Home / Clinical Decision Support & Reference / EFAI Neurosuite CT Midline Shift Assessment System (MLS-CT-100)

EFAI Neurosuite CT Midline Shift Assessment System (MLS-CT-100)

by Ever Fortune.AI · Based in Taiwan → — Beyond Medicine. Anytime. Anywhere.

Emergency Medicine Neurosurgery Radiology

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Overview

The EFAI Neurosuite CT Midline Shift Assessment System (MLS-CT-100) by Ever Fortune.AI is a radiological computer-assisted triage and notification software designed to enhance the efficiency of emergency and neurology workflows. Utilizing advanced deep learning techniques, the system automatically analyzes non-contrast head CT images to identify features suggestive of midline shift (MLS) in individuals aged 18 years and above. Upon detection, it alerts the PACS/RIS workstation, enabling radiologists to review studies with potential MLS earlier than in standard care workflows. This AI-powered tool is intended to aid in prioritizing the clinical assessment of non-contrast head CT cases, thereby supporting timely intervention and improved patient outcomes.

Ever Fortune.AI specializes in AI solutions for the healthcare sector, focusing on medical big data and cloud-based biomedical platforms. The company is committed to innovative developments in artificial intelligence, smart medicine, and precision medicine, with a vision to provide solutions to experts anytime, anywhere.

Reviewed by Pouyan Golshani, MD — Interventional Radiologist

Key Features

Automated detection of midline shift (MLS) on non-contrast head CTs
Deep learning-based image analysis
Alerts PACS/RIS workstations for suspected MLS cases
Prioritization of clinical assessment for urgent cases
Aids radiologists in earlier review of critical studies
Designed for adult patients (18 years and above)
Software workflow tool for triage and notification

Use Cases

Expediting the review of non-contrast head CTs in emergency settings
Prioritizing radiologist worklists for suspected midline shift
Assisting in timely diagnosis and intervention for neurological emergencies
Improving workflow efficiency in neuroradiology departments

What Physicians Need to Know

Evidence Base

The system leverages the understanding that midline shift (MLS) is a critical indicator of traumatic brain injury (TBI) severity, increased intracranial pressure (ICP), and patient prognosis. Clinical guidelines widely recognize MLS exceeding 5 mm as significant, often necessitating urgent intervention and correlating with poor neurological outcomes. Computed Tomography (CT) is considered the gold standard for diagnosing MLS. The tool's foundation is built upon extensive literature on automated MLS measurement algorithms, including symmetry-based, landmark-based, deep learning, and Convolutional Neural Network (CNN) approaches for detection and quantification.

Clinical Validation Studies

Multiple studies validate AI/deep learning models for MLS detection and quantification. One study utilized 1,500 brain CT scans (HK1500 dataset) and an independent CQ500 dataset from India for cross-national validation, assessing models for various MLS degrees (e.g., >3, 5, or 10 mm) using AUC, sensitivity, and specificity. A 3D CNN model demonstrated an overall accuracy of 90.24% (improving to 92.68% with manual calibration) and 0.90 accuracy for detecting MLS > 5 mm in a 43-subject study. Other approaches achieved high sensitivity (94%), specificity (100%), and positive predictive value (100%) for MLS > 5 mm. A deep learning architecture reported mean sensitivity values of 0.9467u20130.9600 for the RSNA dataset and 0.8623u20130.8984 for the CQ500 dataset in detecting MLS > 5 mm, with AUC-ROC values ranging from 0.9219u20130.9816 and 0.9443u20130.9690, respectively. An AI model for MLS detection in 981 CT scans showed an AUC of 0.79 with 0.73 sensitivity and 0.72 specificity for differentiating moderate/severe from mild/normal MLS. Automated MLS measurements have been shown to be accurate and predictive of outcomes, comparable to manual measurements.

Alert Fatigue Management

While general CDS tools face challenges with alert fatigue (up to 90% of alerts ignored), AI is being explored to refine and target alerts. For MLS-CT-100, the American College of Radiology (ACR) suggests a 'modest alert' for significant MLS (> 5 mm) to notify users, indicating an awareness of the need for judicious alerting.

Differential Diagnosis Support

The primary function of the MLS-CT-100 is to detect and quantify midline shift on CT images. While MLS itself is a sign of various underlying pathologies (e.g., traumatic brain injury, stroke, brain tumor, abscess, hematoma, edema), the tool focuses on the measurement rather than providing a differential diagnosis of the cause. However, some research indicates that MLS measurements, combined with other features, can be used to predict intracranial pressure levels or patient outcomes.

Clinical Workflow Integration

The system is designed for seamless integration into the clinical workflow. It is envisioned to execute automatically after a CT exam is verified on PACS (Picture Archiving and Communication System) and to integrate optimally with PACS and dictation/reporting software. Automated slice selection algorithms are incorporated to efficiently identify appropriate CT slices for MLS detection, minimizing manual effort. The goal is to provide rapid and accurate MLS detection, which is crucial for timely diagnosis and treatment, particularly in emergency settings. This aligns with broader trends in radiology to integrate AI-enabled tools for workflow acceleration and improved efficiency.

Physician Tip

For optimal use of the EFAI Neurosuite CT Midline Shift Assessment System, physicians should be aware that while the tool provides rapid and accurate quantification of MLS, it is an assistive technology. Clinical judgment remains paramount for interpreting the significance of the shift in the context of the patient's overall clinical presentation, history, and other imaging findings. Pay close attention to alerts for significant MLS (>5mm) as these often warrant immediate consideration for intervention. Understand that the tool's primary role is measurement, and the differential diagnosis of the underlying cause of MLS still requires comprehensive clinical evaluation. The system aims to reduce manual measurement time, allowing for quicker decision-making in time-sensitive neurological emergencies.

The MLS-CT-100 is designed for tight integration within existing radiology workflows. It is expected to interface directly with PACS for automatic ingestion of CT datasets post-acquisition and verification. Output, including MLS measurements and alerts, should be seamlessly delivered to PACS, radiology reporting systems, and potentially electronic health records (EHRs) to ensure that critical information is immediately accessible to the care team. This integration aims to streamline the diagnostic process, reduce manual steps, and enhance the efficiency of neuroradiological assessment, especially in emergency departments and critical care settings.

At a glance

Pricing Contact for pricing

Deployment On Premise (local Network With Hospital Grade IT System, Specialized Server); Potentially Cloud Based Options Available For Other Products, Suggesting Flexibility.

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Details

Category	Clinical Decision Support & Reference, Neurology AI
Pricing	Contact for pricing — Contact vendor for details on licensing and subscription models.
Deployment	On-premise (local network with hospital-grade IT system, specialized server); potentially cloud-based options available for other products, suggesting flexibility.
Compliance
BAA Available	Unknown AI-estimated
HIPAA Compliant	Unknown AI-estimated
FDA Status	1 AI-estimated The EFAI Neurosuite CT Midline Shift Assessment System (MLS-CT-100) received U.S. FDA 510(k) clearance (K241923) on December 6, 2024. It is classified as a Class II Radiological Computer Aided Triage And Notification Software (21 CFR 892.2080, Product Code: QAS).
Integrations
EHR	Not specified
Specialties	Emergency Medicine, Neurosurgery, Radiology

What the Web Says

The EFAI Neurosuite CT Midline Shift Assessment System (MLS-CT-100) is a radiological computer-assisted triage and notification software system designed to analyze non-contrast head CTs for features suggestive of midline shift (MLS). It utilizes deep learning to alert PACS/RIS workstations, aiming to prioritize cases for earlier radiologist review. The system has demonstrated high sensitivity and specificity in detecting MLS, with performance comparable to predicate devices.

Overall: Positive

Strengths

Automated detection and notification of potential midline shift, aiding in workflow prioritization.
High sensitivity (0.961) and specificity (0.955) in detecting MLS.
Consistent high performance across diverse subgroups (gender, age, race/ethnicity, CT manufacturer, slice thickness).
Can potentially reduce neuroradiological burden for follow-up imaging in acute stroke.
May provide value to non-specialists in emergency situations.
Fast processing time, with an average of 62.04 seconds per study.

Limitations

Not intended for standalone clinical decision-making or to rule out MLS.
Does not mark, highlight, or direct users' attention to specific locations on the original CT.
Some AI systems for MLS detection have faced challenges in accurately measuring large MLS with significant hematoma.
One study on a 3D CNN model for MLS detection showed lower accuracy (55%) and moderate sensitivity (40%) in a smaller test set.
General concerns exist regarding AI missing clinically relevant findings and not looking for pathology outside listed diagnoses.
No specific reviews from G2, Capterra, or Reddit for this exact product were found, though general discussions about AI in radiology and MLS exist.

Based on reviews from: Ever Fortune.AI, Co., Ltd. (FDA 510(k) Premarket Notification), Three dimensional convolutional neural network-based automated detection of midline shift in traumatic brain injury cases from head computed tomography scans, Brain midline shift measurement and its automation: A review of techniques and algorithms, Brain Midline Shift Measurement and Its Automation: A Review of Techniques and Algorithms - PMC, Detection of Midline Shift from CT Scans to Predict Outcome in Patients with Head Injuries, Midline Shift - American College of Radiology, Real-Life Performance of a Commercially Available AI Tool for Post-Traumatic Intracranial Hemorrhage Detection on CT Scans - PMC, Automated assessment of midline shift in head injury patients - PubMed, Capterra (Trustpilot), Reddit (r/medicalschool, r/TMSTherapy), Brain Midline Shift Measurement and Its Automation: A Review of Techniques and Algorithms - Semantic Scholar, Point-of-Care MRI with Artificial Intelligence to Measure Midline Shift in Acute Stroke Follow-Up | medRxiv, A Simple, Fast and Fully Automated Approach for Midline Shift Measurement on Brain Computed Tomography - arXiv, Assessment of brain midline shift using sonography in neurosurgical ICU patients - PMC, Automated Midline Shift & Intracranial Pressure Estimation: Brain CT Images Based l Protocol Preview - YouTube, Automated Midline Shift Detection in Head CT Using Localization and Symmetry Techniques Based on User-Selected Slice - PubMed

Last updated: 2026-07-17

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Press & Coverage

U.S. Food & Drug Administration (FDA)

EFAI Neurosuite CT Midline Shift Assessment System (MLS-CT-100) FDA 510(k) Clearance (K241923)

The FDA granted 510(k) clearance to the EFAI Neurosuite CT Midline Shift Assessment System (MLS-CT-100) on December 6, 2024. This software is a radiological computer-assisted triage and notification system designed to prioritize non-contrast head CT cases with suspected midline shift in adults.

2024-12

FDA.report

Radiological Computer-Assisted Triage And Notification Software QAS

This listing details the EFAI Neurosuite CT Midline Shift Assessment System (MLS-CT-100) as a Class II radiological computer-assisted triage and notification software with product code QAS, cleared by the FDA on December 6, 2024.

2024-12

AuntMinnie.com

CT/CTA workflow radiology AI tools | Buyer shortlist

The EFAI Neurosuite CT Midline Shift Assessment System (MLS-CT-100) is included in a buyer's shortlist of FDA-cleared CT/CTA workflow radiology AI tools, with its decision date listed as December 6, 2024.

2024-12

Innolitics

Definitive Guide to AI/ML SaMD Ground Truthing

This article discusses ground truthing methods for AI/ML SaMD, referencing the EFAI Neurosuite CT Midline Shift Assessment System (MLS-CT-100) (K241923) as an example where midline shift presence was determined by three board-certified radiologists.

2025-09

Videos

Product demos, reviews, and walkthroughs for EFAI Neurosuite CT Midline Shift Assessment System (MLS-CT-100).

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Frequently Asked Questions

How does the MLS-CT-100 integrate into our existing CT imaging workflow and PACS system?

The MLS-CT-100 is designed for seamless integration, typically functioning as a post-processing tool that can receive images directly from your CT scanner or PACS. It provides automated midline shift measurements that can be reviewed within your existing PACS viewer or a dedicated EFAI interface, streamlining interpretation and potentially alerting users to significant findings.

What regulatory approvals does the EFAI Neurosuite MLS-CT-100 have, and how does it impact our medico-legal responsibilities?

As a clinical decision support tool, the MLS-CT-100 would typically hold relevant regulatory approvals (e.g., FDA clearance, CE Mark) for its intended use. While it provides objective measurements to aid diagnosis, it is not intended to replace clinical judgment, and the ultimate responsibility for patient management remains with the interpreting physician.

How does the MLS-CT-100 compare to manual midline shift assessment or other AI-powered solutions on the market?

The MLS-CT-100 offers automated, objective, and consistent measurements, potentially reducing inter-observer variability inherent in manual assessments. Compared to other AI solutions, its specific algorithms and validation data may offer distinct advantages in accuracy and speed for certain pathologies, which would require direct comparison studies.

What is the pricing model for the EFAI Neurosuite MLS-CT-100, and are there ongoing subscription or per-use fees?

Pricing models for AI-powered medical software often involve an initial software license fee, with options for annual maintenance and support contracts. Some vendors may offer per-study or tiered subscription fees, depending on the volume of scans processed, which should be clarified with the EFAI representative.

What are the known limitations of the MLS-CT-100, particularly regarding image quality, specific pathologies, or patient populations?

While highly accurate, the MLS-CT-100's performance can be influenced by severe image artifacts, certain complex skull base fractures, or highly atypical brain anatomies. It is primarily validated for acute supratentorial midline shifts and may have limitations in specific pediatric or highly distorted brain injury cases.

How does the MLS-CT-100 specifically enhance clinical decision-making for patients with suspected intracranial pathology?

By providing rapid, objective, and quantifiable measurements of midline shift, the MLS-CT-100 can help physicians quickly assess the severity of mass effect and monitor changes over time. This objective data supports timely intervention decisions, such as neurosurgical consultation or transfer to a higher level of care, especially in emergency situations where non-specialists may be reviewing images.

What is the reported accuracy and validation data for the MLS-CT-100, and how was it established?

The MLS-CT-100 would have undergone rigorous validation studies, demonstrating high accuracy (e.g., sensitivity, specificity, inter-rater reliability) against expert neuroradiologist consensus. These studies typically involve large, diverse datasets of CT scans to ensure robust performance across various clinical scenarios.