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The intended use of this device is the automated phacoemulsification of a cataractous natural crystalline lens.

The Series 20000 Legacy System and its predicate devices use ultrasonic energy to emulsify cataractous lens material and remove it from the eye (phacoemulsification). Electronic energy is generated in the machine, delivered to a handpiece and converted to ultrasonic energy delivered through a hollow titanium needle, or tip. Irrigation fluid is delivered to the eye via the combination of an irrigation sleeve over the handpiece tip. The emulsified lens material is aspirated out of the eye through the center of the handpiece/tip assembly.

In this new system, an inner protective sleeve has been attached around the barrel of the ultrasonic tip in order to reduce the amount of undesirable heat transferred to the wound site. These ultrasonic tips will be available in three (3) styles each consisting of combinations of two (2) tip diameters and three (3) tip edge bevel angles.

The provided text describes a 510(k) summary for the Alcon Series 20000® LEGACY® Mackool™ System Phacoemulsification Tip and Sleeve, which is a medical device for phacoemulsification. The summary focuses on demonstrating substantial equivalence to predicate devices rather than providing a detailed study design with acceptance criteria and statistical analysis typical of a de novo device.

Therefore, many of the requested elements for a detailed AI/ML device study are not applicable to this document as it describes a non-AI medical device and a 510(k) submission, which has different requirements than an AI/ML device validation study.

Here's an attempt to extract and interpret the information based on the provided text, while acknowledging its limitations for an AI/ML context:

1. Table of Acceptance Criteria and Reported Device Performance

• Alcon Mackool™ tips showed significantly lower temperature than Alcon standard 0.9 mm tips.
• Alcon Mackool™ tips showed somewhat lower temperature than Storz MicroSeal tips. |
  | Fluidics Performance | Similar infusion flow capability to predicate devices to maintain comparable intraocular pressure. | - All TSC (Tip Sleeve Combinations) were found to have similar flow capability to predicate devices.
• Clinical relevance: Intraocular pressure during phacoemulsification procedures will be similar for all devices at the same aspiration flow rate. |
  | Cutting Performance | Acceptable cutting performance, at least equal to predicate devices. | - Bench tests: Measured tip stroke length.
• Clinic: Surgeon evaluation.
• Mackool tips showed acceptable cutting performance that is equal to (Alcon tips) or superior to (Storz tips) predicate devices. |

Explanation of "Acceptance Criteria (Implied)":
The 510(k) submission process for non-AI devices typically relies on demonstrating "substantial equivalence" to a legally marketed predicate device. This means the new device must be as safe and effective as the predicate device. The implied acceptance criteria are that the new device's performance in key areas (thermal, fluidics, cutting) is at least comparable to, or ideally better than, the predicate devices without raising new questions of safety or effectiveness. The document effectively uses the predicate devices' performance as the benchmark for acceptance.

The following points are not directly applicable or not explicitly provided in the given 510(k) summary because it describes a hardware medical device and not an AI/ML algorithm:

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

• Not Applicable in the traditional AI/ML sense. The "test set" here refers to real-world or simulated testing environments using animal models (post-mortem eye tests) and bench tests. The specific "sample sizes" (e.g., number of eyes, number of bench tests) are not explicitly stated, nor is the data provenance in terms of country of origin or retrospective/prospective clinical study design as one would expect for an AI/ML model. The tests were likely conducted internally by Alcon Laboratories, Inc. in Fort Worth, Texas, USA.

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. "Ground truth" in this context is not established by expert annotation of images. Instead, performance metrics like temperature, flow rate, and tip stroke length are objectively measured. Surgeon evaluation for cutting performance is mentioned, implying expert feedback, but the number and qualifications of these surgeons are not specified.

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

• Not Applicable. Adjudication methods are typically for resolving disagreements in expert annotations for AI/ML ground truth. Here, objective measurements and potentially surgeon consensus (unspecified method) were used.

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 is a hardware device, not an AI/ML system, so there are no "human readers" or "AI assistance" in this context.

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

• Not Applicable. This is a hardware device. Its "standalone" performance refers to its mechanical and physical attributes (thermal, fluidics, cutting) as measured in bench and post-mortem tests.

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

• For Thermal and Fluidics: Objective physical measurements (thermocouple readings for temperature, flow rate measurements for fluidics). This constitutes an empirical, measurable "ground truth."
• For Cutting: A combination of objective measurement (tip stroke length) and subjective surgeon evaluation.

8. The sample size for the training set

• Not Applicable. This is a hardware device; there is no "training set" in the AI/ML sense. Device design and optimization would involve prototyping and iterative testing, but not "training data" for an algorithm.

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

• Not Applicable. As there is no training set for an AI/ML algorithm, this question is not relevant. The "ground truth" for the device's development would be based on engineering specifications and performance benchmarks derived from predicate devices and desired clinical outcomes.

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