Koios DS

K242130

Koios Medical, Inc. · cleared 2024-11-15 · product code POK · Radiology

Premarket evidence — what FDA accepted

Device typesamd
source quote (p.6)
“Koios Decision Support (DS) is a software application designed to assist trained interpreting physicians in analyzing breast and thyroid ultrasound images.”
Algorithmdeep-learning derived cancer risk assessment; state-of-the-art computer vision and machine learning techniques; machine learning and rule-based methods for OCR
source quote (p.6)
“The software generates a set of user-editable sonographic nodule descriptor recommendations (Composition, Echogenicity, Shape, Margin, Echogenic Foci) along with an optional, deep-learning derived cancer risk assessment of the suspected nodule from two orthogonal views. Breast and Thyroid Diagnostic Core AI Engines enabled by state-of-the-art computer vision and machine learning techniques capable of reading, interpreting, analyzing, classifying and generating findings from ultrasound image data resulting in an automated risk assessment for breast lesions and thyroid nodules suspicious for cancer. the Koios DS Optical Character Recognition engine uses machine learning and rule-based methods to create a system which is capable of retrieving fast, accurate transcriptions of the text overlaid on ultrasound images.”
Adaptive (vs locked)No
source quote (p.9)
“The underlying Breast and Thyroid engines draw upon knowledge learned from a large database of known cases, tying image features to their eventual diagnosis, to form a predictive model.”
PCCPFDA source did not state this
Cybersecurity addressedFDA source did not state this

Validation studies (7)

Bench

n=900 patients

Bench

n=500 patients

Bench

n=650 cases

endpoints: non-inferiority testing to demonstrate that the use of the Smart Click engine does not degrade diagnostic performance when compared to physician-selected calipers; Dice Similarity Coefficient

Bench

n=1,600 cases

Bench

n=1,910 scans

endpoints: accuracy (percent correct) for each of the structured fields predicted by the engine

Retrospective clinical

n=900 patients

endpoints: impact on Interpreting Physician (Reader) performance as defined by the area under the Receiver Operating Characteristic (ROC) Curve (AUC) when Koios DS Breast and an ultrasound examination are combined (USE + DS), compared to USE Alone

Retrospective clinical

n=650 cases

endpoints: effect on reader performance as defined by measuring the area under the Receiver Operating Characteristic (ROC) Curve (AUC) when Koios DS and an ultrasound examination were combined (USE + DS), compared to unassisted TI-RADS based Reader performance (USE Alone)

Reported performance (14 observations)

sensitivity0.976CI [0.960, 0.992]
source quote (p.19)
“System performance on the 900 cases reported an AUC of 94.5%, with a Sensitivity of 0.976 [0.960, 0.992] and a Specificity of 0.632 [0.588, 0.676].”
specificity0.632CI [0.588, 0.676]
source quote (p.19)
“System performance on the 900 cases reported an AUC of 94.5%, with a Sensitivity of 0.976 [0.960, 0.992] and a Specificity of 0.632 [0.588, 0.676].”
aurocas written: “auc”0.945CI [0.932, 0.959]
source quote (p.19)
“System performance on the 900 cases reported an AUC of 94.5%, with a Sensitivity of 0.976 [0.960, 0.992] and a Specificity of 0.632 [0.588, 0.676].”
aurocas written: “Thyroid AUC (AI Adapter and descriptor predictors)”0.798
source quote (p.24)
“When applied to diagnoses made using ACR TI-RADS guidelines, the AI Adapter and descriptor predictors achieved an AUC of 79.8%, demonstrating a significant increase over the average physician AUC.”
sensitivityas written: “Thyroid Sensitivity (recommending biopsy)”0.644CI [0.545, 0.744]
source quote (p.24)
“When recommending biopsy, the system's sensitivity is 0.644 [0.545, 0.744] and specificity is 0.612 [0.566, 0.658].”
specificityas written: “Thyroid Specificity (recommending biopsy)”0.612CI [0.566, 0.658]
source quote (p.24)
“When recommending biopsy, the system's sensitivity is 0.644 [0.545, 0.744] and specificity is 0.612 [0.566, 0.658].”
sensitivityas written: “Thyroid Sensitivity (recommending follow-up)”0.879CI [0.812, 0.946]
source quote (p.24)
“When recommending follow-up, the system's sensitivity and specificity are 0.879 [0.812, 0.946] and 0.495 [0.446, 0.544], respectively.”
specificityas written: “Thyroid Specificity (recommending follow-up)”0.495CI [0.446, 0.544]
source quote (p.24)
“When recommending follow-up, the system's sensitivity and specificity are 0.879 [0.812, 0.946] and 0.495 [0.446, 0.544], respectively.”
aurocas written: “Breast Engine End-to-End AUC”0.946
source quote (p.29)
“AUC = 0.946”
sensitivityas written: “Breast Engine End-to-End Sensitivity”0.975
source quote (p.29)
“Sensitivity = 0.975”
specificityas written: “Breast Engine End-to-End Specificity”0.637
source quote (p.29)
“Specificity = 0.637”
aurocas written: “Thyroid Engine End-to-End AUC”0.801
source quote (p.29)
“AUC = 0.801”
sensitivityas written: “Thyroid Engine End-to-End Sensitivity”0.67
source quote (p.29)
“Sensitivity = 0.670”
specificityas written: “Thyroid Engine End-to-End Specificity”0.603
source quote (p.29)
“Specificity = 0.603”

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 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/K242130