The goal of this study was to evaluate the maturity of current Deep Learning classification techniques for their application in a real maternal-fetal clinical environment. A large dataset of routinely acquired maternal-fetal screening ultrasound images (which will be made publicly available) was collected from two different hospitals by several operators and ultrasound machines. All images were manually labeled by an expert maternal fetal clinician. Images were divided into 6 classes: four of the most widely used fetal anatomical planes (Abdomen, Brain, Femur and Thorax), the mother’s cervix (widely used for prematurity screening) and a general category to include any other less common image plane. Fetal brain images were further categorized into the 3 most common fetal brain planes (Trans-thalamic, Trans-cerebellum, Trans-ventricular) to judge fine grain categorization performance. The final dataset is comprised of over 12,400 images from 1,792 patients, making it the largest ultrasound dataset to date. We then evaluated a wide variety of state-of-the-art deep Convolutional Neural Networks on this dataset and analyzed results in depth, comparing the computational models to research technicians, which are the ones currently performing the task daily. Results indicate for the first time that computational models have similar performance compared to humans when classifying common planes in human fetal examination. However, the dataset leaves the door open on future research to further improve results, especially on fine-grained plane categorization.

The objective of this study was to evaluate the performance of a new version of quantusFLM®, a software tool for prediction of neonatal respiratory morbidity (NRM) by ultrasound, which incorporates a fully automated fetal lung delineation based on Deep Learning techniques. A set of 790 fetal lung ultrasound images obtained at 24 + 0–38 + 6 weeks’ gestation was evaluated. Perinatal outcomes and the occurrence of NRM were recorded. quantusFLM® version 3.0 was applied to all images to automatically delineate the fetal lung and predict NRM risk. The test was compared with the same technology but using a manual delineation of the fetal lung, and with a scenario where only gestational age was available. The software predicted NRM with a sensitivity, specificity, and positive and negative predictive value of 71.0%, 94.7%, 67.9%, and 95.4%, respectively, with an accuracy of 91.5%. The accuracy for predicting NRM obtained with the same texture analysis but using a manual delineation of the lung was 90.3%, and using only gestational age was 75.6%. To sum up, automated and non-invasive software predicted NRM with a performance similar to that reported for tests based on amniotic fluid analysis and much greater than that of gestational age alone.

Objective: To develop and validate an artificial intelligence-based tool for the diagnosis of osteoporosis/osteopenia using hip radiographs. The tool aims to classify femurs into risk-based categories for osteoporosis/osteopenia, enabling patient prioritization, enhancing preventive medicine through incidental detection, and assisting clinicians’ diagnosis in general. Materials and methods: The AI tool was designed to perform three preprocessing tasks before the osteoporosis/osteopenia prediction: (1) splitting images into single femurs, (2) identifying and discarding femurs with prostheses, and (3) cropping images to isolate the proximal femur. A total of 2691 anteroposterior hip radiographs from 1654 patients were included in the study. The osteoporosis/osteopenia prediction model was trained on 3227 single femur images and tested on 826. Additionally, a final evaluation experiment was conducted on 313 new radiographs from 239 patients to assess the tool’s applicability. Results: The tool demonstrated high performance in the preprocessing tasks, achieving 99.0% accuracy in classifying single vs. double femur images, 99.3% accuracy in identifying prosthetic femurs, and 99.2% pixel accuracy in delineating the proximal femur before cropping. The final prediction model achieved an area under the curve of 86.6% for detecting osteoporosis/osteopenia in the test set and 81.0% in the final evaluation experiment. Conclusions: The obtained results demonstrate the potential of the proposed AI-based pipeline for prediction of osteoporosis/osteopenia using hip radiographs. This study suggests that a tool based on the proposed methods could support DXA triage, incidental osteoporosis detection, and clinical decision-making in settings with limited access to bone densitometry.

The objective of this study was to evaluate the performance of quantitative ultrasound of fetal lung texture analysis in predicting neonatal respiratory morbidity (NRM) in twin pregnancies. This was an ambispective study involving consecutive cases. Eligible cases included twin pregnancies between 27.0 and 38.6 weeks of gestation, for which an ultrasound image of the fetal thorax was obtained within 48 h of delivery. Images were analyzed using quantusFLM^® version 3.0. The primary outcome of this study was neonatal respiratory morbidity, defined as the occurrence of either transient tachypnea of the newborn or respiratory distress syndrome. The performance of quantusFLM^® in predicting NRM was analyzed by matching quantitative ultrasound analysis and clinical outcomes. This study included 166 images. Neonatal respiratory morbidity occurred in 12.7% of cases, and it was predicted by quantusFLM^® analysis with an overall sensitivity of 42.9%, specificity of 95.9%, positive predictive value of 60%, and negative predictive value of 92.1%. The accuracy was 89.2%, with a positive likelihood ratio of 10.4, and a negative likelihood ratio of 0.6. The results of this study demonstrate the good prediction capability of NRM in twin pregnancies using a non-invasive lung texture analysis software. The test showed an overall good performance with high specificity, negative predictive value, and accuracy.

Introduction: Prematurity is a major global health issue. Twin pregnancies are a group at especially high risk of preterm birth. Sonographic mid‐trimester cervical length has limited accuracy in predicting preterm birth. This study aimed to evaluate the association between mid‐trimester sonographic markers of early cervical remodeling and cervical inflammatory biomarkers and fetal fibronectin, alone or in combination, as predictors of preterm birth before 34+0 weeks in asymptomatic twin pregnancies.Material and Methods: Prospective cohort study, including uncomplicated dichorionic or monochorionic‐diamniotic twin pregnancies, recruited and assessed between 18+0 and 24+6 weeks, from a single tertiary referral center between 2020 and 2023. At inclusion, transvaginal ultrasound was performed to assess the following sonographic markers (cervical length, uterocervical angle, cervical consistency index, cervical texture) and an endocervical sample was obtained prior to ultrasound to quantify the following cervical inflammatory biomarkers (tumor necrosis factor alpha, interleukins 1b, 6, 8, 18, matrix metalloproteinase‐8 and 9) and fetal fibronectin. The diagnostic performance of those sonographic and biochemical markers independently associated with spontaneous preterm birth before 34 weeks was analyzed by receiver operating characteristic curves and assessed through sensitivity and specificity analysis for several cutoffs.Results: Of the 172 women included, cervical length was shorter (36 mm vs. 40 mm; p = 0.025) and uterocervical angle was wider (137° vs. 120°; p = 0.004) in the preterm group. Cervical consistency index, cervical texture score, cervical inflammatory biomarkers, and fetal fibronectin were similar among the study groups. The area under the curve to predict spontaneous preterm birth before 34+0 weeks was 0.722 (95% CI 0.577 to 0.866) for cervical length, 0.789 (95% CI 0.683 to 0.895) for uterocervical angle, and 0.852 (95% CI 0.752 to 0.952) for a combination of both. Based on the receiver operating characteristics curve cutoff, sensitivity and specificity for cervical length ≤37 mm was 55.6% and 66.3%, for an uterocervical angle ≥135° was 77.8% and 76.1%, and for both criteria present 44.4% and 93.3%, respectively.Conclusions: This finding of this study suggests that the combination of cervical length and uterocervical angle in mid‐trimester sonographic assessment may improve the prediction of preterm birth before 34 weeks in asymptomatic and uncomplicated twin pregnancies.