Lung AI (LAI001)

K243239

Exo Inc · cleared 2025-04-24 · product code MYN · Radiology

Premarket evidence — what FDA accepted

Device typesamd
source quote (p.4)
“Lung AI software device is a Computer-Aided Detection (CADe) tool designed to assist in the detection of consolidation/atelectasis and pleural effusion during the review of lung ultrasound scans.”
AlgorithmArtificial intelligence, including non-adaptive machine learning algorithms trained with clinical data. Supervised Deep Learning including Deep Convolutional Neural Networks for Segmentation, Landmark Detection and Classification.
source quote (p.7)
“Artificial intelligence, including non-adaptive machine learning algorithms trained with clinical data. Supervised Deep Learning including Deep Convolutional Neural Networks for Segmentation, Landmark Detection and Classification.”
Adaptive (vs locked)No
source quote (p.7)
“Artificial intelligence, including non-adaptive machine learning algorithms trained with clinical data”
PCCPFDA source did not state this
Cybersecurity addressedYes
source quote (p.8)
“Cybersecurity testing was performed in accordance with Cybersecurity in Medical Devices: Quality System Considerations and Content of Premarket Submissions.”

Validation studies (2)

Standalone

n=359 patients · 6 site(s)

endpoints: Detection Sensitivity; Detection Specificity; Localization Sensitivity; Localization Specificity

standards: IEC 62304:2006/AC:2015

Reader study (MRMC)

n=322 patients

endpoints: Improvement in AUC-ROC; Sensitivity; Specificity

standards: IEC 62304:2006/AC:2015

Reported performance (15 observations)

sensitivity0.88CI 95% CI .86 – .92
source quote (p.10)
“The MRMC analysis for detection of pleural effusion shows Se_unaided = 0.71 (95% CI .68-.75) is significantly improved to Se_aided = 0.88 (95% CI.86 – .92) with a Se improvement of ∆Se-PLEFF = Seaided - Seunaided = 0.18 (95% CI .14 – .20)”
specificity0.93CI 95% CI .88 – .95
source quote (p.10)
“The MRMC analysis for detection of pleural effusion shows Sp_unaided = 0.96 (95% CI .95 – .97) is slightly decreased to Sp_aided = 0.93 (95% CI .88 – .95) with ∆Sp-CONS = Spaided - Spunaided = -0.03 (95% CI -.08 to -.02)”
aurocas written: “auc”0.96CI 95% CI .95 - .98
source quote (p.10)
“The MRMC analysis for detection of pleural effusion shows AUC_unaided = 0.93 (95% CI .92 - .94) is improved to AUC_aided = 0.96 (95% CI .95 - .98) with a AUC improvement of ∆AUC-PLEFF = AUCaided - AUCunaided = 0.035 (95% CI 025 – .047), which passes the acceptance criteria.”
sensitivityas written: “Pleural Effusion Detection Sensitivity”0.97CI 95% CI 0.94 – 0.99
source quote (p.9)
“Se = 0.97 (95% CI 0.94 – 0.99)”
specificityas written: “Pleural Effusion Detection Specificity”0.91CI 95% CI 0.87 – 0.96
source quote (p.9)
“Sp = 0.91 (95% CI 0.87 – 0.96)”
sensitivityas written: “Consolidation / Atelectasis Detection Sensitivity”0.97CI 95% CI 0.94 – 0.99
source quote (p.9)
“Se = 0.97 (95% CI 0.94 – 0.99)”
specificityas written: “Consolidation / Atelectasis Detection Specificity”0.94CI 95% CI 0.90 – 0.98
source quote (p.9)
“Sp = 0.94 (95% CI 0.90 – 0.98)”
sensitivityas written: “Pleural Effusion Localization Sensitivity”0.85CI 95% CI 0.80 – 0.89
source quote (p.9)
“Se = 0.85 (95% CI 0.80 – 0.89)”
specificityas written: “Pleural Effusion Localization Specificity”0.91CI 95% CI 0.87 – 0.96
source quote (p.9)
“Sp = 0.91 (95% CI 0.87 – 0.96)”
sensitivityas written: “Consolidation / Atelectasis Localization Sensitivity”0.86CI 95% CI 0.81 – 0.90
source quote (p.9)
“Se = 0.86 (95% CI 0.81 – 0.90)”
specificityas written: “Consolidation / Atelectasis Localization Specificity”0.94CI 95% CI 0.90 – 0.98
source quote (p.9)
“Sp = 0.94 (95% CI 0.90 – 0.98)”
sensitivityas written: “MRMC Pleural Effusion Sensitivity_unaided”0.71CI 95% CI .68-.75
source quote (p.10)
“The MRMC analysis for detection of pleural effusion shows Se_unaided = 0.71 (95% CI .68-.75)”
specificityas written: “MRMC Pleural Effusion Specificity_unaided”0.96CI 95% CI .95 – .97
source quote (p.10)
“The MRMC analysis for detection of pleural effusion shows Sp_unaided = 0.96 (95% CI .95 – .97)”
sensitivityas written: “MRMC Consolidation/Atelectasis Sensitivity_aided”0.89CI 95% CI .88 – .93
source quote (p.10)
“The MRMC analysis for detection of consolidation shows Se_unaided = 0.73 (95% CI .72 - .80) is improved to Se_aided = 0.89 (95% CI .88 – .93)”
specificityas written: “MRMC Consolidation/Atelectasis Specificity_aided”0.91CI 95% CI .87 – .93
source quote (p.11)
“The MRMC analysis for detection of consolidation shows Sp_unaided = 0.92 (95% CI .88 – .93) is slightly decreased to Sp_aided = 0.91 (95% CI .87 – .93)”

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
-100%
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 Radiology 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/K243239