Fluid management in obstetric care is crucial due to the complex physiological conditions of pregnancy, which complicate clinical manifestations and fluid balance management. This expert review examines the use of point-of-care ultrasound (POCUS) to evaluate and monitor the response to fluid therapy in pregnant patients. Pregnancy induces significant physiological changes, including increased cardiac output and glomerular filtration rate, decreased systemic vascular resistance, and plasma oncotic pressure. Conditions like preeclampsia further complicate fluid management due to decreased intravascular volume and increased capillary permeability. Traditional methods of assessing fluid volume status, such as physical examination and invasive monitoring, are often unreliable or inappropriate. POCUS provides a non-invasive, rapid, and reliable means to assess fluid responsiveness, which is essential in managing fluid therapy in pregnant patients. This review details various POCUS modalities used to measure dynamic changes in fluid status, focusing on the evaluation of the inferior vena cava (IVC), lung ultrasound (Lung US), and the left ventricular outflow tract (LVOT). IVC ultrasound in spontaneously breathing patients determines diameter variability, predicting fluid responsiveness and being feasible even late in pregnancy. Lung ultrasound is critical for detecting early signs of pulmonary edema before clinical symptoms arise and is more accurate than traditional radiography. The LVOT velocity-time integral (VTI) assesses stroke volume response to fluid challenges, providing a quantifiable measure of cardiac function, especially beneficial in critical care settings where rapid and accurate fluid management is essential. The expert review synthesizes current evidence and practice guidelines, suggesting integrating POCUS as a fundamental aspect of fluid management in obstetrics. It calls for ongoing research to enhance techniques and validate their use in broader clinical settings, aiming to improve outcomes for pregnant patients and their babies by preventing complications associated with both under- and over-resuscitation.

BACKGROUND: Optimization of ultrafiltration during hemodialysis is a critical parameter in achieving therapeutic efficacy and ensuring hemodynamic stability. While various modalities such as blood volume monitoring, inferior vena cava diameter assessment, natriuretic peptide levels, bioimpedance assay, and lung ultrasound have been widely explored in the context of maintenance hemodialysis, the concept of volume-guided ultrafiltration in dialysis patients with acute kidney injury remains unexplored.
METHODS: Adult patients with acute kidney injury requiring dialysis, who were hemodynamically stable and not on ventilator support, without underlying lung pathology or cardiac failure, were randomized into two groups. All patients underwent 28-zone lung ultrasound before dialysis. The ultrafiltration was decided based on the treating physician\'s clinical judgment in controls. In the intervention group, the ultrafiltration orders prescribed by the treating physician were modified, based on the Kerley B line scores obtained by lung ultrasound. The rest of the dialysis prescriptions were similar. A postdialysis lung ultrasound was done in both groups to assess the postdialysis volume status 30 min after the dialysis session.
RESULTS: A total of 74 patients undergoing hemodialysis for acute kidney injury were randomized. The baseline characteristics were comparable except for higher baseline B line score scores in the intervention arm. All patients received similar dialysis prescriptions. The lung ultrasound-guided ultrafiltration arm had a higher change in B line scores (BLS) from baseline (4 [0-9.5] vs. 0 [0-4]; p value 0.004) during the first dialysis session. The predialysis BLS indexed to ultrafiltration (mL/kbw/h) were significantly lower in controls, reflecting a relatively higher rate of ultrafiltration in controls compared with intervention (p = 0.006). The total number of dialysis sessions done in the control and intervention arm were 61 and 59, respectively. Among controls, 23/61 sessions (37.7%) had intradialytic adverse events, whereas, in the intervention arm, only 4/59 sessions (6.7) had any adverse intradialytic events (p < 0.01).
CONCLUSIONS: Lung ultrasound-guided ultrafiltration was associated with a better safety profile, as demonstrated by reduced intradialytic events.

BACKGROUND: Machine learning (ML) models can yield faster and more accurate medical diagnoses; however, developing ML models is limited by a lack of high-quality labeled training data. Crowdsourced labeling is a potential solution but can be constrained by concerns about label quality.
OBJECTIVE: This study aims to examine whether a gamified crowdsourcing platform with continuous performance assessment, user feedback, and performance-based incentives could produce expert-quality labels on medical imaging data.
METHODS: In this diagnostic comparison study, 2384 lung ultrasound clips were retrospectively collected from 203 emergency department patients. A total of 6 lung ultrasound experts classified 393 of these clips as having no B-lines, one or more discrete B-lines, or confluent B-lines to create 2 sets of reference standard data sets (195 training clips and 198 test clips). Sets were respectively used to (1) train users on a gamified crowdsourcing platform and (2) compare the concordance of the resulting crowd labels to the concordance of individual experts to reference standards. Crowd opinions were sourced from DiagnosUs (Centaur Labs) iOS app users over 8 days, filtered based on past performance, aggregated using majority rule, and analyzed for label concordance compared with a hold-out test set of expert-labeled clips. The primary outcome was comparing the labeling concordance of collated crowd opinions to trained experts in classifying B-lines on lung ultrasound clips.
RESULTS: Our clinical data set included patients with a mean age of 60.0 (SD 19.0) years; 105 (51.7%) patients were female and 114 (56.1%) patients were White. Over the 195 training clips, the expert-consensus label distribution was 114 (58%) no B-lines, 56 (29%) discrete B-lines, and 25 (13%) confluent B-lines. Over the 198 test clips, expert-consensus label distribution was 138 (70%) no B-lines, 36 (18%) discrete B-lines, and 24 (12%) confluent B-lines. In total, 99,238 opinions were collected from 426 unique users. On a test set of 198 clips, the mean labeling concordance of individual experts relative to the reference standard was 85.0% (SE 2.0), compared with 87.9% crowdsourced label concordance (P=.15). When individual experts\' opinions were compared with reference standard labels created by majority vote excluding their own opinion, crowd concordance was higher than the mean concordance of individual experts to reference standards (87.4% vs 80.8%, SE 1.6 for expert concordance; P<.001). Clips with discrete B-lines had the most disagreement from both the crowd consensus and individual experts with the expert consensus. Using randomly sampled subsets of crowd opinions, 7 quality-filtered opinions were sufficient to achieve near the maximum crowd concordance.
CONCLUSIONS: Crowdsourced labels for B-line classification on lung ultrasound clips via a gamified approach achieved expert-level accuracy. This suggests a strategic role for gamified crowdsourcing in efficiently generating labeled image data sets for training ML systems.

BACKGROUND: Point-of-Care Ultrasound (POCUS) consists of a range of increasingly important imaging modalities across a variety of specialties. Despite a variety of accreditation pathways available in the UK, lung POCUS training remains difficult to deliver and accreditation rates remain suboptimal. We describe a multidisciplinary, multi-centre, and multi-pronged approach to lung POCUS education within a region.
METHODS: A survey was conducted in a region. From these results, bottlenecks were identified for improvement. We utilised key stages in an established accreditation pathway, and the Action Learning process. Analysing participant feedback, consensus amongst the team, regional educational needs, and leveraging the expertise within the faculty, we implemented several solutions which were multidisciplinary, multi-centre, and multi-pronged. We also set up a database across several accreditation pathways to facilitate supervision and assessment of rotational trainees.
RESULTS: Utilising the Action Learning process, we implemented several improvements at elements of the lung ultrasound accreditation pathways. An initial regional survey identified key barriers to accreditation: lack of courses (52%), lack of mentors (93%), and difficulty arranging directly supervised scans (73%). A multidisciplinary team of trainers was assembled. Regular courses were organised and altered based on feedback and anecdotal educational needs within the region. Courses were set up to also facilitate continuing professional development and exchange of knowledge and ideas amongst trainers. The barrier of supervision was removed through the organisation of regular supervision sessions, facilitating up to fifty scans per half day per trainer. We collected feedback from courses and optimised them. Remote mentoring platforms were utilised to encourage asynchronous supervision. A database of trainers was collated to facilitate triggered assessments. These approaches promoted a conducive environment and a commitment to learning. Repeat survey results support this.
CONCLUSIONS: Lung ultrasound accreditation remains a complex educational training pathway. Utilising an education framework, recruiting a multidisciplinary team, ensuring a multi-pronged approach, and fostering a commitment to learning can improve accreditation success.

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Introduction Lung diseases are the most frequently encountered form of diseases primarily affecting infants under one year of age. Although the chest X-ray is the first modality of choice, ultrasonography (USG) has emerged as an alternative. Lung ultrasound (LUS) finds its application in the evaluation of several pediatric lung diseases. Objective To assess the use of LUS in acute lower respiratory infections and assess the correlation between etiological diagnosis and radiological diagnosis. Methods This was a hospital-based prospective observational study conducted with children presenting with upper respiratory infections. Around 97 children were included in the study. Clinical diagnosis was made by the pediatrician. LUS was performed by a trained radiologist, using the two-dimensional (2D) ultrasound mode and motion mode (M mode) to assess the LUS in the respective areas of the chest, thereby assessing bilateral lung fields for these patients. Results The majority of our study participants were under one year old (87%), and more than half were male (55%). Bronchiolitis and lower respiratory tract infections (LRIs) were the most commonly seen clinical diagnoses. The distribution of USG findings was statistically significant across the clinical diagnosis (p-value < 0.05). Conclusion Our study found that LUS can serve as an important tool for diagnosing several acute respiratory diseases. It also showed that LUS can replace X-rays in cases of children diagnosed with acute respiratory diseases.

Background: Lung ultrasound (LUS) is a tool of growing interest in Rheumatoid Arthritis (RA) oligo- symptomatic ILD to avoid. Objective: We aimed to evaluate (i) the prevalence of pleural (PLUS) and parenchymal (PAUS) abnormalities in LUS in the RA population and their possible correlation to biomarkers; (ii) the predictivity of gender, smoking habits, previous infections (past COVID-19 tuberculosis), and treatments; (iii) the differences in LUS between sexes. Methods: We collected the data of 155 (15 early and 140 late) RA patients with mild respiratory symptoms, evaluating PLUS and PAUS, in fourteen lung areas and also summing the scores (LUS-T). Results: Only 13/155 (8.4%) were completely negative; LUS correlated to age (all parameters p 0.0001), rheumatoid factor IgM (PLUS p 0.0006, PAUS p 0.02, LUS-T p 0.001) and ACPA (p 0.001, 0.006, 0.001, respectively), and PLUS also correlated to IL6 (p 0.02). The male gender was predictive of all LUS evaluations (p 0.001, 0.05, 0.001, respectively), which were higher than in women (p 0.001, 0.01, 0.001, respectively). Other potential risk factors were independent, except biological treatments, which showed a low predictivity to PLUS (p < 0.05). Conclusions: We can conclude that LUS is a useful technique in RA low respiratory symptoms and correlates with age, the most important RA biomarkers, and male sex.

OBJECTIVE: our study aimed to characterize the lung ultrasound (LUS) patterns noted immediately after delivery in term and near-term neonates, and to investigate whether the LUS scores or patterns observed at that point could anticipate the need for respiratory support in the sample of patients studied.
METHODS: We performed two ultrasound examinations: one in the delivery room and the second at one hour of age. The anterior and lateral regions of both lungs were examined. We assessed the correlation between the LUS scores or patterns and the gestational age, umbilical arterial blood gases, the need for respiratory support (CPAP or mechanical ventilation), the presence of respiratory distress, and the need for the administration of oxygen.
RESULTS: LUS scores were significantly higher in the delivery room examination (8.05 ± 1.95) than at 1 h of age (6.4 ± 1.75) (p < 0.001). There were also statistically significant differences between the LUS patterns observed in different lung regions between the delivery room exam and the exam performed at 1 h of age (p values between 0.001 and 0.017). There were also differences noted regarding the LUS patterns between different lung regions at the exam in the delivery room (the right anterior region LUS patterns were significantly worse than the right lateral LUS patterns (p < 0.004), left anterior LUS patterns (p < 0.001), and left lateral LUS patterns (p < 0.001)). A statistically significant correlation was found between LUS scores and the gestational age of the patients (r = 0.568, p < 0.001-delivery room; r = 4.0443, p < 0.001-one hour of age). There were statistically significant associations between LUS scores, patterns at delivery (p < 0.001) and 1 h of age (p < 0.001), and the need for respiratory support (CPAP or mechanical ventilation).
CONCLUSIONS: LUS in the delivery room offers important information regarding lung fluid elimination and aeration of the lungs, and early LUS features are significantly associated with the risk of respiratory distress and the need for respiratory support.

BACKGROUND: Pneumonia is a ubiquitous health condition with severe outcomes. The advancement of ultrasonography techniques allows its application in evaluating pulmonary diseases, providing safer and accessible bedside therapeutic decisions compared to chest X-ray and chest computed tomography (CT) scan. Because of its aforementioned benefits, we aimed to confirm the diagnostic accuracy of lung ultrasound (LUS) for pneumonia in adults.
METHODS: A systematic literature search was performed of Medline, Cochrane and Crossref, independently by two authors. The selection of studies proceeded based on specific inclusion and exclusion criteria without restrictions to particular study designs, language or publication dates and was followed by data extraction. The gold standard reference in the included studies was chest X-ray/CT scan or both.
RESULTS: Twenty-nine (29) studies containing 6702 participants were included in our meta-analysis. Pooled sensitivity, specificity and PPV were 92% (95% CI: 91-93%), 94% (95% CI: 94 to 95%) and 93% (95% CI: 89 to 96%), respectively. Pooled positive and negative likelihood ratios were 16 (95% CI: 14 to 19) and 0.08 (95% CI: 0.07 to 0.09). The area under the ROC curve of LUS was 0. 9712.
CONCLUSIONS: LUS has high diagnostic accuracy in adult pneumonia. Its contribution could form an optimistic clue in future updates considering this condition.

UNASSIGNED: Lung ultrasound (LUS) as an assessment tool has seen significant expansion in adult, paediatric, and neonatal populations due to advancements in point-of-care ultrasound over the past two decades. However, with fewer experts and learning platforms available in low- and middle-income countries and the lack of a standardised supervised training programme, LUS is not currently effectively used to the best of its potential in neonatal units.
UNASSIGNED: A cross-sectional survey assessed the efficacy of learning LUS via a mentor-based online teaching module (NEOPOCUS). The questionnaire comprised the clinicians\' demographic profile, pre-course skills, and self-assessment of skill acquisition after course completion with ongoing hands-on practice.
UNASSIGNED: A total of 175 clinicians responded to the survey, with the majority (87.9%) working in level 3 and 4 neonatal intensive care units. Clinicians had variable clinical experience. Of them, 53.2% were consultant paediatricians/neonatologists with over 10 years of experience. After the course, there was a significant increase in clinician confidence levels in diagnosing and assessing all LUS pathology, as evidenced by the increase in median cumulative scores [from baseline 6 (interquartile range, IQR, 6-9) to 20 (IQR 16-24), p < 0.001] with half of them gaining confidence within 3 months of the course.
UNASSIGNED: An online curriculum-based neonatal lung ultrasound training programme with clinician image demonstration and peer review of images for image optimisation increases self-reported confidence in diagnosing and managing neonatal lung pathology. Web-based online training in neonatal lung ultrasound has merits that can help with the delivery of training globally, and especially in low- and middle-income countries.