Uses deep learning algorithms to identify lung nodules and abnormalities with high accuracy.
Designed to integrate with existing radiology workflows and PACS without disruption.
Backed by extensive clinical studies and regulatory approvals from FDA and CE Mark.
Provides annotation, reporting, and follow-up tracking tools to streamline radiology tasks.
Allows setting thresholds for abnormality detection based on clinical protocols.
Offers flexible deployment models to suit various IT infrastructures.
Algorithms improve over time with new data and feedback.
Assists in early detection of lung cancer from screening CT scans in high-risk populations.
Identifies signs of tuberculosis and other infectious diseases in chest X-rays for prompt diagnosis.
Helps detect pneumonia and other respiratory infections from imaging studies.
Tracks changes in lung nodules over time for monitoring disease progression or treatment response.
Speeds up interpretation in urgent care settings by quickly highlighting critical findings.
Supports remote radiology consultations by providing AI insights for off-site reviews.
Used in clinical studies for data analysis and outcome measurement in pulmonary research.
Aids in training radiologists and students by highlighting findings and improving diagnostic skills.
Ensures consistency and accuracy in radiology reports across departments or institutions.
Screens large populations for lung diseases in public health programs or community screenings.
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