Lung mass density prediction using machine learning based on ultrasound surface wave elastography and pulmonary function testing

Predicting the onset of overweight in Chinese high school students: a machine-learning approach in a one-year prospective cohort study

OBJECTIVE

This study aimed to develop and evaluate machine-learning models for predicting the onset of overweight in adolescents aged 14‒17, utilizing easily collectible personal information.

METHODS

This study was a one-year prospective cohort study. Baseline data were collected through anthropometric measurements and questionnaires, and the incidence of overweight was calculated one year later via anthropometric measurements. Predictive factors were selected through univariate analysis. Six machine-learning models were developed for predicting the onset of overweight. The SHapley Additive exPlanations (SHAP) was used for global and local interpretation of the models.

RESULTS

Out of 1,241 adolescents, 204 (16.4%) were identified as overweight after one year. Nineteen features were associated with the overweight incidence in univariable analysis. Participants were randomly divided into a training group and a testing group in a 7:3 ratio. The Light Gradient Boosting Machine (LGBM) algorithm achieved outperformed other models, achieving the following metrics: Accuracy (0.956), Recall (0.812), Specificity (0.983), F1-score (0.855), AUC (0.961). Importance ranking revealed that the top 11 minimal feature set can maintain the stability of model performance.

CONCLUSIONS

The onset of overweight in adolescents was accurately predicted using easily collectible personal information. The LGBM-based model exhibited superior performance. Oversampling technique notably improved model performance. The model interpretation technique provided innovative strategies for managing adolescent overweight/obesity.

Lung Ultrasound Spectroscopy Applied to the Differential Diagnosis of Pulmonary Diseases: An In Vivo Multicenter Clinical Study

Lung ultrasound (LUS) is an important imaging modality to assess the state of the lung surface. However, current LUS approaches are based on subjective interpretation of imaging artifacts, which results in poor specificity as quantitative evaluation lacks. The latter could be improved by adopting LUS spectroscopy of vertical artifacts. Indeed, parameterizing these artifacts with native frequency, bandwidth, and total intensity ( [Formula: see text]) already showed potentials in differentiating pulmonary fibrosis (PF). In this study, we acquired radio frequency (RF) data from 114 patients. These data (representing the largest LUS RF dataset worldwide) were acquired by utilizing a multifrequency approach, implemented with an ULtrasound Advanced Open Platform (ULA-OP). Convex (CA631) and linear (LA533) probes (Esaote, Florence, Italy) were utilized to acquire RF data at three (2, 3, and 4 MHz), and four (3, 4, 5, and 6 MHz) imaging frequencies. A multifrequency analysis was conducted on vertical artifacts detected in patients having cardiogenic pulmonary edema (CPE), pneumonia, or PF. These artifacts were characterized by the three abovementioned parameters, and their mean values were used to project each patient into a feature space having up to three dimensions. Binary classifiers were used to evaluate the performance of these three mean features in differentiating patients affected by CPE, pneumonia, and PF. Acquisitions of multifrequency data performed with linear probe lead to accuracies up to 85.43% in the differential diagnosis of these diseases (convex probes' maximum accuracy was 74.51%). Moreover, the results showed high potentials of mean [Formula: see text] (by itself or combined with other features) in improving LUS specificity.

Artificial intelligence - based ultrasound elastography for disease evaluation - a narrative review

Ultrasound elastography (USE) provides complementary information of tissue stiffness and elasticity to conventional ultrasound imaging. It is noninvasive and free of radiation, and has become a valuable tool to improve diagnostic performance with conventional ultrasound imaging. However, the diagnostic accuracy will be reduced due to high operator-dependence and intra- and inter-observer variability in visual observations of radiologists. Artificial intelligence (AI) has great potential to perform automatic medical image analysis tasks to provide a more objective, accurate and intelligent diagnosis. More recently, the enhanced diagnostic performance of AI applied to USE have been demonstrated for various disease evaluations. This review provides an overview of the basic concepts of USE and AI techniques for clinical radiologists and then introduces the applications of AI in USE imaging that focus on the following anatomical sites: liver, breast, thyroid and other organs for lesion detection and segmentation, machine learning (ML) - assisted classification and prognosis prediction. In addition, the existing challenges and future trends of AI in USE are also discussed.

Does multidimensional daily information predict the onset of myopia? A 1-year prospective cohort study

PURPOSE

This study aimed to develop an interpretable machine learning model to predict the onset of myopia based on individual daily information.

METHOD

This study was a prospective cohort study. At baseline, non-myopia children aged 6-13 years old were recruited, and individual data were collected through interviewing students and parents. One year after baseline, the incidence of myopia was evaluated based on visual acuity test and cycloplegic refraction measurement. Five algorithms, Random Forest, Support Vector Machines, Gradient Boosting Decision Tree, CatBoost and Logistic Regression were utilized to develop different models and their performance was validated by area under curve (AUC). Shapley Additive exPlanations was applied to interpret the model output on the individual and global level.

RESULT

Of 2221 children, 260 (11.7%) developed myopia in 1 year. In univariable analysis, 26 features were associated with the myopia incidence. Catboost algorithm had the highest AUC of 0.951 in the model validation. The top 3 features for predicting myopia were parental myopia, grade and frequency of eye fatigue. A compact model using only 10 features was validated with an AUC of 0.891.

CONCLUSION

The daily information contributed reliable predictors for childhood's myopia onset. The interpretable Catboost model presented the best prediction performance. Oversampling technology greatly improved model performance. This model could be a tool in myopia preventing and intervention that can help identify children who are at risk of myopia, and provide personalized prevention strategies based on contributions of risk factors to the individual prediction result.

Machine learning in radiology: the new frontier in interstitial lung diseases

Challenges for the effective management of interstitial lung diseases (ILDs) include difficulties with the early detection of disease, accurate prognostication with baseline data, and accurate and precise response to therapy. The purpose of this Review is to describe the clinical and research gaps in the diagnosis and prognosis of ILD, and how machine learning can be applied to image biomarker research to close these gaps. Machine-learning algorithms can identify ILD in at-risk populations, predict the extent of lung fibrosis, correlate radiological abnormalities with lung function decline, and be used as endpoints in treatment trials, exemplifying how this technology can be used in care for people with ILD. Advances in image processing and analysis provide further opportunities to use machine learning that incorporates deep-learning-based image analysis and radiomics. Collaboration and consistency are required to develop optimal algorithms, and candidate radiological biomarkers should be validated against appropriate predictors of disease outcomes.

State of the Art in Lung Ultrasound, Shifting from Qualitative to Quantitative Analyses

Lung ultrasound (LUS) has been increasingly expanding since the 1990s, when the clinical relevance of vertical artifacts was first reported. However, the massive spread of LUS is only recent and is associated with the coronavirus disease 2019 (COVID-19) pandemic, during which semi-quantitative computer-aided techniques were proposed to automatically classify LUS data. In this review, we discuss the state of the art in LUS, from semi-quantitative image analysis approaches to quantitative techniques involving the analysis of radiofrequency data. We also discuss recent in vitro and in silico studies, as well as research on LUS safety. Finally, conclusions are drawn highlighting the potential future of LUS.

Introduction to the special issue on lung ultrasound

The potential of lung ultrasound (LUS) has become manifest in the light of the recent COVID-19 pandemic. The need for a point-of care, quantitative, and widely available assessment of lung condition is critical. However, conventional ultrasound imaging was never designed for lung assessment. This limits LUS to the subjective and qualitative interpretation of artifacts and imaging patterns visible on ultrasound images. A number of research groups have begun to tackle this limitation, and this special issue reports on their most recent findings. Through in silico, in vitro, and in vivo studies (preclinical animal studies and pilot clinical studies on human subjects), the research presented aims at understanding and modelling the physical phenomena involved in ultrasound propagation, and at leveraging these phenomena to extract semi-quantitative and quantitative information relevant to estimate changes in lung structure. These studies are the first steps in unlocking the full potential of lung ultrasound as a relevant tool for lung assessment.

Lung Ultrasound Surface Wave Elastography for Assessing Patients With Pulmonary Edema

B-Mode ultrasound insonation of lungs that are dense with extravascular lung water (EVLW) produces characteristic reverberation artifacts termed B-lines. The number of B-lines present demonstrates reasonable correlation to the amount of EVLW. However, analysis of B-line artifacts generated by this modality is semi-quantitative relying on visual interpretation, and as a result, can be subject to inter-observer variability. The purpose of this study was to translate the use of a novel, quantitative lung ultrasound surface wave elastography technique (LUSWE) into the bedside assessment of pulmonary edema in patients admitted with acute congestive heart failure. B-mode lung ultrasound and LUSWE assessment of the lungs were performed using anterior and lateral intercostal spaces in the supine patient. 14 patients were evaluated at admission with reassessment performed 1-2 days after initiation of diuretic therapy. Each exam recorded the total lung B-lines, lung surface wave speeds (at 100, 150, and 200 Hz) and net fluid balance. The patient cohort experienced effective diuresis (average net fluid balance of negative 2.1 liters) with corresponding decrease in pulmonary edema visualized by B-mode ultrasound (average decrease of 13 B-Lines). In addition, LUSWE demonstrated a statistically significant reduction in the magnitude of wave speed from admission to follow-up. The reduction in lung surface wave speed suggests a decrease in lung stiffness (decreased elasticity) mediated by successful reduction of pulmonary edema. In summary, LUSWE is a noninvasive technique for quantifying elastic properties of superficial lung tissue that may prove useful as a diagnostic test, performed at the bedside, for the quantitative assessment of pulmonary edema.