Automated Quantification of Pneumonia Infected Volume in Lung CT Images: A Comparison with Subjective Assessment of Radiologists.

Seyedehnafiseh MirniaharikandeheiAlireza AbdihamzehkolaeiAngel ChoquehuancaMarco AedoWilmer PachecoLaura EstacioVictor CahuiLuis HuallpaKevin QuiñonezValeria CalderónAna Maria GutierrezAna VargasDery GameroEveling Castro-GutierrezYuchen QiuBin ZhengJavier A Jo

Published in: Bioengineering (Basel, Switzerland) (2023)

This study demonstrates the feasibility of developing a new DL model to automatically segment disease-infected regions and quantitatively predict disease severity, which may help avoid tedious effort and inter-reader variability in subjective assessment of disease severity in future clinical practice.

Keyphrases

clinical practice
deep learning
machine learning
sleep quality
artificial intelligence
optical coherence tomography
image quality
convolutional neural network
intensive care unit
magnetic resonance
contrast enhanced
acute respiratory distress syndrome