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510(k) Data Aggregation
(239 days)
Cellinew is indicated for use in dermatologic and general surgical procedures for electrocoagulation and hemostasis.
The CELLINEW generates radiofrequency(RF) energy by means of high RF at 6.78Mhz. The RF energy is delivered through the skin into the target tissue via a handpiece equipped with an electrode tip. As the RF energy passes through the tissue, it generates an electrothermal reaction with is capable of coagulation the tissue. In detail, the main body uses the "reverse thermal gradient" principle to deliver RF energy, and thus, heat is generated and selective coagulation occurs while cooling the epidermis, resulting in denaturation and contraction of collagen fibers.
The CELLINEW Device is a non-invasive device for coagulation using high frequency current. The device consists of a main body, handpiece, two disposable electrodes, a neutral electrode, neutral electrode cable, foot switch(optional) and power cord, and is controlled by software.
The provided text describes the 510(k) summary for the CELLINEW device, but it lacks the specific details required to fully address your request regarding acceptance criteria and a detailed study report. The document states that "No clinical studies were conducted as part of this submission," and the performance testing described focuses on technical aspects and an ex-vivo thermal test.
However, I can extract the available information and highlight what is missing based on your request.
Missing Information:
The document does not provide:
- A formal table of acceptance criteria and reported device performance against those criteria beyond what is inferred from the technical specifications comparison.
- Details on sample size, data provenance, number or qualifications of experts, or adjudication methods for any test set, as no clinical studies were performed.
- Information about multi-reader multi-case (MRMC) comparative effectiveness studies.
- Details on standalone algorithm performance, as this is a physical medical device, not an AI algorithm.
- The type of ground truth used for any clinical performance evaluation.
- Sample size for a training set or how ground truth was established for a training set, as no AI/machine learning component or related training data is mentioned.
Based on the available text, here is the information that can be extracted:
1. Table of Acceptance Criteria and Reported Device Performance
A formal table of acceptance criteria and the device's performance against them is not explicitly provided in the document. However, implied acceptance is based on the comparison to a predicate device and technical performance tests.
Here is a summary of the technical specifications and their comparison to the predicate, which serves as an informal set of performance criteria for equivalence:
Specification | Acceptance Criteria (Implied by Predicate) | Reported Device Performance (CELLINEW) | Comparison / Outcome |
---|---|---|---|
Output energy type | Radiofrequency | Radiofrequency | Same |
User Interface | Color touch panel | Color touch panel | Same |
Operating Frequency | 6.78MHz | 6.78MHz | Same |
Max power | 400W | Up to 400W | Same |
Electrode Tips | 0.25cm², 4cm², 16cm² | 0.25cm², 4cm² | Different (Cellinew does not include the 16cm² tip), but deemed not to impact safety/effectiveness. |
Coolant Solution | Cryogen | Cryogen | Same |
Temperature Range | 65~75°C | 65~75°C | Same |
RF Time | 50 ~ 800 ms | 0-1050ms | Different. Marginally higher for CELLINEW, but deemed not to impact safety or effectiveness. |
Impedance | 75 - 400 Ω | 75 - 400 Ω | Same |
Style of Electrode Tip | Monopolar, Bipolar | Monopolar only | Different (only monopolar for Cellinew), but deemed not to impact safety or effectiveness (implied by substantial equivalence). |
2. Sample size used for the test set and the data provenance
For the performance testing mentioned:
- Sample Size: Not specified for the "thermal testing" on porcine skin.
- Data Provenance: The thermal testing was performed on "porcine skin," indicating an ex-vivo (animal tissue) study, not human data. The country of origin for this data is not specified.
For clinical evidence:
- "No clinical studies were conducted as part of this submission."
3. Number of experts used to establish the ground truth for the test set and the qualifications of those experts
Not applicable, as no clinical studies were performed and no "ground truth" for diagnostic or treatment outcome accuracy was established in the provided documentation based on human assessment. The "thermal testing" involved demonstrating the device's ability to coagulate tissue, likely measured by technical means rather than expert interpretation of images.
4. Adjudication method for the test set
Not applicable, as no clinical studies or human-interpreted test sets were reported.
5. If a Multi-Reader Multi-Case (MRMC) comparative effectiveness study was done, and the effect size of how much human readers improve with AI vs without AI assistance
Not applicable. The CELLINEW device is an electrosurgical device, not an AI diagnostic or assistance system. No MRMC studies were conducted.
6. If a standalone (i.e., algorithm only without human-in-the-loop performance) was done
Not applicable. This is a physical electrosurgical device; it does not operate as a standalone algorithm in the context of diagnostic or interpretive performance. The software controls the device, and its performance was evaluated via software verification and validation, but not as an "algorithm only" performance study.
7. The type of ground truth used
For the "thermal testing" on porcine skin, the "ground truth" was the observed ability of the device to "coagulate tissue," which is a direct physical effect. This is not comparable to expert consensus, pathology, or outcomes data typically used in diagnostic or screening device evaluations.
8. The sample size for the training set
Not applicable. The device is not an AI/machine learning system that requires a "training set" in the conventional sense.
9. How the ground truth for the training set was established
Not applicable. There is no mention of a training set for an AI/machine learning component.
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