Tyto Insights for Crackles Detection

K240555

Tyto Care Ltd. · cleared 2024-07-02 · product code PHZ · Anesthesiology

Premarket evidence — what FDA accepted

Device typesamd
source quote (p.4)
“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).”
AlgorithmArtificial Intelligence (AI) enabled Algorithm for Crackles detection utilizing the CRNN (Convolutional Recurrent Neural Network) model for sound event detection, integrating CNNs (Convolutional Neural Networks) and RNNs (Recurrent Neural Networks).
source quote (p.15)
“The Algorithm of the proposed device is Artificial Intelligence (AI) enabled Algorithm for Crackles detection when the Algorithm of the primary predicate device is AI enabled Algorithm for Wheezes detection. In both devices, the data is being analyzed by AI Machine Learning algorithm to determine the presence of abnormal lung sound in the lungs sound recording. Both the proposed device and the primary predicate device utilize the CRNN (Convolutional Recurrent Neural Network) model for sound event detection, integrating CNNs (Convolutional Neural Networks) and RNNs (Recurrent Neural Networks).”
Adaptive (vs locked)Yes
source quote (p.1)
“FDA's substantial equivalence determination also included the review and clearance of your Predetermined Change Control Plan (PCCP). Under section 515C(b)(1) of the Act, a new premarket notification is not required for a change to a device cleared under section 510(k) of the Act, if such change is consistent with an established PCCP granted pursuant to section 515C(b)(2) of the Act.”
PCCPYes
source quote (p.1)
“FDA's substantial equivalence determination also included the review and clearance of your Predetermined Change Control Plan (PCCP).”
Cybersecurity addressedYes
source quote (p.17)
“Cybersecurity- all the applicable information to reflect effective cybersecurity management and to address the FDA's recommendations described in Cybersecurity in Medical Devices: Refuse to Accept Policy for Cyber Devices and Related Systems Under Section 524B of the FD&C Act, issue date March 2023, Cybersecurity in Medical Devices: Quality System Considerations and Content of Premarket Submissions, issue date September 2023 and in the other FDA's applicable policies have been included in this submission.”

Validation studies (1)

Reader study (MRMC)

n=445 patients

endpoints: establish that the lower bound of 95% two-sided CI for the difference in AUCs between the Tyto Insights for Crackles Detection vs. clinical readers is higher than non-inferiority margin of -0.05; the repeatability of the software as compared to the clinical readers

standards: ANSI AAMI ISO 14971:2019, Medical devices - Application of risk management to medical devices, ANSI AAMI IEC 62304:2006/A1:2016, Medical device software - Software life cycle processes, ISO 15223-1 Fourth edition 2021-07, Medical devices - Symbols to be used with information to be supplied by the manufacturer - Part 1: General requirements., ANSI AAMI IEC 62366-1:2015+AMD1:2020 (Consolidated Text) Medical devices Part 1: Application of usability engineering to medical device.

Reported performance (5 observations)

sensitivity0.72CI 0.63-0.79
source quote (p.18)
“Sensitivity: 0.72 (0.63-0.79)”
specificity0.99CI 0.97–1.00
source quote (p.18)
“Specificity: 0.99 (0.97–1.00)”
aurocas written: “auc”0.97CI 0.95–0.98
source quote (p.19)
“AUC Tyto Insights for Crackles Detection: 0.97 (0.95–0.98).”
ppvas written: “Positive Predictive Value (PPV)”0.63CI 0.4–0.87
source quote (p.18)
“Positive Predictive Value (PPV) 0.63 (0.4–0.87)”
npvas written: “Negative Predictive Value (NPV)”0.99CI 0.98–0.99
source quote (p.18)
“Negative Predictive Value (NPV) 0.99 (0.98–0.99)”

Each value carries its own analysis unit and task — never compare or pool across devices. Source: 510(k) summary PDF.

Predicate network

Postmarket — what happened after clearance

0
recalls in product code, 24mo
0
MAUDE reports in code, 12mo
—
vs code's own 3-yr baseline
0
drift signals on this device

Recall and MAUDE counts are product-code-level (reports aren't reliably attributable to one device); a recall is shown as device-attributed only when the recall record itself lists this clearance number. Signals are descriptive observables with sources — never a judgment that the device is unsafe or drifting. Snapshot 2026-07-08.

Reimbursement — how devices like this got paid

Not yet tracked — no payment pathway indexed for this clearance (the reimbursement corpus is a growing seed set).

Applicable FDA guidance — what the submission is measured against

FDA guidance documents and guiding principles applicable to 510(k) AI/ML devices in the Anesthesiology panel. A curated reference index, not legal or regulatory advice — each item states its own status, and a draft is never binding.

Applicability is derived from the device's FDA advisory panel and pathway — cross-cutting guidances apply to every AI/ML device; panel-specific ones are flagged. Titles, dates, and links verified against fda.gov as of July 2026.

Constat Precedent · public FDA/CMS data · descriptive decision-support, not regulatory or reimbursement advice. Share this page: constat.dev/precedent/device/K240555