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Emily's Care Nourish Test System (Model 1) quantitatively measures the concentration of protein, fat (triglycerides), and carbohydrates (lactose) in human milk. It also provides calculated values for calories (energy). These measurements, in conjunction with other clinical assessments, may be used to aid in the nutritional management of newborns, including preterm, and infants.

This device is intended for use in healthcare by trained healthcare personnel at point of care or clinical laboratory settings.

The device is for prescription use only.

The Emily's Care Nourish Test System (Model 1) is an in vitro diagnostic device that consists of the following:

• 1. iPhone models 12 mini. 12 pro. 12 pro max. 13. 13 pro max, 14 or 14 pro max running iOS 16 or 17
• 2. Free Emily's Care app to be downloaded on the iPhone app store
• 3. Single-use test strips, in a desiccant-lined vial
• 4. Single-use reference card to place the test strip on to be read
• 5. Pipettes and tubes to collect milk
• 6. Lightbox

The provided text is a 510(k) summary from the FDA for the Emily's Care Nourish Test System (Model 1). It primarily focuses on the regulatory aspects of device clearance rather than a detailed study report on acceptance criteria and performance. Therefore, I cannot provide a complete answer to all parts of your request based solely on the provided text.

Specifically, the document does not contain:

• A table of acceptance criteria and reported device performance (only ranges are given for measuring various components).
• Details on sample sizes used for the test set, data provenance, or details about training sets.
• Information on the number or qualifications of experts, adjudication methods, or MRMC comparative effectiveness studies.
• Information on standalone algorithm performance or the type of ground truth used beyond stating that the device measures "macronutrients."

However, I can extract the following information relevant to your request based on the provided text:

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

The document does not explicitly present a table of acceptance criteria with corresponding performance metrics. It indicates acceptable measuring ranges for the macronutrients, which can be seen as implicit acceptance criteria for the device's measurement capabilities.

The document states that "The Emily's Care Nourish Test System underwent bench testing to evaluate its performance characteristics, including precision, accuracy (bias), linearity, sensitivity, and interference." While these are the types of performance characteristics evaluated, the specific acceptance criteria and the numerical results are not provided in this summary.

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

This information is not provided in the given 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 provided in the given text.

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

This information is not provided in the given text.

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 provided in the given text, and it's generally not applicable to this type of IVD device which measures macronutrients, rather than an AI-assisted diagnostic imaging device for human readers.

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

The device is described as an "in vitro diagnostic device" that quantitatively measures macronutrients using an iPhone running an app, single-use test strips, a reference card, pipettes, tubes, and a lightbox. This implies a "standalone" measurement system in the sense that the device itself performs the measurement and calculation, but it is explicitly intended for "use in healthcare by trained healthcare personnel." Therefore, it's not an "algorithm-only" performance study in the sense of AI-driven image analysis without human interaction, but rather a system performance. No separate "standalone" study is specifically detailed, as the entire system's performance is intended for the test.

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

The document states the device "quantitatively measures the concentration of protein, fat (triglycerides), and carbohydrates (lactose) in human milk." For such a device, ground truth would typically be established by reference laboratory methods (e.g., gold-standard chemical assays or spectroscopy) that are highly accurate for measuring these macronutrients in breast milk. The text does not explicitly state which specific "gold standard" methods were used as ground truth for comparison.

8. The sample size for the training set

This information is not provided in the given text. Given the device uses a colorimetric principle with an iPhone camera, it would likely involve some form of calibration or training data for the image analysis algorithm, but specifics are absent.

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

This information is not provided in the given text.

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