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Pneumonia is one of the most common acute respiratory infections among pediatric populations worldwide. Ultrasound is becoming increasingly important in the diagnosis of lung diseases as a more portable and safer alternative to X-ray imaging. In the current work, we present a new automatic system for detection of B-lines, one of the distinctive features of pneumonic ultrasound scans, using amplitude modulation-frequency modulation (AM-FM). Features are evaluated on 109 videos obtained from 100 pediatric patients using a Verasonics V1 scanner. Further, the results were compared to the ones obtained with a previously published spectral feature (SF) method. Sensitivities of 92% and 83% and specificities of 91% and 70% were obtained on zone-1 and zone-2 of the lungs, respectively. In contrast, the SF method provided sensitivities of 72% and 68% in zone-1 and zone-2, respectively, and specificities of 68% and 46% in zone-1 and zone-2, respectively. In addition, the AM-FM method allowed increasing the F1-score when compared to the SF method from 70% to 87% and from 61% to 78% in zone-1 and zone-2, respectively. The results suggest the proposed method may be useful for the computer assisted diagnosis of pneumonia.

Pediatric Pneumonia is one of the principal causes of death by year on children under the age of five worldwide. The diagnosis is commonly made by clinical criteria with support from imaging tools like radiography. Lung ultrasound has been considered a low-cost and portable alternative for pneumonia imaging; however, interpretation is subjective and requires adequate training. In the present work, a pneumonia detection algorithm based on the measurement of the fundamental bandwidth downshift over depth of ultrasound radiofrequency (RF) signals is presented. RF-data was obtained from lung ultrasound samples of children aged between six months and five years. Sampling was performed using a 6.6 MHz linear transducer. The sample consisted of 10 positive-and 10 negative-diagnosed RF cine-loops selected by a medical expert and captured in a local pediatric health institute. For each frame, several regions of interest were outlined starting from the pleural line. Corresponding functions for each RF-line of the maximum frequency decrement rate over depth from the fundamental spectra at a fixed bandwidth were estimated and linearly fitted. Finally, a descriptor function was build concatenating all fitted values from the RF-lines for each frame respectively. Each descriptor function was later thresholded to differentiate between healthy and pneumonic regions frame-wise. The optimal threshold was found to be 0.46 MHz/cm and was selected based on a receiver operator characteristic (ROC) curve analysis. The results revealed an accuracy rate higher than 90% on the sample.

According to World Health Organization, pneumonia is the respiratory disease with the highest pediatric mortality rate accounting for 15% of all deaths of children under 5 years old worldwide. The diagnosis of pneumonia is commonly made by clinical criteria with support from ancillary studies and also laboratory findings. Chest imaging is commonly done with chest X-rays and occasionally with a chest CT scan. Lung ultrasound is a promising alternative for chest imaging; however, interpretation is subjective and requires adequate training. In the present work, a two-class classification algorithm based on four Gray-level co-occurrence matrix texture features (i.e., Contrast, Correlation, Energy and Homogeneity) extracted from lung ultrasound images from children aged between six months and five years is presented. Ultrasound data was collected using a L14-5/38 linear transducer. The data consisted of 22 positive- and 68 negative-diagnosed B-mode cine-loops selected by a medical expert and captured in the facilities of the Instituto Nacional de Salud del Nino (Lima, Peru), for a total number of 90 videos obtained from twelve children diagnosed with pneumonia. The classification capacity of each feature was explored independently and the optimal threshold was selected by a receiver operator characteristic (ROC) curve analysis. In addition, a principal component analysis was performed to evaluate the combined performance of all the features. Contrast and correlation resulted the two more significant features. The classification performance of these two features by principal components was evaluated. The results revealed 82% sensitivity, 76% specificity, 78% accuracy and 0.85 area under the ROC.

The incidence rate of diagnosed thyroid cancer has increased over the last decades. Although ultrasonic imaging has increased the malignancy detection rate, current ultrasonography markers do not provide a sufficient level of diagnostic accuracy to replace the fine needle aspiration biopsy. Recently, studies have reported that significant differences were observed in the values of quantitative ultrasound (QUS) parameters derived from a thyroid cancer rodent model between normal/benign and malignant tissues. In the present study, the performance of a multi-parametric classification for the differentiation of thyroid cancer in this rodent model has been evaluated. The experimental database consisted of 32 mice having different predispositions to developing thyroid abnormalities; 6 of them developed thyroid cancer papillary carcinoma (PTC), 5 follicular variant papillary thyroid carcinoma (FV-PTC), 6 developed benign tumors (c-cell adenoma) and 15 did not develop any thyroid abnormalities. Backscattered data was obtained from excised thyroid tissues using a 40 MHz, f/3 single element transducer. A total of five QUS parameters were derived from the ultrasound data: two from backscatter coefficients (i.e., the effective scatterer diameter (ESD) and effective acoustic concentration (EAC)), two from envelope statistics (i.e., the μ and k parameters), and one from ultrasound attenuation (i.e., attenuation coefficient slope). A two-class classification between normal/benign and malignant cases was performed using linear discriminant analysis with both one- and two-dimensional feature spaces. When using a two-dimensional feature space, it was found that the combination of EAC and 10/μ resulted in both a sensitivity and specificity of 100%.

The incidence of diagnosed thyroid cancer has increased significantly over the last decades. Although advances in ultrasonic imaging have increased the malignancy detection rate, current ultrasonic imaging markers do not provide a sufficient level of diagnostic accuracy to replace biopsy. Recent studies suggest that ultrasound parameters derived from backscatter coefficients may allow differentiating among different types of thyroid tumors and normal tissues in a rodent model ex vivo. In this work, the potential use of attenuation coefficient (AC) estimates for the same purpose was explored. A sample set of 24 excised mice thyroids were scanned using a 40-MHz, f/3 single element transducer. The experimental dataset contained six animals that developed papillary thyroid carcinoma (PTC), five that developed follicular variant papillary thyroid carcinoma (FV-PTC), five that developed c-Cell adenoma (c-Cell) and eight that did not develop thyroid abnormalities (control). AC slope maps were generated with a spectral log difference method using 0.5mm by 0.5mm data blocks. Outliers of each slice due to artifacts in AC estimation were discarded using the Thompson Tau method. Finally, a Kruskal-Wallis test was conducted to analyze if statistically significant differences in the mean AC slope among the four groups existed. The median and interquartile range for each group were 1.29 and 0.22 dB/cm-MHz for the control group, 1.64 and 0.09 dB/cm-MHz for c-Cell, 1.16 and 0.12 dB/cm-MHz for PTC and 1.33 and 0.08 dB/cm-MHz for FV-PTC, respectively. These values are consistent with previous reports of attenuation in thyroid tissues. The Kruskal-Wallis test reported statistically significant differences between the c-Cell group and the other groups of study and between the PTC and FV-PTC groups (p