The reliability of deep learning models in assessing the shoulder arthroscopic field’s visual clarity in relation to bleeding

The trained DenseNet169 predictive model was reliable enough to be utilized as an objective measure of the visual clarity of the shoulder arthroscopic field for further research.

Authors

Institutions

Publication Details

Journal
JSES International
Published
2025-03-05
DOI
https://doi.org/10.1016/j.jseint.2025.02.003
Primary Topic
Shoulder Injury and Treatment
Type
article
Field-Weighted Citation Impact
0.00
Controls
|||
ALL TIME
JAN
FEB
MAR
APR
MAY
JUN
JUL
AUG
SEP
OCT
article

The reliability of deep learning models in assessing the shoulder arthroscopic field’s visual clarity in relation to bleeding

Thun Itthipanichpong, Thanathep Tanpowpong, Son Tran, Danaithep Limskul et al.
JSES International
Shoulder Injury and Treatment
article

The reliability of deep learning models in assessing the shoulder arthroscopic field’s visual clarity in relation to bleeding

Thun Itthipanichpong, Thanathep Tanpowpong, Son Tran, Danaithep Limskul, Napatpong Thamrongskulsiri, Minh Cong Bui, D. T. Nguyen
article en

Abstract

The trained DenseNet169 predictive model was reliable enough to be utilized as an objective measure of the visual clarity of the shoulder arthroscopic field for further research.

JSES InternationalVol. 9(3)
Vietnam National University Ho Chi Minh City (VN), Chulalongkorn University (TH), Can Tho University (VN)
Openalex Percentile: Top 93%
Shoulder Injury and Treatment
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.