ArticleThe Journal of the Acoustical Society of America2021
Lung mass density prediction using machine learning based on ultrasound surface wave elastography and pulmonary function testing.
Article in The Journal of the Acoustical Society of America, 2021. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 4 papers.
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The trial behind it
Trials whose registry record cites this paper, or whose number appears in the abstract. A trial that started after this paper was published is citing it as background, not reporting it.
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Who cites it
4 citing papers in PubMed.
- Predicting the onset of overweight in Chinese high school students: a machine-learning approach in a one-year prospective cohort study.Endocrine · 2024Article
- Does multidimensional daily information predict the onset of myopia? A 1-year prospective cohort study.Biomedical engineering online · 2023Article
- 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
Corrections and comments
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Authors and funding
5 authors.
Funding
Abstract
objectiveThe objective of this study is to predict in vivo lung mass density for patients with interstitial lung disease using different gradient boosting decision tree (GBDT) algorithms based on measurements from lung ultrasound surface wave elastography (LUSWE) and pulmonary function testing (PFT).
methodsAge and weight of study subjects (57 patients with interstitial lung disease and 20 healthy subjects), surface wave speeds at three vibration frequencies (100, 150, and 200 Hz) from LUSWE, and predicted forced expiratory volume (FEV1% pre) and ratio of forced expiratory volume to forced vital capacity (FEV1%/FVC%) from PFT were used as inputs while lung mass densities based on the Hounsfield Unit from high resolution computed tomography (HRCT) were used as labels to train the regressor in three GBDT algorithms, XGBoost, CatBoost, and LightGBM. 80% (20%) of the dataset was used for training (testing).
resultsThe results showed that predictions using XGBoost regressor obtained an accuracy of 0.98 in the test dataset.
conclusionThe obtained results suggest that XGBoost regressor based on the measurements from LUSWE and PFT may be able to noninvasively assess lung mass density in vivo for patients with pulmonary disease.
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Registered trials
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