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The Breeze Nebulizer (NBR-101) is a vibrating mesh nebulizer system designed to aerosolize liquid medications for inhalation by the patient. The device may be used with pediatric (ages 2 years old and above) and adult patients in the home. It is not intended for use with Pentamidine.

Breeze Nebulizer (NBR-101) is a vibrating mesh nebulizer that delivers aerosolized medication to the lower respiratory tract by using a vibrating mesh to create aerosol and provide fine particles to the patient's lungs. It is similar to the predicate device, the FDA-cleared Model NE-U22 Micro Air Vibrating Mesh Nebulizer, cleared under 510(k) K062263. They are identical in purpose, function, core technology and method of operation.

Breeze nebulizer (NBR-101) is a portable size, curvaceous body design that is convenient to hold, and ability to detect the amount of medications available and to turn off automatically. The open button is made of soft materials and gives off an ice blue light, coupled with an overall elegant white exterior.

Breeze Nebulizer (NBR-101) is battery powered, 4 "AAA" and the dimensions is 58(W) X 145(H) X 70(D). The medication container capacity is 8ml maximum and the residual volume is approximately 0.1ml.

This document describes the 510(k) premarket notification for the Breeze Nebulizer (NBR-101), specifically focusing on its substantial equivalence to a predicate device rather than a study demonstrating it meets specific clinical performance acceptance criteria. As such, information regarding AI, ground truth, sample sizes for training/test sets, or expert involvement in establishing ground truth is not applicable.

The core of the submission relies on non-clinical testing to demonstrate safety and effectiveness.

1. Table of Acceptance Criteria and Reported Device Performance

The document lists various standards and regulations applied for different testing items. The reported performance is generally stated as meeting the requirements of these standards. Specific numerical acceptance criteria or detailed performance metrics are not provided in this summary.

The document also provides a comparative table of characteristics between the proposed device and the predicate device, highlighting their similarities and differences. While this is not "acceptance criteria" in the traditional sense, it demonstrates how the device's characteristics are compared against a legally marketed device.

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

The document does not specify a "test set" in the context of a clinical performance study. The studies mentioned are non-clinical (bench tests, safety tests), and the sample size for these engineering and regulatory compliance tests is not provided in this summary. The data provenance is from digiO2 International Co., Ltd. in New Taipei City, Taiwan, performing internal non-clinical testing. These are assumed to be prospective tests conducted for the purpose of the 510(k) submission.

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

Not applicable. This is a submission for a medical device that relies on non-clinical engineering and regulatory compliance testing rather than clinical data or expert-derived ground truth.

4. Adjudication method for the test set

Not applicable.

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 not an AI-powered diagnostic tool, and no MRMC study was performed.

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

Not applicable. This device is not an algorithm.

7. The type of ground truth used

For the non-clinical tests, the "ground truth" implicitly refers to the specified performance parameters and safety limits defined by the referenced international standards (e.g., IEC 60601-1, ISO 10993, EN 13544-1). Compliance with these standards serves as the benchmark.

8. The sample size for the training set

Not applicable. There is no mention of a "training set" as this is not an AI/machine learning device.

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

Not applicable.

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The Thermo Pal Infrared Ear Thermometer (Model no.: ETH-101) is an infrared thermometer intended for the intermittent measurement of human body temperature in people of all ages.

The Thermo Pal Infrared Ear Thermometer (Model no.: ETH-101) is hand-held, non-sterile, reusable, battery operated device that can measure human body temperature via the human ear. Operation is based on the measuring of the natural thermal infrared radiation emitted from the ear tympanic.

1. Table of Acceptance Criteria and Reported Device Performance:

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

The document mentions "A Clinical Test Report conducted according to ASTM E1965: 2003" but does not specify the sample size used for the test set.

The data provenance is described as "conducted by manufacturer," suggesting it was conducted by Digio2 International Co., LTD. The country of origin of the data is not explicitly stated, but the manufacturer is based in Taipei Hsien, Taiwan. The study appears to be prospective as it was "carried out in such a way that compared the accuracy performance between Thermo Pal Infrared Ear Thermometer (Model no.: ETH-101) and the predicate device."

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

The document does not provide information on the number of experts used or their qualifications for establishing the ground truth in the clinical test.

4. Adjudication method for the test set:

The document does not specify any adjudication method used for the test set.

5. If a multi-reader multi-case (MRMC) comparative effectiveness study was done, and the effect size of how much human readers improve with AI vs without AI assistance:

This is not applicable. The device described is an infrared ear thermometer, which is a standalone measurement device and does not involve human readers or AI assistance in the context of diagnostic interpretation.

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

Yes, a standalone performance test was done, as the device is an infrared ear thermometer. The "Clinical Test Report" assessed the device's accuracy against a predicate device without human-in-the-loop performance.

7. The type of ground truth used:

The type of ground truth used for checking the accuracy of the Thermo Pal Infrared Ear Thermometer was comparison with a predicate device (BRAUN THERMOSCAN IRT 4000 series). Specifically, the report "compared the accuracy performance between Thermo Pal Infrared Ear Thermometer (Model no.: ETH-101) and the predicate device according to the method recommended in ASTM E1965 standard." This implies that the predicate device's readings served as the reference or "ground truth" for comparison.

8. The sample size for the training set:

The document does not mention a training set as this device is a hardware measurement instrument and not an AI/machine learning algorithm that requires training data.

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

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

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Care Pal (Model no. CPW-10X) is indicated for use in non-clinical settings to collect and transmit historical medical information to healthcare professionals to help support effective management of their patients.

The product is not intended to provide automated treatment decisions nor for use as a substitute for a health care professional's judgement.

The Care Pal ("CP") remote patient monitoring system is for use in non-clinical settings as an accessory device to collect and transmit historical patient information to healthcare providers. It is intended to be used in combination with a variety of external devices. The CP remote patient monitoring system serves as the remote communication link between compatible external devices and the compatible healthcare facility at another location. The product is not intended to provide automated treatment decisions nor for use as a substitute for a health care professional's judgment.

The CP appliance contains software that can be activated to function with specific medical devices (including blood glucose meter, blood pressure and weight scale). The CP appliance with device connectivity retrieves data from a specific medical device and transmits to a remote healthcare provider using standard digital communication technologies. The CP appliance is not used directly on the patient, and poses no significant risk to the patient or other people within the patient's home.

Care Pal provides interfaces to the following connecting peripheral devices and back end server as well a. BT

Selected device (Brand/Model): weight scale, A&D/UC-321PBT

• b. USB
  Selected device (Brand/Model): glucose meter, Johnson & Johnson LifeScan / OneTouch Ultra II (K053529)

c. RS-232 (Serial Port) Selected device (Brand/Model): blood pressure meter, A&D UA-787PC (K012013)

d. Internet (Ethernet/wireless) connection to backend server

The provided text describes the 510(k) summary for the "Care Pal" device (Model no. CPW-10X). However, it does not contain specific acceptance criteria for device performance or a detailed study demonstrating how the device meets those criteria in the way typically expected for an AI/ML medical device submission (e.g., accuracy, sensitivity, specificity studies).

Instead, this document focuses on substantial equivalence to a predicate device based on intended use, technological characteristics, and conformance to safety and EMC standards.

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

1. Table of Acceptance Criteria and Reported Device Performance:

• The document primarily references general performance summaries related to operating specifications, safety, and EMC requirements.
• It states that the device conforms to applicable standards, including IEC 60601-1 and IEC 60601-1-2 requirements. These are general safety and electromagnetic compatibility standards for medical electrical equipment.
• It also states that "bench testing contained in this submission demonstrate that any differences in their technological characteristics do not raise any new questions of safety or effectiveness." However, specific acceptance criteria for this bench testing (e.g., data transfer accuracy percentage, successful connection rate) and the detailed results are not provided in this summary.

Without the actual study report, it's impossible to create a precise table. Based on the summary, a conceptual table would look like this:

Missing Information (Crucial for an AI/ML device, but this isn't one):

• Specific quantitative performance metrics like accuracy, sensitivity, specificity, or F1-score.
• Thresholds for passing these metrics.

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

• Not applicable / Not specified. This device is a data management/transmission system, not an AI/ML algorithm that predicts or diagnoses based on a test dataset. The "bench testing" mentioned would focus on the device's ability to connect and transmit data reliably, not on a "test set" of patient data for diagnostic evaluation.
• The document mentions "historical medical information" being transmitted, but this refers to the type of data the device handles, not a dataset used for performance evaluation of the device itself.

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

• Not applicable / Not specified. As a data transmission device, ground truth for diagnostic or prognostic purposes is not established by experts for its functional evaluation. The "ground truth" for this device would likely be the accurate transfer of data from the peripheral device to the server, which would be verified through technical means rather than expert clinical review.

4. Adjudication method for the test set:

• Not applicable / Not specified. No clinical test set requiring expert adjudication is described in this summary.

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 not an AI algorithm designed to assist human readers or perform diagnostic tasks. It's a data transmission system.

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

• Not applicable. This is not an algorithm, but a hardware and software system for data collection and transmission. Its "standalone" performance would relate to its ability to perform its transmission function independently, which would be covered under bench testing and EMC/safety compliance.

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

• Not applicable / Not specified in detail. For this type of device, ground truth would relate to the successful and accurate transmission of data. For instance, if a glucose meter reads "120 mg/dL," the ground truth for the Care Pal's performance would be that "120 mg/dL" is accurately received by the remote server. This is typically verified through technical validation, not clinical ground truth like pathology or expert consensus.

8. The sample size for the training set:

• Not applicable. This device is not an AI/ML algorithm that requires a training set.

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

• Not applicable. This device is not an AI/ML algorithm that requires a training set.

In summary:

The provided document describes a "Care Pal" device which is a data management/transmission system, not an AI/ML diagnostic or therapeutic device. Therefore, it does not contain the types of performance data (e.g., accuracy, sensitivity, specificity, expert ground truth, sample sizes for training/test sets) that are typically associated with AI/ML device approval. The "study" mentioned is "bench testing" and compliance with general safety and EMC standards, aimed at demonstrating substantial equivalence to a predicate device rather than statistical performance on a clinical dataset.

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