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The Propeller System includes the Propeller Sensor is an accessory device intended for singlepatient use to assist physicians and patients in recording and monitoring the actuations of prescribed DPI usage for the Ellipta devices.

The Propeller Mobile Application records, stores, and transmits usage events from Propeller Sensors, or via manual user entry, to a remote storage system. With the Propeller Mobile Application the user can review information collected from the DPI sensor, and review symptoms and other information about their disease management and its impact. The user may also share their information with their caregivers, physician, and healthcare providers.

The Propeller Web Application is software that, like the Propeller Mobile Application, is intended to allow users to review the collected information and characteristics of their DPI and its use, to capture other patient-reported information and outcomes, and to allow that information to be shared with their caregivers, physicians, and health care providers.

When used with a prescribed DPI, the system can report on information captured during the normal course of use, such as the time between actuations that can be helpful in assessing DPI technique.

The Propeller System is intended to be used in populations from Child (>2 years) to Adult.

The Propeller System can be used both indoors home, work, and clinical settings, as well as on aircraft.

The Propeller System may also be used in clinical trials where researchers need to know information about the use of DPI medication(s) by a participant.

The output of the Propeller System is not intended to diagnosis provided by a licensed physician. The Propeller System is not intended for use as an DPI dose counter, nor is it intended to indicate the quantity of medication remaining in an DPI.

Dry Power Inhaler (DPI) / Ellipta Accessory that monitors a patient's inhaler usage. The portable, polycarbonate sensor clips on top of a DPI mouthpiece cover and passively records inhalation events when the inhaler is used using infra-red (IR) sensors. The sensor then sends the event information via Bluetooth to the mobile phone or wireless gateway.

The provided text does not contain detailed acceptance criteria or a study proving that the device meets those criteria with specific performance metrics. This document is a 510(k) summary, which focuses on demonstrating substantial equivalence to a predicate device rather than providing intricate details of performance studies against defined acceptance criteria.

However, based on the information available, I can infer some aspects and present the information as requested, using placeholders where specific data is absent.

Here's a breakdown of what can be extracted and what is missing:

1. Table of Acceptance Criteria and Reported Device Performance

The document mentions that "Test results indicate that the Propeller Sensor Model 2015-E... complies with predetermined specifications." However, the specific predetermined specifications (acceptance criteria) and the corresponding reported device performance values are not detailed in this summary.

2. Sample Size Used for the Test Set and Data Provenance

The document states, "Bench testing included battery performance testing and particle size distribution (PSD) testing." It also references "Software verification and validation testing." However, no specific sample sizes for these test sets are provided, nor is the data provenance (e.g., country of origin, retrospective or prospective) mentioned.

3. Number of Experts Used to Establish Ground Truth and Qualifications

This information is not provided. The testing mentioned (software, electrical safety, biocompatibility, battery, PSD) does not typically involve expert review for ground truth establishment in the same way as performance studies for diagnostic algorithms.

4. Adjudication Method

This information is not applicable and therefore not provided. The testing described does not involve an adjudication process.

5. Multi-Reader Multi-Case (MRMC) Comparative Effectiveness Study

No MRMC comparative effectiveness study was done or reported. The device is a sensor for monitoring inhaler usage, not a diagnostic tool requiring human reader interpretation in comparison to AI assistance. The document explicitly states: "Clinical Testing No clinical testing was required."

6. Standalone (Algorithm Only) Performance Study

The device is a hardware sensor that passively records inhalation events and transmits data. Its "performance" is primarily about accurately detecting these events and transmitting them reliably. While software verification and validation were performed, no standalone "algorithm-only" performance study in the context of, for example, diagnostic image analysis, is described. The core function is sensing and data transmission, for which specific accuracy metrics are implied to be part of the "predetermined specifications" but are not detailed.

7. Type of Ground Truth Used

For the various tests mentioned:

• Software verification and validation: Ground truth would be defined by the software requirements and specifications.
• Compliance to standards (IEC, ISO): Ground truth is the adherence to the requirements outlined in those standards.
• EMC, Wireless Coexistence: Ground truth is defined by the technical limits and requirements of relevant standards and regulations.
• Battery performance, PSD: Ground truth would be the expected physical or electrical characteristics defined by the manufacturer's specifications.

8. Sample Size for the Training Set

This information is not provided and likely not relevant in the conventional sense of machine learning training data for this type of device. The device is a hardware sensor primarily. While there is a "Propeller System" that includes mobile and web applications, the document focuses on the sensor itself. Any "training" for the sensor would be during its design and calibration, not typically described as a "training set" in this context.

9. How the Ground Truth for the Training Set Was Established

Not applicable/Not provided given the nature of the device and the absence of a distinct "training set" as understood in AI/ML performance studies.

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