Lung-CAD

by Imagen Technologies  · Based in United States → — Making High-Quality Healthcare Accessible To Everyone
Internal Medicine Pulmonology Radiology
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Regulatory Status Disclosed

Overview

Imagen Technologies is a physician-led diagnostic imaging practice that delivers technology-enabled imaging services to improve diagnostic quality, efficiency, cost, and access to care. Their practice is powered by FDA-cleared, AI-enabled software, including Lung-CAD, which is fully integrated into PACS environments. This AI software supports radiologists and other physicians in working more efficiently and confidently while maintaining high standards of diagnostic accuracy. Imagen’s AI solutions aim to reduce medical expenses by aiding in early detection and intervention, preventing unnecessary ER and specialist visits, and improving accuracy to cut down on unnecessary referrals. The company combines clinical leadership with advanced technology to modernize radiology, equipping healthcare professionals with practical tools that enhance patient care and support sustainable, high-quality practice.

Reviewed by Pouyan Golshani, MD — Interventional Radiologist

Key Features

  • Detects and highlights interstitial thickening on chest radiographs
  • Detects and highlights lung hyperinflation on chest radiographs
  • AI algorithm trained on findings suggestive of Pulmonary Fibrosis and COPD
  • 48% relative reduction in misses of interstitial thickening in clinical study
  • 40% relative reduction in misses of lung hyperinflation in clinical study
  • 93% of physicians showed improvement when assisted by Lung-CAD in a clinical study
  • Generates DICOM Presentation State file (output overlay)
  • Integrated into PACS environment
  • Concurrent reading aid for physicians
  • Secure cloud-based processing and delivery

Use Cases

  • Assisting physicians in interpreting chest X-rays
  • Early detection of interstitial thickening
  • Early detection of lung hyperinflation
  • Improving diagnostic quality and efficiency in radiology
  • Supporting radiologists in working more efficiently and confidently
  • Reducing diagnostic errors in chest radiograph interpretation

What Physicians Need to Know

Key Capabilities
Detects and highlights radiographic findings of interstitial thickening and lung hyperinflation on chest X-rays. The AI algorithm was trained on findings suggestive of Pulmonary Fibrosis and Chronic Obstructive Pulmonary Disease (COPD). It generates DICOM Presentation State files with bounding boxes around detected regions of interest (ROIs) and text overlays.
Clinical Utility
Serves as a concurrent reading aid for physicians, improving diagnostic accuracy. Clinical studies showed a 48% relative reduction in misses of interstitial thickening and a 40% relative reduction in misses of lung hyperinflation when using Lung-CAD, with 93% of physicians showing overall improvement. It aims to enhance clinician experience by improving productivity and reducing turnaround times. It is not intended for clinical diagnosis and does not replace the physician's role.
Integration Options
Designed to integrate with existing Picture Archiving and Communication System (PACS) viewers, allowing physicians to toggle the Lung-CAD overlay on or off for concurrent review. It generates DICOM Presentation State files, facilitating standard integration into imaging workflows.
Compliance Status
FDA-cleared as a computer-assisted detection (CADe) software device. All Imagen devices, including Lung-CAD, are cleared by the U.S. Food and Drug Administration (FDA) for their specific indications.
Pricing Model
Specific pricing for Imagen AI's Lung-CAD is not publicly disclosed, as is common for enterprise-level medical AI solutions. General medical AI CAD systems often utilize pay-per-use, one-off perpetual software licenses, or yearly subscription models.
User Experience
The AI overlay can be easily toggled within the PACS viewer for flexible review. The design aims to enhance the clinician experience by streamlining workflows and improving efficiency, with AI results readily visible to assist radiologists.
Support Quality
Specific details regarding support quality for Lung-CAD were not explicitly found in the provided search results. Imagen generally emphasizes its commitment to improving patient outcomes and working closely with clinicians.
Implementation Complexity
The tool is designed for seamless integration into existing PACS configurations, utilizing DICOM Presentation State files for output. This approach aims to minimize implementation complexity and ensure a smooth workflow.
Evidence Base
The AI algorithm was trained on extensive datasets related to Pulmonary Fibrosis and COPD. Clinical studies demonstrate its efficacy, showing a significant reduction in missed findings (48% for interstitial thickening, 40% for lung hyperinflation) and overall physician improvement (93%). Standalone performance assessments reported high sensitivity (0.913), specificity (0.866), and AUC (0.961) for interstitial thickening detection. A multiple reader, multiple case (MRMC) study confirmed superior accuracy when physicians were aided by Lung-CAD.
Physician Tip

Lung-CAD is a valuable concurrent reading aid for chest X-rays, particularly for detecting subtle signs of interstitial thickening and lung hyperinflation, which can be indicative of conditions like pulmonary fibrosis and COPD. Remember that this tool is designed to assist, not replace, your clinical judgment and diagnostic process. Utilize the toggle feature within your PACS viewer to integrate its findings seamlessly into your workflow, enhancing efficiency and potentially reducing diagnostic oversights. Its FDA clearance provides assurance of its regulatory compliance for its stated indications.

Lung-CAD is engineered for straightforward integration into existing radiology workflows, primarily through Picture Archiving and Communication Systems (PACS). The output is delivered as DICOM Presentation State files, which allows for consistent display of AI findings (bounding boxes and text overlays) directly within your preferred PACS viewer. This standard approach minimizes disruption and facilitates a smooth adoption process within clinical environments.

Details

Category Oncology AI, Radiology & Imaging AI
Pricing Contact for pricing — Enterprise pricing; contact Imagen Technologies for details.
DeploymentCloud-based
Compliance
BAA AvailableUnknown AI-estimated
HIPAA CompliantUnknown AI-estimated
FDA Status 1 AI-estimated

Lung-CAD (K230085) is an FDA-cleared computer-assisted detection (CADe) software device. It analyzes chest radiograph studies to identify and highlight radiographic findings of interstitial thickening and lung hyperinflation, intended as a concurrent reading aid for physicians interpreting chest X-rays in adults.
Integrations
EHR Not specified
Specialties Internal Medicine, Pulmonology, Radiology

What the Web Says

Lung-CAD by Imagen.ai is a computer-assisted detection (CADe) software designed to enhance the accurate detection of lung hyperinflation in chest radiographs. Clinical studies have shown that using Lung-CAD improves the accuracy of radiologists in detecting lung nodules, with higher sensitivity and Area Under the Curve (AUC) values compared to unaided readings. The software utilizes deep learning and computer vision techniques to analyze images and highlight regions of interest, acting as a concurrent reading aid for physicians.

Overall: Mixed

Strengths

  • Increases sensitivity in detecting lung nodules.
  • Improves overall reader accuracy (AUC) for lung hyperinflation detection.
  • Reduces radiologists' interpretation time in some cases.
  • Assists in identifying lung nodules that might be missed by radiologists alone.
  • Provides consistent analysis regardless of reader fatigue.
  • High accuracy, sensitivity, and specificity in standalone performance assessments.

Limitations

  • Can produce a high number of false positives, potentially increasing workload.
  • May have lower specificity compared to unaided radiologist readings in some studies.
  • Earlier CAD systems sometimes missed small cancers that radiologists found.
  • Legal responsibility for AI misdiagnosis is not clearly defined, hindering clinical application.
  • Many models rely on single-center data, leading to potential performance decline with varied equipment/protocols.
  • Limited detailed descriptions of radiologists' workflow with AI assistance in some studies.

Based on reviews from: accessdata.fda.gov, AuntMinnie, AJR Special Series on AI Applications, arXiv, MDPI, Journal of Thoracic Disease, Science.gov, ResearchGate, DOKUMEN.PUB, Scribd, Institute of Management Research and Development, Shirpur

Last updated: 2026-07-18

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Press & Coverage

Imagen Technologies
Lung-CAD (Lung Hyperinflation) FDA Clearance
Imagen Technologies received FDA clearance for Lung-CAD on October 3, 2023, an AI-based software that assists physicians by analyzing chest X-ray images to identify regions of lung hyperinflation. It highlights these regions to support concurrent reading and improve detection accuracy without replacing clinical diagnosis.
2023-10
Imagen Technologies
Lung-CAD (Interstitial Thickening) FDA Clearance
Imagen Technologies also received FDA clearance for Lung-CAD on September 13, 2023, for its AI-powered software that helps physicians by analyzing chest X-rays to identify areas showing interstitial thickening. This serves as a concurrent reading aid to improve detection in adults.
2023-09
Imagen Technologies
AI Software - Imagen
Imagen's Lung-CAD AI algorithm was trained to detect findings suggestive of Pulmonary Fibrosis and Chronic Obstructive Pulmonary Disease (COPD). Clinical studies showed a significant reduction in missed interstitial thickening and lung hyperinflation when physicians used Lung-CAD.
unknown
medRxiv
Assessment of the accuracy of lung lesions diagnosis in adolescents with osteosarcoma using artificial intelligence - medRxiv
A study published on June 11, 2026, assessed the diagnostic accuracy of an AI algorithm in adolescents with osteosarcoma for lung lesions. The study found that while AI sensitivity in adolescent CT analysis was comparable to or higher than some other systems, its diagnostic accuracy was lower in pediatric patients compared to adult datasets.
2026-06
PMC (PubMed Central)
A Systematic Review of AI Performance in Lung Cancer Detection on CT Thorax - PMC
A systematic review published on June 24, 2025, concluded that AI models for detecting and classifying pulmonary lesions on CT scans have the potential to augment CT thorax interpretation. These models generally showed higher sensitivity but lower specificity compared to radiologists for nodule detection.
2025-06
PMC (PubMed Central)
Narrative review of the application of artificial intelligence-related technologies in the diagnosis of pulmonary nodules with recommendations for clinical practice and future research - PMC
This narrative review, updated on August 28, 2025, highlights that AI-assisted imaging diagnosis can significantly improve the quality of low-dose CT images and shorten CAD film reading time. AI models have shown high performance in differentiating benign and malignant pulmonary nodules.
2025-08
accessdata.fda.gov
K243239 - Jacqueline Murray - accessdata.fda.gov
An FDA 510(k) summary from April 24, 2025, details 'Lung AI' (LAI001), a Computer-Aided Detection (CADe) tool designed to assist in the analysis of lung ultrasound images. This device, which lists Imagen Technologies' Lung-CAD (K230085) as its predicate device, is intended for adults to detect consolidation/atelectasis and pleural effusion.
2025-04

Videos

Product demos, reviews, and walkthroughs for Lung-CAD.

View all on YouTube

Frequently Asked Questions

Lung-CAD systems are designed to integrate directly into Picture Archiving and Communication Systems (PACS), allowing radiologists to view AI analysis alongside images without switching software. These systems primarily assist in identifying, characterizing, and prognosticating lung nodules on CT scans, aiming to improve detection sensitivity, especially for smaller nodules, and potentially reduce interpretation time.
For market access, Lung-CAD systems require regulatory clearances such as FDA 510(k) in the US and CE Mark in Europe, which validate their safety and effectiveness. Physicians should be aware that these devices must comply with ongoing regulations including registration, labeling, medical device reporting, and quality system requirements, with increasing emphasis on cybersecurity.
The primary alternative to AI-powered Lung-CAD is traditional manual interpretation by radiologists. While AI can enhance sensitivity and efficiency, particularly for high-volume screenings, human expertise remains crucial for complex cases and nuanced decision-making. Other AI-powered tools from various vendors also exist, offering different algorithms and performance profiles for lung nodule detection and characterization.
Lung-CAD systems typically offer pricing models such as pay-per-use, one-off perpetual software licenses, and yearly subscriptions, with additional costs for installation, training, hosting, and support. While per-case costs can vary, studies suggest that AI-assisted decision-making can be cost-effective, potentially leading to lower overall costs and higher effectiveness in lung cancer screening.
Lung-CAD AI systems can generate false positives, which may interrupt radiologist workflow and increase reading times, and some studies indicate AI may have lower specificity than human readers. Their performance can vary with nodule size, morphological type (e.g., ground-glass opacities can be challenging), and location. Additionally, some algorithms may be narrowly focused on specific tasks or imaging modalities, limiting their generalizability.
Practical challenges include ensuring seamless integration with existing PACS and RIS systems to avoid workflow disruptions and the need for radiologists to switch between different software. There are also concerns about generalizability across diverse patient populations and practice patterns, as well as the need for further clinical validation to demonstrate clear added value in patient-oriented outcomes.

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Suggest an Edit → | Last Verified: 2026-04-18 | First Added: 2026-04-18
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