Chest X-ray lung imaging features in pediatric COVID-19 and comparison with viral lower respiratory infections in young children.

RATIONALE: Chest radiography (CXR) is a noninvasive imaging approach commonly used to evaluate lower respiratory tract infections (LRTIs) in children. However, the specific imaging patterns of pediatric coronavirus disease 2019 (COVID-19) on CXR, their relationship to clinical outcomes, and the possible differences from LRTIs caused by other viruses in children remain to be defined. METHODS: This is a cross-sectional study of patients seen at a pediatric hospital with polymerase chain reaction (PCR)-confirmed severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) (n = 95). Patients were subdivided in infants (0-2 years, n = 27), children (3-10 years, n = 27), and adolescents (11-19 years, n = 41). A sample of young children (0-2 years, n = 68) with other viral lower respiratory infections (LRTI) was included to compare their CXR features with the subset of infants (0-2 years) with COVID-19. RESULTS: Forty-five percent of pediatric patients with COVID-19 were hospitalized and 20% required admission to intensive care unit (ICU). The most common abnormalities identified were ground-glass opacifications (GGO)/consolidations (35%) and increased peribronchial markings/cuffing (33%). GGO/consolidations were more common in older individuals and perihilar markings were more common in younger subjects. Subjects requiring hospitalization or ICU admission had significantly more GGO/consolidations in CXR (p < .05). Typical CXR features of pediatric viral LRTI (e.g., hyperinflation) were more common in non-COVID-19 viral LRTI cases than in COVID-19 cases (p < .05). CONCLUSIONS: CXR may be a complemental exam in the evaluation of moderate or severe pediatric COVID-19 cases. The severity of GGO/consolidations seen in CXR is predictive of clinically relevant outcomes. Hyperinflation could potentially aid clinical assessment in distinguishing COVID-19 from other types of viral LRTI in young children.

Ultrasound-Based Phenotyping of Lateral Ventricles to Predict Hydrocephalus Outcome in Premature Neonates.

OBJECTIVE: Prediction of post-hemorrhagic hydrocephalus (PHH) outcome-i.e., whether it requires intervention or not-in premature neonates using cranial ultrasound (CUS) images is challenging. In this paper, we present a novel fully-automatic method to perform phenotyping of the brain lateral ventricles and predict PHH outcome from CUS. METHODS: Our method consists of two parts: ventricle quantification followed by prediction of PHH outcome. First, cranial bounding box and brain interhemispheric fissure are detected to determine the anatomical position of ventricles and correct the cranium rotation. Then, lateral ventricles are extracted using a new deep learning-based method by incorporating the convolutional neural network into a probabilistic atlas-based weighted loss function and an image-specific adaption. PHH outcome is predicted using a support vector machine classifier trained using ventricular morphological phenotypes and clinical information. RESULTS: Experiments demonstrated that our method achieves accurate ventricle segmentation results with an average Dice similarity coefficient of 0.86, as well as very good PHH outcome prediction with accuracy of 0.91. CONCLUSION: Automatic CUS-based ventricular phenotyping in premature newborns could objectively and accurately predict the progression to severe PHH. SIGNIFICANCE: Early prediction of severe PHH development in premature newborns could potentially advance criteria for diagnosis and offer an opportunity for early interventions to improve outcome.

Pilot study of the potential of 3D ultrasound to measure tonsillar volume and hypertrophy.

OBJECTIVE: Obstructive sleep apnea (OSA), results in approximately 4-5 million outpatient visits per year in the United States. In pediatric patients, OSA is primarily caused by adenotonsillar hypertrophy, and therefore, adenotonsillectomy remains an effective surgical treatment. We investigate whether 3D ultrasound (3DUS) imaging can accurately and objectively assess tonsillar hypertrophy for the potential identification and stratification of candidates for adenotonsillectomy. METHODS: A prospective study was performed evaluating pediatric patients (N = 17) between the ages of 4-14 years who were undergoing adenotonsillectomy for OSA symptoms. On the day of surgery, tonsillar ultrasound was performed by a single attending radiologist. Tonsillectomy was performed and each tonsils' principal axes and physical volume by water submergence were measured. The findings were compared using paired T-test, Pearson correlation coefficient and Bland-Altman analysis. RESULTS: The average tonsillar physical measurements of length, width and height were 1.54 ± 0.28, 2.0 ± 0.31 cm and 2.72 ± 0.41 cm, and 1.73 ± 0.17, 1.61 ± 0.21 mm and 2.98 ± 0.28 mm from physical and 3DUS estimations, respectively (P < 0.001 for all measurements). The average tonsillar volume was 3.84 ± 1.23 ml and 4.30 ± 1.15 ml from physical and 3DUS measurements, respectively (p = 0.04). The Bland-Altman mean difference ±â€¯95% limit of agreement between length, width, height, and volume results from the two measurements were -0.186 ± 2.01 cm, -0.393 ± 6.33 cm, 0.25 ± 7.71 cm, and 0.45 ± 2.32 ml, respectively. CONCLUSION: While 3DUS is feasible, it may not be an accurate estimate of tonsillar volume for assessing hypertrophy. A larger study will be required to establish the accuracy of 3DUS measurements of tonsillar volume.

Ventricular shape evaluation on early ultrasound predicts post-hemorrhagic hydrocephalus.

BACKGROUND: To compare the ability of ventricular morphology on cranial ultrasound (CUS) versus standard clinical variables to predict the need for temporizing cerebrospinal fluid drainage in newborns with intraventricular hemorrhage (IVH). METHOD: This is a retrospective study of newborns (gestational age <29 weeks) diagnosed with IVH. Clinical variables known to increase the risk for post-hemorrhagic hydrocephalus were collected. The first CUS with IVH was identified and a slice in the coronal plane was selected. The frontal horns of the lateral ventricles were manually segmented. Automated quantitative morphological features were extracted from both lateral ventricles. Predictive models of the need of temporizing intervention were compared. RESULTS: Sixty-two newborns met inclusion criteria. Fifteen out of the 62 had a temporizing intervention. The morphological features had a better accuracy predicting temporizing interventions when compared to clinical variables: 0.94 versus 0.85, respectively; p < 0.01 for both. By considering both morphological and clinical variables, our method predicts the need of temporizing intervention with positive and negative predictive values of 0.83 and 1, respectively, and accuracy of 0.97. CONCLUSION: Early cranial ultrasound-based quantitative ventricular evaluation in premature newborns can predict the eventual use of a temporizing intervention to treat post-hemorrhagic hydrocephalus. This may be helpful for early monitoring and treatment.

Automatic Segmentation of Neonatal Ventricles from Cranial Ultrasound for Prediction of Intraventricular Hemorrhage Outcome.

Intraventricular hemorrhage (IVH) followed by post hemorrhagic hydrocephalus (PHH) in premature neonates is one of the recognized reasons of brain injury in newborns. Cranial ultrasound (CUS) is a noninvasive imaging tool that has been used widely to diagnose and monitor neonates with IVH. In our previous work, we showed the potential of quantitative morphological analysis of lateral ventricles from early CUS to predict the PHH outcome in neonates with IVH. In this paper, we first present a new automatic method for ventricle segmentation in 2D CUS images. We detect the brain bounding box and brain mid-line to estimate the anatomical positions of ventricles and correct the brain rotation. The ventricles are segmented using a combination of fuzzy c-means, phase congruency, and active contour algorithms. Finally, we compare this fully automated approach with our previous work for the prediction of the outcome of PHH on a set of 2D CUS images taken from 60 premature neonates with different IVH grades. Experimental results showed that our method could segment ventricles with an average Dice similarity coefficient of 0.8 ± 0.12. In addition, our fully automated method could predict the outcome of PHH based on the extracted ventricle regions with similar accuracy to our previous semi-automated approach (83% vs. 84%, respectively, p-value = 0.8). This method has the potential to standardize the evaluation of CUS images and can be a helpful clinical tool for early monitoring and treatment of IVH and PHH.

Early Detection of Ureteropelvic Junction Obstruction Using Signal Analysis and Machine Learning: A Dynamic Solution to a Dynamic Problem.

PURPOSE: We sought to define features that describe the dynamic information in diuresis renograms for the early detection of clinically significant hydronephrosis caused by ureteropelvic junction obstruction. MATERIALS AND METHODS: We studied the diuresis renogram of 55 patients with a mean ± SD age of 75 ± 66 days who had congenital hydronephrosis at initial presentation. Five patients had bilaterally affected kidneys for a total of 60 diuresis renograms. Surgery was performed on 35 kidneys. We extracted 45 features based on curve shape and wavelet analysis from the drainage curves recorded after furosemide administration. The optimal features were selected as the combination that maximized the ROC AUC obtained from a linear support vector machine classifier trained to classify patients as with or without obstruction. Using these optimal features we performed leave 1 out cross validation to estimate the accuracy, sensitivity and specificity of our framework. Results were compared to those obtained using post-diuresis drainage half-time and the percent of clearance after 30 minutes. RESULTS: Our framework had 93% accuracy, including 91% sensitivity and 96% specificity, to predict surgical cases. This was a significant improvement over the same accuracy of 82%, including 71% sensitivity and 96% specificity obtained from half-time and 30-minute clearance using the optimal thresholds of 24.57 minutes and 55.77%, respectively. CONCLUSIONS: Our machine learning framework significantly improved the diagnostic accuracy of clinically significant hydronephrosis compared to half-time and 30-minute clearance. This aids in the clinical decision making process by offering a tool for earlier detection of severe cases and it has the potential to reduce the number of diuresis renograms required for diagnosis.

Cranial ultrasound-based prediction of post hemorrhagic hydrocephalus outcome in premature neonates with intraventricular hemorrhage.

Premature neonates with intraventricular hemorrhage (IVH) followed by post hemorrhagic hydrocephalus (PHH) are at high risk for brain injury. Cranial ultrasound (CUS) is used for monitoring of premature neonates during the first weeks after birth to identify IVH and follow the progression to PHH. However, the lack of a standardized method for CUS evaluation has led to significant variability in decision making regarding treatment. We propose a quantitative imaging tool for the evaluation of PHH on CUS for premature neonates based on morphological features of the cerebral ventricles. We retrospectively studied 64 extremely premature neonates born less than 29 weeks gestational age, less than 1,500 grams weight at birth, admitted to our center within two weeks of life, and diagnosed with different grades of IVH. We extracted morphological features of the lateral ventricles from CUS imaging using image analysis techniques to compare neonates who needed a temporizing intervention to treat PHH to the ones who did not. From the original set of features, an optimal ranking was obtained based on linear support vector machine. A subset of features was subsequently selected that maximizes the overall accuracy level. Regarding whether or not there was a need for temporizing intervention, we predicted the outcome of PHH with an improved accuracy level of 84%, compared to the 76% rate obtained by linear manual measurement. The proposed imaging tool allowed us to establish a quantitative method for PHH evaluation on CUS in extremely premature neonates with IVH. Further studies will help standardize the evaluation of CUS in those neonates to institute treatments earlier and improve outcomes.

Shape-based acetabular cartilage segmentation: application to CT and MRI datasets.

PURPOSE: A new method for acetabular cartilage segmentation in both computed tomography (CT) arthrography and magnetic resonance imaging (MRI) datasets with leg tension is developed and tested. METHODS: The new segmentation method is based on the combination of shape and intensity information. Shape information is acquired according to the predictable nonlinear relationship between the U-shaped acetabulum region and acetabular cartilage. Intensity information is obtained from the acetabular cartilage region automatically to complete the segmentation procedures. This method is evaluated using 54 CT arthrography datasets with two different radiation doses and 20 MRI datasets. Additionally, the performance of this method in identifying acetabular cartilage is compared with four other acetabular cartilage segmentation methods. RESULTS: This method performed better than the comparison methods. Indeed, this method maintained good accuracy level for 74 datasets independent of the cartilage modality and with minimum user interaction in the bone segmentation procedures. In addition, this method was efficient in noisy conditions and in detection of the damaged cartilages with zero thickness, which confirmed its potential clinical usefulness. CONCLUSIONS: Our new method proposes acetabular cartilage segmentation in three different datasets based on the combination of the shape and intensity information. This method executes well in situations where there are clear boundaries between the acetabular and femoral cartilages. However, the acetabular cartilage and pelvic bone information should be obtained from one dataset such as CT arthrography or MRI datasets with leg traction.

Acetabular cartilage segmentation in CT arthrography based on a bone-normalized probabilistic atlas.

PURPOSE: Determination of acetabular cartilage loss in the hip joint is a clinically significant metric that requires image segmentation. A new semiautomatic method to segment acetabular cartilage in computed tomography (CT) arthrography scans was developed and tested. METHODS: A semiautomatic segmentation method was developed based on the combination of anatomical and statistical information. Anatomical information is identified using the pelvic bone position and the contact area between cartilage and bone. Statistical information is acquired from CT intensity modeling of acetabular cartilage and adjacent tissue structures. This method was applied to the identification of acetabular cartilages in 37 intra-articular CT arthrography scans. RESULTS: The semiautomatic anatomical-statistical method performed better than other segmentation methods. The semiautomatic method was effective in noisy scans and was able to detect damaged cartilage. CONCLUSIONS: The new semiautomatic method segments acetabular cartilage by fully utilizing the statistical and anatomical information in CT arthrography datasets. This method for hip joint cartilage segmentation has potential for use in many clinical applications.