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2 min read

Lung AI Solution Is A Powerful, Fast And Reliable Second Opinion

Lung AI Solution Is A Powerful, Fast And Reliable Second Opinion

Arterys' Lung AI solution helps physicians analyze and track lung nodules by automatically processing Lung CT scans to deliver improved speed, efficiency, and accuracy of diagnostic decisions.  Here you can review a video that delves into the platform's strengths through a radiologist's extensive experience with the platform.

Dr Eleanna Saloura_3

Dr. Eleanna Saloura is an Attending Radiologist at the Department of Computed Tomography at Sakarellos Diagnostic Center in Greece. She has been using Lung AI, an advanced lung imaging technology that employs artificial intelligence (AI), for over a year.

Dr. Saloura gives insight into her experience with the capabilities of the technology:

"(Arterys' Lung AI solution) has a high sensitivity in detecting nodules, larger than 3 millimeters, but it also can detect smaller nodules...In parallel, we need to take into consideration the medical history of the patient, and the Lung AI solution provides more information related to the size and density of the nodules. With this additional information, the physicians can make more accurate diagnostic and treatment decisions. Overall I think the Lung AI Solution is fast and reliable as a second opinion."

Dr. Saloura shares further details in this video:

HubSpot Video


Lung AI Features

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. This reduces tedious and operator-dependent manual tasks and produces an average time saving of 44%.

Consistent Results: 

Lung AI increases inter-radiologist agreement for the presence or absence of nodules from a low of 67% to 87%, facilitating more consistent and accurate diagnoses.

Accurate Results:

Lung AI delivers clear, easy-to-interpret customizable reporting that helps physicians reduce missed nodule detections by up to 70%.

Optimized Workflow:

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.  

Lung AI Features

Intelligent Analysis:

  • Automated and single-click nodule segmentation 
  • Real-time updates to quantitative measurements for segmentation edits
  • Long axis, short axis, volume, Hounsfield units, and comparison values are calculated and displayed 

Multi-Criteria Support:

  • General, Fleischner, or L-RADS user criteria selection for appropriate follow-up recommendations
  • Dedicated scoring panel that captures key calculations and comparative values between studies 
  • Ability to change baseline exam date for more accurate recommendations of characteristic changes 

Longitudinal Tracking: 

  • Automatic longitudinal tracking of lung nodules across multiple exams
  • Quantification of nodule progression with key metrics of nodule changes over time
  • Text and graphical display results 


  • Incorporate results directly into existing PACS, EHR, worklist, notification, and dictation systems
  • Ensuring exams are read more consistently, and users have fewer clicks 
  • Customizable output options of relevant clinical information
  • Image workflow and information workflow selectable outputs supported

Learn More About AI-Assisted Lung Imaging 

Discover how AI is expanding the possibilities for nodule detection in our white paper Innovations in Lung Imaging. The information presented includes:

  • Thoracic CT: A Key to Lung Cancer Survival
  • LDCT Imaging Challenges
  • Artificial Intelligence and Applications to CT
  • Deep Learning and Nodule Detection
  • Deep Learning and Workflow, Tracking, and Reporting
  • Cloud Computing: The Key to Advanced Imaging Analytics

lung imaging


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