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The Keikku is an electronic stethoscope that enables amplification, filtering, and transmission of auscultation data of the patient (heart, lungs, bowel, arteries, and veins), whereby a clinician at one location on a network can listen to the auscultation data of a patient on site or at a different location on the network. The Keikku is intended for use on pediatric and adult patients. The Keikku is intended to be used by professional users in a clinical environment or by lay users in a nonclinical environment. The device is not intended for self-diagnosis.

The Keikku (Rx) is a digital stethoscope device designed for use by health care professionals in clinical settings and by lay users in non-clinical environments under healthcare provider supervision. The Keikku electronically amplifies, filters and transfers body sounds through the accompanying mobile application and is used for storage, sharing and transmitting the data for telemedicine use. It also enables lay users, under supervision from a healthcare provider, to listen to their body sounds (lungs, heart, arteries, veins, gastrointestinal tract, etc.), record and share it with their physicians during telehealth sessions. The Keikku consists of two primary components: 1. The Keikku device is an electronic stethoscope. The Keikku device is used for recording audio, converting it to digital data, and transmitting the data to a mobile device via Bluetooth®. It includes volume adjustment via rotation, tap feature for starting and ending the recording, and an LED light indicator for indicating the status of the device. 2. The Keikku App. The app captures audio data from the Keikku device and provides data visualization and annotation, secure data storage, audio playback, and sharing features. These features enable a healthcare professional to monitor patients, seek second opinions from a specialist or use the device for telemedicine use.

The provided text describes the Keikku Electronic Stethoscope and its substantial equivalence determination to predicate devices. However, it does not contain specific acceptance criteria or a detailed study that proves the device meets such criteria in the format explicitly requested.

The document states that "The test passed and met the predefined acceptance criteria" for performance testing related to audio frequency and NSR response, but it does not specify what those acceptance criteria were or present the reported device performance in a table. It also refers to usability evaluation as having "passing results" without detailing the study or its criteria.

Therefore, I cannot fully complete the requested table and answer all questions due to the lack of explicit information in the provided text.

Based on the available information, here is what can be extracted and what is missing:

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

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 for Test Set: Not specified in the provided text for any of the performance studies.
• Data Provenance: Not specified in the provided 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)

• Not specified. The document repeatedly refers to "tests" and "evaluations" but does not mention expert involvement in establishing ground truth for any test sets beyond general usability studies.

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

• Not specified.

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

• No MRMC comparative effectiveness study is mentioned. The device's primary function is as an electronic stethoscope for amplification, filtering, and transmission of auscultation data, not an AI-assisted diagnostic tool for human readers.

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

• This question is not directly applicable in the context of an electronic stethoscope as described. The device itself (Keikku Electronic Stethoscope) performs sound capture, amplification, and filtering. It is inherently a "standalone" device in its primary functionality. However, it works with an accompanying mobile application for data visualization, storage, and sharing, and facilitates human practitioners' listening. There's no separate "algorithm only" performance reported that would be distinct from the device's inherent operation.

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

• The document primarily describes engineering and validation testing (biocompatibility, cleaning/disinfection, electrical safety, EMC, software). For the "Performance Testing," it states the purpose was "to verify the Keikku's performance is similar to that of its predicate and reference devices, Eko Core and 3M Littmann electronic stethoscope, in terms of audio frequency and NSR response." This implies the "ground truth" for performance was defined by the established performance characteristics of the predicate and reference devices, rather than a clinical ground truth like pathology or expert consensus on clinical diagnoses.

8. The sample size for the training set

• Not applicable as the document does not describe any machine learning models that would require a "training set." The Keikku device provides amplification, filtering, and transmission, but it's not described as having an AI component that is "trained" in the typical sense.

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

• Not applicable (see point 8).

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