McCormick Place, Chicago - Coreline Soft, a leader in medical AI technology, is making its mark at the Radiological Society of North America (RSNA) 2024 meeting, taking place from December 1 to 5 at McCormick Place, Chicago.
The company, exhibiting at booth #4949, is highlighting its AVIEW suite of AI diagnostic products, focusing on improvements in both diagnostics and workflow efficiency. This focus aligns perfectly with this year’s theme, "Building Intelligent Connections," highlights the integration of new technologies in radiology.
Coreline Soft's presence at RSNA is substantial, with three out of six presentations in the "Chest Imaging (ILD) session" featuring research utilizing their AVIEW Lung Texture AI solution. This success underscores the growing acceptance and impact of Coreline Soft's technology within the U.S. hospital and chest imaging communities.
James Lee, North America Branch Head of Coreline Soft, expressed pride in this achievement, emphasizing the company's commitment to accelerating research in lung diseases through quantitative evaluation using AI. He highlighted the honor of contributing to advancements in this critical field and seeing their work recognized on a global platform.
Key Research Presented at RSNA 2024:
Coreline Soft's contributions to the 'Chest Imaging (ILD) session' include the following studies:
- Improving Interreader Agreement in CT Pattern Classification for Diagnosis of Idiopathic Pulmonary Fibrosis Using Content-Based Image Retrieval (M3-SSCH03-2): Presented by Sohee Park. This research explores the use of content-based image retrieval to enhance the consistency of CT pattern classification in diagnosing IPF.
- Deep Learning for Separate Quantification of Pulmonary Arteries and Veins on Non-Enhanced Chest CT: Prognostic Value in Patients with Connective Tissue Disease (M3-SSCH03-5): Presented by Jooae Choe. This study investigates the use of deep learning to separately quantify pulmonary arteries and veins on non-enhanced CT scans, aiming to determine its prognostic value in patients with connective tissue disease.
- Development and Multi-Institutional Validation of Estimating Forced Vital Capacity in Pulmonary Fibrosis Using Quantitative Chest CT Data (M3-SSCH03-6): Presented by Steven Rothenberg. This research focuses on developing and validating a method for estimating forced vital capacity (FVC) in pulmonary fibrosis patients using quantitative data from chest CT scans.
This announcement demonstrates Coreline Soft's leadership in using AI to improve the diagnosis and management of lung disease.
