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DeepRESP is an aid in the diagnosis of various sleep disorders where subjects are often evaluated during the initiation or follow-up of treatment of various sleep disorders. The recordings to be analyzed by DeepRESP can be performed in a hospital, a patient's home, or an ambulatory setting. It is indicated for use with adults (18 years and above) in a clinical environment by or on the order of a medical professional.

DeepRESP is intended to mark sleep study signals to aid in the identification of events and annotations of traces; automatically calculate measures obtained from recorded signals (e.g., magnitude, time, frequency, and statistical measures of marked events); and infer sleep staging with arousals with EEG and in the absence of EEG. All output is subject to verification by a medical professional.

DeepRESP is a cloud-based software as a medical device (SaMD), designed to perform analysis of sleep study recordings, with and without EEG signals, providing data for the assessment and diagnosis of sleep-related disorders. Its algorithmic framework provides the derivation of sleep staging, including arousals, scoring of respiratory events, and key parameters such as the Apnea-Hypopnea Index (AHI) and Central Apnea-Hypopnea Index (CAHI).

DeepRESP (K252330) is hosted on a serverless stack. It consists of:

• A web Application Programming Interface (API) intended to interface with a third-party client application, allowing medical professionals to access DeepRESP's analytical capabilities.
• Predefined sequences called Protocols that run data analyses, including artificial intelligence and rule-based models for the scoring of sleep studies, and a parameter calculation service.
• A Result storage using an object storage service to temporarily store outputs from the DeepRESP Protocols.

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

1. Table of Acceptance Criteria and Reported Device Performance

The document does not explicitly state pre-defined acceptance criteria (e.g., "DeepRESP must achieve AHI ≥ 5 PPA% of at least X%"). Instead, it reports the performance of the device and compares it to predicate devices to demonstrate substantial equivalence and non-inferiority. The "Observed paired differences" columns, particularly those where the confidence interval does not cross zero, imply a comparison to show that the new DeepRESP v2.0 performs at least as well as the previous version or the additional predicate.

For the purpose of this analysis, I will present the reported performance of the Subject Device (DeepRESP v2.0) as the "reported device performance." Since no explicit acceptance criteria thresholds are given, the comparison to predicates and the demonstration of non-inferiority served as the implicit acceptance path for the FDA clearance.

Reported Device Performance (DeepRESP v2.0)

PPA%: Positive Percent Agreement, NPA%: Negative Percent Agreement, OPA%: Overall Percent Agreement.

2. Sample Sizes and Data Provenance

The clinical validation was conducted using retrospective data.

• Test Set Sample Size:

  • Type I/II Scoring Validation: 4,030 PSG recordings
  • Type III Scoring Validation: 5,771 sleep recordings
    • This comprised 4,037 Type I recordings and 1,734 Type II recordings, processed as Type III by using only the relevant subset of signals.

• Data Provenance:

  • Country of Origin: United States.
  • Data Type: Manually scored sleep recordings from sleep clinics, collected as part of routine clinical work for patients suspected of suffering from sleep disorders.
  • Settings: Urban, suburban, and rural areas.
  • Demographics: Included individuals in all age groups (18-21, 22-35, 36-45, 46-55, 56-65, >65) and all BMI groups (<25, 25-30, <30). The recording collection for Type I/II scoring consisted of 44% Females, and for Type III scoring, 35% Females. High level of race/ethnicity diversity (Caucasian or White, Black or African American, Other).

3. Number of Experts and Qualifications for Ground Truth

• Number of Experts: Not explicitly stated. The document refers to "manually scored sleep recordings" and "medical professional" for verification. It also mentions "board-certified sleep physicians" in the context of the training set. However, the specific number of experts used to establish the ground truth for the test set is not detailed.
• Qualifications of Experts: For the test set, it's implied that "medical professionals" performed the manual scoring, as the data originated from "routine clinical work." For the training set, "board-certified sleep physicians" were involved in establishing the ground truth.

4. Adjudication Method for the Test Set

The document does not explicitly describe an adjudication method (e.g., 2+1, 3+1) for establishing the ground truth on the test set. It mentions "manually scored sleep recordings" but does not detail how potential disagreements between multiple scorers (if any were used) were resolved.

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

No, a multi-reader multi-case (MRMC) comparative effectiveness study was not done.

The study design was a retrospective data study comparing the automatic scoring of DeepRESP to manual scoring (ground truth) and also comparing DeepRESP's performance to two predicate devices (DeepRESP K241960 and Nox Sleep System K192469). This is a standalone performance evaluation against expert-derived ground truth, with a direct comparison to existing automated systems, not an MRMC study assessing human reader improvement with AI assistance.

6. Standalone Performance Study

Yes, a standalone performance study was done. The reported PPA, NPA, and OPA values for DeepRESP v2.0 (Subject Device) represent its performance as a standalone algorithm without human-in-the-loop assistance. The subsequent comparison to the predicate devices also evaluated their standalone performance. The document explicitly states: "All output is subject to verification by a medical professional," indicating that while the device is intended to aid in diagnosis, its performance evaluation was conducted on its automated output before any human review.

7. Type of Ground Truth Used

The ground truth used was expert consensus (manual scoring). The study used "manually scored sleep recordings" from "routine clinical work" as the reference standard against which DeepRESP's automatic scoring was compared.

8. Sample Size for the Training Set

The document does not report the sample size used for the training set. It only states the sample sizes for the validation (test) sets: 4,030 PSG recordings for Type I/II validation and 5,771 sleep recordings for Type III validation.

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

The document states that the ground truth for the training set "was established through a rigorous process involving multiple board-certified sleep physicians." This implies an expert-driven process, likely involving consensus or reconciliation among several highly qualified professionals. However, the exact methodology (e.g., number of physicians, adjudication rules) is not detailed beyond "rigorous process."

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DeepRESP is an aid in the diagnosis of various sleep disorders where subjects are often evaluated during the initiation or follow-up of treatment of various sleep disorders. The recordings to be analyzed by DeepRESP can be performed in a hospital, patient home, or an ambulatory setting. It is indicated for use with adults (22 years and above) in a clinical environment by or on the order of a medical professional.

DeepRESP is intended to mark sleep study signals to aid in the identification of events and annotation of traces; automatically calculate measures obtained from recorded signals (e.g., magnitude, time, frequency, and statistical measures of marked events); infer sleep staging with arousals with EEG and in the absence of EEG. All output is subject to verification by a medical professional.

DeepRESP is a cloud-based software as a medical device (SaMD), designed to perform analysis of sleep study recordings, with and without EEG signals, providing data for the assessment and diagnosis of sleep-related disorders. Its algorithmic framework provides the derivation of sleep staging including arousals, scoring of respiratory events and key parameters such as the Apnea-Hypopnea Index (AHI).

DeepRESP is hosted on a serverless stack. It consists of:

• A web Application Programming Interface (API) intended to interface with a third-party client application, allowing medical professionals to access DeepRESP's analytical capabilities.
• Predefined sequences called Protocols that run data analyses, including artificial intelligence and rule-based models for the scoring of sleep studies, and a parameter calculation service.
• A Result storage using an object storage service to temporarily store outputs from the DeepRESP Protocols.

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

1. Table of Acceptance Criteria & Reported Device Performance:

The document doesn't explicitly state "acceptance criteria" as a separate table, but it compares DeepRESP's performance against manual scoring and predicate devices. I've extracted the performance metrics that effectively serve as acceptance criteria given the "non-inferiority" and "superiority" claims against established devices.

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

• Type I/II Studies (EEG present): 2,224 sleep recordings
• Type III Studies (No EEG): 3,488 sleep recordings (including 2,213 Type I recordings and 1,275 Type II recordings, processed to utilize only Type III relevant signals).
• Provenance: Retrospective study. Data originated from sleep clinics in the United States, collected as part of routine clinical work for patients suspected of sleep disorders. The patient population showed diversity in age, BMI, and race/ethnicity (Caucasian or White, Black or African American, Other, Not Reported) and was considered representative of patients seeking medical services for sleep disorders in the United States.

3. Number of Experts and Qualifications for Ground Truth:

The document explicitly states that the studies used "manually scored sleep recordings" but does not specify the number of experts or their specific qualifications (e.g., "radiologist with 10 years of experience"). It implicitly relies on the quality of "manual scoring" from routine clinical work in US sleep clinics as the ground truth.

4. Adjudication Method for the Test Set:

The document does not describe any specific adjudication method (e.g., 2+1, 3+1). It refers to "manual scoring" as the established ground truth.

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

No, a MRMC comparative effectiveness study was not reported. The study design was a retrospective data analysis comparing the algorithm's performance against existing manual scoring (ground truth) and established predicate devices. There is no information about human readers improving with AI vs. without AI assistance. The device is intended to provide automatic scoring subject to verification by a medical professional.

6. Standalone (Algorithm Only) Performance:

Yes, the study report describes the standalone performance of the DeepRESP algorithm. The reported PPA, NPA, OPA percentages, and ICC values represent the agreement of the automated scoring by DeepRESP compared to the manual ground truth. The device produces output "subject to verification by a medical professional," but the performance metrics provided are for the algorithmic output itself.

7. Type of Ground Truth Used:

The ground truth used was expert consensus (manual scoring). The document states "It used manually scored sleep recordings... The studies were done by evaluating the agreement in scoring and clinical indices resulting from the automatic scoring by DeepRESP compared to manual scoring."

8. Sample Size for the Training Set:

The document does not explicitly state the sample size used for the training set. The clinical validation study is described as a "retrospective study" used for validation, but details about the training data are not provided in this summary.

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

The document does not specify how the ground truth for the training set was established. It only describes the ground truth for the validation sets as "manually scored sleep recordings" from routine clinical work.

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