(230 days)
Noxturnal Web is intended to be used for the diagnostic evaluation by a physician to assess sleep quality and as an aid for the diagnosis of sleep and respiratory-related sleep disorders in adults only.
Noxturnal Web is a software-only medical device to be used to analyze physiological signals and manually score sleep study results, including the staging of sleep, AHI, and detection of sleep disordered breathing events including obstructive apneas.
It is intended to be used under the supervision of a clinician in a clinical environment.
Noxturnal Web is a web-based software that can be utilized to screen various sleep and respiratoryrelated sleep disorders. The users of Noxturnal Web are medical professionals who have received training in the areas of hospital/clinical procedures, physiological monitoring of human subjects, or sleep disorder investigation. Users can input a sleep study recording stored on the cloud (electronic medical record repository) using their established credentials. Once the sleep study data has been retrieved, the Noxturnal Web software can be used to display, manually analyze, generate reports and print the prerecorded physiological signals.
Noxturnal Web is used to read sleep study data for the display, analysis, summarization, and retrieval of physiological parameters recorded during sleep and awake. Noxturnal Web facilitates a user to review or manually score a sleep study either before the initiation of treatment or during the treatment follow-up for various sleep and respiratory-related sleep disorders.
Noxturnal Web presents information from the input sleep study data in an organized layout. Multiple visualization layouts (e.g., Study Overview, Respiratory Signal Sheet, etc.) are available to allow the users to optimize the visualization of key data components. The reports generated by Noxturnal Web allow the inclusion of custom user comments, and these reports can then be viewed on the screen and/or printed.
The provided document is a 510(k) summary for the medical device Noxturnal Web. It states that clinical data were not relied upon for a determination of substantial equivalence. Therefore, there is no information in this document regarding a clinical study or a test set with expert-established ground truth.
However, the document does describe the performance expectations and how suitability was determined through non-clinical testing, specifically software verification and validation.
Here's the information based on the provided text, focusing on the non-clinical and comparative aspects:
1. A table of acceptance criteria and the reported device performance
The document does not present explicit quantitative acceptance criteria for performance in a table format with reported numerical device performance. Instead, it describes functional equivalence to the predicate device through comparative analysis and states that the software meets its pre-specified requirements and performs as intended.
The comparison table on pages 8-9 highlights the functional equivalences:
| Acceptance Criteria (Inferred from Functional Equivalence) | Reported Device Performance (as stated in document) |
|---|---|
| Aid/Assist in the diagnosis of sleep and respiratory-related sleep disorders | Yes (Same as predicates) |
| Arousal Scoring | Yes (Same as predicates) |
| Respiratory Events Scoring | Yes (Same as predicates) |
| Leg Movement Events Scoring | Yes (Same as predicates) |
| Sleep Study Scoring Method (Manual) | Manual (Same as primary predicate; additional predicate also has automatic) |
| Sleep Stage Scoring (W, N1/N2/N3, R) | Yes (Same as predicates) |
| Report Generation | Yes (Same as predicates) |
| Calculation of AASM standardized indices | Yes (Same as predicates) |
| Data Inputs (EEG, EOG, EMG, ECG, Chest/Abdomen movements, Airflow, Oxygen Saturation, Body Position/Activity) | All "Yes" (Same as predicates for all relevant inputs) |
| Software Type (Web-based) | Web-based (Same as additional predicate; primary predicate is computer program) |
| Physical Characteristics (Web-based operating in the cloud with Windows or Mac OS) | Web-based software operating in the cloud with Windows or Mac OS (Similar to additional predicate) |
| Standard of Scoring Manual | The American Academy of Sleep Medicine (AASM) Manual for the Scoring of Sleep and Associated Events (Same as predicates) |
| Backend implementation | Identical to corresponding qualitative and quantitative functionality implemented in the reference device (Nox Sleep System, K192469) |
| Cybersecurity controls | Implemented in accordance with FDA's Guidance "Cybersecurity for Networked Medical Devices Containing Off-the-Shelf (OTS) Software" and "Cybersecurity in Medical Devices: Quality System Considerations and Content of Premarket Submissions" |
The general acceptance criterion is that the Noxturnal Web is "as safe and effective as the predicate devices" and "meets its pre-specified requirements."
2. Sample size used for the test set and the data provenance
The document explicitly states: "Clinical data were not relied upon for a determination of substantial equivalence." Therefore, there is no clinical test set of patient data with ground truth as would be used in a clinical study.
The testing performed was "Software verification and validation testing... to demonstrate safety and performance based on current industry standards," and "Verification and Validation testing of all requirement specifications defined for Noxturnal Web was conducted and passed." This implies that the 'test set' consisted of various software functions and their outputs, but not a large set of patient physiological recordings serving as a "test set" in the context of a clinical performance study. The data provenance and size of this kind of "test set" (software test cases) are not detailed in this summary.
3. Number of experts used to establish the ground truth for the test set and the qualifications of those experts
Given that "Clinical data were not relied upon," there was no clinical test set requiring expert-established ground truth in the traditional sense for demonstrating substantial equivalence. The summary highlights that the device supports manual scoring completed by medical professionals who have received training in relevant areas (page 7). This implies that the human-in-the-loop performance is based on the expertise of the user, rather than the device itself establishing ground truth.
4. Adjudication method for the test set
Not applicable, as no clinical test set with established ground truth was used for assessing substantial equivalence.
5. If a multi reader multi case (MRMC) comparative effectiveness study was done, If so, what was the effect size of how much human readers improve with AI vs without AI assistance
No MRMC study was done, as indicated by the statement "Clinical data were not relied upon for a determination of substantial equivalence." The device's primary function is to facilitate manual scoring by a clinician, not to provide AI-assisted automated interpretations that would then be compared to human-only interpretations via an MRMC study.
6. If a standalone (i.e. algorithm only without human-in-the-loop performance) was done
The device is described as "software-only medical device to be used to analyze physiological signals and manually score sleep study results" and "It is intended to be used under the supervision of a clinician in a clinical environment." This indicates that the device is not intended for standalone (algorithm only) performance without human-in-the-loop interaction for interpretation and scoring. The comparative table also notes that both the subject device and the primary predicate "rely on manual scoring."
7. The type of ground truth used
For the purpose of regulatory clearance, the "ground truth" for the device's functionality was its ability to replicate the features and performance of legally marketed predicate devices, as demonstrated through "comparative analysis, software and performance testing." The ground truth for interpreting sleep studies using this device resides with the trained medical professional who manually scores the data according to the "American Academy of Sleep Medicine (AASM) Manual for the Scoring of Sleep and Associated Events."
8. The sample size for the training set
Not applicable, as this device appears to be a software tool for manual scoring and analysis, rather than an AI/ML algorithm that requires a "training set" in the context of deep learning or machine learning models. The summary makes no mention of AI/ML or training data; its emphasis is on providing tools for manual clinician review.
9. How the ground truth for the training set was established
Not applicable, for the same reasons as point 8.
§ 882.1400 Electroencephalograph.
(a)
Identification. An electroencephalograph is a device used to measure and record the electrical activity of the patient's brain obtained by placing two or more electrodes on the head.(b)
Classification. Class II (performance standards).