Enhancing the Diagnosis of Sarcopenia Through Low-Dose Chest CT and Artificial Intelligence-Based Segmentation: Optimizing Resource Utilization in Healthcare

Utilization of the LDCT protocol in diagnosing sarcopenia appears to be a valuable approach. By applying AI-based segmentation to these scans, it has become possible to accurately measure the entire muscle mass.

Authors

Institutions

Publication Details

Journal
Journal of the Korean Society of Radiology
Published
2025-01-01
DOI
https://doi.org/10.3348/jksr.2023.0108
Citations
1
Primary Topic
Nutrition and Health in Aging
Type
article
Field-Weighted Citation Impact
1.16
Controls
|||
ALL TIME
JAN
FEB
MAR
APR
MAY
JUN
JUL
AUG
SEP
OCT
article

Enhancing the Diagnosis of Sarcopenia Through Low-Dose Chest CT and Artificial Intelligence-Based Segmentation: Optimizing Resource Utilization in Healthcare

H J Lee, Jung Keun Hyun, Sang Yoon Kim
1 citations
Journal of the Korean Society of Radiology
Nutrition and Health in Aging
1.16
article

Enhancing the Diagnosis of Sarcopenia Through Low-Dose Chest CT and Artificial Intelligence-Based Segmentation: Optimizing Resource Utilization in Healthcare

H J Lee, Jung Keun Hyun, Sang Yoon Kim
article en
1 citations

Abstract

Utilization of the LDCT protocol in diagnosing sarcopenia appears to be a valuable approach. By applying AI-based segmentation to these scans, it has become possible to accurately measure the entire muscle mass.

Journal of the Korean Society of RadiologyVol. 86(4)
Dankook University Hospital (KR), Dankook University (KR)
Openalex Percentile: Top 19%
Nutrition and Health in Aging
1.16
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.