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510(k) Data Aggregation
(104 days)
The Juniper Cooling Device is intended for use as a skin cooling device to minimize pain and thermal injury during laser and dermatological treatments. Alternative uses include skin cooling as a local anesthetic for procedures that induce minor local discomfort.
The Juniper Cooling Device a thermoelectric cooling device that applies controlled cooling to a treatment site.
The provided text describes a 510(k) summary for the Juniper Cooling Device. However, it does not include detailed acceptance criteria or a study proving the device meets specific performance metrics in the way typically expected for a scientific or clinical study.
Instead, the submission establishes substantial equivalence to predicate devices based on:
- Intended Use: The indications for use are the same as predicate devices.
- Technological Comparison: The device is described as a thermoelectric cooling device, similar to the predicates.
- Bench Testing: "Bench testing demonstrate that the Juniper Cooling Device is functionally equivalent to the predicate devices."
Therefore, most of the requested information regarding detailed acceptance criteria and a study demonstrating the device meets those criteria is not present in the provided text. The document primarily focuses on demonstrating substantial equivalence to already marketed devices.
Here's a breakdown of the available information based on your request:
1. Table of acceptance criteria and the reported device performance
| Acceptance Criterion | Reported Device Performance |
|---|---|
| Functional Equivalence to Predicate Devices | "Bench testing demonstrate that the Juniper Cooling Device is functionally equivalent to the predicate devices." |
| Indications for Use Match Predicate Devices | "The indications for use for the Juniper Cooling Device are the same as predicate devices cited in this application." |
Note: No specific numerical or quantitative acceptance criteria (e.g., cooling temperature range, cooling rate, pain reduction percentage) are provided in this summary. The "performance data" section is very general.
2. Sample size used for the test set and the data provenance
Not applicable/Not provided. The submission refers to "bench testing" but does not detail the sample size, specifics of the "test set" (e.g., number of devices tested, number of simulated skin models), or data provenance (country of origin, retrospective/prospective). This is typical for a 510(k) submission focused on substantial equivalence where detailed clinical trial data might not be required if technological equivalence and intended use are sufficient.
3. Number of experts used to establish the ground truth for the test set and the qualifications of those experts
Not applicable/Not provided. There is no mention of human experts establishing ground truth for any test data in this submission. The evaluation is based on technical comparison and bench testing for functional equivalence.
4. Adjudication method for the test set
Not applicable/Not provided. No adjudication method is described, as there isn't a human expert-based ground truth establishment process detailed.
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
Not applicable/Not provided. This device is a cooling device, not an AI-assisted diagnostic or imaging tool. Therefore, an MRMC study related to human reader performance or AI assistance is not relevant or described.
6. If a standalone (i.e. algorithm only without human-in-the loop performance) was done
Not applicable/Not provided. The device is a physical cooling device, not an algorithm. Therefore, a standalone algorithm performance study is not relevant. The "performance data" refers to the device's functional equivalence.
7. The type of ground truth used (expert consensus, pathology, outcomes data, etc.)
Not applicable/Not provided. The evaluation appears to be based on the device's functional characteristics observed during "bench testing" compared to predicate devices, rather than a "ground truth" derived from clinical outcomes, pathology, or expert consensus on patient data.
8. The sample size for the training set
Not applicable/Not provided. This device is not an AI/ML algorithm that requires a "training set."
9. How the ground truth for the training set was established
Not applicable/Not provided. As it's not an AI/ML algorithm, there is no training set or associated ground truth.
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