(132 days)
The Diagnostic Ultrasound System Aplio 500 Model TUS-A500, Aplio 400 Model TUS-A400 and Aplio 300 Model TUS-A300 is indicated for the visualization of structures, and dynamic processes with the human body using ultrasound and to provide image information for diagnosis in the following clinical applications: fetal, abdominal, intra-operative (abdominal), pediatric, small organs, trans-vaginal, trans-rectal, neonatal cephalic, adult cephalic, cardiac (both adult and pediatric), peripheral vascular, transesophageal, and musculo-skeletal (both conventional and superficial).
The Aplio 500 Model TUS-A500, Aplio 400 Model TUS-A400 and Aplio 300 Model TUS-A300 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 12 MHz.
This is a 510(k) premarket notification for modifications to an ultrasound system, not for an AI device. The document describes the device as the "Aplio 500 Model TUS-A500, Aplio 400 Model TUS-A400 and Aplio 300 Model TUS-A300" diagnostic ultrasound systems. The submission is for "Modification of a cleared device" that "improves upon existing features including the image visualization of blood flow."
Therefore, the prompt's request for "acceptance criteria and the study that proves the device meets the acceptance criteria" in the context of an AI device, along with details like "sample size used for the test set," "number of experts used to establish the ground truth," "adjudication method," "MRMC comparative effectiveness study," "standalone performance," and "training set," is not applicable to this document.
The document does not describe an Artificial Intelligence (AI) / Machine Learning (ML) enabled device. It is a traditional medical device modification.
Here's what can be extracted regarding performance testing, although it's not in the context of AI acceptance criteria:
1. A table of acceptance criteria and the reported device performance:
This document does not provide specific quantitative acceptance criteria or detailed performance metrics in the format typically seen for AI device evaluations. The submission states:
- Acceptance Criteria (Implicit): The device modifications meet the requirements for improved/added features. The device is safe and effective for its intended use.
- Reported Device Performance: The modifications improve existing features, specifically "the image visualization of blood flow." The document also lists the various clinical applications and modes of operation for which the system and its transducers are indicated (e.g., Fetal, Abdominal, Cardiac, Peripheral Vascular, etc., and B-mode, M-mode, PWD, CWD, Color Doppler, etc.). However, it does not provide quantitative results like sensitivity, specificity, or image quality scores for these improvements or listed functionalities, as would be expected for an AI device.
2. Sample sized used for the test set and the data provenance:
- Sample Size: Not specified for any test set.
- Data Provenance: "acquisition of representative clinical images" was conducted as part of the testing. No country of origin is mentioned, and it is implied to be retrospective, as the images are "acquired."
3. Number of experts used to establish the ground truth for the test set and the qualifications of those experts:
- Not applicable as this is not an AI device submission requiring expert human ground truth for algorithm performance evaluation. Testing involved "bench testing and the acquisition of representative clinical images."
4. Adjudication method for the test set:
- Not applicable as this is not an AI device submission requiring adjudication of human expert annotations or ground truth.
5. If a multi reader multi case (MRMC) comparative effectiveness study was done, If so, what was the effect size of how much human readers improve with AI vs without AI assistance:
- No MRMC comparative effectiveness study was done, as this is not an AI device.
6. If a standalone (i.e. algorithm only without human-in-the-loop performance) was done:
- Not applicable, as this is not an AI device.
7. The type of ground truth used:
- For the "acquisition of representative clinical images", the ground truth is implicitly the clinical reality captured by the ultrasound imaging, verified by standard clinical interpretation and potentially other diagnostic methods. However, the document does not elaborate on how this "ground truth" was formally established or used to evaluate the new features of the device (like improved blood flow visualization) beyond stating that the features met requirements.
8. The sample size for the training set:
- Not applicable, as this is not an AI device and thus has no training set in the AI/ML sense.
9. How the ground truth for the training set was established:
- Not applicable, as this is not an AI device and thus has no training set.
§ 892.1550 Ultrasonic pulsed doppler imaging system.
(a)
Identification. An ultrasonic pulsed doppler imaging system is a device that combines the features of continuous wave doppler-effect technology with pulsed-echo effect technology and is intended to determine stationary body tissue characteristics, such as depth or location of tissue interfaces or dynamic tissue characteristics such as velocity of blood or tissue motion. This generic type of device may include signal analysis and display equipment, patient and equipment supports, component parts, and accessories.(b)
Classification. Class II.