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510(k) Data Aggregation
(278 days)
The Phaser Lipoplasty System is intended to be used for liquefaction and aspiration of localized subcutaneous fatty deposits for the purpose of aesthetic body contouring.
The Phaser Lipoplasty System is indicated for use in aesthetic body contouring.
The Phaser Lipoplasty System is a surgical liposuction device that utilizes pressurized, heated and pulsed saline streams ("the Phaser stream") in conjunction with a specialized cannula to transition adipose tissue from a solid state to a liquid or gel state so as to facilitate its removal from the body. The physician can employ standard surgical liposuction techniques when using the device, the low pressure and temperature allow adipose tissue removal without burning or otherwise damaging non-target tissues within the surgical field.
This looks like a 510(k) summary for the Andrew Technologies' Phaser Lipoplasty System. The document focuses on demonstrating substantial equivalence to predicate devices rather than providing detailed acceptance criteria and a study proving those criteria were met in the same way a de novo or PMA submission might.
Therefore, the requested information in the form of a table of acceptance criteria and reported performance, especially regarding AI-specific elements like sample sizes for test sets, ground truth establishment by experts, adjudication methods, MRMC studies, or standalone algorithm performance, is not available in this document. This device is a physical surgical liposuction system, not an AI/ML powered device, which explains the absence of such information.
However, I can extract the available performance data described in the document:
1. Table of Acceptance Criteria and Reported Device Performance
As this is a 510(k) submission for a physical device, typical "acceptance criteria" are not explicitly stated as they would be for an AI/ML device. Instead, the performance data focuses on demonstrating safety and comparable effects to existing devices. The primary performance assessment was based on thermal effects and histological evidence in a porcine model.
| Metric (Phaser System) | Reported Device Performance (Phaser) | Comparator (SAL) Performance | Acceptance Criteria (Implicit) |
|---|---|---|---|
| Average Skin Temperature | 31.4 to 35.8 ℃ | 26.2 to 31.1 ℃ | Not to cause thermal damage |
| Average Cannula Temperature | 36.7 to 40.2 ℃ | 28.5 to 33.8 ℃ | Not to cause thermal damage |
| Histological Evidence of Thermal Damage | None seen | None seen | No histological evidence of thermal damage |
| User Interface/Human Factors | Performed and functioned as intended | N/A | Device performs and functions as intended in use environment |
Study Proving Device Meets Performance:
The company conducted in-vivo testing in a porcine model and a simulated use study.
2. Sample size used for the test set and the data provenance:
- Sample Size for Test Set: Not explicitly stated (e.g., number of pigs, number of test sites). The document mentions "a porcine model."
- Data Provenance: Porcine model (animal study, in-vivo), likely conducted by the manufacturer or contracted research organization. Retrospective/prospective is not specified but animal tests are typically prospective. Country of origin not specified.
3. Number of experts used to establish the ground truth for the test set and the qualifications of those experts:
- Experts: Not explicitly stated. Histological examination typically involves a pathologist, but the number and qualifications are not provided.
- Qualifications: Not provided.
4. Adjudication method (e.g., 2+1, 3+1, none) for the test set:
- Not applicable/Not provided.
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, an MRMC study was not done. This is a physical device, not an AI/ML system, so human reader performance with or without AI assistance is not relevant.
6. If a standalone (i.e. algorithm only without human-in-the loop performance) was done:
- Not applicable. This is a physical device, not an algorithm.
7. The type of ground truth used (expert consensus, pathology, outcomes data, etc.):
- For Thermal Damage: Histological examination (pathology).
- For User Interface: "Evaluations" of human factor considerations, which would likely be observational assessments against intended function.
8. The sample size for the training set:
- Not applicable. This is a physical device, not an AI/ML algorithm that requires a training set.
9. How the ground truth for the training set was established:
- Not applicable. No training set for an AI/ML algorithm.
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