Research on the Application of Lung Ground- Glass Nodule Screening Based on Gene Methylation Combined with Spiral CT and AI Recognition System in Teaching Practice

The application of gene methylation combined with spiral CT in the screening of lung ground-glass nodules (GGN) and the integration of AI recognition systems represent cutting-edge advancements in medical technology. This study explores the practical application of these techniques in teaching settings, aiming to enhance students’ understanding and proficiency in modern medical imaging and diagnosis. By incorporating these methods into educational curricula, we seek to assess their effectiveness in improving diagnostic accuracy, efficiency, and overall student engagement. The findings of this research have implications for enhancing medical education, particularly in the field of radiology and imaging sciences, ultimately leading to improved patient care and outcomes.

Authors

Institutions

Publication Details

Journal
Oncology Treatment Discovery
Published
2025-04-02
DOI
https://doi.org/10.26689/otd.v3i1.9949
Primary Topic
Radiomics and Machine Learning in Medical Imaging
Type
article
Field-Weighted Citation Impact
0.00
Controls
|||
ALL TIME
JAN
FEB
MAR
APR
MAY
JUN
JUL
AUG
SEP
OCT
article

Research on the Application of Lung Ground- Glass Nodule Screening Based on Gene Methylation Combined with Spiral CT and AI Recognition System in Teaching Practice

Yingying Xu, Yong Zhou, Jie Ming, Wenbo Jiang et al.
Oncology Treatment Discovery
Radiomics and Machine Learning in Medical Imaging
article

Research on the Application of Lung Ground- Glass Nodule Screening Based on Gene Methylation Combined with Spiral CT and AI Recognition System in Teaching Practice

Yingying Xu, Yong Zhou, Jie Ming, Wenbo Jiang, Xiaojing Gan, Cheng Zhou
article en

Abstract

The application of gene methylation combined with spiral CT in the screening of lung ground-glass nodules (GGN) and the integration of AI recognition systems represent cutting-edge advancements in medical technology. This study explores the practical application of these techniques in teaching settings, aiming to enhance students’ understanding and proficiency in modern medical imaging and diagnosis. By incorporating these methods into educational curricula, we seek to assess their effectiveness in improving diagnostic accuracy, efficiency, and overall student engagement. The findings of this research have implications for enhancing medical education, particularly in the field of radiology and imaging sciences, ultimately leading to improved patient care and outcomes.

Oncology Treatment DiscoveryVol. 3(1)
Xinjiang Medical University (CN)
Openalex Percentile: Top 90%
Radiomics and Machine Learning in Medical Imaging
AI Navigator

Ask Laika to Summarize, Analyze, and Connect papers live on the map.

Summarize Papers & Methodologies

Extract key findings, datasets, and comparative methods across publications.

Benchmark Rankings & Visual Analytics

Rank top research institutions, authors, funders, topics, and journals by Field-Weighted Citation Impact (FWCI) and paper volume with instant charts.

Connect Distant Disciplines

Bridge topological clusters on the map to find hidden collaborative intersections.