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510(k) Data Aggregation
(196 days)
SonarMed AirWave Airway Monitoring System
The SonarMed AirWave Airway Monitoring System is used to assist in verifying placement of the endotracheal tube (ETT), to assist in detecting movement of the ETT tip, to assist in detecting obstruction of the ETT, and to assist in listening to breath sounds.
The SonarMed AirWave Airway Monitoring System is intended for use by qualified personnel to assist with artificial airway management for patients in an in-hospital setting (intensive care, operating room, and emergency department settings, as well as intra-hospital transport).
The SonarMed AirWave Airway Monitoring System is to be used as an adjunct to normal clinical practice and is a not a stand-alone diagnostic system.
It is intended for use with neonates, infants, children, adolescents, and adults (sizes 2.5 mm to 9.0 mm).
The SonarMed AirWave is comprised of a SonarMed Monitor (Monitor) that is used in conjunction with a single-patient use SonarMed Sensor (Sensor) and software that operates the Monitor and Sensor. The Monitor is powered from an external power supply and has a battery backup. When in use, the SonarMed Sensor is placed in-line between the ventilator circuit and the proximal end of the endotracheal tube (ETT) of a patient who is connected to a ventilator.
Using acoustic reflection technology, signals from the Sensor are displayed on the Monitor showing the clinician:
- The baseline location of the ETT tip as established by the clinician
- Estimation of passageway around the tipof the ETT, relative to the . ETT diameter
- ETT movement relative to the baseline location ●
- . ETT occlusion / obstruction information including percent obstructed and location of the obstruction
- The clinician can choose whether to view information about the . patient's airway in either a waveform or graphic on the Monitor's LCD. Additionally, the clinician can use the microphones to listen to breath sounds. The information provided by the device is to be used in conjunction with normal clinical practice to assist with management of the artificial airway of the patient.
The provided text describes the SonarMed AirWave Airway Monitoring System and its substantial equivalence determination by the FDA. However, it does not explicitly detail the acceptance criteria and a study proving the device meets these criteria in the typical quantitative manner (e.g., sensitivity, specificity, AUC values) usually associated with AI/ML device performance.
The document focuses on demonstrating substantial equivalence to a predicate device (K143042) through various bench tests and by addressing modifications. The "acceptance criteria" appear to be implicit in the comparative performance against the predicate device, aiming for "as accurate or more accurate."
Based on the provided text, here's an attempt to answer your questions to the extent possible, acknowledging the limitations of the information provided for a typical AI/ML device performance study:
1. A table of acceptance criteria and the reported device performance
The document doesn't provide explicit quantitative acceptance criteria or corresponding reported performance metrics (like percentage accuracy thresholds) in a tabular format. Instead, it describes the conclusion of the bench testing:
| Performance Aspect | Implied Acceptance Criterion / Comparison | Reported Device Performance |
|---|---|---|
| Passageway Detection Accuracy | Accurately estimates diameter of passageway around ETT tip (compared to predicate) | "The Passageway Detection Study documented that the subject device accurately estimates the diameter of the passageway around the tip of the ETT for all patient populations." |
| ETT Movement Detection | Accurately detects distance and direction of ETT tip movement (compared to predicate) | "The Movement Study documented that the subject device accurately detects the distance and direction of the ETT tip movement both upward and downward for all patient populations." |
| Obstruction Detection | Accurately detects percentage of obstruction in ETT (compared to predicate) | "The Obstruction Detection Study documented that the subject device accurately detects the percentage of obstruction in the ETT for all patient populations." |
| Overall Performance | As accurate or more accurate than the predicate. | Stated under the "Performance" section of the comparison table: "As accurate or more accurate than predicate." |
2. Sample size used for the test set and the data provenance
The document refers to "bench testing" and "studies" (Passageway Detection Study, Movement Study, Obstruction Detection Study) but does not specify the sample sizes used for these tests.
The data provenance is not mentioned. It is implied these are pre-clinical/bench test data, not clinical patient data, given the description of testing on the device itself. Therefore, concepts like "country of origin of the data," "retrospective," or "prospective" as applied to patient cohorts are not applicable here.
3. Number of experts used to establish the ground truth for the test set and the qualifications of those experts
The document does not specify the number of experts or their qualifications for establishing ground truth. Given that the testing is described as "bench testing" on the device's accuracy for physical measurements (ETT movement, obstruction, passageway diameter), it's more likely that the "ground truth" was established by calibrated instruments or known physical alterations to the test setup, rather than human expert interpretation of images/data.
4. Adjudication method for the test set
The document does not mention any adjudication method, as it does not describe a study involving multiple human readers or interpretations. The "studies" appear to be objective measurements from the device against a known physical ground truth during bench testing.
5. If a multi-reader multi-case (MRMC) comparative effectiveness study was done
No, an MRMC comparative effectiveness study involving human readers is not mentioned in the provided text. The evaluation focuses on the device's intrinsic measurement capabilities in a bench test setting, not on how human readers' performance with or without the device.
6. If a standalone (i.e., algorithm only without human-in-the-loop performance) was done
The "bench testing" described appears to be a standalone performance evaluation of the device's sensors and algorithms, given that it aims to demonstrate the device "accurately detects" or "accurately estimates" various parameters.
7. The type of ground truth used
The ground truth used appears to be objective, known physical parameters established by the bench test setup for ETT tip location, obstruction percentage, and airway diameter. This is inferred from the description of "Passageway Detection Study," "Movement Study," and "Obstruction Detection Study." It is not based on expert consensus, pathology, or outcomes data in a clinical sense.
8. The sample size for the training set
The document does not mention a training set size. This device relies on acoustic reflection technology and embedded processing, which are described as being similar to the predicate device. It's not explicitly stated as an AI/ML device that requires a large, dedicated "training set" in the common sense of deep learning. Updates were made to software (e.g., O2 ranges, waveform screen functionality), but the underlying "technology" (acoustic reflectometry) remains the same as the predicate.
9. How the ground truth for the training set was established
As no training set is explicitly mentioned for a machine learning model, the method for establishing ground truth for such a set is not described.
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(457 days)
SonarMed AirWave, Airway Monitoring System
The SonarMed AirWave Airway Monitoring System is used to assist in verifying placement of the endotracheal tube (ETT), to assist in detecting movement of the ETT tip, to assist in detecting obstruction of the ETT, and to assist in listening to breath sounds.
The SonarMed AirWave Airway Monitoring System is intended for use by qualified personnel to assist with artificial airway management for patients in an in-hospital setting (intensive care, operating room, and emergency department settings, as well as intra-hospital transport).
The SonarMed AirWave Airway Monitoring System is to be used as an adjunct to normal clinical practice, and is not to be used as a stand-alone diagnostic system.
It is intended for use with patients who use ET tube sizes 2.5, 3.0, 3.5, and for sizes 6.5, 7.0, 7.5, 8.0, 8.5, and 9.0 mm.
The SonarMed AirWave is comprised of a SonarMed Monitor (Monitor) that is used in conjunction with a single-patient use SonarMed Sensor (Sensor) and software that operates the Monitor and Sensor. The Monitor is powered from an external power supply and has a battery backup. When in use, the SonarMed Sensor is placed in-line between the ventilator circuit and the proximal end of the endotracheal tube (ETT) of a patient who is connected to a ventilator.
Using acoustic reflection technology, signals from the Sensor are displayed on the Monitor showing the clinician:
• The baseline location of the ETT tip as established by the clinician
• Quantification of diameter of the anatomical structure around the tip of the ETT
• ETT movement relative to the baseline location
- ETT occlusion / obstruction information including percent obstructed and location of the obstruction
The clinician can choose whether to view information about the patient's airway in either a waveform or graphic on the Monitor's LCD. Additionally the clinician can use the microphones to listen to breath sounds. This information should only be used in an adjunctive manner to assist with management of the artificial airway of the patient.
The monitor allows the user to manually adjust the sound of speed. This is to compensate for the changes in the sound of speed with high oxygen concentrations and the use of anesthesia gases. The monitor also has an auto-speed of sound mode. This mode automatically changes the speed of sound.
The monitor allows the user to manually enter the distance to the carina from the chest x-ray. This is an optional feature that the user can elect from the setting menu. Once selected the algorithm will calculate the distance to the carina based on the movement of the tip of the tube.
The provided text describes the SonarMed™ AirWave Airway Monitoring System (K143042) and its conformity with a predicate device through non-clinical testing. It focuses on demonstrating substantial equivalence to a previously cleared device (SonarMed Airway Management System K092611).
Here's an analysis of the acceptance criteria and study information based on the provided text:
1. Table of Acceptance Criteria and Reported Device Performance
The document does not explicitly state formal "acceptance criteria" in a quantitative format typical for clinical trials or performance goals. Instead, the performance is demonstrated through comparisons to the predicate device and verification of intended function in various tests. The key performance aspects evaluated are:
| Acceptance Criteria (Implied) | Reported Device Performance |
|---|---|
| ETT Tip Movement & Passageway Size Accuracy (Infant vs. Adult Size Sensor) | The new, infant sized sensors are at least as accurate as the predicate, adult sized sensors in detecting movement and passageway size. This was concluded from bench testing and a pre-clinical animal study. The pre-clinical study concluded that the tracking algorithm is accurate in determining ETT tip movement and passageway size. |
| ETT Obstruction Accuracy (Infant vs. Adult Size Sensor) | The new, infant sized sensors are at least as accurate as the predicate, adult sized sensors in detecting obstruction. This was concluded from bench testing. |
| Functionality during Prolonged Use (Heated, Humidified, Positive Pressure Ventilation) | The SonarMed AirWave Sensor functions as intended during prolonged use for over 30 days. This was demonstrated in a long-term bench study. |
| Biocompatibility | Testing for cytotoxicity, sensitization, and intracutaneous reactivity all passed. Exhaustive E&L (extractable and leachable) testing determined compounds were present at levels well below health consequences for the neonatal population with large safety margins. |
| Safety and Effectiveness Equivalence to Predicate | The conclusions drawn from the nonclinical testing demonstrate that the device is as safe, as effective, and performs as well as the predicate device. This is the overall conclusion of the substantial equivalence determination for non-clinical aspects. |
2. Sample Size Used for the Test Set and Data Provenance
- Bench Testing: The text mentions "Several bench tests were performed" and "A long-term bench study was conducted." It compares "movement and passageway accuracy of the infant sized sensor to the predicate, adult sized sensor" and "obstruction accuracy of the infant sized sensor to the predicate, adult sized sensor." However, specific sample sizes for the bench tests (e.g., number of ETTs, number of tests run) are not provided.
- Pre-clinical Animal Study: "The study utilized an animal model (rabbit)." The number of rabbits used in the study is not specified.
- Data Provenance: The studies were conducted by SonarMed, Inc. and are described as non-clinical bench and pre-clinical animal studies. The text does not refer to human data or data from a specific country of origin in terms of patient population.
3. Number of Experts Used to Establish the Ground Truth for the Test Set and Qualifications of Those Experts
The document does not mention the use of human experts to establish ground truth for the test sets. The ground truth in the bench and animal studies would have been established through controlled experimental setups (e.g., precisely measured ETT movements, intentionally created obstructions, known ETT tip positions).
4. Adjudication Method for the Test Set
Since human expert assessment for ground truth is not mentioned, an adjudication method for test set ground truth is not applicable or described in this context. The "adjudication" would be inherent in the controlled experimental design of the bench and animal studies.
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 Multi-Reader Multi-Case (MRMC) comparative effectiveness study was not performed or described. The device is an airway monitoring system for direct measurement, not an AI-assisted diagnostic imaging interpretation tool that would involve "human readers" interpreting "cases."
6. If a Standalone (i.e., algorithm only without human-in-the-loop performance) Was Done
The performance described (accuracy in determining ETT tip movement, passageway size, and obstruction) is the standalone performance of the device's sensing and algorithm. The device is intended to provide information to a clinician, but the accuracy studies themselves evaluated the device's ability to measure these parameters directly. It is stated: "The SonarMed AirWave Airway Monitoring System is to be used as an adjunct to normal clinical practice, and is not to be used as a stand-alone diagnostic system." This statement regarding its clinical use as an adjunct does not negate that the performance evaluation of its measurements (as described in the non-clinical tests) is essentially its standalone accuracy.
7. The Type of Ground Truth Used
- Bench Testing: The ground truth for bench testing would be known, controlled, and precisely measured physical parameters (e.g., exact movement distances, known obstruction percentages, measured passageway sizes) created within the experimental setup.
- Pre-clinical Animal Study: For the animal study, the ground truth for ETT movement would be based on known physical manipulations of the ETT within the rabbit trachea, bronchus, and esophagus, likely verified by external measurement or direct observation during the procedure.
8. The Sample Size for the Training Set
The document does not mention a concept of a "training set" as would be relevant for machine learning or AI algorithm development in the traditional sense. The device appears to be based on more traditional acoustic reflection technology and algorithms, which likely don't involve a distinct "training set" in the way a deep learning model would. If an algorithm was "trained," the details are not provided.
9. How the Ground Truth for the Training Set Was Established
As no training set is described, this question is not applicable.
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