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The Aesculap-Meditec Phacolase™ Er:YAG laser is intended for cutting, incision, excision, vaporization, and ablation of soft tissue in ophthalmology: Anterior capsulotomy Soft tissue surrounding the orbit of the eye

The Aesculap-Meditec Phacolase™ Er: YAG laser emits a beam of coherent light at 2.94 microns.

The provided text is a 510(k) summary for the Aesculap-Meditec Phacolase™ Er:YAG laser. This document describes the device's intended use and claims substantial equivalence to predicate devices based on preclinical and clinical testing. However, it does not contain detailed acceptance criteria, specific performance metrics, sample sizes for test or training sets, information about expert adjudication, or discussions of multi-reader multi-case studies or standalone algorithm performance.

Therefore, many of your requested details cannot be extracted from this specific document.

Here's what can be extracted and what cannot:

1. A table of acceptance criteria and the reported device performance

• This information is not available in the provided text. The document states "The results of a preclinical and clinical testing were submitted in support of the claim for incision and fragmentation of cataractous lenses during cataract surgery," but no specific performance metrics or acceptance criteria are listed.

2. Sample size used for the test set and the data provenance (e.g. country of origin of the data, retrospective or prospective)

• This information is not available in the provided text.

3. Number of experts used to establish the ground truth for the test set and the qualifications of those experts (e.g. radiologist with 10 years of experience)

• This information is not available in the provided text. The device is a laser for surgical procedures, so "ground truth" in the context of diagnostic AI is not directly applicable. For a surgical device, performance would typically be evaluated based on surgical outcomes, safety, and efficacy during the procedure itself.

4. Adjudication method (e.g. 2+1, 3+1, none) for the test set

• This information is not applicable/not available. Adjudication methods like 2+1 or 3+1 are typically used for establishing ground truth in diagnostic studies involving multiple human readers, not for evaluating the performance of a surgical laser device based on preclinical and clinical testing.

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

• This information is not applicable/not available. An MRMC study is relevant for AI-assisted diagnostic tools. This document describes a surgical laser, not an AI diagnostic system. Therefore, it does not involve "human readers" in the sense of interpreting medical images with or without AI assistance.

6. If a standalone (i.e. algorithm only without human-in-the loop performance) was done

• This information is not applicable/not available. This is a surgical laser, not an algorithm.

7. The type of ground truth used (expert consensus, pathology, outcomes data, etc)

• For a surgical laser, "ground truth" would generally refer to outcomes observed during preclinical (e.g., in-vitro, animal) and clinical (human) studies, such as successful incision, vaporization, absence of adverse events, and achievement of the surgical goal. However, the specific type of ground truth (e.g., detailed pathological findings, specific surgical outcome metrics) used in the submitted testing is not detailed in this summary. It only broadly mentions "preclinical and clinical testing."

8. The sample size for the training set

• This information is not applicable/not available. "Training set" is a term used in machine learning for AI development. This document is about a medical device (laser), not an AI algorithm.

9. How the ground truth for the training set was established

• This information is not applicable/not available. As above, "training set" is not relevant to this device type.

In summary, the provided 510(k) summary for the Aesculap-Meditec Phacolase™ Er:YAG laser confirms that preclinical and clinical testing were performed to demonstrate substantial equivalence for its intended indications. However, it lacks the detailed methodology and performance metrics typically found in a study report for a diagnostic AI device.

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The Aesculap-Meditec CO2 Laser is intended for the ablation, vaporization, incision, excision, or cutting of soft tissue in oral surgery, E.N.T., gynecology, and dermatology.

The Aesculap-Meditec MultiPulse-CO2 laser emits a beam of coherent light at 10.6 microns.

The provided document is a 510(k) summary for the Aesculap-Meditec MultiPulse CO2 laser, not a study report detailing acceptance criteria and performance.

The document explicitly states:

"Performance Data: None. The specifications and indications for use of the Aesculap-Meditec MultiPulse CO2 laser are the same or very similar to those of the claimed predicate device. Because of this, performance data were not required."

Therefore, based on the provided text, the device did not undergo a study to prove it meets specific acceptance criteria as it was determined to be substantially equivalent to a predicate device without requiring new performance data.

Thus, I cannot provide the requested information from the given text.

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The MCL 29 Dermablate laser is intended for coagulation, vaporization, ablation, or cutting of soft tissue (skin) in dermatology, plastic surgery (including aesthetic surgery), and in oral and maxillofacial surgery, and ophthalmology (oculoplasty).

The MCL 29 Dermablate laser is an erbium: YAG of power at 2.94um.

This looks like a 510(k) premarket notification for a medical device, specifically a laser system for soft tissue ablation. In the context of 510(k) submissions, the “acceptance criteria” and “study that proves the device meets the acceptance criteria” often refer to the demonstration of substantial equivalence to a predicate device, rather than a clinical trial with specific performance metrics like sensitivity and specificity for an AI algorithm.

Here's an analysis based on the provided text, addressing your questions where applicable within this regulatory framework:

1. Table of Acceptance Criteria and Reported Device Performance

For 510(k) applications demonstrating substantial equivalence, the "acceptance criteria" are typically that the new device shares basic features, intended use, and similar technological characteristics with a legally marketed predicate device, and does not raise different questions of safety and effectiveness.

In this document, the "performance" is assessed by comparing the specifications and intended use of the MCL 29 Dermablate laser with two predicate devices.

Summary of Device Performance: "The specifications and intended uses of the MCL 29 Dermablate laser are the same or very similar to those of the claimed predicate devices. There are no significant differences between the devices under conditions of intended use."

2. Sample size used for the test set and the data provenance (e.g. country of origin of the data, retrospective or prospective)

The document explicitly states: "None. Because of this, performance data were not required." This indicates that no separate "test set" of empirical data (clinical or otherwise) was used to evaluate the device's performance in the way one would for an AI algorithm. The demonstration of substantial equivalence relies on a comparison of characteristics with predicate devices already on the market.

3. Number of experts used to establish the ground truth for the test set and the qualifications of those experts (e.g. radiologist with 10 years of experience)

Not applicable, as no performance data based on a test set requiring expert ground truth was generated or submitted for this 510(k).

4. Adjudication method (e.g. 2+1, 3+1, none) for the test set

Not applicable, as no performance data based on a test set requiring adjudication was generated or submitted.

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. This device is a laser system, not an AI-assisted diagnostic or therapeutic tool for human readers. No MRMC study was conducted.

6. If a standalone (i.e. algorithm only without human-in-the-loop performance) was done

Not applicable. This device is a laser system, not an algorithm.

7. The type of ground truth used (expert consensus, pathology, outcomes data, etc)

Not applicable. The "ground truth" for this 510(k) submission is the established safety and effectiveness of the predicate devices based on their prior marketing clearance. The new device is deemed safe and effective if it doesn't raise new questions of safety and effectiveness compared to these predicates.

8. The sample size for the training set

Not applicable. This is a hardware device; there is no "training set" in the context of machine learning.

9. How the ground truth for the training set was established

Not applicable. There is no training set for this device.

In summary, for K964128, the "study that proves the device meets the acceptance criteria" is the detailed comparison of its specifications and intended use against two legally marketed predicate devices. The acceptance criteria essentially boil down to demonstrating that the new device is "substantially equivalent" to these predicates, meaning it does not raise new questions of safety or effectiveness. The document explicitly states, "Because of this, performance data were not required," indicating that a clinical trial or empirical performance study was not deemed necessary by the FDA for this particular submission.

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