EFAI ChestSuite XR Pneumothorax Assessment System

by Ever Fortune AI Co.  · Based in Taiwan → — Beyond Medicine. Anytime. Anywhere.
Critical Care Emergency Medicine Radiology
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Regulatory Status Disclosed

Overview

The EFAI ChestSuite XR Pneumothorax Assessment System is a radiological computer-assisted triage and notification software system developed by Ever Fortune AI Co.. It utilizes deep learning techniques to automatically analyze Posteroanterior (PA) chest X-rays, identifying suspected findings of pneumothorax. The system is designed to send notification messages to Picture Archiving and Communication Systems (PACS) or workstations, facilitating worklist prioritization and triage for medical professionals.

This software workflow tool is intended to aid the clinical assessment of adult (22 years of age or older) PA Chest X-Ray cases that exhibit features suggestive of pneumothorax. It provides a passive notification to radiologists, indicating cases that may benefit from prioritization, but it does not mark, highlight, or direct users’ attention to specific locations on the original chest X-ray. The system is not intended for stand-alone clinical decision-making, nor is it meant to rule out pneumothorax or otherwise preclude a comprehensive clinical assessment of X-ray cases. The EFAI ChestSuite XR Pneumothorax Assessment System demonstrates high performance with a reported sensitivity of 97% and specificity of 98% for pneumothorax detection. Deployment is recommended within a local network with an existing hospital-grade IT system, requiring installation on a specialized server that supports deep learning processing.

Reviewed by Pouyan Golshani, MD — Interventional Radiologist

Key Features

  • Radiological computer-assisted triage and notification software
  • Utilizes deep learning techniques for automated analysis of PA chest x-rays
  • Identifies suspected findings of pneumothorax
  • Sends notification messages to PACS/workstation for worklist prioritization or triage
  • Aids clinical assessment of adult (22+ years) Posteroanterior (PA) view Chest X-Ray cases
  • Not intended for stand-alone clinical decision-making
  • Not intended to direct attention to specific portions or anomalies of an image
  • Not intended to rule out pneumothorax or otherwise preclude clinical assessment
  • High sensitivity (97%) and specificity (98%) for pneumothorax detection
  • Recommended deployment in a local network with an existing hospital-grade IT system

Use Cases

  • Prioritization and triage of adult PA chest X-ray images for suspected pneumothorax
  • Aid for radiologists and clinicians in identifying potential pneumothorax cases
  • Improving workflow efficiency in radiology departments by flagging critical cases

What Physicians Need to Know

DICOM Support & Standards
The system accepts DICOM-formatted images as input.
PACS Integration Method
Integrates by sending notification messages to the Picture Archiving and Communication System (PACS) or workstation. This enables worklist prioritization or triage of suspicious pneumothorax findings. It can also send results as a secondary capture DICOM.
AI Model Architecture (deep learning approach)
Utilizes deep learning techniques to automatically analyze chest X-rays.
Processing Speed (per study)
The average performance time for the EFAI ChestSuite XR Pneumothorax Assessment System is 23.3 seconds (95% CI [23.2, 23.4]) per study.
FDA Clearance Pathway (510k/De Novo)
Received Traditional 510(k) clearance from the FDA.
Supported Modalities (CT/MRI/X-ray/US)
Specifically supports X-ray images, including Posteroanterior (PA) and Anteroposterior (AP) views of the chest.
Sensitivity & Specificity Data
Demonstrated an overall sensitivity of 0.97 (95% CI=0.94-0.99) and a specificity of 0.98 (95% CI=0.96-0.99) for pneumothorax detection.
RSNA/ACR Validation
Performance validation involved a standalone dataset with ground truth established by majority agreement among three US board-certified radiologists. The performance acceptance criteria required the lower bounds of 95% confidence intervals for both sensitivity and specificity to exceed 0.8.
Physician Tip

This AI tool is designed to assist in the triage and prioritization of chest X-rays for suspected pneumothorax, not to serve as a standalone diagnostic solution. Radiologists should use the passive notifications to prioritize their worklist and perform a comprehensive review of all images. The system is specifically validated for adult PA and AP chest X-rays. It does not provide image-level markings or highlights, requiring the radiologist to confirm the presence and location of pneumothorax. Always integrate AI findings with full clinical context and patient history.

The EFAI ChestSuite XR Pneumothorax Assessment System is designed for integration within a local network with existing hospital-grade IT systems. It requires installation on a specialized server capable of deep learning processing. Seamless integration with PACS/workstations is achieved via DICOM standards for image input and notification outputs, facilitating worklist prioritization. Configuration of input and output destinations is typically managed by the manufacturer.

Details

Category Radiology & Imaging AI, Triage & ER/ICU AI
Pricing Contact for pricing
DeploymentOn-premise (local network, specialized server)
Compliance
BAA AvailableUnknown AI-estimated
HIPAA CompliantUnknown AI-estimated
FDA Status 1 AI-estimated

Cleared by the U.S. FDA on May 31, 2022, under 510(k) K221552. It is classified as a Radiological Computer Aided Triage and Notification Software, intended to aid in the clinical assessment of adult (22 years of age or older) Posteroanterior (PA) view Chest X-Ray cases with features suggestive of pneumothorax.
Integrations
EHR Not specified
Specialties Critical Care, Emergency Medicine, Radiology

What the Web Says

The EFAI ChestSuite XR Pneumothorax Assessment System is an AI-powered software tool designed to help clinicians prioritize chest X-ray cases suspected of pneumothorax. It uses deep learning to analyze posteroanterior chest X-rays and provides case-level notifications within existing PACS/workstation workflows, aiming for faster identification and assessment of patients. The system is intended to aid in triage and clinical decision-making, but not for stand-alone diagnosis.

Overall: Positive

Strengths

  • High sensitivity (0.97) and specificity (0.98) for pneumothorax detection.
  • Fast performance, with an average analysis time of approximately 23 seconds per case.
  • Integrates with existing PACS/workstation workflows, providing case-level notifications for prioritization.
  • Aids in triage and supports clinical decision-making, potentially reducing delays in diagnosis.
  • Consistent and reliable performance across various X-ray manufacturers and confounding conditions.
  • Can improve diagnostic accuracy, especially for less experienced clinicians, and narrow the performance gap between junior and senior readers.

Limitations

  • Not intended for stand-alone diagnosis; requires human clinician review.
  • Does not mark specific image areas or direct attention to particular anomalies.
  • Performance may decrease with multiple findings or for smaller targets.
  • Can produce false-positive results, which could lead to unnecessary imaging and costs.
  • General-purpose AI models, while showing promise, may have distinct diagnostic biases and varying performance profiles.
  • Some AI models for pneumothorax detection have shown reduced performance on real-world clinical images compared to curated datasets.

Based on reviews from: FDA Radiology AI Device (EFAI ChestSuite XR Pneumothorax Assessment System), Ever Fortune AI | Radiology AI Companies - X-ray Interpreter, accessdata.fda.gov (Ever Fortune AI Co., Ltd. 510(k) Premarket Notification), accessdata.fda.gov (Ever Fortune.AI, Co., Ltd. 510(k) Premarket Notification), Muriel Steele Society (Artificial Intelligence Tool for Reads Chest X-Rays Approved by FDA), EverFortune.AI (ChestSuite), Semantic Scholar (Multidisciplinary Evaluation of an AI-Based Pneumothorax Detection Model), GE Healthcare (Critical Care Suite on-device AI), PMC (Evaluating AI Models for Pneumothorax Detection on Chest Radiographs: Diagnostic Accuracy and Clinical Trade-Offs), PMC (Impact of AI Assistance in Pneumothorax Detection on Chest Radiographs Among Readers of Varying Experience), medRxiv (Evaluation of an artificial intelligence model for detection of pneumothorax and tension pneumothorax on chest radiograph), Diagnostic Imaging (FDA-Cleared AI Triage Software for Chest X-Rays Offers Enhanced Detection of Pleural Effusion and Pneumothorax), FDA Clears Critical Care Suite With AI-Powered Pneumothorax Detection, Radiologists Outperformed AI in Identifying Lung Diseases on Chest X-ray, PubMed (Comparison of emergency physicians and artificial intelligence models in pneumothorax detection: A multi-reader retrospective study), Intel (GE Healthcare Accelerates Pneumothorax Detection by Embedding AI with the X-ray System), PLOS Medicine (Automated detection of moderate and large pneumothorax on frontal chest X-rays using deep convolutional neural networks: A retrospective study), CTV News (AI system more accurately identifies collapsed lungs using chest x-rays), Artificial Intelligence Tool for Reads Chest X-Rays Approved by FDA

Last updated: 2026-07-18

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Frequently Asked Questions

The EFAI ChestSuite XR Pneumothorax Assessment System is designed as a software workflow tool that integrates with your Picture Archiving and Communication System (PACS) or workstation. Its primary function is to automatically analyze adult Posteroanterior (PA) view chest X-rays for features suggestive of pneumothorax and provide case-level output for worklist prioritization or triage. It aims to aid in the clinical assessment by flagging suspicious cases for radiologists, rather than directing attention to specific image anomalies or being used for standalone diagnosis.
The EFAI ChestSuite XR Pneumothorax Assessment System has received FDA 510(k) clearance, indicating it can be marketed as a medical device. As a healthcare provider, you are responsible for maintaining human oversight, logging performance, and cooperating on post-market monitoring, especially as AI tools in radiology are often classified as 'high-risk' under regulations like the EU AI Act. Additionally, compliance involves ensuring ethical use, data privacy (HIPAA-aligned), and understanding that the AI is a tool to supplement, not replace, professional judgment.
Pricing for AI radiology solutions often follows subscription-based or pay-per-use models, with costs varying significantly based on volume and specific customer needs. Diagnostic AI systems in radiology can range from $50,000 to $300,000, with larger enterprise implementations potentially exceeding $300,000. Beyond software fees, cost considerations include hardware investments if on-premise deployment is preferred, integration with existing systems, data preparation, regulatory compliance validation, and ongoing monitoring and maintenance.
The EFAI ChestSuite XR is not intended for standalone clinical decision-making, nor is it designed to rule out pneumothorax or preclude a full clinical assessment of X-ray cases. AI systems, in general, can have limitations such as reduced performance with poor image quality, potential biases if trained on non-representative datasets, and lower accuracy for small or subtle findings or when multiple pathologies are present. It's crucial for physicians to understand that AI is a supplementary tool and human expertise remains essential.
Yes, several other FDA-cleared AI-powered systems for pneumothorax detection on chest X-rays are available, such as GE Healthcare's Critical Care Suite and AZmed's AZchest. While EFAI ChestSuite XR focuses on triage and prioritization without specific image marking, some alternatives offer features like highlighting suspected areas, providing confidence levels, or detecting multiple abnormalities. Physicians should evaluate alternatives based on specific workflow needs, reported accuracy, and integration capabilities.
While specific accuracy metrics for EFAI ChestSuite XR were not detailed in the provided context beyond its FDA clearance as a triage and notification system, similar AI pneumothorax detection systems have reported high AUC values (e.g., 0.965) and sensitivities ranging from 63% to 90% and specificities from 99% to 100% for pneumothorax detection. AI can improve the performance of readers, especially less experienced ones, but its performance can decrease for smaller findings or complex cases with multiple abnormalities. The combined performance of human and machine is often superior to either alone.
While specific details for EFAI ChestSuite XR were not provided, AI systems in healthcare must adhere to stringent data privacy regulations like HIPAA. Reputable AI vendors typically ensure that patient health information (PHI) is handled securely, often through federated learning approaches where models are trained without centralizing sensitive imaging metadata. Hospitals deploying such systems are responsible for establishing governance processes, ensuring data quality, and maintaining robust risk management systems to protect patient data.

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