Search Results

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.

Ask a specific question about this device

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.

Ask a specific question about this device

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.

Ask a specific question about this device

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.

Ask a specific question about this device

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.

Ask a specific question about this device

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).

Ask a specific question about this device