Auto Lung Nodule Detection

by Samsung Electronics Co.  · Based in South Korea → — AI-Powered Lung Nodule Detection for Enhanced Diagnostic Confidence
Oncology Pulmonology Radiology
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Regulatory Status Disclosed

Overview

The Auto Lung Nodule Detection (ALND) tool by Samsung Electronics Co. is an artificial intelligence (AI)-based, computer-assisted detection (CADe) software designed to aid physicians in the diagnosis of pulmonary nodules. Integrated as an on-device solution within Samsung Digital X-ray Imaging systems, such as the AccE GC85A, ALND utilizes advanced AI algorithms to detect lung nodules ranging from 10 to 30mm in size on posteroanterior chest radiographs of adults.

This tool enhances diagnostic workflow by automatically indicating the location of suspected lung nodules on X-ray images. It offers an ‘Autorun’ option to perform nodule detection immediately after chest X-ray imaging and supports PACS transmission, streamlining the user’s workflow within a hospital environment. ALND has undergone extensive external clinical validation across multiple university hospitals, demonstrating a sensitivity of 80% or more and a low false positive rate of 0.15 per image.

While ALND specifically focuses on nodule detection, Samsung’s broader commitment to AI in medical imaging includes collaborations, such as with Lunit Inc., to expand chest X-ray analysis solutions for detecting various abnormalities and prioritizing critical cases.

Reviewed by Pouyan Golshani, MD — Interventional Radiologist

Use Cases

  • Aiding radiologists in diagnosing lung nodules on chest X-rays
  • Improving workflow efficiency in radiology departments
  • Supporting early detection of lung cancer
  • Enhancing diagnostic confidence for physicians
  • Prioritizing patient exams with suspected abnormalities (in conjunction with other solutions)
  • Integration into general chest radiography workflows

What Physicians Need to Know

Key Capabilities
On-device, AI-powered computer-assisted detection (CADe) for pulmonary nodules on PA chest radiographs of adults. Detects nodules between 10 and 30mm using a deep learning algorithm. Features an 'Autorun' option for automatic detection post-X-ray imaging. Outputs annotation overlays in DICOM format within <10 seconds.
Clinical Utility
Aids physicians as a 'second reader' to enhance diagnostic accuracy and workflow. Clinically verified with a sensitivity of 80% or more, demonstrating statistically significant improvement in nodule detection performance for radiologists. Extensive external validation with diverse images and low false-positive rates (0.15 per image). Intended to assist in identifying suspected lung nodules, not for primary diagnosis or other lung lesions.
Integration Options
Native integration with Samsung Digital X-ray Imaging systems as part of the S-Station operation software. Offers PACS transmission options. Input and output are DICOM compatible. Deployment is locally virtualized (VM, Docker). Samsung is collaborating with Vuno and Lunit to expand AI capabilities for broader chest abnormality detection and triage.
Compliance Status
FDA 510(k) cleared (Class II), CE Certified (Class IIa MDD), and UK Conformity Assessed (UKCA). Samsung Healthcare's Quality System adheres to 21 CFR Part 820 and ISO 13485:2016.
Pricing Model
Subscription-based, with pricing dependent on the number of installations. Specific pricing details are not publicly disclosed and vary by region and negotiation.
User Experience
Designed to simplify workflow with an 'Autorun' feature that automatically performs nodule detection. Provides clear circular regions of interest (ROI) to highlight suspected nodules. Fast processing time of less than 10 seconds.
Implementation Complexity
Integrated directly into Samsung Digital X-ray Imaging systems, requiring compatible Samsung hardware. Deployment involves local virtualization.
Evidence Base
Supported by extensive clinical validation across multiple university hospitals (e.g., Freiburg, Massachusetts General, Samsung Medical Center). Studies show improved radiologist detection of malignant lung nodules with the DCNN-based software. Trained on large datasets, including 17,210 radiographs for training and 800 for testing.
Physician Tip

Utilize Auto Lung Nodule Detection as a valuable 'second reader' to augment your review of adult PA chest radiographs, especially for nodules between 10-30mm. Remember it's an assistive tool, not a replacement for your primary diagnosis, and is not indicated for lung lesions other than abnormal nodules. Leverage the 'Autorun' feature for streamlined workflow and pay attention to the highlighted regions of interest.

This tool is exclusively integrated with Samsung Digital X-ray Imaging systems and operates within their S-Station software. It supports DICOM for image input and output, facilitating seamless integration into existing PACS environments. Future collaborations, such as with Lunit, aim to broaden its capabilities for detecting a wider range of chest abnormalities, enhancing its utility within the Samsung ecosystem.

Details

Category Oncology AI, Radiology & Imaging AI
Pricing Contact vendor — Not publicly available; integrated with Samsung Digital X-ray Imaging systems.
DeploymentOn-device (integrated with Samsung Digital X-ray Imaging systems)
Compliance
BAA AvailableUnknown AI-estimated
HIPAA CompliantUnknown AI-estimated
FDA Status 1 AI-estimated

Received FDA 510(k) clearance (K201560) in October 2021. It is a computer-assisted detection (CADe) software for detecting pulmonary nodules between 10 and 30mm in size on posteroanterior chest radiographs of adults.
Integrations
EHR Not specified
Specialties Oncology, Pulmonology, Radiology

What the Web Says

Samsung Healthcare's Auto Lung Nodule Detection (ALND) is an AI-powered, computer-assisted detection (CADe) tool designed to help physicians identify suspected lung nodules (10-30mm) on chest X-rays. It functions as a 'second reader' to improve diagnostic accuracy and workflow. The system has received FDA clearance and has been clinically verified in multiple university hospitals, demonstrating improved nodule detection performance with statistical significance.

Overall: Mixed

Strengths

  • Aids physicians in detecting lung nodules on chest X-rays.
  • Utilizes deep learning AI algorithms for nodule prediction.
  • Clinically verified with over 80% sensitivity.
  • Improves radiologists' nodule detection performance with statistical significance.
  • Simplifies user workflow with automatic nodule detection and PACS transmission options.
  • Extensive external clinical validation with diverse images and low false positives.

Limitations

  • Only available with Samsung Electronics X-ray systems.
  • Cannot be used on patients with lung lesions other than abnormal nodules.
  • Some studies indicate a relatively low positive predictive value (PPV) and sensitivity compared to radiologist reports for nodule detection in real-world settings.
  • Potential for misidentifying normal or variant anatomy as abnormalities.
  • Challenges remain with high false-positive rates and generalizability across diverse populations for AI lung nodule detection tools in general.
  • Skepticism among healthcare professionals regarding AI tools in general.

Based on reviews from: Health AI Register, Practical Patient Care, Imaging Technology News, accessdata.fda.gov, VUMC News, Journal of Clinical Imaging Science, e-Century Publishing Corporation, Reddit

Last updated: 2026-07-19

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Press & Coverage

Practical Patient Care
Samsung gets FDA approval for AI tool to detect lung nodules in chest X-rays
NeuroLogica, a Samsung Electronics subsidiary, received FDA approval for its Auto Lung Nodule Detection (ALND) tool, an AI-powered solution for detecting pulmonary nodules 10-30mm in chest X-rays. The tool is integrated into Samsung Digital X-ray Imaging systems and aims to assist physicians in diagnosis.
2021-10
Samsung Healthcare News Center
Samsung Healthcare Introduces Lunit AI Solutions
Samsung Healthcare, through Boston Imaging, announced a partnership with Lunit Inc. to integrate AI solutions like Lunit INSIGHT CXR and Lunit Insight CXR Triage into Samsung's digital radiology products. This collaboration aims to enhance chest abnormality detection, building upon Samsung's existing Auto Lung Nodule Detection (ALND) offering.
2024-01
Samsung Healthcare News Center
Samsung's Push To Leverage Artificial Intelligence on Display at RSNA 2021
At RSNA 2021, Samsung showcased its advancements in AI for diagnostic imaging, including the recently FDA-cleared Auto Lung Nodule Detection (ALND) tool. ALND is an on-device CADe software that uses an AI algorithm to detect pulmonary nodules between 10 and 30mm in size on chest X-rays.
2021-11
Imaging Technology News
Samsung Receives FDA Clearance for AI Algorithms that Detect Lung Nodules in Chest X-rays
NeuroLogica Corp. announced FDA 510(k) clearance for its Auto Lung Nodule Detection (ALND) tool, an AI-powered CADe solution for detecting pulmonary nodules 10-30mm in chest X-rays. Clinical evaluations showed statistically significant improvement in nodule detection performance with ALND.
2021-10
Cardiopulmonary Imaging
NeuroLogica Receives US FDA Clearance for AI-based Auto Lung Nodule Detection Tool
Samsung NeuroLogica received FDA clearance for its Auto Lung Nodule Detection (ALND) tool, an AI-based CADe solution for identifying pulmonary nodules 10-30mm on adult PA chest radiographs. The deep-learning technology has been clinically verified with over 80% sensitivity.
2021-11
PMC (National Library of Medicine)
Evaluating the performance of artificial intelligence software for lung nodule detection on chest radiographs in a retrospective real-world UK population
This peer-reviewed study evaluated the performance of Samsung Electronics' Auto Lung Nodule Detection (ALND) software in a real-world UK population. The software, based on a deep convolutional neural network, demonstrated a sensitivity of 54.5% and specificity of 83.2% for nodule detection compared to radiologist reports.
2023-11
PMC (National Library of Medicine)
Current and emerging artificial intelligence applications in chest imaging: a pediatric perspective
This article discusses current and emerging AI applications in chest imaging, including Samsung Electronics' Auto Lung Nodule Detection for CR. It highlights the role of AI in detecting lung nodules and other abnormalities on chest radiographs, with a focus on adult data.
unknown
Samsung Healthcare News Center
Samsung Unveils its Latest Radiology Innovations at RSNA 2019
At RSNA 2019, Samsung showcased its Auto Lung Nodule Detection, an AI-based CAD software designed to identify and detect lung nodules in adult chest PA radiographs. At the time, this feature was available for sale in Europe (CE marked) and Korea.
2019-12

Videos

Product demos, reviews, and walkthroughs for Auto Lung Nodule Detection.

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Frequently Asked Questions

AI-powered lung nodule detection systems typically integrate seamlessly into existing Picture Archiving and Communication Systems (PACS) as a 'second reader' or intelligent assistant. They analyze CT scans in the background, flagging suspicious areas for radiologist review, thereby enhancing efficiency and potentially reducing missed nodules in high-volume settings or screening programs.
These AI systems require regulatory clearance, such as FDA 510(k) in the US or CE Mark in Europe, as medical devices to ensure their safety and effectiveness for intended clinical use. While these approvals validate the technology, the radiologist remains ultimately responsible for the final diagnosis and patient management decisions.
AI, particularly deep learning, often demonstrates superior performance to older CAD systems by learning complex patterns from extensive datasets. While AI can achieve high sensitivity, sometimes matching or exceeding human performance in specific detection tasks, some studies indicate it may have lower specificity, leading to more false positives compared to radiologists.
Cost models for AI solutions vary, often including subscription fees, per-study charges, or perpetual licenses. Key factors influencing the overall investment include the specific vendor, the complexity of integration with existing IT infrastructure, the volume of studies processed, and the level of ongoing support and maintenance required.
Limitations include the potential for false positives, which can lead to unnecessary follow-up procedures and patient anxiety, and false negatives, which risk delayed diagnoses, especially for atypical nodule presentations. Performance can also vary significantly based on the diversity and quality of the training data, potentially leading to reduced accuracy across different patient populations, scanner types, or nodule characteristics.
Reputable AI solutions are designed to comply with stringent data privacy regulations like HIPAA and GDPR by implementing robust security measures. These typically include anonymization or de-identification of patient data, secure data transfer protocols, strict access controls, and often require Business Associate Agreements (BAAs) in the US to protect sensitive health information.

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Suggest an Edit → | Last Verified: 2026-04-19 | First Added: 2026-04-19
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