Accuracy of upper airway volume measurements using different software products: a comparative analysis

This study provides the first evaluation of NemoStudio's volumetric accuracy for CBCT-based airway measurements, offering novel insights into software reliability and the impact of algorithm selection in clinical and academic settings.

Authors

Institutions

Publication Details

Journal
Dentomaxillofacial Radiology
Published
2025-03-14
DOI
https://doi.org/10.1093/dmfr/twaf023
Citations
1
Primary Topic
Radiomics and Machine Learning in Medical Imaging
Type
article
Field-Weighted Citation Impact
1.36
Controls
|||
ALL TIME
JAN
FEB
MAR
APR
MAY
JUN
JUL
AUG
SEP
OCT
article

Accuracy of upper airway volume measurements using different software products: a comparative analysis

Dilara Nil Günaçar, Busra Beser, Taha Emre Köse, Muhammed Enes Naralan et al.
1 citations
Dentomaxillofacial Radiology
Radiomics and Machine Learning in Medical Imaging
1.36
article

Accuracy of upper airway volume measurements using different software products: a comparative analysis

Dilara Nil Günaçar, Busra Beser, Taha Emre Köse, Muhammed Enes Naralan, Merve Gonca
article en
1 citations

Abstract

This study provides the first evaluation of NemoStudio's volumetric accuracy for CBCT-based airway measurements, offering novel insights into software reliability and the impact of algorithm selection in clinical and academic settings.

Dentomaxillofacial RadiologyVol. 54(5)
Recep Tayyip Erdoğan University (TR), Eskişehir Osmangazi University (TR)
Openalex Percentile: Top 23%
Radiomics and Machine Learning in Medical Imaging
1.36
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.