AISAP Cardio V1.0

K234141

Aisap · cleared 2024-08-01 · product code POK · Radiology

Premarket evidence — what FDA accepted

Device typesamd
source quote (p.7)
“Software as a medical device (SaMD)”
Algorithmmachine learning NN (neural network) models trained to recognize patterns and make decisions. AISAP CARDIO V1.0 contains classification models which identify categories within data, regression models which predict numerical values, and instance segmentation models that detect and segment objects within images.
source quote (p.5)
“AISAP CARDIO V1.0 uses machine learning NN (neural network) models trained to recognize patterns and make decisions. AISAP CARDIO V1.0 contains classification models which identify categories within data, regression models which predict numerical values, and instance segmentation models that detect and segment objects within images.”
Adaptive (vs locked)FDA source did not state this
PCCPFDA source did not state this
Cybersecurity addressedYes
source quote (p.8)
“Risk assessment, performance, and cybersecurity of AISAP CARDIO V1.0 have been evaluated and verified in accordance with pre-defined software specifications and applicable performance standards through software verification testing.”

Validation studies (4)

Standalone

n=200 cases

endpoints: Left Ventricular Ejection Fraction (LVEF); IVC maximal diameter; Left atrium (LA) area; Right atrium (RA) area; LV end diastolic diameter; Right ventricle (RV) fractional area change (FAC); Aortic root diameter

standards: American Society of Echocardiography (ASE) guidelines

Standalone

n=329 cases

endpoints: Mitral Regurgitation (MR); Aortic Stenosis (AS); Aortic Regurgitation (AR); Tricuspid Regurgitation (TR)

Reader study (MRMC)

n=260 cases

endpoints: AUCaided; AUCunaided; Kappa agreement; Accuracy agreement

Bench

n=2,000 other

endpoints: Correct identification of the video clip view classification

Reported performance (36 observations)

sensitivityas written: “MR Sensitivity”0.953CI (90.5,98.9)
source quote (p.10)
“95.3% (90.5,98.9)”
specificityas written: “MR Specificity”0.902CI (86.3,93.9)
source quote (p.10)
“90.2% (86.3,93.9)”
aurocas written: “MR AUC”0.975CI (0.960,0.987)
source quote (p.10)
“0.975 (0.960,0.987)”
agreement_kappaas written: “MR Kappa”0.879CI (0.852,0.906)
source quote (p.10)
“0.879 (0.852,0.906)”
sensitivityas written: “AS Sensitivity”0.865CI (76.2,95.7)
source quote (p.10)
“86.5% (76.2,95.7)”
specificityas written: “AS Specificity”0.945CI (91.3,97.3)
source quote (p.10)
“94.5% (91.3,97.3)”
aurocas written: “AS AUC”0.969CI (0.950,0.984)
source quote (p.10)
“0.969 (0.950,0.984)”
agreement_kappaas written: “AS Kappa”0.865CI (0.825,0.901)
source quote (p.10)
“0.865 (0.825,0.901)”
sensitivityas written: “AR Sensitivity”0.965CI (91.3,100.0)
source quote (p.10)
“96.5% (91.3,100.0)”
specificityas written: “AR Specificity”0.97CI (94.9,98.9)
source quote (p.10)
“97.0% (94.9,98.9)”
aurocas written: “AR AUC”0.993CI (0.986,0.999)
source quote (p.10)
“0.993 (0.986,0.999)”
agreement_kappaas written: “AR Kappa”0.913CI (0.892,0.932)
source quote (p.10)
“0.913 (0.892,0.932)”
sensitivityas written: “TR Sensitivity”0.935CI (86.9,98.5)
source quote (p.10)
“93.5% (86.9,98.5)”
specificityas written: “TR Specificity”0.893CI (85.2,93.1)
source quote (p.10)
“89.3% (85.2,93.1)”
aurocas written: “TR AUC”0.973CI (0.955,0.987)
source quote (p.10)
“0.973 (0.955,0.987)”
agreement_kappaas written: “TR Kappa”0.879CI (0.854,0.905)
source quote (p.10)
“0.879 (0.854,0.905)”
agreement_kappaas written: “MR Kappa aided”0.881CI (0.872,0.890)
source quote (p.11)
“0.881 (0.872,0.890)”
accuracyas written: “MR Accuracy aided”0.736CI (71.9%,75.2%)
source quote (p.11)
“73.6% (71.9%,75.2%)”
agreement_kappaas written: “MR Kappa unaided”0.756CI (0.737,0.774)
source quote (p.11)
“0.756 (0.737,0.774)”
accuracyas written: “MR Accuracy unaided”0.616CI (59.8%,63.4%)
source quote (p.11)
“61.6% (59.8%,63.4%)”
agreement_kappaas written: “TR Kappa aided”0.881CI (0.871,0.892)
source quote (p.11)
“0.881 (0.871,0.892)”
accuracyas written: “TR Accuracy aided”0.753CI (73.6%,77.0%)
source quote (p.11)
“75.3% (73.6%,77.0%)”
agreement_kappaas written: “TR Kappa unaided”0.765CI (0.747,0.783)
source quote (p.11)
“0.765 (0.747,0.783)”
accuracyas written: “TR Accuracy unaided”0.641CI (62.2%,65.9%)
source quote (p.11)
“64.1% (62.2%,65.9%)”
agreement_kappaas written: “AR Kappa aided”0.913CI (0.905,0.921)
source quote (p.11)
“0.913 (0.905,0.921)”
accuracyas written: “AR Accuracy aided”0.806CI (79.1%,82.1%)
source quote (p.11)
“80.6% (79.1%,82.1%)”
agreement_kappaas written: “AR Kappa unaided”0.815CI (0.798,0.831)
source quote (p.11)
“0.815 (0.798,0.831)”
accuracyas written: “AR Accuracy unaided”0.717CI (70.0%,73.3%)
source quote (p.11)
“71.7% (70.0%,73.3%)”
agreement_kappaas written: “AS Kappa aided”0.85CI (0.834,0.864)
source quote (p.11)
“0.850 (0.834,0.864)”
accuracyas written: “AS Accuracy aided”0.747CI (73.0%,76.3%)
source quote (p.11)
“74.7% (73.0%,76.3%)”
agreement_kappaas written: “AS Kappa unaided”0.792CI (0.773,0.809)
source quote (p.11)
“0.792 (0.773,0.809)”
accuracyas written: “AS Accuracy unaided”0.698CI (68.1%,71.6%)
source quote (p.11)
“69.8% (68.1%,71.6%)”
accuracyas written: “PLAX Accuracy”1
source quote (p.12)
“PLAX - 500/500”
accuracyas written: “PSAX Accuracy”0.992
source quote (p.12)
“PSAX - 496/500”
accuracyas written: “A4C Accuracy”0.99
source quote (p.12)
“A4C - 495/500”
accuracyas written: “SC IVC Accuracy”0.988
source quote (p.12)
“SC IVC - 494/500”

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). 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).

RIGOR™ Precedent · public FDA/CMS data · descriptive decision-support, not regulatory or reimbursement advice. Share this page: radar.healthai.com/precedent/device/K234141