ArticleUltrasonics2018
Lung mass density analysis using deep neural network and lung ultrasound surface wave elastography.
Article in Ultrasonics, 2018. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 15 papers.
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Who cites it
15 citing papers in PubMed, 35 citations in OpenAlex.
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- Artificial intelligence - based ultrasound elastography for disease evaluation - a narrative review.Frontiers in oncology · 2023Review
- State of the Art in Lung Ultrasound, Shifting from Qualitative to Quantitative Analyses.Ultrasound in medicine & biology · 2022Review
- Semantic Analysis Technology of English Translation Based on Deep Neural Network.Computational intelligence and neuroscience · 2022Article
- Lung mass density prediction using machine learning based on ultrasound surface wave elastography and pulmonary function testing.The Journal of the Acoustical Society of America · 2021Article
- Ultrasound Elastography for Lung Disease Assessment.IEEE transactions on ultrasonics, ferroelectrics, and frequency control · 2020Article
- Predicting lung mass density of patients with interstitial lung disease and healthy subjects using deep neural network and lung ultrasound surface wave elastography.Journal of the mechanical behavior of biomedical materials · 2020Article
- Artificial neural network to estimate micro-architectural properties of cortical bone using ultrasonic attenuation: A 2-D numerical study.Computers in biology and medicine · 2019Article
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- Predicting Structural Properties of Cortical Bone by Combining Ultrasonic Attenuation and an Artificial Neural Network (ANN): 2-D FDTD Study.Image Analysis and Recognition: International Conference, ICIAR ... : proceedings. ICIAR · 2019Article
- Lung Ultrasound Surface Wave Elastography for Assessing Interstitial Lung Disease.IEEE transactions on bio-medical engineering · 2019Article
- A quantitative method for measuring the changes of lung surface wave speed for assessing disease progression of interstitial lung disease.Ultrasound in medicine & biology · 2019Article
- The effect of pleural fluid layers on lung surface wave speed measurement: Experimental and numerical studies on a sponge lung phantom.Journal of the mechanical behavior of biomedical materials · 2019Article
Corrections and comments
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Authors and funding
2 authors at 1 institution in 1 country.
Funding
Abstract
Lung mass density is directly associated with lung pathology. Computed Tomography (CT) evaluates lung pathology using the Hounsfield unit (HU) but not lung density directly. We have developed a lung ultrasound surface wave elastography (LUSWE) technique to measure the surface wave speed of superficial lung tissue. The objective of this study was to develop a method for analyzing lung mass density of superficial lung tissue using a deep neural network (DNN) and synthetic data of wave speed measurements with LUSWE. The synthetic training dataset of surface wave speed, excitation frequency, lung mass density, and viscoelasticity from LUSWE (788,000 in total) was used to train the DNN model. The DNN was composed of 3 hidden layers of 1024 neurons for each layer and trained for 10 epochs with a batch size of 4096 and a learning rate of 0.001 with three types of optimizers. The test dataset (4000) of wave speeds at three excitation frequencies (100, 150, and 200 Hz) and shear elasticity of superficial lung tissue was used to predict the lung density and evaluate its accuracy compared with predefined lung mass densities. This technique was then validated on a sponge phantom experiment. The obtained results showed that predictions matched well with test dataset (validation accuracy is 0.992) and experimental data in the sponge phantom experiment. This method may be useful to analyze lung mass density by using the DNN model together with the surface wave speed and lung stiffness measurements.
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Registered trials
Read under generation 80e0d062 · epoch 390. Bibliography from PubMed, PubMed Central and OpenAlex; grants from NIH RePORTER; trial links from ClinicalTrials.gov; estimates, votes and beliefs from the OpenQuestion graph.