Open. Review. Confirm. Report. It's that easy.
Automatic detection* of solid, part solid, and ground glass nodules. Our interactive workflow allows the user to add, edit, or delete detections with automatic updates to quantitative information.
*Detection is for Research use only in US
Automatically assess each lesion's evolution over time.
Whether you have one or many studies to compare, Arterys has your back. Automatically track nodules over time with tabular and graphical progression.
Volumetric segmentation of nodules
With the Arterys Lung AI application, precise volumetric segmentation of lung nodules is available automatically for both detected and user found nodules. Deep Learning reduces variability across analyses.
Built-in Lung-RADS calculations
Standardize reporting of screening exams using the integrated Lung-RADS workflow. Share and communicate results effectively with patients and among colleagues. Arterys Lung AI supports Lung-RADS versions 1.0 and 1.1, as well as linear and volumetric measurements.
Fast Diagnostic and Treatment Decisions
Lung AI helps physicians quickly analyze lung nodules by automatically processing Lung CT scans -- delivering clear, easy-to-interpret customizable reporting that helps physicians reduce missed nodule detections by up to 70%6 7 -- facilitating more consistent and accurate diagnoses.
When applying AI across the Lung CT workflow, Lung AI increases inter-radiologist agreement for the presence or absence of nodules from a low of 67% to 87% per with study1, 3-5 -- facilitating more consistent and accurate diagnoses.
Increase Productivity and Decrease Reporting Time
By incorporating AI across the Lung CT workflow, Lung AI automatically analyzes CT scans, providing physicians with automated measured segmentations, longitudinal tracking, and customizable reporting -- that reduces tedious and operator-dependent manual tasks and produces an average time saving of 44% per study 1, 2.
Lung AI does not require on-premises hardware and results are accessible through a diagnostic quality zero footprint viewer and an internet connection. It seamlessly integrates with existing PACS, EHR, worklist, notification, and dictation systems ensuring exams are read accordingly and consistently.
- National Cancer Institute - NLST database: https://biometry.nci.nih.gov/cdas/nlst/
- Centre Hospitalier de Valenciennes, Avenue Désandrouin CS 50479, 59322 Valenciennes, FRANCE
- Groupe hospitalier Paris Saint-Joseph, 185, rue Raymond Losserand - 75014 Paris, FRANCE
- Fleiss, J. L. (1971) “Measuring nominal scale agreement among many raters.” Psychological Bulletin, Vol. 76, No. 5 pp. 378–382 5. Landis, J.R.; Koch, G.G. (1977). “The measurement of observer agreement for categorical data”. Biometrics. 33 (1): 159–174
- Arterys Lung AI Nodule Detection study - University of California, San Diego
- US FDA clearance for detection pending