Clinical Feasibility of Quantitative Ultrasound Texture Analysis: A Robustness Study Using Fetal Lung Ultrasound Images

Deep learning approaches for automated classification of neonatal lung ultrasound with assessment of human-to-AI interrater agreement

Neonatal respiratory disorders pose significant challenges in clinical settings, often requiring rapid and accurate diagnostic solutions for effective management. Lung ultrasound (LUS) has emerged as a promising tool to evaluate respiratory conditions in neonates. This evaluation is mainly based on the interpretation of visual patterns (horizontal artifacts, vertical artifacts, and consolidations). Automated interpretation of these patterns can assist clinicians in their evaluations. However, developing AI-based solutions for this purpose is challenging, primarily due to the lack of annotated data and inherent subjectivity in expert interpretations. This study aims to propose an automated solution for the reliable interpretation of patterns in LUS videos of newborns. We employed two distinct strategies. The first strategy is a frame-to-video-level approach that computes frame-level predictions from deep learning (DL) models trained from scratch (F2V-TS) along with fine-tuning pre-trained models (F2V-FT) followed by aggregation of those predictions for video-level evaluation. The second strategy is a direct video classification approach (DV) for evaluating LUS data. To evaluate our methods, we used LUS data from 34 neonatal patients comprising of 70 exams with annotations provided by three expert human operators (3HOs). Results show that within the frame-to-video-level approach, F2V-FT achieved the best performance with an accuracy of 77% showing moderate agreement with the 3HOs. while the direct video classification approach resulted in an accuracy of 72%, showing substantial agreement with the 3HOs, our proposed study lays down the foundation for reliable AI-based solutions for newborn LUS data evaluation.

Simultaneous imaging of ultrasonic relative backscatter and attenuation coefficients for quantitative liver steatosis assessment

Prevalence of liver disease is continuously increasing and nonalcoholic fatty liver disease (NAFLD) is the most common etiology. We present an approach to detect the progression of liver steatosis based on quantitative ultrasound (QUS) imaging. This study was performed on a group of 55 rats that were subjected to a control or methionine and choline deficient (MCD) diet known to induce NAFLD. Ultrasound (US) measurements were performed at 2 and 6 weeks. Thereafter, animals were humanely euthanized and livers excised for histological analysis. Relative backscatter and attenuation coefficients were simultaneously estimated from the US data and envelope signal-to-noise ratio was calculated to train a regression model for: (1) fat fraction percentage estimation and (2) performing classification according to Brunt's criteria in grades (0 <5%; 1, 5-33%; 2, >33-66%; 3, >66%) of liver steatosis. The trained regression model achieved an [Formula: see text] of 0.97 (p-value < 0.01) and a RMSE of 3.64. Moreover, the classification task reached an accuracy of 94.55%. Our results suggest that in vivo QUS is a promising noninvasive imaging modality for the early assessment of NAFLD.

A Comparison of Ultrasound Imaging Texture Analyses During the Early Postpartum With the Mode of Delivery

BACKGROUND

Breastfeeding is beneficial to infants. However, cesarean section is reported to be a risk factor for unsuccessful breastfeeding.

RESEARCH AIMS

(1) To extract discriminating data from texture analysis of breast ultrasound images in the immediate postpartum period; and (2) to compare the analysis results according to delivery mode.

METHODS

A cross-sectional, prospective non-experimental design with a questionnaire and observational components was used. Participants (N = 30) were women who delivered neonates at a center from September 2020 to December 2020. The participants underwent ultrasound examination of bilateral breasts 7-14 days after delivery. Ultrasound images were collected for texture analysis. A questionnaire about breastfeeding patterns was given to the participants on the day of the ultrasound examination.

RESULTS

No significant differences were found in texture analysis between the breasts of participants who had undergone Cesarean section and vaginal deliveries. The mean volume of total human milk produced in 1 day was significantly greater in the vaginal delivery group than in the cesarean section group (M = 350.87 ml, SD = 183.83 vs. M = 186.20 ml, SD = 184.02; p = .017). The pain score due to breast engorgement measured subjectively by participants was significantly lower in the vaginal delivery group than in the cesarean section group (M = 2.8, SD = 0.86 vs. M = 3.4, SD = 0.63; p = .047).

CONCLUSION

Texture analysis of breast ultrasound images did not demonstrate difference between the cesarean section and vaginal delivery groups in the immediate postpartum period; nevertheless, cesarean section was independently associated with less successful breastfeeding.

Fetal MRI radiomics: non-invasive and reproducible quantification of human lung maturity

OBJECTIVES

To assess the reproducibility of radiomics features extracted from the developing lung in repeated in-vivo fetal MRI acquisitions.

METHODS

In-vivo MRI (1.5 Tesla) scans of 30 fetuses, each including two axial and one coronal T2-weighted sequences of the whole lung with all other acquisition parameters kept constant, were retrospectively identified. Manual segmentation of the lungs was performed using ITK-Snap. One hundred radiomics features were extracted from fetal lung MRI data using Pyradiomics, resulting in 90 datasets. Intra-class correlation coefficients (ICC) of radiomics features were calculated between baseline and repeat axial acquisitions and between baseline axial and coronal acquisitions.

RESULTS

MRI data of 30 fetuses (12 [40%] females, 18 [60%] males) at a median gestational age of 24 + 5 gestational weeks plus days (GW) (interquartile range [IQR] 3 + 3 GW, range 21 + 1 to 32 + 6 GW) were included. Median ICC of radiomics features between baseline and repeat axial MR acquisitions was 0.92 (IQR 0.13, range 0.33 to 1), with 60 features exhibiting excellent (ICC > 0.9), 27 good (> 0.75-0.9), twelve moderate (0.5-0.75), and one poor (ICC < 0.5) reproducibility. Median ICC of radiomics features between baseline axial and coronal MR acquisitions was 0.79 (IQR 0.15, range 0.2 to 1), with 20 features exhibiting excellent, 47 good, 29 moderate, and four poor reproducibility.

CONCLUSION

Standardized in-vivo fetal MRI allows reproducible extraction of lung radiomics features. In the future, radiomics analysis may improve diagnostic and prognostic yield of fetal MRI in normal and pathologic lung development.

KEY POINTS

• Non-invasive fetal MRI acquired using a standardized protocol allows reproducible extraction of radiomics features from the developing lung for objective tissue characterization. • Alteration of imaging plane between fetal MRI acquisitions has a negative impact on lung radiomics feature reproducibility. • Fetal MRI radiomics features reflecting the microstructure and shape of the fetal lung could complement observed-to-expected lung volume in the prediction of postnatal outcome and optimal treatment of fetuses with abnormal lung development in the future.

Artificial Intelligence in Obstetric Ultrasound: An Update and Future Applications

Artificial intelligence (AI) can support clinical decisions and provide quality assurance for images. Although ultrasonography is commonly used in the field of obstetrics and gynecology, the use of AI is still in a stage of infancy. Nevertheless, in repetitive ultrasound examinations, such as those involving automatic positioning and identification of fetal structures, prediction of gestational age (GA), and real-time image quality assurance, AI has great potential. To realize its application, it is necessary to promote interdisciplinary communication between AI developers and sonographers. In this review, we outlined the benefits of AI technology in obstetric ultrasound diagnosis by optimizing image acquisition, quantification, segmentation, and location identification, which can be helpful for obstetric ultrasound diagnosis in different periods of pregnancy.

Relationship between the overall survival in glioblastomas and the radiomic features of intraoperative ultrasound: a feasibility study

PURPOSE

Predicting the survival of patients diagnosed with glioblastoma (GBM) is essential to guide surgical strategy and subsequent adjuvant therapies. Intraoperative ultrasound (IOUS) can contain biological information that could be correlated with overall survival (OS). We propose a simple extraction method and radiomic feature analysis based on IOUS imaging to estimate OS in GBM patients.

METHODS

A retrospective study of surgically treated glioblastomas between March 2018 and November 2019 was performed. Patients with IOUS B-mode and strain elastography were included. After preprocessing, segmentation and extraction of radiomic features were performed with LIFEx software. An evaluation of semantic segmentation was carried out using the Dice similarity coefficient (DSC). Using univariate correlations, radiomic features associated with OS were selected. Subsequently, survival analysis was conducted using Cox univariate regression and Kaplan-Meier curves.

RESULTS

Sixteen patients were available for analysis. The DSC revealed excellent agreement for the segmentation of the tumour region. Of the 52 radiomic features, two texture features from B-mode (conventional mean and the grey-level zone length matrix/short-zone low grey-level emphasis [GLZLM_SZLGE]) and one texture feature from strain elastography (grey-level zone length matrix/long-zone high grey-level emphasis [GLZLM_LZHGE]) were significantly associated with OS. After establishing a cut-off point of the statistically significant radiomic features, we allocated patients in high- and low-risk groups. Kaplan-Meier curves revealed significant differences in OS.

CONCLUSION

IOUS-based quantitative texture analysis in glioblastomas is feasible. Radiomic tumour region characteristics in B-mode and elastography appear to be significantly associated with OS.