(24 days)
The Respironics BiPAP® Pro Bi-level System delivers positive airway pressure therapy for the treatment of Obstructive Sleep Apnea only.
The Respironics BiPAP® Pro Bi-level System is a microprocessor controlled blower based bi-level positive pressure system that delivers two different positive pressure levels (IPAP/EPAP). The dual pressure levels provide a more natural means of delivering pressure support therapy to the patient resulting in improved patient comfort. Respironics is adding an additional therapy feature to the existing BiPAP Pro Bi-level System Software. This feature will ease the transition from the end of inspiration to the beginning of exhalation. The BiPAP® Pro Bi-level System is intended for use with a patient circuit that is used to connect the device to the patient interface device (mask). A typical patient circuit consists of a six-foot disposable or reusable smooth lumen 22mm tubing, an exhalation device, and a patient interface device.
Here's an analysis of the provided text regarding the acceptance criteria and study for the Respironics BiPAP® Pro Bi-level System, structured according to your request.
Please note: The provided document is a 510(k) summary and FDA approval letter for a medical device. It focuses on demonstrating substantial equivalence to a predicate device rather than detailing specific, quantitative acceptance criteria and a comprehensive "study" report in the way one might expect for a novel AI/software product. Therefore, much of the requested information (like specific performance metrics, sample sizes for test/training sets, expert qualifications, etc.) is not explicitly present in the given text. The device described here is a hardware-based ventilator with a software enhancement, not a standalone AI diagnostic tool.
1. A table of acceptance criteria and the reported device performance
Based on the provided text, specific, quantitative acceptance criteria and their corresponding reported performance values are not detailed. The submission states that "All tests were verified to meet the required acceptance criteria," but these criteria themselves are not listed. The focus is on demonstrating substantial equivalence.
| Acceptance Criterion (Implicit) | Reported Device Performance (Implicit) |
|---|---|
| Intended Use Equivalence: Same intended use as predicate device. | "Same intended use." |
| Operating Principle Equivalence: Same operating principle as predicate device. | "Same operating principle." |
| Technology Equivalence: Same technology as predicate device. | "Same technology." |
| Manufacturing Process Equivalence: Same manufacturing process as predicate device. | "Same manufacturing process." |
| Safety and Effectiveness: Modifications have no impact on safety and effectiveness. | "Respironics has determined that the modifications have no impact on the safety and effectiveness of the device." |
| Compliance with Standards: Complies with applicable standards for software in medical devices. | "The modified device complies with the applicable standards referenced in the Guidance for FDA Reviewers and Industry 'Guidance for the Content of Pre-market Submissions for Software Contained in Medical Devices', May 1998." |
| Design Verification: All design verification tests meet required acceptance criteria (tests not specified). | "Design verification tests were performed... All tests were verified to meet the required acceptance criteria." |
2. Sample sized 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 given text. The document refers to "design verification tests" but does not specify the sample size of patients or data used, nor its provenance. Given the nature of a software enhancement to a CPAP machine, these tests likely involved bench testing and potentially limited human usability testing rather than a large-scale clinical trial with patient data sets.
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 given text. The "ground truth" for a device like this would typically be established by engineering specifications and functional testing, not expert consensus on diagnostic images or clinical outcomes in the same way an AI diagnostic tool would.
4. Adjudication method (e.g. 2+1, 3+1, none) for the test set
This information is not provided in the given text.
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 done, or at least not reported in this 510(k) summary. This type of study is relevant for AI-assisted diagnostic tools that impact human interpretation, which is not the primary function of this device. The device is a CPAP system with a software enhancement designed to "ease the transition from the end of inspiration to the beginning of exhalation," implying a focus on patient comfort and device function, not human diagnostic accuracy.
6. If a standalone (i.e. algorithm only without human-in-the-loop performance) was done
The device itself is a "blower based bi-level positive pressure system" with a microprocessor-controlled software component. While the software operates "standalone" in its functional execution, it's an embedded system controlling a physical device, not a diagnostic algorithm evaluated for its "standalone" performance against a human reader in an interpretative task. The "design verification tests" would assess the software's performance in controlling the device's pressure delivery characteristics.
7. The type of ground truth used (expert consensus, pathology, outcomes data, etc)
For this type of device and software enhancement, the "ground truth" for design verification would primarily be engineering specifications and established physiological principles for pressure delivery in respiratory therapy. For example, testing would verify that the device delivers the specified IPAP/EPAP levels within acceptable tolerances, and that the new feature accurately manages the inspiratory-expiratory transition according to its design. It would not be expert consensus on diagnoses, pathology reports, or long-term clinical outcomes data in the way an AI diagnostic system would use it.
8. The sample size for the training set
This information is not provided in the given text. The software enhancement is likely based on established control algorithms and engineering principles rather than a large machine learning training set derived from patient data.
9. How the ground truth for the training set was established
This information is not provided in the given text. Similar to point 7, if a "training set" (in the machine learning sense) was used, its ground truth would likely be based on engineering models, physiological data, or simulated inputs rather than expert annotation of clinical cases. However, it's more probable that this software enhancement involved traditional control system design and verification rather than an AI/ML training paradigm.
§ 868.5905 Noncontinuous ventilator (IPPB).
(a)
Identification. A noncontinuous ventilator (intermittent positive pressure breathing-IPPB) is a device intended to deliver intermittently an aerosol to a patient's lungs or to assist a patient's breathing.(b)
Classification. Class II (performance standards).