(91 days)
The Diagnostic Ultrasound System Xario 200 Model TUS-X200 and Xario 200 Model TUS-X200S are indicated for the visualization of structures, and dynamic processes with the human body using ultrasound and to provide image information for diaqnosis in the following clinical applications: fetal, abdominal, intra-operative(abdominal), laparoscopic, pediatric, small orqans, neonatal cephalic, adult cephalic, trans-rectal, trans-vaginal, musculo-skeletal (conventional), musculo-skeletal (superficial), cardiac adult, cardiac pediatric, trans-esoph(cardiac) and peripheral vessel.
The Xario200 Model TUS-X200 and Model TUS-X200S 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.
The provided text describes a 510(k) premarket notification for the Toshiba Medical Systems Corporation's Xario 200 Diagnostic Ultrasound System V5.0. This submission focuses on modifications to a previously cleared device (Xario 200, V3.0) and the introduction of new features and transducers.
The document does not describe acceptance criteria or a specific study proving the device meets those criteria in the way one might expect for a new algorithmic device with quantifiable performance metrics. Instead, it describes substantial equivalence to a predicate device and verification/validation testing against recognized standards.
Here's an analysis based on the provided text:
1. Table of Acceptance Criteria and Reported Device Performance:
The document doesn't provide a table of acceptance criteria in terms of specific performance metrics (e.g., sensitivity, specificity, accuracy) for an AI component. Instead, it demonstrates performance by stating that the updates "do not raise new questions of safety and effectiveness" and that the added features perform "as expected" and are "substantially equivalent" to predicate devices.
The closest to "reported device performance" is the statement regarding the Superb Micro Vascular Imaging (SMI) feature:
| Performance Characteristic | Acceptance Criteria / Predicate Equivalence | Reported Device Performance (SMI) |
|---|---|---|
| Image Quality (SMI) | Demonstrates imaging of low velocity flow with significant reduction in clutter noise; capable of imaging with high frame rate (based on predicate equivalence and performance described) | "SMI was capable of imaging low velocity flow with a significant reduction in clutter noise and was capable of imaging with a high frame rate." |
| Image Quality (Precision Plus Imaging) | Image quality improvements (based on predicate equivalence) | "Image quality improvement(s) to existing feature" |
| Safety and Effectiveness | Substantially equivalent to predicate device (K143027) and reference device (K151451); compliance with international standards | Device is safe and effective for its intended use; conforms to IEC60601-1, IEC 60601-1-2, IEC 60601-2-37, IEC 62304, AIUM RTD2-2004, and ISO 10993-1. |
2. Sample size used for the test set and the data provenance:
The document mentions "Representative clinical images of volunteers were obtained to demonstrate that the implementation of SMI onto the subject device performed as expected." However, it does not specify the sample size (number of images or volunteers) used for this performance testing.
Data provenance: The document does not explicitly state the country of origin or if the data was retrospective or prospective. It only mentions "clinical images of volunteers," which suggests prospective data collection in a clinical setting, but further details are not provided.
3. Number of experts used to establish the ground truth for the test set and the qualifications of those experts:
The document does not specify the number of experts, their qualifications, or how they established the ground truth for any of the performance testing mentioned. This is typical for submissions focused on substantial equivalence where the primary assessment is whether the new features perform comparably to predicate devices, rather than establishing de novo clinical performance metrics against a defined ground truth.
4. Adjudication method for the test set:
The document does not describe any adjudication method used for the test set.
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:
A multi-reader multi-case (MRMC) comparative effectiveness study was not performed or described in this submission. The device is an ultrasound system with modified and new imaging features, not an AI-assisted diagnostic tool that directly aids human readers in interpretation or diagnosis in a quantifiable way measurable by effect size in improved reader performance. The "AI" mentioned (SMI) is an imaging enhancement technique, not an interpretive AI.
6. If a standalone (i.e. algorithm only without human-in-the loop performance) was done:
The performance evaluation described ("SMI was capable of imaging low velocity flow with a significant reduction in clutter noise and was capable of imaging with a high frame rate") appears to be a standalone assessment of the algorithm's technical capabilities in imaging. It describes the intrinsic performance of the SMI algorithm, without explicitly involving human interpretation performance as an outcome measure, which aligns with device modifications for image quality rather than diagnostic AI.
7. The type of ground truth used (expert consensus, pathology, outcomes data, etc):
The document does not specify a formal ground truth (like pathology or outcomes data) for the evaluation of SMI. The description "SMI was capable of imaging low velocity flow with a significant reduction in clutter noise and was capable of imaging with a high frame rate" implies that the assessment was likely based on qualitative evaluation by imaging experts or quantitative technical measurements of flow and noise reduction, rather than a clinical ground truth for a specific diagnosis.
8. The sample size for the training set:
Ultrasound systems and their imaging enhancements (like SMI, ApliPure, Precision Imaging, etc.) are typically developed through engineering and signal processing, often using simulated data, phantom studies, and then clinical images for refinement and validation. The concept of a distinct "training set" in the context of machine learning, as opposed to engineering development and system optimization, is not mentioned in this document.
9. How the ground truth for the training set was established:
As no "training set" in the machine learning sense is described, there is no information on how ground truth for a training set was established. The development likely involved iterative engineering adjustments and testing against known benchmarks or expert qualitative assessment of image quality.
Summary of Missing Information:
The provided 510(k) summary is typical for showcasing substantial equivalence for an ultrasound imaging device with updated features rather than a novel AI-driven diagnostic algorithm. Therefore, detailed information about acceptance criteria in terms of clinical performance metrics (sensitivity, specificity), sample sizes for test/training sets, expert qualifications, adjudication methods, or MRMC studies for AI interpretation is largely absent. The focus is on demonstrating that the device remains safe and effective and comparable to previously cleared devices, with new features performing as expected in a technical sense.
§ 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.