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The Tyto Insights for Rhonchi Detection is a prescription-use artificial intelligence (AI) enabled decision support software system used in the evaluation of lung sounds in adults and pediatrics (2 years and older). It automatically analyzes the acoustic signal of the lung as recorded by the FDA 510k cleared compatible Tyto Stethoscope and identifies recordings where a specific abnormal lung sound suggestive of "Rhonchi" is suspected. It is not intended to detect other abnormal or normal lung sounds. A licensed health care professional's advice is required to understand the meaning of the Tyto Insights for Rhonchi Detection result. Healthcare providers should consider the device result in conjunction with recording and other relevant patient data.

The Tyto Insights for Rhonchi Detection is a web-based AI-enabled software system designed to aid in the clinical assessment of lungs auscultation sound data by analyzing recorded lung sounds to determine whether a Rhonchi is detected within the recorded sound data. The Tyto Insights for Rhonchi Detection Software is intended to process recordings from the FDA-cleared compatible Tyto Stethoscope (Tyto Stethoscope, K181612). The acquisition of the acoustic data (recordings) is carried out by a professional user in a clinical environment or by a lay- user in a non-medical environment, in compliance with the labeling of the Tyto Stethoscope. The system is composed of the following sub-systems:

1. The Tyto Insights for Rhonchi Detection Application Server (APS) communicates with the Tyto Insights for Rhonchi Detection Algorithm Server (ALS) and implements an application programming interface (API) for communication with the telehealth server.

2. The Tyto Insights for Rhonchi Detection Algorithm Server (ALS) receives an audio file as input and returns an analysis result of positive or negative regarding whether a Rhonchi was detected as output.

3. The Tyto Insights for Rhonchi Detection Web Server (WBS) provides a graphic indication whether a Rhonchi is detected in the recording. It can be utilized both in the patient and clinician side.

All the software subsystems (servers and storage) are hosted in the cloud and communicate through IP network.

The recordings from the compatible Tyto Stethoscope (K181612) are sent by the third-party point of care app to the clinician app through the telehealth server. The telehealth server sends the set of the lung sound recordings to the Tyto Insights for Rhonchi Detection web server using its dedicated API. The AI enabled algorithm runs automatically and returns a response for each audio file with the indication of rhonchi to the telehealth server which sends a response to both the clinician side and the patient side.

Here's a breakdown of the acceptance criteria and study details for the Tyto Insights for Rhonchi Detection, based on the provided FDA 510(k) clearance letter:

Acceptance Criteria and Device Performance

1. Table of Acceptance Criteria and Reported Device Performance

Study Details

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

• Sample Size: 400 recordings (100 Rhonchi positive and 300 negative) from 400 patients.
• Data Provenance: Retrospective validation dataset composed of recordings obtained from the real-world use of the Tyto Care FDA-cleared compatible Tyto Stethoscope (K181612). The country of origin is not explicitly stated, but Tyto Care Ltd. is based in Israel, suggesting possible international data acquisition or a mix.

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

• Number of Experts: Three.
• Qualifications of Experts: Blinded experienced Pulmonologists. Specific years of experience are not mentioned.

4. Adjudication method for the test set

• Adjudication Method: Majority vote (2+1) of the three blinded Pulmonologists. The binary ground truth was determined if at least two out of the three experts agreed.

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 study was performed comparing the device's AUC to that of clinical readers (non-Pulmonologists).
• Effect Size: The difference in AUC (Tyto Insights for Rhonchi Detection – clinical readers) was 0.16 (0.13–0.21). This indicates that the device itself performed better than the clinical readers (non-Pulmonologists) in detecting rhonchi, establishing non-inferiority with a positive margin. The study did not evaluate how much human readers improve with AI assistance (i.e., human-in-the-loop performance), but rather compared standalone AI performance to standalone human reader performance.

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

• Yes, a standalone performance evaluation was done. Table 3 explicitly provides "The stand-alone accuracy of the Tyto Insights for Rhonchi Detection" with Sensitivity, Specificity, PPV, and NPV.

7. The type of ground truth used

• Type of Ground Truth: Expert consensus. Specifically, a majority vote from three experienced Pulmonologists.

8. The sample size for the training set

• The document does not explicitly state the sample size for the training set. It only mentions the retrospective validation dataset of 400 recordings. The PCCP section mentions "Re-training of the ML model with additional data," which implies a training set was used to develop the initial model, but its size is not provided in this document.

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

• The document does not explicitly describe how the ground truth for the training set was established. It only details the ground truth establishment method for the validation dataset (majority vote by three pulmonologists). It's reasonable to infer a similar method might have been used for the training data, but it's not stated.

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The Tyto Insights for Crackles Detection is an over-the-counter artificial intelligence (AI) enabled decision support software system used in the evaluation of lung sounds in adults and pediatrics (2 years and older). It automatically analyzes the acoustic signal of the lung as recorded by the FDA 510k cleared compatible Tyto Stethoscope and identifies recordings where a specific abnormal lung sound suggestive of "Crackle" is suspected. It is not intended to detect other abnormal or normal lung sounds. A licensed health care professional’s advice is required to understand the meaning of the Tyto Insights for Crackles Detection result. Healthcare providers should consider the device result in conjunction with recording and other relevant patient data.

The Tyto Insights for Crackles Detection is a web-based (AI) enabled software system designed to aid in the clinical assessment of lungs auscultation sound data by analyzing recorded lung sounds to determine whether a Crackle is detected within the recorded sound data. The Tyto Insights for Crackles Detection Software is intended to process recordings from the FDA-cleared compatible Tyto Stethoscope (Tyto Stethoscope, K181612). The acquisition of the acoustic data (recordings) is carried out by a professional user in a clinical environment or by a lay- user in a non-medical environment, in compliance with the labeling of the Tyto Stethoscope. The system is composed of the following sub-systems:

• The Tyto Insights for Crackles Detection Application Server (APS) communicates with 1. the Tyto Insights for Crackles Detection Algorithm Server (ALS) and implements an application programming interface (API) for communication with the telehealth server.
• The Tyto Insights for Crackles Detection Algorithm Server (ALS) receives an audio file 2. as input and returns an analysis result of positive or negative regarding whether a Crackles was detected as output.
• The Tyto Insights for Crackles Detection Web Server (WBS) provides a graphic 3. indication whether a Crackles is detected in the recording. It can be utilized both in patient and clinician side.
  All the software subsystems (servers and storage) are hosted in the cloud and communicate through IP network.

Here's a breakdown of the acceptance criteria and the study proving the device meets them, based on the provided FDA 510(k) summary for "Tyto Insights for Crackles Detection":

Product Information:

• Trade/Device Name: Tyto Insights for Crackles Detection
• Regulation Number: 21 CFR 868.1900
• Regulation Name: Diagnostic Pulmonary-Function Interpretation Calculator
• Regulatory Class: Class II
• Product Code: PHZ
• Intended Use/Indications for Use: An over-the-counter AI-enabled decision support software system for evaluating lung sounds (adults and pediatrics 2+ years) recorded by the compatible Tyto Stethoscope. It identifies recordings where "Crackle" is suspected. It is not intended to detect other abnormal/normal lung sounds. Requires a licensed healthcare professional's advice to interpret results, which should be considered with other patient data.

1. Table of Acceptance Criteria and Reported Device Performance

Note: The acceptance criteria for Sensitivity and Specificity (LCI > 0.6279 and LCI > 0.9668, respectively) are specifically laid out in the "Predetermined Change Control Plan (PCCP)" section for modifications to the device. For the initial submission, the primary endpoint focused on the non-inferiority of the device's AUC compared to clinical readers. The reported standalone sensitivity and specificity are actual performance metrics from the validation study.

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

• Test Set Sample Size: 446 recordings (120 Crackles positive, 326 Crackles negative). This corresponded to a total of 445 patients.
• Data Provenance: Retrospective validation dataset. Recordings were obtained from the real-world use of the Tyto Care FDA-cleared compatible Tyto Stethoscope (K181612). The document does not specify the country of origin for the data.

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

• Number of Experts: Three (3) blinded experienced Pulmonologists.
• Qualifications: "Experienced Pulmonologists" are specified. Further details on years of experience or specific board certifications are not provided in the summary.

4. Adjudication Method for the Test Set

• Adjudication Method: Binary ground truth was determined by a majority vote of the three blinded Pulmonologists. This is a form of 3+ Consensus.

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

• MRMC Study Done?: A comparative effectiveness study was done comparing the device (AI) performance to "clinical readers" (Physicians non-Pulmonologists). However, this was an AI-only vs. human-only comparison, not a "human reading with AI assistance vs. human reading without AI assistance" MRMC study.
• Effect Size of Human Improvement with AI: This study did not assess how much human readers improve with AI assistance. It directly compared the AI algorithm's performance to human clinical readers.
  • Device AUC: 0.97 (0.95–0.98)
  • Clinical Readers AUC: 0.77 (0.73–0.8)
  • Difference in AUC (Device - Clinical Readers): 0.2 (0.17–0.23). This indicates the AI algorithm significantly outperformed the clinical readers in this specific comparison, establishing non-inferiority (and superiority) based on the defined margin.

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

• Standalone Study Done?: Yes.
• Performance Metrics:
  • Sensitivity: 0.72 (0.63-0.79)
  • Specificity: 0.99 (0.97–1.00)
  • Positive Predictive Value (PPV): 0.63 (0.4-0.87)
  • Negative Predictive Value (NPV): 0.99 (0.98-0.99)

7. The Type of Ground Truth Used

• Type of Ground Truth: Expert Consensus. Specifically, a majority vote of three blinded experienced Pulmonologists on the presence or absence of "Crackle" in the lung sound recordings.

8. The Sample Size for the Training Set

• The document does not specify the exact sample size for the training set. It mentions that the device utilizes a CRNN (Convolutional Recurrent Neural Network) model and that "Each network is trained based on the target clinical class." However, it only provides details for the retrospective validation dataset.

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

• The document does not explicitly describe how the ground truth for the training set was established. It primarily focuses on the ground truth establishment for the validation set used for performance evaluation. It's generally assumed that similar expert-driven annotation methods would be used for training data, but this is not detailed in the provided summary.

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The Tyto Insights for Wheeze Detection is an over the-counter artificial intelligence (Al) enabled decision support software system used in the evaluation of lung sounds in adults and pediatrics (2 years and older). It automatically analyzes the acoustic signal of the lung as recorded by the FDA cleared compatible Tyto Stethoscope and identifies recordings where a specific abnormal lung sound suggestive of "Wheeze" is suspected. It is not intended to detect other abnormal or normal lung sounds. A licensed health care professional's advice is required to understand the meaning of the Tyto Insights for Wheeze Detection result. Healthcare providers should consider the device result in conjunction with recording and other relevant patient data.

The Tyto Insights for Wheeze Detection is a web-based (AI) enabled software system designed to aid in the clinical assessment of lungs auscultation sound data by analyzing recorded lung sounds to determine whether a Wheeze is detected within the recorded sound data.

The Tyto Insights for Wheeze Detection Software is intended to process recordings from the FDA-cleared compatible Tyto Stethoscope (Tyto Stethoscope, K181612). The acquisition of the acoustic data (recordings) is carried out by a professional user in a clinical environment or by a lay- user in a non-medical environment, in compliance with the labeling of the Tyto Stethoscope. The system is composed of the following sub-systems:

• 1. The Tyto Insights for Wheeze Detection Application Server (APS) communicates with the Tyto Insights for Wheeze Detection Algorithm Server (ALS) and implements an application programming interface (API) for communication with the telehealth server.
• 2. The Tyto Insights for Wheeze Detection Algorithm Server (ALS) receives an audio file as input and returns an analysis result of positive or negative regarding whether a wheeze was detected as output.
• The Tyto Insights for Wheeze Detection Web Server (WBS) provides a graphic 3. indication whether a wheeze is detected in the recording. It can be utilized both in patient and clinician side.

All the software subsystems (servers and storage) are hosted in the cloud and communicate through IP network.

Here's a breakdown of the acceptance criteria and study details for the "Tyto Insights for Wheeze Detection" device, based on the provided FDA 510(k) summary:

1. Table of Acceptance Criteria and Reported Device Performance

The document doesn't explicitly state "acceptance criteria" as a pass/fail threshold, but rather focuses on comparing the device's performance to its predicate and demonstrating non-inferiority. The key performance metrics are sensitivity, specificity, and Area Under the Curve (AUC).

Conclusion regarding acceptance criteria: The device met the primary endpoint of non-inferiority to the predicate device based on AUC, as the lower bound of the 95% CI for the difference in AUCs (0.0289) was well above the non-inferiority margin of -0.05.

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

• Sample Size for Test Set: 371 recordings, corresponding to 359 patients. (86 recordings were Wheeze positive and 285 negative).
• Data Provenance: Retrospective validation dataset sourced from real-world use of the FDA cleared compatible Tyto Stethoscope. The country of origin is not explicitly stated, but Tyto Care Ltd. is based in Israel.

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

• Number of Experts: Three blinded experienced Pulmonologists.
• Qualifications of Experts: Described as "experienced Pulmonologists." Specific details like years of experience are not provided.

4. Adjudication Method for the Test Set

• Adjudication Method: Binary ground truth was determined by a majority vote of the three blinded Pulmonologists. This implies a "3+1" approach where if at least two out of three experts agreed on the presence or absence of wheeze, that was considered the ground truth.

5. If a Multi-Reader Multi-Case (MRMC) Comparative Effectiveness Study was Done

• There is no indication of a Multi-Reader Multi-Case (MRMC) comparative effectiveness study being performed with human readers with and without AI assistance. The study focused on the stand-alone performance of the AI algorithm and its non-inferiority to a predicate device's algorithm, not on the improvement of human readers with AI assistance.

6. If a Standalone Study (Algorithm Only Without Human-in-the-Loop Performance) was Done

• Yes, a standalone study was done. The performance evaluation section explicitly states: "For the characterization of the stand-alone accuracy, the automated binary result of the software has been compared to ground truth and specificity and sensitivity were calculated." and "The primary end point was area under the Curve (AUC) for the detection of wheezes by the proposed device compared to the TytoCare Lung Sounds Analyzer (K221614)".

7. The Type of Ground Truth Used

• Type of Ground Truth: Expert consensus. Specifically, the binary ground truth was determined by a majority vote of three blinded experienced Pulmonologists.

8. The Sample Size for the Training Set

• The sample size for the training set is not explicitly provided in the document. The text states: "The AI Algorithm was trained with recordings acquired by the real-world use of the compatible Tyto Stethoscope."

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

• The document states that the AI algorithm was "trained with recordings acquired by the real-world use of the compatible Tyto Stethoscope." However, it does not explicitly detail how the ground truth for this training set was established. It can be inferred that a similar expert labeling process might have been used, but this is not confirmed in the provided text.

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The TytoCare Lung Sounds Analyzer is an over-the-counter decision support software system used in the evaluation of lung sounds in adults and children (2 years and older). It automatically analyzes the acoustic signal of the lung as recorded by the FDA cleared compatible Tyto Stethoscope and identifies recordings where a specific abnormal lung sound suggestive of "Wheeze" is suspected. It is not intended to detect other abnormal or normal lung sounds. A licensed health care professional's advice is required to understand the meaning of the TytoCare Lung Sounds Analyzer result. Healthcare provider should consider the device result in conjunction with recording and other relevant patient data.

The TytoCare Lung Sounds Analyzer is a web-based software system designed to aid in the clinical assessment of lungs auscultation sound data by analyzing recorded lung sounds to determine whether a Wheeze is detected within the recorded sound data.

The TytoCare Lung Sounds Analyzer Software is intended to process recordings from the FDA-cleared compatible Tyto Stethoscope (Tyto Stethoscope, K181612). The acquisition of the acoustic data (recordings) is carried out by a professional user in a clinical environment or by a lay- user in a non-medical environment, in compliance with the labeling of the Tyto Stethoscope.

The system is composed of the following sub-systems:

• 1. The TytoCare Lung Sounds Analyzer Application Server (APS) communicates with the TytoCare Lung Sounds Analyzer Algorithm Server (ALS) and implements an application programming interface (API) for communication with the telehealth server.
• 2. The TytoCare Lung Sounds Analyzer Algorithm Server (ALS) receives an audio file as input and returns an analysis result of positive or negative regarding whether a wheeze was detected as output.
• 3. The TytoCare Lung Sounds Analyzer Web Server (WBS) provides a graphic indication whether a wheeze is detected in the recording. It can be utilized both in patient and clinician side.

All the software subsystems (servers and storage) are hosted in the cloud and communicate through IP network.

Here's a breakdown of the acceptance criteria and the study details for the TytoCare Lung Sounds Analyzer, based on the provided document:

Acceptance Criteria and Device Performance

Study Details

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

   • Sample Size: 371 recordings (86 Wheeze positive, 285 negative) from 359 patients.
   • Data Provenance: Retrospective validation dataset of Tyto Stethoscope recordings sourced from real-world use of the Tyto Care FDA cleared Tyto Stethoscope. The dataset included recordings from patients with known pre-existing conditions (COPD or Asthma) (7.28%).

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

   • Number of Experts: Three.
   • Qualifications: Blinded experienced Pulmonologists. Specific years of experience are not mentioned.

3. Adjudication method for the test set:

   • Adjudication Method: Majority vote of the three blinded Pulmonologists ("binary ground truth was determined by majority vote of these three Pulmonologists"). This is a 3-reader consensus method.

4. If a Multi-Reader Multi-Case (MRMC) comparative effectiveness study was done:

   • Yes, a comparative study was done. The clinical performance section compares the device's AUC to that of "clinical readers (Physicians non-Pulmonologists)."
   • Effect size of how much human readers improve with AI vs. without AI assistance: The study primarily focused on the device's accuracy being non-inferior to human readers, rather than human readers with AI assistance. The reported AUC for the device (0.91) was higher than the AUC for clinical readers (0.83), with a difference of 0.09. This suggests the device performs better than the un-assisted clinical readers. The study did not test human readers with AI assistance, so an effect size for human improvement with AI assistance cannot be determined from this document.

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

   • Yes, a standalone accuracy assessment was performed. Table 3 explicitly presents "The stand-alone accuracy of the TytoCare Lung Sounds Analyzer" with sensitivity and specificity results.

6. The type of ground truth used:

   • Type of Ground Truth: Expert consensus. Specifically, a majority vote of three blinded experienced Pulmonologists.

7. The sample size for the training set:

   • The document does not provide the sample size for the training set. It only mentions the validation dataset.

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

   • The document does not provide information on how the ground truth for the training set was established. It only describes the ground truth establishment for the validation dataset.

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wheezo is intended to detect and record abnormal breath sounds (continuous adventitious breath sounds/CABS) at the windpipe (trachea), reported as WheezeRate in adults and children (2 years and older). A licensed health care professional's advice is required to understand the meaning and importance of the wheezo readings.

The Respiri wheezo WheezeRate Detector contains the following components (1) wheezo Sensing Device (2) wheezo App and (3) Secure cloud server. The wheezo transfers a user's breath sound data to the App using a Smart Device. The sound data is analysed in the alqorithm, which is integrated inside the App and runs on the Smart Device.

It is a hand-held, battery-operated, computer-based, pulmonary sound detector that utilises microphones to acquire, amplify, filter, record and quantify the presence of wheezing. The breath sound is transferred using Bluetooth® technology to smartphone for detection and quantification of wheeze presence, expressed as wheeze rate.

The wheezo samples breathing and ambient sounds, and streams the audio data wirelessly via Bluetooth SPP profile to a connected Smart Device Mobile App. It does not store any recorded data into persistent memory. The Mobile App, through user interaction, must perform the standard Bluetooth pairing process to the wheezo prior to use. Wheeze rate is calculated by the Mobile App using the audio data received from the sensing device. A remote device can connect to the wheezo Sensing Device via Standard Bluetooth. The device supports Bluetooth Serial Port Protocol (SPP) mode, where asynchronous serial packets are transported using Bluetooth RFCOMM protocol. When not paired, the sensing device will be in discoverable mode.

Here's a breakdown of the acceptance criteria and the study details for the wheezo WheezeRate Detector, based on the provided FDA 510(k) summary:

Acceptance Criteria and Device Performance:

The document doesn't explicitly state quantitative acceptance criteria for "wheeze rate accuracy" for the wheezo device to be considered substantially equivalent. Instead, it relies on a comparison to a predicate device (SonoSentry) and a clinical validation study demonstrating agreement with expert assessment. The key "performance" is the detection and quantification of wheezing.

Given the information, the primary "acceptance criteria" appear to be:

• Accuracy in Wheeze Rate Calculation: The wheezo's calculated wheeze rate must align with expert manual calculations.
• Safety and Effectiveness: The device must not raise new questions of safety or effectiveness compared to the predicate device, despite technological differences.

Table of Acceptance Criteria and Reported Device Performance:

Here's the breakdown of the study details:

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

• Sample Size: 189 recordings from 56 patients and 20 healthy individuals.
• Data Provenance: Not explicitly stated (e.g., country of origin, retrospective/prospective). However, the study describes "patients and healthy individuals," suggesting it was prospective data collection specifically for validation.

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

• Number of Experts: A panel of three respiratory experts.
• Qualifications of Experts: Only stated as "respiratory experts." Specific qualifications like years of experience or board certification are not provided in this summary.

4. Adjudication method for the test set:

• The document states that the wheezo's calculated wheeze rate was "compared to a panel of three respiratory experts' manual calculation of the wheeze rate." It does not specify an explicit adjudication method like "2+1" or "3+1" for reaching a consensus among experts on each recording. It implies that the experts' manual calculations individually formed the ground truth against which the device was validated, rather than a single adjudicated ground truth per case.

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 explicitly described. The clinical testing section refers to "Validation of the wheezo device output... compared to a panel of three respiratory experts' manual calculation of the wheeze rate." This suggests a standalone performance evaluation against expert opinion, not an evaluation of human readers using the AI device compared to human readers without it.

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

• Yes, a standalone performance study was done. The clinical testing section explicitly describes "Validation of the wheezo device output (calculated wheeze rate)... compared to a panel of three respiratory experts' manual calculation of the wheeze rate." This is a direct measure of the algorithm's performance in calculating the WheezeRate independently.

7. The type of ground truth used:

• Expert Consensus / Expert Opinion: The ground truth for the clinical validation was established by the "manual calculation of the wheeze rate" by a "panel of three respiratory experts."

8. The sample size for the training set:

• Not provided. The document describes the clinical validation study but does not provide information about the training set used for the wheezo's algorithm.

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

• Not provided. As the training set details are not included, how its ground truth was established is also not available in this summary.

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The SonoSentry™ WheezeRate™ Detector is intended to detect and record abnormal breath sounds (continuous adventitious breath sounds/CABS) at the windpipe (trachea), reported as WheezeRate™ in adults and children (2 years and older). WheezeRate™ represents the percentage of abnormal breath sound detected during the measurement time. A licensed health care professional's advice is required to understand the meaning and importance of the SonoSentry™ readings.

The SonoSentry™ WheezeRate™ Detector is a hand-held electronic measurement device that utilizes an acoustic contact sensor to acquire, amplify, filter, record and analyze breath sounds at the trachea for the presence of continuous adventitious breath sounds/CABS. The device calculates and outputs a WheezeRate™ based on the amount of abnormal breath sounds detected in a given time. The device is designed for use in adults and children (ages 2 years and older).

The SonoSentry™ WheezeRate™ Detector consists of:

• . An acoustic contact sensor
• . An air-coupled electret microphone for ambient noise reiection module
• LCD screen to display measurement results .
• . 4 user buttons
• . Signal conditioning and digitization PCB
• Dedicated DSP .
• . SDRAM memory
• . Embedded software

Here's a breakdown of the acceptance criteria and study information for the SonoSentry™ WheezeRate™ Detector, based on the provided text:

1. Table of Acceptance Criteria and Reported Device Performance

The document does not explicitly state quantitative acceptance criteria or a specific performance metric table with pass/fail thresholds. However, it does state the device's intended use and the outcome of the clinical study.

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

• Test Set Sample Size: The document does not specify the exact number of participants (pediatric and adult) in the clinical study that formed the test set. It mentions "pediatric and adult study subjects."
• Data Provenance: Not explicitly stated (e.g., country of origin). The study subjects had "a previous diagnosis of moderate to severe asthma." The study appears to be prospective since it describes "Sound files... were analyzed by the SonoSentry and an Expert Panel."

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

• Number of Experts: An "Expert Panel" was used, but the exact number of experts is not specified.
• Qualifications of Experts: The Expert Panel consisted of "Board Certified Pulmonologists."

4. Adjudication Method for the Test Set

The document states that an "Expert Panel consisting of Board Certified Pulmonologists" analyzed the sound files. It doesn't explicitly detail a specific adjudication method like "2+1" or "3+1." It implies a consensus or agreement by the panel.

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

• Was an MRMC study done? Yes, the document explicitly states, "The Multiple Reader/Multiple Case Study demonstrated an acceptable agreement between the output of the SonoSentry and physician detection of CABS."
• Effect size of human readers improve with AI vs without AI assistance: The document does not provide any specific effect size or quantitative measure of how much human readers improved with AI assistance compared to without it. It only states that there was "acceptable agreement" between the device and physician detection. The study seemed to compare the device's performance against physician assessment, rather than human performance with the device versus without it.

6. Standalone (Algorithm Only) Performance Study

Yes, a standalone study was done. The clinical study describes the SonoSentry analyzing sound files and its output being compared to physician assessment. This indicates a standalone evaluation of the algorithm's performance without direct human-in-the-loop assistance for the physicians in the comparison.

7. Type of Ground Truth Used

The ground truth used was expert consensus / physician assessment. Specifically, it was established by an "Expert Panel consisting of Board Certified Pulmonologists" who analyzed the same breath sounds.

8. Sample Size for the Training Set

The document does not provide any information regarding the sample size used for the training set of the SonoSentry™ WheezeRate™ Detector or its underlying algorithm.

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

The document does not provide any information on how the ground truth for the training set was established. It only discusses the clinical study used for performance evaluation (test set).

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The PulmoTrack TM 2020 is intended for the analysis, interpretation and documentation of lung sounds. The PulmoTrack TM 2020 is indicated for use by or under the supervision of a physician while carrying out a provocation test, administering a bronchodilator or performing a physical examination in pulmonary function testing environment when there is a need for performing an acoustic pulmonary function measurement that quantifies the presence of wheezing. It is also indicated when there is a need to listen to amplified and filtered breath sounds. The PulmoTrack TM 2020 is indicated for patient population above two years old

The PulmoTrackTM 2020 is a computer based electronic stethoscope that utilizes an acoustic contact sensor to acquire, amplify, filter, record and analyze pulmonary sounds from the trachea and thorax and provides high fidelity audio outputs, visual displays, printed reports and automated identification of lung sounds. The data transfer may be done via Bluetooth wireless communication.

The PulmoTrack™ 2020 is a computer-based electronic stethoscope that acquires, amplifies, filters, records, and analyzes pulmonary sounds. Its intended use is for the analysis, interpretation, and documentation of lung sounds, specifically quantifying the presence of wheezing during pulmonary function testing, provocation tests, bronchodilator administration, or physical examinations. It is indicated for patients above two years old, to be used by or under the supervision of a physician.

This device did not undergo a clinical study as it claimed substantial equivalence to its predicate device, PulmoTrack™ 2010 (WIM-PC™) (K071955), without raising new safety and/or effectiveness concerns. The only technical modification was the data transfer from A/D to the PC via a Bluetooth wireless channel. Therefore, the performance validation focused on demonstrating that the PulmoTrack™ 2020 performed according to its specifications and as well as the predicate device, particularly concerning biocompatibility and wireless functionality.

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

1. Table of Acceptance Criteria and Reported Device Performance

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

A specific "test set" for a clinical performance study (as would be typical for a novel device) is not explicitly described. Because this was a 510(k) submission based on substantial equivalence to a predicate device with only a minor technological change (Bluetooth), the performance validation was likely focused on technical specifications and functional testing rather than a clinical study with a patient test set. The document states "All testing results demonstrated satisfactory performance," implying internal verification and validation activities. There is no information provided about country of origin of data or whether it was retrospective or prospective.

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

Not applicable. No clinical study involving a test set and expert ground truth establishment is described for the PulmoTrack™ 2020 in this 510(k) submission.

4. Adjudication Method for the Test Set

Not applicable. No clinical study involving a test set and adjudication 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. No MRMC study was conducted or described. The PulmoTrack™ 2020 is an electronic stethoscope that provides analysis and interpretation of lung sounds, but the provided documentation does not detail a study comparing human reader performance with and without AI assistance.

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

The device itself is an electronic stethoscope with automated identification of lung sounds. The "Performance Validation" section broadly states that the device "underwent validation testing to ensure performance according to its specifications." This would imply standalone performance testing for its ability to analyze and identify lung sounds as per its design. However, specific details of such standalone testing (e.g., against a known dataset of pre-recorded lung sounds with established ground truth) are not provided in this 510(k) summary.

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

The document does not explicitly state the type of ground truth used for validating the automated identification of lung sounds. For a device like this, ground truth would typically come from:

• Expert auscultation/consensus: A panel of experienced physicians listening to the same lung sounds and agreeing on the presence or absence of wheezing.
• Spirometry/pulmonary function tests: Objective measures of lung function that correlate with the presence of wheezing.

Given the nature of the submission (substantial equivalence with minor changes), it's more likely that the validation focused on ensuring the device accurately captured and processed sounds, and that its automated identification algorithm performed comparably to the predicate device, which would have had its own original validation methods for ground truth.

8. The sample size for the training set

Not applicable. The document does not describe a new training set for an AI/algorithm. The device is claiming substantial equivalence to a predicate, implying it uses the same underlying technology/algorithms as the PulmoTrack™ 2010.

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

Not applicable. As no new training set is described for the PulmoTrack™ 2020, how its ground truth was established is not part of this submission. It would rely on the original methodologies used to develop and validate the predicate device's technology.

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The PERSONAL WHEEZOMETER™ is intended for quantifying the presence of wheezing. This device should be used under the direction of a physician or licensed healthcare professional for monitoring acoustic pulmonary functions.

The PERSONAL WHEEZOMETER™ ™ is a hand-held electronic measurement device that utilizes an acoustic contact sensor to acquire, amplify, filter, record and analyze pulmonary sounds from the trachea for the presence of wheczes. The device outputs a wheezerate score based on the amount of wheezing detected in a given time. The PERSONAL WHEEZOMETER™ (PW) is intended to be a home use version of the PulmoTrack® (K980878) and PulmoTrack model 2010 (WIM-PC) (K071955), providing wheeze-rate information for both home and clinical settings.

The PERSONAL WHEEZOMETER™ device consists of:

• An acoustic contact sensor
• An air-coupled electret microphone for ambient noise rejection module.
• LCD screen to display measurement results
• 4 user buttons
• Signal conditioning and digitization PCB
• Dedicated DSP
• SDRAM memory
• Embedded software.

This 510(k) summary does not contain sufficient information to comprehensively answer all aspects of your request. Specifically, it lacks detailed quantitative acceptance criteria and the results of the performance study against those criteria, as well as specific information about ground truth establishment and training set details.

Here's what can be extracted and what is missing:

1. Table of Acceptance Criteria and Reported Device Performance

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

• Test Set Sample Size: Not specified for the clinical study.
• Data Provenance: The document states that KarmelSonix is an Israeli company and the contact person is from Israel. The clinical study was likely conducted in Israel, but this is not explicitly stated. The study is prospective, as it's a "clinical usability study" performed with the device.

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

• Number of Experts: Not specified.
• Qualifications of Experts: Not specified. (It's unclear if experts were even used to establish ground truth for wheezing, as the study primarily focused on usability.)

4. Adjudication method for the test set

• Not specified. Given the focus on usability, a formal adjudication process for wheeze detection may not have been the primary objective.

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

• A MRMC comparative effectiveness study was not conducted. The study described is a "clinical usability study" of the device itself, not a comparative study with human readers or AI assistance.

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

• The device, PERSONAL WHEEZOMETER™, is essentially a standalone algorithm for quantifying wheezing, displaying a "wheezerate score." The "clinical usability study" would inherently assess its standalone performance in a real-world user context. However, specific metrics of algorithm-only performance (e.g., sensitivity/specificity against a gold standard) are not detailed.

7. The type of ground truth used

• The document does not explicitly state the type of ground truth used for evaluating wheezing detection. Given the clinical usability study context, it's possible that clinical assessment by physicians (expert consensus) or correlation with other diagnostic tools might have been informally used, but this is not detailed.

8. The sample size for the training set

• Not specified. The document does not mention details about a training set, implying a pre-trained algorithm or an algorithm developed without a distinct "training set" in the context of this submission. The device is a home-use version of previously cleared devices (PulmoTrack® and PulmoTrack model 2010), so its core algorithms might have been developed and validated previously.

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

• Not specified, as details about a training set are not provided.

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The WIM-PCTM is intended for the analysis, interpretation and documentation of lung sounds. The WIM-PC™ is indicated for use by or under the supervision of a physician while carrying out a provocation test, administering a bronchodilator or performing a physical examination in pulmonary function testing environment when there is a need for performing an acoustic pulmonary function measurement that quantifies the presence of wheezing. It is also indicated when there is a need to listen to amplified and filtered breath sounds.

The WIM-PC™ is a computer based electronic stethoscope that utilizes two contact sensors simultaneously to acquire, amplify, filter, record and analyze pulmonary sounds from the trachea and thorax and provides high fidelity audio outputs, visual displays and printed reports. The WIM-PCTM system consists of: Acoustic sensors (attached to the patient using adhesive pads), Sensor pod with a built-in dielectric microphone for ambient noise pick-up, Tension-sensitive respiration belt, A/D data acquisition device, USB cable and signal cable, Laptop PC unit with Data Analysis software.

The provided document is a 510(k) summary for the WIM-PCTM device by KarmelSonix Israel Ltd. It focuses on demonstrating substantial equivalence to a predicate device (PulmoTrack™ model 1010) and outlines the validation tests performed. However, it does not contain detailed information about specific acceptance criteria, comprehensive study results (like sensitivity/specificity, effect sizes), or the detailed methodology of how ground truth was established for a testing or training set in the way a clinical study report would.

Therefore, I can only extract limited information based on what is present in the document.

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

1. Table of Acceptance Criteria and Reported Device Performance

The document mentions "Wheeze detection validation test" and "Breath detection validation test" as performed on the software, but it does not provide specific numerical acceptance criteria (e.g., minimum sensitivity, specificity, or agreement rates) nor does it report the quantitative performance results of these tests.

2. Sample Size and Data Provenance for the Test Set

The document does not specify the sample size used for the "Wheeze detection validation test" or "Breath detection validation test." It also does not mention the data provenance (e.g., country of origin, retrospective or prospective nature of the data).

3. Number of Experts and Qualifications for Ground Truth Establishment (Test Set)

The document does not mention the number of experts used or their qualifications for establishing ground truth in the reported validation tests.

4. Adjudication Method (Test Set)

The document does not describe any adjudication method (e.g., 2+1, 3+1, none) used for the test set.

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

The document does not mention a multi-reader multi-case (MRMC) comparative effectiveness study. Therefore, no effect size for human reader improvement with AI assistance can be determined from this text.

6. Standalone Performance Study

Yes, a standalone validation was performed for the device's software components. The document explicitly states:

• "The following validation tests were performed on the WIM-PCTM software:
  • Wheeze detection validation test.
  • Breath detection validation test."
    However, the actual performance metrics (e.g., sensitivity, specificity, accuracy) are not reported.

7. Type of Ground Truth Used

The document does not explicitly state the type of ground truth used for the "Wheeze detection validation test" or "Breath detection validation test." Given the nature of pulmonary sound analysis, it's highly likely to be expert consensus (e.g., auscultation by physicians) or potentially correlated with other clinical findings, but this information is not provided.

8. Sample Size for the Training Set

The document does not specify the sample size used for training the software algorithms.

9. How Ground Truth for the Training Set was Established

The document does not describe how the ground truth for the training set was established.

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The VR Lung Electrosonograph is intended for use in monitoring and recording lung sounds.

The VR Lung Electrosonograph is intended for use in monitoring and recording lung sounds. The VR Lung Electrosonograph is a non-invasive device consisting of three primary components: 1) Electronic stethoscopes designed to collect lung sounds via dermal contact with the human thorax; 2) a Digital Collection Module ("DCM") for the conversion of analog data to digital data; and 3) a mobile computer workstation to assist in processing, displaying, and/or storing recorded information. The VR Lung Electrosonograph is intended to be used by trained healthcare practitioners, and has been designed to accommodate most clinic, treatment center, or hospital settings. While the VR Lung Electrosonograph may aid in diagnosis, the device is not intended to be used as a diagnostic instrument.

The provided text is a 510(k) summary for the Deep Breeze VR Lung Electrosonograph. This document is primarily focused on demonstrating substantial equivalence to predicate devices and does not contain detailed information about specific acceptance criteria, performance studies with quantitative results, or information typically found in clinical validation studies for AI/ML devices. Therefore, I cannot fully complete the requested table and answer many of the questions.

Here's what can be extracted and what information 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)

This information is not provided in the document. The filing does not describe a performance study with a test set of data.

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)

This information is not provided in the document. The filing does not describe a performance study with a ground truth established by experts.

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

This information is not provided in the document. The filing does not describe a performance study with an adjudication method.

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

There is no indication that an MRMC comparative effectiveness study was done, nor any mention of AI assistance. The device is described as an "Electrosonograph" for monitoring and recording lung sounds, not an AI-driven interpretive tool.

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

The device is described as a non-invasive tool to "aid in diagnosis," but "not intended to be used as a diagnostic instrument." It's an electronic stethoscope system designed for "trained healthcare practitioners" to collect, process, display, and store lung sound information. This suggests a human-in-the-loop scenario where the device provides data to the practitioner. There is no mention of standalone algorithmic performance.

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

This information is not provided in the document, as no specific performance study with ground truth is described.

8. The sample size for the training set

This information is not provided in the document. The 510(k) summary focuses on substantial equivalence, not on the development or training of an algorithm.

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

This information is not provided in the document.

Summary of the Study (Based on available information):

The provided document describes a 510(k) submission for the VR Lung Electrosonograph. The study is implicitly a substantial equivalence comparison to predicate devices rather than a direct performance study with acceptance criteria and a test set against ground truth.

• The device, a "VR Lung Electrosonograph," is intended for "monitoring and recording lung sounds."
• It is compared for substantial equivalence to the "Meditron Stethoscope System" (K991367) and the "STG Monitor Multichannel Lung Sound Analysis System" (K012387).
• The argument for substantial equivalence rests on similar intended use and technological characteristics (design, materials, energy source, function).
• The device is explicitly stated as being able to "aid in diagnosis" but "not intended to be used as a diagnostic instrument." This implies that the device provides data to a healthcare practitioner, who then uses their clinical judgment for diagnosis, rather than the device performing a diagnosis itself.
• The regulatory pathway chosen (510(k)) and the content provided focus on demonstrating that the new device is as safe and effective as a legally marketed predicate device, rather than proving performance against specific quantitative criteria.

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