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510(k) Data Aggregation
(78 days)
The Diagnostic Ultrasound System is applicable for adults, pregnant women, pediatric patients and neonates. It is intended for use in gynecology, obstetric, abdominal, pediatric, small parts (breast, testes, thyroid, etc.), neonatal cephalic, transcranial, cardiac, transvaginal, peripheral vascular, urology, orthopedic, and musculoskeletal (conventional and superficial) exams.
The Navi e/ Navi s/Navi X Diagnostic Ultrasound System is a touch screen controlled ultrasonic system. Its function is to acquire and display ultrasound data in B-Mode, M-Mode, Color-Mode, Power (Dirpower)-Mode, PW-Mode, CW-mode, and the combined mode. The system can also measure anatomical structures and offer software analysis packages performance to provide information based on which the competent health care professionals can make the diagnosis. The Navi e/ Navi x/Navi X Diagnostic Ultrasound System consists of the main unit named Navi series, ultrasound probes, power adapter, connecting cable, probe extender, needle-guided bracket, batteries, mobile trolley, ECG module and batteries. Three models for the main units are included in this submission, that is Navi s, Navi e and Navi X. Eight different models of probes are available for the Navi series.
The provided text describes the Navi e/Navi s/Navi X Diagnostic Ultrasound System and its substantial equivalence to predicate devices, but it does not contain detailed acceptance criteria or a specific study proving the device meets those criteria, especially not in the context of AI/ML performance.
Instead, the document outlines various non-clinical tests conducted to ensure the device's safety and performance in a general sense, largely by comparing it to predicate ultrasound systems.
Here's an analysis of what information is available and what is missing based on your request:
1. Table of Acceptance Criteria and Reported Device Performance:
The document doesn't provide a specific table of acceptance criteria with corresponding performance metrics for the device. It generally states that "all of the tested parameters met the predefined acceptance criteria" for performance testing, but the criteria themselves are not enumerated.
2. Sample size used for the test set and the data provenance:
- Test Set Sample Size: Not applicable as no specific test set (e.g., imaging dataset for AI/ML evaluation) is mentioned. The performance testing refers to general ultrasound system performance.
- Data Provenance: Not applicable.
3. Number of experts used to establish the ground truth for the test set and the qualifications of those experts:
Not applicable, as no ground truth establishment for a test set (in the context of AI/ML or specific clinical performance evaluation) is mentioned.
4. Adjudication method (e.g., 2+1, 3+1, none) for the test set:
Not applicable, as no clinical test set with ground truth requiring adjudication is mentioned.
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:
- MRMC Study: No, an MRMC comparative effectiveness study was not done.
- Effect Size: Not applicable, as there's no mention of AI assistance for human readers or a study evaluating it.
6. If a standalone (i.e. algorithm only without human-in-the-loop performance) was done:
No, a standalone algorithm performance study was not done. The document does not describe any specific AI/ML algorithms that would require such a study. The software verification and validation are for the overall system software, deemed "moderate" level of concern.
7. The type of ground truth used (expert consensus, pathology, outcomes data, etc.):
Not applicable. The document discusses regulatory compliance, biocompatibility, electrical safety, and general performance of the ultrasound system, not the performance of an AI/ML component against a clinical ground truth.
8. The sample size for the training set:
Not applicable, as no AI/ML training set is mentioned or described.
9. How the ground truth for the training set was established:
Not applicable, as no AI/ML training set or its ground truth establishment is mentioned.
Summary of what the document does include regarding performance:
The document primarily focuses on demonstrating substantial equivalence to predicate ultrasound devices through:
- Biocompatibility testing: According to ISO 10993-1, ISO 10993-5, and ISO 10993-10, for cytotoxicity, sensitization, and skin irritation of probes and glue.
- Electrical safety and electromagnetic compatibility (EMC) testing: Compliance with IEC 60601-1:2012 and IEC 60601-1-2:2007.
- Performance testing: According to IEC 60601-2-37:2007 for ultrasonic medical diagnostic and monitoring equipment. It also mentions evaluation of "clinic measurement accuracy and system sensitivity" where "all of the tested parameters met the predefined acceptance criteria."
- Acoustic output measurement and real-time display: Compliance with NEMA UD 2:2004 and NEMA UD 3:2004.
- Software Verification and Validation Testing: Conducted per FDA guidance for "moderate" level of concern software.
Conclusion:
This submission for the Navi e/Navi s/Navi X Diagnostic Ultrasound System is for a conventional ultrasound imaging system. It does not describe any AI/ML components or their performance characteristics. Therefore, the specific information requested about AI acceptance criteria, clinical study designs (MRMC, standalone), ground truth, and training data is not present in the provided text. The "performance testing" mentioned refers to the general technical and functional performance of the ultrasound system itself, not the diagnostic performance of an AI algorithm.
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(368 days)
The Clover 50/Clover60/Clover70 Diagnostic Ultrasound System is applicable for adults, pregnant women, pediative patients and neonates. It is intended for use in gynecology, obstetric, abdominal, pediatric, small parts (breast, testes, thyroid, etc.), neonatal cephalic, transcranial, cardiac, transvaginal, peripheral vascular, urology, orthopedic, and musculoskeletal (conventional and superficial) exams.
The Clover 50/Clover60/Clover70 Diagnostic Ultrasound System is a mobile software controlled ultrasonic system. Its function is to acquire and display ultrasound data in B-Mode, M-Mode, Color-Mode, Power (Dirpower)-Mode, PW-Mode, CW-mode, and the combined mode. The system can also measure anatomical structures and offer software analysis packages performance to provide information based on which the competent health care professionals can make the diagnosis.
The Clover 50/Clover60/Clover70 Diagnostic Ultrasound System consists of the main unit named clover series, ultrasound probes, power adapter, connecting cable, probe extender, needle-guided bracket, batteries, mobile trolley and travelling case.
Three models for the main units are included in this submission that is Clover 50, Clover 60 and Clover 70. Seven different models of probes are available for the Clover series.
The provided text describes the Clover 50/Clover60/Clover70 Diagnostic Ultrasound System and its substantial equivalence to predicate devices, focusing on non-clinical performance testing. It does not contain information about acceptance criteria and a study proving the device meets acceptance criteria in a clinical setting with human subjects, nor does it detail a standalone algorithm performance, MRMC study, or ground truth establishment relevant to AI.
However, it does describe the performance testing criteria and results for various measurement accuracies and modes of operation. It considers these performance tests as evidence for substantial equivalence, implying they serve as acceptance criteria for the device's technical functionality relative to the predicate devices.
Here's a breakdown of the information that can be extracted, addressing your points where possible:
1. Table of Acceptance Criteria and Reported Device Performance
The device's performance was compared against the listed predicate device (Mindray M7/M7T, K131690). The "acceptance criteria" are implied by the performance of the predicate device, which the new device aims to be "substantially equivalent" to or better. The table shows the performance of the Clover 70 model and compares it to the predicate device.
Note: The predicate device's performance appears to set the acceptance criteria for the new device. Both devices are marked 'S' (Same) indicating substantial equivalence in these performance metrics.
| Items | Acceptance Criteria (from predicate M7/M7T) | Clover 70 Reported Performance | Substantial Equivalence |
|---|---|---|---|
| Precision of 2D Images | |||
| Distance | Within ±3%; or when the measured value is less than 40mm, the error is less than 1.5mm | Max Error: 1.4% (Full screen) | S |
| Area (Trace) | Within ±7%; or when the measured value is less than 16 cm², the error is less than 1.2 cm² | -5.11% (Full screen) | S |
| Area (ellipse, circle) | Within ±7%; or when the measured value is less than 16 cm², the error is less than 1.2 cm² | 0.8% (Full screen) | S |
| Circumference | Within ±7%; or when the measured value is less than 16 cm², the error is less than 1.2 cm² | -0.47% (Full screen) | S |
| Angle | Within ±3% | -1.89% (Full screen) | S |
| Volume | Within ±10%; or when the measured value is less than 64 cm³, the error is less than 6.4 cm³ | 0.51% (Full screen) | S |
| Basic Time/Motion measurements | |||
| Distance | Within ±3%; or when the measured value is less than 40mm, the error is less than 1.5mm | -2% (Full screen) | S |
| Time | Within ±2% | 0 (Timeline Display) | S |
| Heart rate | Within ±4% | 0 (15-999 beats per minute) | S |
| Velocity (PW mode) | When angle ≤ 60°, ≤5% | C5-1: 4.3% max; L15-4: 3.3% max; LH15-6: 3.1% max; P4-1: 4.8% max; EV10-4: 3.3% max; P7-3: 5.0% max | S |
| Velocity (CW mode) | When angle ≤ 60°, ≤5% | P4-1: 4.8% max; P7-3: 4.3% max | S |
2. Sample size used for the test set and the data provenance
The document refers to "Performance testing was conducted on the Clover 50/Clover60/Clover70 Diagnostic Ultrasound System, to evaluate the clinic measurement accuracy and system sensitivity, and all of the tested parameters met the predefined acceptance criteria." However, it does not specify the sample size used for this performance testing. It also does not mention data provenance (e.g., country of origin, retrospective or prospective) as this was non-clinical performance data, likely gathered in a lab or testing environment rather than a clinical dataset from patients.
3. Number of experts used to establish the ground truth for the test set and the qualifications of those experts
This information is not provided. The ground truth for this type of performance testing would typically be based on highly accurate physical measurements using calibrated equipment rather than expert human interpretation.
4. Adjudication method for the test set
This information is not provided. Given it's non-clinical performance metrics, an adjudication method (like 2+1, 3+1) would not be applicable in the same way as for clinical studies involving human interpretation.
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
There is no mention of an MRMC comparative effectiveness study or any AI component. The device described appears to be a traditional diagnostic ultrasound system and not an AI-powered device.
6. If a standalone (i.e. algorithm only without human-in-the-loop performance) was done
There is no mention of a standalone algorithm or AI performance.
7. The type of ground truth used (expert consensus, pathology, outcomes data, etc.)
For the performance testing metrics (distance, area, velocity, etc.), the ground truth would likely be established using precise physical phantoms and calibrated measurement tools, rather than clinical expert consensus, pathology, or outcomes data. The document does not explicitly state the method, but this is standard for ultrasound system calibration and performance verification.
8. The sample size for the training set
There is no mention of a training set, as this is not an AI/ML device.
9. How the ground truth for the training set was established
There is no mention of a training set or its ground truth, as this is not an AI/ML device.
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