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The SNOO Smart Sleeper bassinet plus the SNOO Sleep Sack are jointly intended to facilitate a supine position during sleep. Infants who are placed in a supine sleep position are at lower risk of SIDS/SUID. The device is intended for home use by caregivers of infants from birth to 6 months of age, who are not yet able to roll over consistently.

The SNOO Smart Sleeper (SNOO) is comprised of the SNOO bassinet and the SNOO Sleep Sack that iointly constitute an infant securement device intended to facilitate a supine position during sleep. The device is intended for home use by caregivers of infants from birth to 6 months of age, who are not yet able to roll over consistently. The SNOO Sleep Sack is a cotton swaddle with fixed wings made of woven cotton fabric that extend to the right and left of the infant's body. Three sizes of sleep sacks (small 5-12lbs, medium 12-18lbs. and large 18-25lbs) are provided with each bassinet to accommodate the growing baby. Small loops at the end of each SNOO Sleep Sack wing are slid over the safety clips of the SNOO bassinet to securely position the swaddled infant on the back. The SNOO Sleep Sack has corresponding wings that are designed to be physically attached to those clips after the baby is wrapped in the sleep sack - thereby keeping the infant securely positioned on the back during sleep and to prevent rolling. The SNOO bassinet also provides sound and motion. White noise and rocking can be automatically increased in response to a baby's cries. There is a SNOO App which can be used to adjust the levels of motion.

The provided text describes the SNOO Smart Sleeper, an infant supine sleep system, and presents information related to its regulatory acceptance. While it details acceptance criteria and study results for various aspects, it does not explicitly provide a "study that proves the device meets the acceptance criteria" in a singular, consolidated way, nor does it present the information in the structured format requested for AI/device performance. The request focuses on AI/device performance metrics (such as sensitivity, specificity, FROC, AUC), which are not directly applicable to this mechanical infant sleep system.

However, I can extract the acceptance criteria and reported "performance" based on the provided text, aligning them as closely as possible to the spirit of the request by interpreting "device performance" as the results of the specific tests conducted.

Here's the information extracted and formatted to best answer your request, focusing on the available data:

Acceptance Criteria and Device Performance for SNOO Smart Sleeper

1. Table of acceptance criteria and the reported device performance

2. Sample size used for the test set and the data provenance

• Zipper Durability Test: 10 zippers per batch.
• Loop Hook & Unhooking: 3 SNOO Sacks (1 Small, 1 Medium, 1 Large).
• Washing Durability Test: 10 pieces per batch.
• Velcro Durability Test: 10 pieces per batch.
• Human Factors Validation: 15 test participants.
• Supine Position Facilitation (Quantitative): 1012 users.
• Supine Position Facilitation (Qualitative): 93 hours of video recordings (number of infants not specified, but "all observed infants").
• SIDS/SUID and Adverse Events: 106,723 SNOO users.

Data Provenance:
The clinical data described (supine position, SIDS/SUID, adverse events) were collected in the United States. The study comparing SIDS/SUID incidence used real-world evidence (RWE) for SNOO users compared to historical data from the CDC Wide-ranging Online Data for Epidemiological Research (WONDER) database. This indicates the data for SNOO users is prospective/real-world observational, while the comparative SIDS/SUID data is retrospective/historical.

3. Number of experts used to establish the ground truth for the test set and the qualifications of those experts

The document does not describe the use of "experts" in the context of establishing ground truth for a test set in the way one would for diagnostic interpretative tasks (e.g., radiologists interpreting images). The "ground truth" for this device's performance is established by:

• Pre-defined engineering and material standards (e.g., ASTM, ISO, IEC, UL).
• Direct observation (e.g., video recordings for supine position).
• User feedback in human factors testing.
• Compilation of real-world adverse event reports.
• Comparison with established epidemiological databases (CDC WONDER).

No specific number or qualification of "experts" for establishing ground truth in the requested sense is mentioned.

4. Adjudication method for the test set

Not applicable in the context of this device. The tests performed are largely objective engineering, material, or observational assessments with pre-defined pass/fail criteria or direct measurement. Human factors testing involved observation of tasks, which would typically involve an evaluator judging successful completion against a rubric, but no specific adjudication method (like 2+1 review) is described.

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 an infant sleep system, not an AI-powered diagnostic tool requiring human reader interpretation or MRMC studies.

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

Not applicable. The SNOO Smart Sleeper is a physical device providing securement, motion, and sound, not an algorithm. Its "performance" in securing infants is a direct physical function. While it has an app and automation features (white noise and rocking in response to cries), the core function evaluated for its regulatory claim is physical securement.

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

The "ground truth" for the various performance aspects can be categorized as:

• Engineering Standards/Measurements: For zipper, loop, washing, Velcro durability, electrical safety, EMC, bassinet standards, biomechanical stability.
• Direct Observation/Video Analysis: For demonstrating supine position facilitation.
• User Performance (Human Factors): For human factors usability.
• Outcomes Data/Epidemiological Comparison: For SIDS/SUID rates and adverse event reporting.

8. The sample size for the training set

Not applicable. This is a physical device; there is no "training set" in the machine learning sense. The device's design and engineering would be based on general engineering principles and possibly iterative testing, but not a formal "training set" like an AI model.

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

Not applicable, as there is no training set mentioned for this type of device.

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