Automated Approach for Enhancing Fetal Head Station Assessment in Labor with Transperineal Ultrasound.

RATIONALE AND OBJECTIVES: Accurate assessment of fetal head station (FHS) is crucial during labor management to reduce the risk of complications and plan the mode of delivery. Although digital vaginal examination (DVE) has been associated with inaccuracies in FHS assessment, ultrasound (US) evaluation remains dependent on sonographer expertise. This study aimed at investigating the reliability and accuracy of an automatic approach to assess the FHS during labor with transperineal US (TPU). MATERIALS AND METHODS: In this prospective observational study, 27 pregnant women in the second stage of labor, with fetuses in cephalic presentation, underwent conventional labor management with additional TPU examination. A total of 45 2D B-mode TPU acquisitions were performed at different FHS, before performing DVE. The FHS was assessed by the algorithm (FHSaut) on TPU images and by DVE (FHSdig). The sonographic assessment of FHS by expert sonographer (FHSexp) on the same TPU acquisition used for the automatic measurement served as gold standard. The performance and accuracy were assessed through Spearman's ρ, the coefficient of determination (R2), root mean square error (RMSE), and Bland-Altman analysis. RESULTS: A strong correlation between FHSaut and FHSexp (ρ = 0.97, p < 0.001) and a high coefficient of determination (R2 = 0.95) were found. A lower correlation with FHSexp (ρ = 0.66, p < 0.001) and coefficient of determination (R2 = 0.52) was found for DVE. Moreover, the RMSE reported higher accuracy of FHSaut (RMSE = 0.32 cm) compared to FHSdig (RMSE = 0.97 cm). Bland-Altman analysis showed that the algorithm performed with smaller bias and narrower limits of agreement compared to DVE. CONCLUSION: The proposed algorithm can evaluate FHS with high accuracy and low RMSE. This approach could facilitate the use of US in labor, supporting the clinical staff in labor management.

A deep learning approach to identify the fetal head position using transperineal ultrasound during labor.

OBJECTIVES: To develop a deep learning (DL)-model using convolutional neural networks (CNN) to automatically identify the fetal head position at transperineal ultrasound in the second stage of labor. MATERIAL AND METHODS: Prospective, multicenter study including singleton, term, cephalic pregnancies in the second stage of labor. We assessed the fetal head position using transabdominal ultrasound and subsequently, obtained an image of the fetal head on the axial plane using transperineal ultrasound and labeled it according to the transabdominal ultrasound findings. The ultrasound images were randomly allocated into the three datasets containing a similar proportion of images of each subtype of fetal head position (occiput anterior, posterior, right and left transverse): the training dataset included 70 %, the validation dataset 15 %, and the testing dataset 15 % of the acquired images. The pre-trained ResNet18 model was employed as a foundational framework for feature extraction and classification. CNN1 was trained to differentiate between occiput anterior (OA) and non-OA positions, CNN2 classified fetal head malpositions into occiput posterior (OP) or occiput transverse (OT) position, and CNN3 classified the remaining images as right or left OT. The DL-model was constructed using three convolutional neural networks (CNN) working simultaneously for the classification of fetal head positions. The performance of the algorithm was evaluated in terms of accuracy, sensitivity, specificity, F1-score and Cohen's kappa. RESULTS: Between February 2018 and May 2023, 2154 transperineal images were included from eligible participants across 16 collaborating centers. The overall performance of the model for the classification of the fetal head position in the axial plane at transperineal ultrasound was excellent, with an of 94.5 % (95 % CI 92.0--97.0), a sensitivity of 95.6 % (95 % CI 96.8-100.0), a specificity of 91.2 % (95 % CI 87.3-95.1), a F1-score of 0.92 and a Cohen's kappa of 0.90. The best performance was achieved by the CNN1 - OA position vs fetal head malpositions - with an accuracy of 98.3 % (95 % CI 96.9-99.7), followed by CNN2 - OP vs OT positions - with an accuracy of 93.9 % (95 % CI 89.6-98.2), and finally, CNN3 - right vs left OT position - with an accuracy of 91.3 % (95 % CI 83.5-99.1). CONCLUSIONS: We have developed a DL-model capable of assessing fetal head position using transperineal ultrasound during the second stage of labor with an excellent overall accuracy. Future studies should validate our DL model using larger datasets and real-time patients before introducing it into routine clinical practice.

A new standard scoring for interstitial pneumonia based on quantitative analysis of ultrasonographic data: A study on COVID-19 patients.

OBJECTIVE: To assess the effectiveness of 3 novel lung ultrasound (LUS)-based parameters: Pneumonia Score and Lung Staging for pneumonia staging and COVID Index, indicating the probability of SARS-CoV-2 infection. METHODS: Adult patients admitted to the emergency department with symptoms potentially related to pneumonia, healthy volunteers and clinical cases from online accessible databases were evaluated. The patients underwent a clinical-epidemiological questionnaire and a LUS acquisition, following a 14-zone protocol. For each zone, a Pneumonia score from 0 to 4 was assigned by the algorithm and by an expert operator (kept blind with respect to the algorithm results) on the basis of the identified imaging signs and the patient Lung Staging was derived as the highest observed score. The output of the operator was considered as the ground truth. The algorithm calculated also the COVID Index by combining the automatically identified LUS markers with the questionnaire answers and compared with the nasopharyngeal swab results. RESULTS: Overall, 556 patients were analysed. A high agreement between the algorithm assignments and the expert operator evaluations was observed, both for Pneumonia Score and Lung Staging, with the latter having sensitivity and specificity over 92% both in the discrimination between healthy/sick patients and between sick patients with mild/severe pneumonia. Regarding the COVID Index, an area under the curve of 0.826 was observed for the classification of patients with/without SARS-CoV-2. CONCLUSION: The proposed methodology allowed the identification and staging of patients suffering from pneumonia with high accuracy. Moreover, it provided the probability of being infected by SARS-CoV-2.