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Francesco Conversano,1 Giulia Soloperto,1 Antonio Greco,1 Andrea Ragusa,1,2 Ernesto Casciaro,1 Fernanda Chiriacò,1 Christian Demitri,3 Giuseppe Gigli,2–5 Alfonso Maffezzoli,3 Sergio Casciaro11National Research Council, Institute of Clinical Physiology, Lecce, Italy; 2National Nanotechnology Laboratory of CNR-NANO, Lecce, Italy; 3University of Salento, Department of Engineering for Innovation, Lecce, Italy; 4Italian Institute of Technology – Center for Biomolecular Nanotechnology (CBN-IIT), Arnesano, Italy; 5University of Salento, Department of Mathematics and Physics ‘Ennio De Giorgi’, Lecce, ItalyPurpose: To evaluate the diagnostic performance of gold nanorod (GNR)-enhanced optoacoustic imaging employing a conventional echographic device and to determine the most effective operative configuration in order to assure optoacoustic effectiveness, nanoparticle stability, and imaging procedure safety.Methods: The most suitable laser parameters were experimentally determined in order to assure nanoparticle stability during the optoacoustic imaging procedures. The selected configuration was then applied to a novel tissue-mimicking phantom, in which GNR solutions covering a wide range of low concentrations (25–200 pM) and different sample volumes (50–200 µL) were exposed to pulsed laser irradiation. GNR-emitted optoacoustic signals were acquired either by a couple of single-element ultrasound probes or by an echographic transducer. Off-line analysis included: (a) quantitative evaluation of the relationships between GNR concentration, sample volume, phantom geometry, and amplitude of optoacoustic signals propagating along different directions; (b) echographic detection of “optoacoustic spots,” analyzing their intensity, spatial distribution, and clinical exploitability. MTT measurements performed on two different cell lines were also used to quantify biocompatibility of the synthesized GNRs in the adopted doses.Results: Laser irradiation at 30 mJ/cm2 for 20 seconds resulted in the best compromise among the requirements of effectiveness, safety, and nanoparticle stability. Amplitude of GNR-emitted optoacoustic pulses was proportional to both sample volume and concentration along each considered propagation direction for all the tested boundary conditions, providing an experimental confirmation of isotropic optoacoustic emission. Average intensity of echographically detected spots showed similar behavior, emphasizing the presence of an “ideal” GNR concentration (100 pM) that optimized optoacoustic effectiveness. The tested GNRs also exhibited high biocompatibility over the entire considered concentration range.Conclusion: An optimal configuration for GNR-enhanced optoacoustic imaging was experimentally determined, demonstrating in particular its feasibility with a conventional echographic device. The proposed approach can be easily extended to quantitative performance evaluation of different contrast agents for optoacoustic imaging.Keywords: photoacoustic imaging, tissue-mimicking phantom, laser, nanoparticle degradation

This article discusses a preliminary study on the feasibility of using an automatic algorithm to assess the likelihood of successful instrumental delivery in obstetrics. The algorithm analyzes ultrasound images to measure parameters such as the occiput position, head-perineum distance, and midline angle, and assigns a color code (green, yellow, or red) indicating the chances of success. The study found that the algorithm had a sensitivity of 0.80 and specificity of 0.82 for the green code, and a sensitivity of 0.64 and specificity of 0.80 for the yellow code. The authors conclude that these preliminary results are encouraging and suggest the potential for further improvements in clinical decision-making for delivery management. [Extracted from the article]

Objectives: To describe a newly developed machine-learning (ML) algorithm for the automatic recognition of fetal head position using transperineal ultrasound (TPU) during the second stage of labor and to describe its performance in differentiating between occiput anterior (OA) and non-OA positions.Methods: This was a prospective cohort study including singleton term (> 37 weeks of gestation) pregnancies in the second stage of labor, with a non-anomalous fetus in cephalic presentation. Transabdominal ultrasound was performed to determine whether the fetal head position was OA or non-OA. For each case, one sonographic image of the fetal head was then acquired in an axial plane using TPU and saved for later offline analysis. Using the transabdominal sonographic diagnosis as the gold standard, a ML algorithm based on a pattern-recognition feed-forward neural network was trained on the TPU images to discriminate between OA and non-OA positions. In the training phase, the model tuned its parameters to approximate the training data (i.e. the training dataset) such that it would identify correctly the fetal head position, by exploiting geometric, morphological and intensity-based features of the images. In the testing phase, the algorithm was blinded to the occiput position as determined by transabdominal ultrasound. Using the test dataset, the ability of the ML algorithm to differentiate OA from non-OA fetal positions was assessed in terms of diagnostic accuracy. The F1 -score and precision-recall area under the curve (PR-AUC) were calculated to assess the algorithm's performance. Cohen's kappa (κ) was calculated to evaluate the agreement between the algorithm and the gold standard.Results: Over a period of 24 months (February 2018 to January 2020), at 15 maternity hospitals affiliated to the International Study group on Labor ANd Delivery Sonography (ISLANDS), we enrolled into the study 1219 women in the second stage of labor. On the basis of transabdominal ultrasound, they were classified as OA (n = 801 (65.7%)) or non-OA (n = 418 (34.3%)). From the entire cohort (OA and non-OA), approximately 70% (n = 824) of the patients were assigned randomly to the training dataset and the rest (n = 395) were used as the test dataset. The ML-based algorithm correctly classified the fetal occiput position in 90.4% (357/395) of the test dataset, including 224/246 with OA (91.1%) and 133/149 with non-OA (89.3%) fetal head position. Evaluation of the algorithm's performance gave an F1 -score of 88.7% and a PR-AUC of 85.4%. The algorithm showed a balanced performance in the recognition of both OA and non-OA positions. The robustness of the algorithm was confirmed by high agreement with the gold standard (κ = 0.81; P < 0.0001).Conclusions: This newly developed ML-based algorithm for the automatic assessment of fetal head position using TPU can differentiate accurately, in most cases, between OA and non-OA positions in the second stage of labor. This algorithm has the potential to support not only obstetricians but also midwives and accoucheurs in the clinical use of TPU to determine fetal occiput position in the labor ward. © 2021 International Society of Ultrasound in Obstetrics and Gynecology. [ABSTRACT FROM AUTHOR]