LVivo Software Application
K200232DiA Imaging Analysis Ltd · cleared 2020-06-23 · product code QIH · Radiology
Premarket evidence — what FDA accepted
source quote (p.4)
“Automated Radiological Image Processing Software- classified as Class 2 QIH, Regulation Number 21 CFR 892.2050 LVivo Software Application”
source quote (p.9)
“The Algorithm combines image processing and Deep Learning Neural Network (NN) for the RV analysis.”
Validation studies (2)
Retrospective clinical
sample size not stated · 1 site(s)
endpoints: compare LVivoRV measurement of FAC to manual FAC measurement; compare LVivoRV measurements to the manual measurements of EDA, ESA, TAPSE, S' and FREE WALL STRAIN; compare RV function by visual assessment to the categorized result from FAC, TAPSE, S' and STRAIN and; evaluate inter and intra observer variability.
standards: ASE guidelines
Retrospective clinical
n=226 images · 1 site(s)
endpoints: compare LVivo Bladder measurement of bladder volume to bladder volume by manual tracing.; expected to have a good agreement between the automated and the manual method (which is an accepted method for BV measurement) based on 200ml threshold, with kappa of at least 0.61 which is considered substantial agreement according to accepted Kappa interoperations.
Reported performance (6 observations)
source quote (p.12)
“high sensitivity and specificity (100 and 80 respectively).”
source quote (p.12)
“high sensitivity and specificity (100 and 80 respectively).”
source quote (p.10)
“The inter-observer reliability between readers within the same group was also tested. Inter-observer reliability between sonographers for EDA, ESA and FAC was 0.85, 0.9 and 0.77 respectively (p<0.0001).”
source quote (p.10)
“The inter-observer reliability between readers within the same group was also tested. Inter-observer reliability between sonographers for EDA, ESA and FAC was 0.85, 0.9 and 0.77 respectively (p<0.0001).”
source quote (p.10)
“The inter-observer reliability between readers within the same group was also tested. Inter-observer reliability between sonographers for EDA, ESA and FAC was 0.85, 0.9 and 0.77 respectively (p<0.0001).”
source quote (p.12)
“excellent Kappa of 0.84”
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
- re_clearance
The FDA AI/ML device list shows a newer 510(k) K243862 (decision 2025-03-17) from DiA Imaging Analysis Ltd. for a matching device line ("LVivo Software Application") — a new clearance for the same line is a change event.
first seen 2026-07-08 · k_number:K243862
- re_clearance
The FDA AI/ML device list shows a newer 510(k) K243235 (decision 2025-03-03) from DiA Imaging Analysis Ltd. for a matching device line ("LVivo Software Application") — a new clearance for the same line is a change event.
first seen 2026-07-08 · k_number:K243235
- re_clearance
The FDA AI/ML device list shows a newer 510(k) K240553 (decision 2024-10-04) from DiA Imaging Analysis Ltd. for a matching device line ("LVivo Software Application") — a new clearance for the same line is a change event.
first seen 2026-07-08 · k_number:K240553
- re_clearance
The FDA AI/ML device list shows a newer 510(k) K210053 (decision 2021-02-05) from DiA Imaging Analysis Ltd for a matching device line ("LVivo Software Application") — a new clearance for the same line is a change event.
first seen 2026-07-08 · k_number:K210053
Recall and MAUDE counts are product-code-level (reports aren't reliably attributable to one device). 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).