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The moorLDLS Laser Doppler Line Scanner is intended for blood flow measurements in the microcirculation.

The moorLDLS laser Doppler line scanner is a device for imaging blood flow in the microcirculation. It uses the established laser Doppler technique to quantify movement of blood cells beneath the skin surface. Unlike the predicate device moorLDI2-IR laser Doppler imager, which use a single low power infrared laser beam, the moorLDLS line scanner sweeps a line of laser light across the tissue to build up a colour coded image of blood flow rapidly.

The document provided is a 510(k) summary for the moorLDLS Laser Doppler Line Scanner, which is a regulatory submission to the FDA. It does not contain a detailed study report with acceptance criteria and specific device performance metrics in the format requested.

However, based on the provided text, I can infer some aspects relevant to your request. The core of this submission is to demonstrate "substantial equivalence" to a predicate device, which inherently means meeting similar performance characteristics.

Here's an attempt to structure the information based on your request, acknowledging the limitations of the provided text:

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

Study Details:

The document describes a comparison test to evaluate the performance of the moorLDLS line scanner and determine its substantial equivalence to the predicate device moorLDI2-IR.

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

• Sample Size: Not specified in the provided text. The text only mentions "a set of comparison tests" including "flow model and image scan using both devices." The number of flow models, images, or subjects/instances used is not detailed.
• Data Provenance: Not explicitly stated as "country of origin of data." The submitter is Moor Instruments Ltd, located in the UK. It is highly probable that the data was generated in the UK. The study appears to be prospective in nature, as it describes tests carried out to evaluate a new device.

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/Not specified. This type of study (comparing a new device against a predicate for substantial equivalence) typically focuses on objective performance measurements (e.g., accuracy, reproducibility, correlation with the predicate) rather than expert-established ground truth for diagnostic interpretation. The "ground truth" would likely be the known output of the flow models or the established measurements from the predicate device.

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

• Not applicable/Not specified. Adjudication methods are typically used in clinical studies where expert consensus is needed to resolve discrepancies in interpretation. This specific submission focuses on objective technical comparison.

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 not an AI-assisted diagnostic device, nor is it a multi-reader, multi-case study in the context of human reader performance. It's a device for measuring blood flow, and the comparison is between two instruments.

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

• Yes, implicitly. The performance data described ("flow model and image scan using both devices") implies a standalone evaluation of the device's ability to measure blood flow, independent of human interpretation or assistance beyond operating the device.

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

• Based on the description "flow model and image scan," the ground truth would likely be physical measurements from controlled flow models and/or measurements produced by the predicate device itself, which is considered a legally marketed and established standard for blood flow measurement.

8. The sample size for the training set

• Not applicable/Not specified. This is not a machine learning or AI device that requires a training set in the conventional sense. The "training" for such a device would be its engineering design and calibration based on known physical principles.

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

• Not applicable (as per point 8). The device's design and calibration would be based on fundamental physics and engineering principles, with validation against established standards and methods in laboratory settings.

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