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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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SKEEPER is an electronic stethoscope that collects, fitters, and amplifies the sound of a person's heart, lungs, and abdomen. It is intended for use by professional users in clinical environments or by lay users in non-clinical environments. It is not intended for self-diagnosis.

SKEEPER is an electronic stethoscope used to collect and measure the sounds inside the body, and it consists of an electronic stethoscope (SM-300) and a mobile application (SKEEPER PRO APP).

When the diaphragm at the bottom of the SM-300 is lightly attached to the body part to be measured, such as the heart, lungs, or abdomen, the diaphragm vibrates due to the sound inside the human body and generates a sound. This sound is collected using a microphone and converted into an electrical signal. After that, the electrical signal is filtered and amplified for each frequency band set according to the measurement site, amplified, and output to earphones or transmitted to the SKEEPER APP installed on the mobile platform, and then the sound is output the earphone or speaker of the corresponding platform.

The SKEEPER PRO APP graphs the waveform of the sound received by the SM-300. It also stores and plays back sound data and analyzes heart rate.

The provided text is a 510(k) summary for the SKEEPER electronic stethoscope. It describes the device, its intended use, and a comparison to predicate and reference devices to demonstrate substantial equivalence. However, it does not contain the detailed acceptance criteria, device performance, or information about specific studies (like a multi-reader multi-case study, standalone performance, sample sizes for test/training sets, expert qualifications, adjudication methods, or ground truth establishment) as requested in your prompt.

Specifically, the document states:

• "Clinical Data was not required to demonstrate the substantial equivalence." This indicates that no clinical study was performed.
• Non-clinical data provided includes electrical safety, electromagnetic compatibility, biocompatibility, software verification and validation, wireless coexistence, and bench testing. These typically involve engineering and performance specifications but not diagnostic accuracy or comparative effectiveness in a clinical context.

Therefore, I cannot provide the requested information from the given text as it does not contain details about:

1. A table of acceptance criteria and reported device performance related to diagnostic accuracy or clinical utility.
2. Sample sizes for test sets, data provenance, number of experts, adjudication methods.
3. Multi-reader multi-case comparative effectiveness study or standalone performance.
4. Types of ground truth used for such studies.
5. Sample size for the training set or how ground truth was established for it.

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The 'eMurmur Heart AI' software is a decision support system in the evaluation of recorded patient heart sounds. The automated analysis by eMurmur Heart Al identifies specific heart sounds that may be present, including S1, S2, physiological heart murmurs, pathological heart murmurs and the absence of a heart murmur.

eMurmur Heart AI is indicated for use in a setting where auscultation would typically be performed by a healthcare provider. It is not intended as a sole means of diagnosis. The heart sound interpretations offered by eMurmur Heart AI are only significant when considered in conjunction with healthcare provider over-read and including all other relevant patient data.

To analyze heart sounds via the eMurmur Heart AI, a digital recording of a patient's heart sounds is required. Recordings are made using a supported digital stethoscope, connected to a front-end client like, e.g., the eMurmur app or the eMurmur web portal. The recorded auscultation data are transmitted from the front-end client to the eMurmur backend, which hosts the eMurmur Heart Al. After analysis by the eMurmur Heart Al, the results of the analysis are returned to the front-end client where they are displayed to the user. The user can utilize the eMurmur Heart AI results to support their decision-making process regarding the potential presence and type of a heart murmur.

eMurmur is a non-medical device software platform which includes the eMurmur backend, eMurmur apps and eMurmur web portal. The platform is used to stream, record, display. replay, and store auscultation data, recorded by means of supported digital stethoscopes.

The eMurmur software platform has functions subject to FDA premarket review, i.e., eMurmur Heart AI, as well as functions that are not subject to FDA premarket review. For this application. FDA assessed those functions only to the extent that they could adversely impact the safety and effectiveness of the functions subject to FDA premarket review.

The provided text describes the eMurmur Heart AI device, but it mainly focuses on its substantial equivalence to a predicate device (eMurmur ID) and states that no new performance data was required because the core algorithm is the same. Therefore, the information needed to fully answer your request regarding acceptance criteria and a new study proving the device meets those criteria is not explicitly present for eMurmur Heart AI.

However, based on the information provided, we can infer some details and present the "Clinical Performance" data listed for the predicate device, eMurmur ID, as it's stated that eMurmur Heart AI shares the same heart sound analysis algorithm and thus identical clinical performance.

Here's the breakdown of the information that can be extracted or reasonably inferred from the document:

1. Table of Acceptance Criteria and Reported Device Performance

The document doesn't explicitly state "acceptance criteria" but lists "Clinical Performance" metrics for the "eMurmur ID" predicate device, which are then declared identical for eMurmur Heart AI. These would serve as the de facto performance metrics considered acceptable for substantial equivalence.

Note: The document explicitly states: "eMurmur Heart AI and the predicate, eMurmur ID (K181988), utilize the same heart sound analysis algorithm, hence no new performance data is required." This implies that the performance established for eMurmur ID is directly applicable and "accepted" for eMurmur Heart AI.

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

The provided text does not specify the sample size used for the test set for either eMurmur Heart AI or the predicate eMurmur ID, nor does it mention data provenance (country of origin, retrospective/prospective). This information would typically be detailed in the original 510(k) submission for eMurmur ID (K181988).

3. Number of Experts Used to Establish Ground Truth for the Test Set and Qualifications

The provided text does not specify the number of experts used or their qualifications for establishing ground truth for the test set of the predicate eMurmur ID.

4. Adjudication Method for the Test Set

The provided text does not specify the adjudication method used for the test set of the predicate eMurmur ID.

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

The provided text does not mention a multi-reader multi-case (MRMC) comparative effectiveness study, nor does it discuss the effect size of human readers improving with AI assistance.

6. Standalone Performance

Yes, a standalone performance study was done based on the predicate device (eMurmur ID). The listed Sensitivity and Specificity (85.0% and 86.7% respectively) represent the algorithm's performance without explicit mention of human-in-the-loop assistance during the reported performance evaluation. The device is described as a "decision support system," emphasizing its role in aiding healthcare providers.

7. Type of Ground Truth Used

The type of ground truth used is not explicitly stated in this document. It mentions the algorithm identifies specific heart sounds, including physiological and pathological murmurs. Typically, for such devices, ground truth would be established by expert consensus based on clinical examination, potentially complemented by other diagnostic tests (e.g., echocardiography) for heart murmur validation.

8. Sample Size for the Training Set

The provided text does not specify the sample size for the training set.

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

The provided text does not specify how the ground truth for the training set was established.

Summary of what's missing and why:

The core reason for much of the missing information is that this document is a 510(k) summary for "eMurmur Heart AI" which leverages the substantial equivalence pathway. It argues that because "eMurmur Heart AI" uses the same heart sound analysis algorithm as the already cleared "eMurmur ID," no new performance data is required. Therefore, the detailed study design, sample sizes, expert qualifications, and ground truth methodologies would have been part of the original 510(k) submission for "eMurmur ID" (K181988), not explicitly reiterated in this document beyond the summary performance metrics.

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The RESP is a non-invasive battery-operated device, including a wearable component, intended to longitudinally acquire, record, and store lung sounds from adult patients in a healthcare setting. The device stores the data for later playback, review, and analysis by a clinician on a mobile app.

The RESP is a non-invasive battery-operated device, including a wearable component, intended to longitudinally acquire, record, and store lung sounds from adult patients in a healthcare setting. The device stores the data for later playback, review, and analysis by a clinician on a mobile app. The Strados Remote Electronic Stethoscope Platform (RESP) is comprised of the Strados Wearable Device (SWD), Strados Charging Station (SCS) and external power supply, and Strados Patient Adhesive (SPA) used to adhere the SWD to the patient. The SWD is controlled by a mobile device running the Strados Mobile Application (SMA) via Bluetooth connection. The Strados Wearable Device (SWD) sits on the chest wall and passively records the patient's lung sounds. The Strados Mobile App (SMA) on a smartphone allows playback of lung sounds from the wearable device in order for clinicians to listen to the patient's lung sounds. The device is used in a healthcare setting.

The provided text does not contain information about acceptance criteria or a study that proves the device meets those criteria. It is an FDA 510(k) clearance letter and associated marketing submission documents for the Strados Remote Electronic Stethoscope Platform (RESP).

The document mainly focuses on establishing substantial equivalence to a predicate device, as required for 510(k) clearance. This involves comparing the device's indications for use, technological characteristics, and non-clinical performance data (e.g., electrical safety, biocompatibility, functional testing).

Therefore, I cannot provide the requested table and study details as they are not present in the provided text. The text explicitly states: "Clinical performance testing was performed to validate the quality of the subject device's recorded lung sounds." However, no details about this clinical performance testing, acceptance criteria, or specific study results are included in the provided document sections.

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The Stethee™ Pro 1 is an electronic stethoscope intended for screening and medical diagnostic purposes only. It may be used for the detection and amplification of sounds from the heart, lungs, arteries, veins, and other internal organs with or without the use of selective frequencies. It can be used on any person undergoing a physical assessment.

Stethee Pro 1 is intended for use with the Stethee Pro Software System, whose features enable recording, playback, visualization, analysis, management and reporting of patient samples, and sharing this data with other authorized users.

The Stethee Pro 1 is an electronic stethoscope intended for medical screening or diagnostic purposes only. Stethee Pro 1 may be used for detection and amplification of sounds from the heart, lungs, arteries, veins, and other internal organs with or without the use of selective filters. It can be used on any person undergoing a physical assessment.

The Stethee Pro 1 consists of hardware and embedded software which controls all of the various features found in the device, such as sound capture, digital signal processing, volume control, haptic feedback for user, LED display ring, and wireless data transfer (via Bluetooth®). No patient data is stored on the Stethee Pro 1 device itself.

After amplification and filtering of the sounds detected, Stethee Pro 1 transfers the sounds to the User's ears via any Bluetooth® connected headphones, or by wired headphones connected to Stethee Pro 1 using an AUX adaptor at the device's USB port.

The Stethee Pro 1 also includes features that permit it to stream sounds to a peripheral smart device (e.g., mobile phone) as an audio buffer to a smart device (iOS or Android) via a Bluetooth® connection. The audio buffer is handled on the smart device by a separate standalone software application called the Stethee Pro Software System.

Stethee Pro Software System (SPS) is a series of software applications (Stethee Pro Mobile Applications and the Stethee Pro Central Web Application) that work with the Stethee Pro 1 electronic stethoscope. Stethee Pro Software System is supported by M3DICINE's proprietary software platform M3DICINE Cloud Services (MCS) which provides various services including database, security, and core business logic services so that services for data sharing and multiple sign-ins from multiple devices can be implemented.

The provided text does not contain detailed information about the acceptance criteria or a specific study proving the device meets those criteria in the typical format of an AI/ML medical device submission. The document is a 510(k) summary for the Stethee Pro 1, an electronic stethoscope, and its accompanying software system.

It primarily focuses on demonstrating substantial equivalence to a predicate device (Stethee Pro 1 and Stethee App [K172296]) and outlining the device's features, intended use, and general compliance with medical device standards. While it mentions "successful clinical validation of the performance accuracy of the device's machine learning analysis algorithms was performed against the listed reference devices," it does not elaborate on the specific acceptance criteria, study design, sample sizes, ground truth establishment, or expert qualifications for this validation.

Therefore, I cannot fully complete the requested table and answer all questions directly from the provided text. However, I can extract what is implied or mentioned in general terms:

Inferred Information from the Document:

The acceptance criteria are implicitly related to the performance accuracy of features like "Heart Rate: Detection and Display" and "Respiration Rate" as validated against "Reference Device Capnostream 35."

Here's an attempt to structure the available information, with acknowledges of missing details:

1. Table of Acceptance Criteria and Reported Device Performance

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

• Sample Size for Test Set: Not explicitly stated in the document for any specific clinical validation. The document states "successful clinical validation of the performance accuracy of the device's machine learning analysis algorithms was performed against the listed reference devices."
• Data Provenance: Not specified (e.g., country of origin, retrospective/prospective).

3. Number of Experts Used to Establish the Ground Truth for the Test Set and Qualifications of Those Experts

• Number of Experts: Not specified.
• Qualifications of Experts: Not specified.

4. Adjudication Method for the Test Set

• Method: Not specified.

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

• The document does not describe an MRMC comparative effectiveness study where human readers' performance with and without AI assistance was evaluated for improvement. The focus is on the device's standalone performance and its equivalence to established devices.

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

• Yes, implicitly. The "successful clinical validation of the performance accuracy of the device's machine learning analysis algorithms" against reference devices (like the Capnostream 35 for respiration rate) suggests a standalone evaluation of the algorithm's performance against a clinical standard, separate from a human-in-the-loop study. The "Heart Rate: Methodology" Section mentions "Performs continuous real-time calculation after initial sampling and updates heart rate display after each heartbeat," indicating an algorithmic function.

7. The Type of Ground Truth Used

• For Heart Rate and Respiration Rate: The ground truth appears to be derived from the "Reference Devices" (Stethee App [K172296] and Capnostream™ 35 [K150272]). For respiration rate, it states "The Respiration Rate feature was clinically validated against the Reference Device Capnostream 35." This implies the Capnostream 35's readings served as the ground truth.

8. The Sample Size for the Training Set

• Not specified. The document only mentions "machine learning analysis algorithms" without detailing training data specifics.

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

• Not specified.

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The Tyto Stethoscope is an electronic stethoscope that enables transmission of auscultation sound data, whereby a clinician at one location on an IP network can listen to the auscultation sounds of a patient on site or at a different location on the IP network with the signal carried on an IP connection between the two locations. The Tyto Stethoscope is intended to be used by professional users in a clinical environment or by lay users in a nonclinical environment. The device is for medical diagnostics purposes only. The device is not intended for self-diagnosis.

The Tyto Stethoscope (OTC) is designed for use by professional as well as lay users in clinical or non-clinical environments. It enables four types of stethoscope exams: Heart, Lungs, Heart Rate and Audio (Audio is for clinician only). The operation process of the Tyto Stethoscope uses four (4) primary functional elements: (1) The Tyto Stethoscope (composed of a Stethoscope Tip and a Base Unit – Tyto Device and supported with proprietary software). (2) A mobile device (e.g., a smartphone, not part of the Tyto Stethoscope device, not supplied by TytoCare, on which the proprietary TytoCare App is running), (3) The Tyto Server platform (composed of server hardware not part of the Tyto Stethoscope device, not supplied by TytoCare, on which the proprietary server software is running). (4) A clinician receiving platform located in a clinical environment (e.g., a PC at the clinic, not part of the Tyto Stethoscope device, not supplied by TytoCare, on which the proprietary Clinician App is running).

The provided text is a 510(k) summary for the Tyto Stethoscope (OTC). It mostly focuses on establishing substantial equivalence to predicate devices and does not contain detailed information about specific acceptance criteria or a study that rigorously proves the device meets those criteria in a quantitative sense as typically seen for AI/ML devices.

However, based on the general information provided about performance testing, we can infer some "acceptance criteria" through the lens of device specifications and the types of tests mentioned.

Here's an attempt to answer the questions based on the available text, acknowledging that much of the detailed study information is not present in this summary.

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

Based on the provided text, the "acceptance criteria" are broad and relate to meeting specifications and demonstrating similar performance and safety to predicate devices. The text doesn't provide specific quantitative "reported device performance" metrics against numeric acceptance thresholds, as it relies on the device being technologically identical to a previously cleared device.

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

The document states, "A testing plan was developed and performed in order to verify that the Tyto Stethoscope (K160401) meets its specifications and demonstrates, based on comparable characteristics, similar performance and safety as compared to its predicate devices". It then lists categories of performance testing.

• Sample Size for Test Set: Specific sample sizes for any of the listed tests (e.g., stethoscope performance, pseudo clinical, heart rate measurement) are not provided in this summary.
• Data Provenance: Not explicitly stated. Given the device is an electronic stethoscope and the context is regulatory, it's highly likely that the performance testing involved simulated scenarios, physical measurements, and potentially human subjects for usability or sound perception. The original clearance (K160401) would contain more details.

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

This information is not provided in the 510(k) summary. The summary refers to "pseudo clinical" and "stethoscope performance" testing, which would typically involve expert assessment, but no details are given.

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

This information is not provided in the 510(k) 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

The Tyto Stethoscope (OTC) is described as an electronic stethoscope for transmitting auscultation sounds to a clinician. It enables the transmission of raw sound data for human clinicians to interpret. There is no indication in this summary that it incorporates AI for interpretation or diagnosis, nor is there any mention of an MRMC comparative effectiveness study involving AI assistance.

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

Since there is no mention of an AI algorithm performing diagnosis or interpretation, this question is not applicable. The device's function is to transmit data for a human clinician.

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

Given the device's function (electronic stethoscope for sound transmission and heart rate), the "ground truth" for performance testing would likely involve:

• For Stethoscope Performance: Comparison to established acoustic standards, reference stethoscopes, or expert audiologist/physician assessments of sound quality.
• For Heart Rate Measurement: Comparison to ECG, manual palpation, or other validated heart rate measurement devices.
• For Pseudo Clinical: Likely comparison to expert clinical assessment of scenarios or validated simulated patient sounds.

However, the specific type of ground truth used for each test is not detailed in this summary.

8. The sample size for the training set

The provided text describes performance testing for an electronic stethoscope, not an AI/ML algorithm that requires training data in the traditional sense. Therefore, the concept of a "training set" as it relates to AI models is not applicable here. The device's functionality is based on hardware and software for sound acquisition and transmission, not data-driven learning.

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

As explained in point 8, the concept of a "training set" for an AI/ML algorithm is not applicable to this device as described.

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The IMEDIPLUS Electronic Stethoscope DS3011A is intended for the detection, amplification and recording of sounds from the heart, lungs, anterior and posterior chest, abdomen, neck, limbs, arteries, veins and other internal organs with selective frequency ranges. And the stethoscope chest-piece is designed for use with child, adolescent and adult patients. It is used for any subject undergoing a physical examination and intended diagnostic purposes in clinic or hospital.

The IMEDIPLUS Electronic Stethoscope DS3011A picks up the sounds from the heart, lung, anterior/posterior chest, abdomen, neck, limbs, arteries, veins and other internal organs from a patient's body. When picking up the sounds, the phonogram of sounds could simultaneously display after pushing the button 'OK' or 'REC' for recording. When you auscultate with DS3011A, the sounds are conducted simultaneously to the user's ears bilaterally by active speaker embedded at the bottom of the IMEDIPLUS Electronic Stethoscope DS3011A. At the meantime, sound processing is operated with the aid of a digital signal processor. The IMEDIPLUS Electronic Stethoscope DS3011A could identify the recording number by 1-D barcode reader, indicate the sound location by intuitive keypad, and record the sounds from different sites. The one-hand user interface includes a full-color OLED display, an intuitive keypad at the anterior part, a barcode reader at the posterior part, a chest-piece at the superior part, a tube connector for output of sounds at the inferior part, and a recording button at the left part. After opening the Electronic Stethoscope DS3011A with your setting password and connection with a wireless device by Bluetooth, the IMEDIPLUS Electronic Stethoscope DS3011A could transmit digital data of recorded sounds under the operation of IMEDIPLUS DS3011A_DM Software (DS3011A Data Management) installed in the connected device. The IMEDIPLUS DS3011A_DM Software (DS3011A Data Management) will only operate under the user's authorization with the seething password. The IMEDIPLUS DS3011A-DM Software (IMEDIPLUS DS3011A-Datamanagement Software) installed in mobile device provides the users to connect with the IMEDIPLUS Electronic Stethoscope DS3011A, receive the recorded data of sounds, storage the data, display the phonograms, and replay the recorded audio data with the speaker of effective frequency range from 20 to 1000Hz. The IMEDIPLUS Electronic Stethoscope DS3011A could also exchange audio data with an external personal computing device by micro SD card. Every single audio file stored in the micro SD card was labeled with the user's ID, recording number and indicated position. The IMEDIPLUS Electronic Stethoscope DS3011A does not incorporate any other off-the-shelf (OTS) software. The recorded audio data only can be replayed by the IMEDIPLUS Electronic Stethoscope DS3011A, the IMEDIPLUS Electronic Stethoscope DS301, and the IMEDIPLUS DS3011A_DM Software (DS3011A_Data Management) installed in the mobile device with the speaker of effective range from 20 to 1000Hz. The IMEDIPLUS Electronic Stethoscope DS3011A operates on one (1) NP-120 lithium battery with an included power management system to prolong the battery life.

Here's a breakdown of the acceptance criteria and the studies that prove the device meets them, based on the provided text:

1. Table of Acceptance Criteria and Reported Device Performance

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The NAS-1000 System is a medical acoustic system intended as an adjunct to standard clinical practice for use in non-invasively monitoring, detecting, recording and displaying acoustic signals in the brain. It is used for any subject undergoing a physical examination and intended only for medical assessment purposes in a clinic or hospital.

The NAS-1000 System is a non-invasive, non-energy emitting device indicated for monitoring and recording of acoustic signals from the brain. The earbud passively receives the acoustic signal from the brain and the result is graphically displayed as an acoustic waveform on the monitor (NAS-1000M).

The NAS-1000 System consists of two components: a non-sterile, disposable, single patient use Headset (NAS-1000H) with four different sized earbuds (XS, S, M, L), and a tablet-based Monitor (NAS-1000M) which contains the software (NAS-1000S).

The provided text describes the Neuro Assessment System (NAS-1000) and its substantial equivalence to a predicate device, the 3M™ Littmann® Electronic Stethoscope, Model 3200 (K083903). While it details non-clinical and clinical testing, it does not explicitly provide an acceptance criterion table with reported device performance or information typically found in a comparative effectiveness study with AI assistance.

Here's a breakdown of the information available and what is missing based on your request:

1. Table of acceptance criteria and the reported device performance

The document does not provide a table of acceptance criteria with corresponding performance metrics. Instead, it describes general findings from phantom bench testing and clinical testing on volunteers. The core comparison is framed around "substantial equivalence" to the predicate device, K083903.

The document states:

• Phantom Bench Testing: "The NAS-1000 System and the 3M Littmann e-Stethoscope performed comparably and were able to graphically display the simulated systolic pulse from the phantom."
• Clinical Testing: "The study demonstrated the ability of the NAS-1000 System to detect and generate waveforms from the brain to provide a tool for monitoring brain acoustic signals / sounds over various time intervals." and "The tablet outputs demonstrate the ability of the NAS-1000 System to display brain acoustic signals / sounds of in the volunteers based on the analysis of waveform data."

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

• Sample Size for Test Set: 50 volunteers.
• Data Provenance: The document states "A performance testing study of the NAS-1000 System was conducted on volunteers" and "A total of 50 volunteers were measured and monitored." This indicates a prospective clinical study on human subjects, but the country of origin is not specified. It is implied to be a direct collection for the device's validation.

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

This information is not provided in the document. The study aimed to demonstrate the device's ability to detect and generate waveforms from the brain, implying the ground truth was the actual detection of acoustic signals rather than a diagnostic interpretation by experts. The device is for "monitoring, detecting, recording and displaying acoustic signals," not for providing a diagnosis based on an expert's assessment.

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

This information is not provided and is not applicable given the reported nature of the study as a technical performance validation rather than a diagnostic accuracy study requiring expert adjudication of ground truth labels.

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

An MRMC comparative effectiveness study was not conducted or described. The NAS-1000 System is presented as an acoustic monitoring device that displays waveforms, not an AI-powered diagnostic tool that assists human readers in making diagnoses. Thus, there is no mention of human reader improvement with AI assistance.

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

The device itself is a standalone system for monitoring and displaying acoustic signals. The clinical testing described directly assesses the system's ability to "detect and generate waveforms" and "display brain acoustic signals," which aligns with a standalone performance assessment of the device's core functionality.

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

The ground truth for the clinical study was the presence and characteristics of brain acoustic signals/sounds in volunteers. The study demonstrated the device's ability to detect and generate waveforms from these signals. For the phantom bench testing, the ground truth was a "simulated systolic pulse" generated by a peristaltic pump, and the device's output was compared to this known input signal.

8. The sample size for the training set

This information is not provided. The document focuses on regulatory submission and doesn't detail the development and training of potential machine learning models, if any are employed beyond signal processing. Given the device's description as a system for monitoring, detecting, recording, and displaying acoustic signals, it's more likely relying on signal processing algorithms rather than a trained AI model in the typical sense that would require a large training set for diagnostic purposes.

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

This information is not provided as the concept of a "training set" for an AI model is not discussed in the context of this device's validation.

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The CliniCloud Stethoscope is a digital stethoscope intended for periodic recording of lung and heart sounds to a smartphone device, via the CliniCloud app. It is intended to be used for persons of all ages. The device is for medical diagnostic purposes only.

The CliniCloud stethoscope is a battery powered electronic stethoscope intended for the periodic recording of lung and heart sounds to a smartphone via a CliniCloud micro-USB to 4-pole audio cable. The CliniCloud Stethoscope features no user interface or speaker and must be used in conjunction with the CliniCloud app on a compatible smartphone. It is intended to be used for persons of all ages.

This is a 510(k) premarket notification for the CliniCloud Stethoscope. The document indicates that no clinical performance data was required for this submission. Therefore, it does not contain a study proving the device meets specific acceptance criteria through clinical trials or human-in-the-loop performance. The approval is based on non-clinical testing demonstrating substantial equivalence to a predicate device.

Here's the breakdown of the information requested, based on the provided text:

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

Since no clinical study was conducted with specific acceptance criteria for a human user or algorithm performance, a traditional table comparing acceptance criteria to reported device performance from such a study cannot be generated. However, the non-clinical tests conducted aimed to prove the device met design specifications and was substantially equivalent to the predicate. The performance aspects assessed are listed:

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

• Test Set Sample Size: Not applicable. No human test set for performance evaluation was used. The non-clinical tests involved laboratory-based evaluations of the device's physical and functional characteristics.
• Data Provenance: Not applicable for a human test set. The non-clinical testing was conducted to verify design specifications.

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 applicable. No human test set requiring expert ground truth establishment was used.

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

Not applicable. No human test set requiring adjudication was used.

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. An MRMC comparative effectiveness study was not performed. The device is a digital stethoscope, not an AI diagnostic algorithm for image or signal interpretation by human readers.

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

Yes, in a way. The performance tests for the device itself (e.g., frequency response, heart rate estimation accuracy, signal transmission) can be considered "standalone" evaluations of the device's technical capabilities, without direct human intervention in the performance measurement. However, it's not a standalone AI algorithm performance evaluation in the typical sense.

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

For the non-clinical performance evaluations:

• Heart rate estimation: Ground truth was established by comparison to a "510(k) cleared pulse oximeter."
• Frequency response: Ground truth was established by comparison against a "reference 510(k) cleared stethoscope."
• Other tests relied on engineering specifications and compliance with established standards (e.g., for safety, EMC, biocompatibility).

8. The sample size for the training set

Not applicable. This device is a digital stethoscope, not an AI algorithm that requires a training set for machine learning.

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

Not applicable. This device is a digital stethoscope, not an AI algorithm that requires a training set.

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The Stethee™ Pro 1 is an electronic stethoscope intended for medical diagnostic purposes only. It may be used for the detection and amplification of sounds from the heart, lungs, arteries, veins, and other internal organs. It can be used on any person undergoing a physical assessment.

The M3DICINE Stethee™ Pro 1 is an electronic stethoscope that picks up sounds of the heart, arteries, veins, lungs and other internal organs. Stethee™ Pro 1 provides capability for auscultation in frequencies from 20-2000Hz.

After amplification and filtering, the sounds are transferred to the user's ears via any Bluetooth connected headphones. The Stethee™ Pro 1 includes features that permit it to record and store sounds on a peripheral smart device (e.g., mobile phone) by transferring the sounds as an audio buffer to the smart device via a Bluetooth® connection. The smart device makes use of a downloaded companion stand-alone Mobile Medical Application (MMA), Stethee™ App, to record, manage and replay the captured patient sounds on the smart device.

Stethee™ App is also able to display the audio file as a phonocardiograph (21 CFR 870.2390) on the user's smart device and can use the audio file to calculate and display a simple Heart Rate. Stethee™ App does not direct or influence the performance of Stethee™ Pro 1.

The Stethee™ Pro 1 device user interface includes a simple push down mechanism ("control knob") to start recording. It has a light-emitting diode (LED) display ring which indicates the device's status state (ON or OFF, charge status, and Bluetooth® synchronization status).

The Stethee™ Pro 1 operates on a rechargeable certified 3.7V lithium ion polymer battery.

The Stethee™ Pro 1 incorporates embedded software. The embedded software controls all the various features found in the Stethee™ Pro 1, such as sound capture, digital signal processing, volume control, LED display ring, and Bluetooth® transfer.

The document describes the Stethee™ Pro 1 electronic stethoscope and its equivalence to a predicate device, the 3M™ Littmann® Model 3200. However, it does not provide acceptance criteria in terms of specific performance metrics with numerical targets or a detailed study proving the device meets those specific acceptance criteria.

Instead, the document focuses on demonstrating substantial equivalence to a predicate device through comparative performance testing.

Here's an attempt to answer your questions based on the provided text, highlighting what information is available and what is not:

1. Table of acceptance criteria and the reported device performance

The document does not explicitly state "acceptance criteria" in the format of defined thresholds for different performance metrics. It rather presents a comparison of technical characteristics between the Stethee™ Pro 1 and the predicate device. The implied "acceptance criteria" is that the Stethee™ Pro 1 performs substantially equivalent to the predicate device in these characteristics.

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

The document states: "M3DICINE submitted performance testing information in this 510(k) demonstrating that the Stethee™ Pro 1 can perform over its intended range of operation (20-2000Hz). Comparative performance testing submitted in the 510(k) demonstrated the two devices perform in a substantially equivalent manner."

However, no specific sample sizes for test sets or data provenance (e.g., country of origin, retrospective/prospective) are mentioned for this comparative performance testing.

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

This information is not provided in the document. The testing described is comparative performance testing against a predicate device, not necessarily a diagnostic study requiring expert ground truth for classification.

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

This information is not provided in the document.

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 was described or performed. The device is an electronic stethoscope with an accompanying app that can display phonocardiographs and heart rate. It's not an AI-assisted diagnostic tool in the sense of improving human reader performance on complex image interpretation. The comparison is primarily on technical specifications and basic functionality.

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

The document describes the performance of the Stethee™ Pro 1 device itself, which includes embedded software for sound capture, digital signal processing, volume control, and Bluetooth transfer. The heart rate calculation by the Stethee™ App can be considered an "algorithm only" function in terms of its calculation process.

• Heart Rate Calculation: The Stethee™ App "calculates and display a simple Heart Rate."
  • Minimum Audio Sample Requirements: Requires initial 0.3-2 second recording (predicate requires 5 seconds).
  • Methodology: Performs continuous real-time calculation after initial 0.3-2 second sampling and updates heart rate display after each heart beat (predicate updates every 2 seconds after initial 5 seconds).
  • Range of Detection and Accuracy: 30-200 BPM with an allowable readout error rate of no greater than ±10 % or ±5 bpm (predicate: 30-199 BPM with ±10% consistency).
• Visualization: "Sounds can be visualized as phonocardiograph using companion software Stethee™ App." This is a standalone function of the software.

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

For the heart rate accuracy, the implied ground truth would be a validated heart rate measurement, likely from an ECG or a highly accurate reference device. However, the exact type of ground truth used for performance testing is not explicitly stated. For the broader "substantial equivalence" claim, the predicate device itself serves as the reference for comparison.

8. The sample size for the training set

The document does not mention a training set or any machine learning/AI model training in the context of diagnostic capabilities beyond basic heart rate calculation. The "embedded software" handles signal processing and device control but isn't described as a learnable AI model.

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

As no training set is mentioned in the context of the device's diagnostic claims, this information is not applicable/provided.

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