Diagnostic Assistance Solution for Big 3 Chest Disease

Automated COPD Analysis AI Solution

Lung Cancer Screening
AI Solution

aview LCS: An AI Solution for Detection and Analysis of
Lung Nodules from Chest CT.

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aview LCS | Lung Cancer Screening AI Solution

Conformity Certifications

MFDS

Republic of Korea
FDA

United States of America
PMDA

Japan
TFDA

Taiwan
CE

Europe
TGA

Australia
HSA

Singapore
ANVISA

Brazil
HC

Canada

Automated Nodule Detection and Lung-RADS Calculation

Detecting and Analyzing Easy to miss Lung Nodules on Low-Dose Chest CT with Imaging Artificial Intelligence.

Efficiently diagnose with awareness of patient conditions.

Finding microscopic nodules provides a variety of information, including basic, number of nodules, size and status, and RADS category. Findings that are likely to develop into lung cancer can also be checked in advance, reducing working time and allowing efficient reading depending on the case. Microscopic Nodule Detection: Providing Comprehensive Information on Number of Nodules, Size, Status, and RADS Category Early Detection of Potential Lung Cancer Development Enables Time-efficient Scans Reading.

aview LCS | Automated Nodule Detection and Lung-RADS Calculation

Detecting Nodules of Various Sizes, from Small to Large

Follow up mode

Automated Matching of Follow-up with Previous Lung CT Scans Instantly Assess Nodule Changes.

Monitoring Nodule Growth is Vital for Accurate Readings.

It autonomously assesses growth and changes by matching lung nodules, going beyond mere image comparisons.
aview LCS efficiently classifies lung nodules into the relevant categories according to the Lung CT Screening Reporting and Data System (Lung-RADS ver 1.1) guidelines as recommended by the American College of Radiology.

Follow-up CT Scan

Previous CT Scan

aview LCS | Automated Matching of Follow-up with Previous Lung CT Scans Instantly Assess Nodule Changes

Clinical Viewer

CT Findings are visualized in 3D.
User-Friendly Viewer for Medical Professionals and Patients.

Advanced 3D Rendering of Patient’s Lung.

2D Images May Not Provide Adequate Information of Abnormal Findings Visualized 3D Model of Patient’s Lung is much more informative and intuitive. Abnormal findings can be easily explained by demonstration of the 3D Model.

aview LCS | CT Findings are visualized in 3D, User-Friendly Viewer for Medical Professionals and Patients

Delivering Comprehensive Analysis Results

Automatically Analyze Lung Cancer Screening Results and
Generate Comprehensive Reports.

Automate Tedious manual Tasks to Save Time.

Reading report

Effortless Generation of Lung Cancer Screening Results for Patient Records and Official Reports.

Baseline CT
CTDIvo1: 0.32 mGy

Radiological Finding
RLL (#198), Non-Solid 32 mm, Category: 3
RLL (#200), Non-Solid 26 mm, Category: 3
LLL (|
Patient Reports and insurance reimbursement reports

Automatically generated reports with detail assist patients understand examination results.

Validations

Clinically Proven: AI Medical Technology Saves
Time and Enhances Reading Accuracy.

0

Time Saving
0

Sensitivity
23

false positivity rate
Reduce Workload

https://dailybulletin.rsna.org/db22/index.cfm?pg=22fri07

Lancaster HL, Zheng S, Aleshina OO, Yu D, Yu Chernina V, Heuvelmans MA, de Bock GH, Dorrius MD, Willem Gratama J, Morozov SP, Gombolevskiy VA, Silva M, Yi J, Oudkerk M. Outstanding negative prediction performance of solid pulmonary nodule volume AI for ultra-LDCT baseline lung cancer screening risk stratification. Lung Cancer. 2022 Jan 6;165:133-140

Publications

Refer to the following research papers

These contents represent summaries of scientific
publications and are unrelated to any form of advertising.

To evaluate performance of AI as a standalone reader in ultra-low-dose CT lung cancer baseline screening, and compare it to that of experienced radiologists. Volumetric nodule measurements were performed by five experienced blinded radiologists, and independently assessed using an AI lung cancer screening prototype (aview LCS, v1.0.34, Coreline Soft, Co. ltd, Seoul, Korea) to automatically detect, measure, and classify solid nodules. Our results suggest that through the use of AI as an impartial reader in baseline lung cancer screening, negative-misclassification results could exceed that of four out of five experienced radiologists, and radiologists’ workload could be drastically diminished by up to 86.7%.

Harriet L. Lancaster, Sunyi Zheng, et al. “Outstanding negative prediction performance of solid pulmonary nodule volume AI for ultra-LDCT baseline lung cancer screening risk stratification.” Lung Cancer
The Korean Lung Cancer Screening Project (K-LUCAS) is a single-arm cohort study that was conducted from February 2017 to evaluate the feasibility of implementing an organized national lung cancer screening program in Korea High-risk population aged 55–74 years with more than a 30-pack-year smoking history was recruited. The screening results were reported using the Lung Imaging Reporting and Data System (Lung-RADS), suggested by the American College of Radiology. K-LUCAS was performed by a network-based diagnosis supporting system using a computer-aided detection (CAD) program (aview LCS, Corelinesoft) to maintain screening quality. K-LUCAS provided promising evidence supporting the implementation of a national lung cancer screening program to detect lung cancer at an early stage and promote smoking cessation for participants in Asian population.

Lee J, Kim Y, Kim HY, et al. “Feasibility of implementing a national lung cancer screening program: Interim results from the Korean Lung Cancer Screening Project (K-LUCAS). Transl Lung Cancer Res.” 2021;10(2):723-736.

More Publications

Lung Cancer Screening AI Solution

aview LCS: An AI Solution for Detection and Analysis of Lung Nodules from Chest CT.