Statistical Characterisation of Fetal Anatomy in Simple Obstetric Ultrasound Video Sweeps.

OBJECTIVE: We present a statistical characterisation of fetal anatomies in obstetric ultrasound video sweeps where the transducer follows a fixed trajectory on the maternal abdomen. METHODS: Large-scale, frame-level manual annotations of fetal anatomies (head, spine, abdomen, pelvis, femur) were used to compute common frame-level anatomy detection patterns expected for breech, cephalic, and transverse fetal presentations, with respect to video sweep paths. The patterns, termed statistical heatmaps, quantify the expected anatomies seen in a simple obstetric ultrasound video sweep protocol. In this study, a total of 760 unique manual annotations from 365 unique pregnancies were used. RESULTS: We provide a qualitative interpretation of the heatmaps assessing the transducer sweep paths with respect to different fetal presentations and suggest ways in which the heatmaps can be applied in computational research (e.g., as a machine learning prior). CONCLUSION: The heatmap parameters are freely available to other researchers (https://github.com/agleed/calopus_statistical_heatmaps).

Respiratory syncytial virus disease morbidity in Australian infants aged 0 to 6 months: a systematic review with narrative synthesis.

BACKGROUND: A significant proportion of the global respiratory syncytial virus (RSV) associated morbidity is accounted for by infants aged 0 to 6 months, who are particularly vulnerable to severe disease. In 2015, 44% of global hospitalisations in infants in this age group were secondary to RSV. The objective of this systematic review is to appraise and synthesise the local evidence of RSV infection morbidity among Australian infants aged 0 to 6 months and to assess the implications for future immunisation strategies. METHODS: Electronic databases (Medline, Embase, Pubmed and Global Health) were searched for full-text articles published between 2000 and 2023 in English language. Studies that examined markers of RSV disease morbidity in infants aged 0 to 6 months in Australia who had laboratory confirmed RSV infection were eligible for inclusion. The outcomes of interest were incidence, prevalence, testing rate, positivity rate, mortality, emergency department visits, community health visits, hospitalisation, intensive care unit admission, supplementary oxygen use, mechanical ventilation, risk factors for disease severity and monoclonal antibody use. RESULTS: The database search identified 469 studies. After removal of duplicates and full-text review, 17 articles were eligible for inclusion. This review was reported according to the Preferred Reporting Items for Systematic Reviews and Meta-Analyses and Synthesis without meta-analysis guidelines. CONCLUSIONS: Qualitative analysis of the included studies showed that Australian infants aged 0 to 6 months have higher rates of RSV testing, positivity and incidence; and more likely to develop severe disease that requires hospitalisation, intensive care unit admission or respiratory support, compared to children and adults of all ages. Aboriginal and Torres Strait Islander infants aged 0 to 6 months demonstrated higher rates of RSV infection and hospitalisation, compared to non-Indigenous infants. Age-related trends persisted in geographic areas with varying seasonal transmission of RSV, and during the SARS-CoV-2 pandemic. Passive immunisation strategies targeting infants in their first 6 months of life, either via vaccination of pregnant women or administration of long-acting monoclonal antibody during infancy, could effectively reduce RSV disease burden in Australia.

Automatic Image Guidance for Assessment of Placenta Location in Ultrasound Video Sweeps.

Ultrasound-based assistive tools are aimed at reducing the high skill needed to interpret a scan by providing automatic image guidance. This may encourage uptake of ultrasound (US) clinical assessments in rural settings in low- and middle-income countries (LMICs), where well-trained sonographers can be scarce. This paper describes a new method that automatically generates an assistive video overlay to provide image guidance to a user to assess placenta location. The user captures US video by following a sweep protocol that scans a U-shape on the lower maternal abdomen. The sweep trajectory is simple and easy to learn. We initially explore a 2-D embedding of placenta shapes, mapping manually segmented placentas in US video frames to a 2-D space. We map 2013 frames from 11 videos. This provides insight into the spectrum of placenta shapes that appear when using the sweep protocol. We propose classification of the placenta shapes from three observed clusters: complex, tip and rectangular. We use this insight to design an effective automatic segmentation algorithm, combining a U-Net with a CRF-RNN module to enhance segmentation performance with respect to placenta shape. The U-Net + CRF-RNN algorithm automatically segments the placenta and maternal bladder. We assess segmentation performance using both area and shape metrics. We report results comparable to the state-of-the-art for automatic placenta segmentation on the Dice metric, achieving 0.83 ± 0.15 evaluated on 2127 frames from 10 videos. We also qualitatively evaluate 78,308 frames from 135 videos, assessing if the anatomical outline is correctly segmented. We found that addition of the CRF-RNN improves over a baseline U-Net when faced with a complex placenta shape, which we observe in our 2-D embedding, up to 14% with respect to the percentage shape error. From the segmentations, an assistive video overlay is automatically constructed that (i) highlights the placenta and bladder, (ii) determines the lower placenta edge and highlights this location as a point and (iii) labels a 2-cm clearance on the lower placenta edge. The 2-cm clearance is chosen to satisfy current clinical guidelines. We propose to assess the placenta location by comparing the 2-cm region and the bottom of the bladder, which represents a coarse localization of the cervix. Anatomically, the bladder must sit above the cervix region. We present proof-of-concept results for the video overlay.

Developing Clinical Artificial Intelligence for Obstetric Ultrasound to Improve Access in Underserved Regions: Protocol for a Computer-Assisted Low-Cost Point-of-Care UltraSound (CALOPUS) Study.

BACKGROUND: The World Health Organization recommends a package of pregnancy care that includes obstetric ultrasound scans. There are significant barriers to universal access to antenatal ultrasound, particularly because of the cost and need for maintenance of ultrasound equipment and a lack of trained personnel. As low-cost, handheld ultrasound devices have become widely available, the current roadblock is the global shortage of health care providers trained in obstetric scanning. OBJECTIVE: The aim of this study is to improve pregnancy and risk assessment for women in underserved regions. Therefore, we are undertaking the Computer-Assisted Low-Cost Point-of-Care UltraSound (CALOPUS) project, bringing together experts in machine learning and clinical obstetric ultrasound. METHODS: In this prospective study conducted in two clinical centers (United Kingdom and India), participating pregnant women were scanned and full-length ultrasounds were performed. Each woman underwent 2 consecutive ultrasound scans. The first was a series of simple, standardized ultrasound sweeps (the CALOPUS protocol), immediately followed by a routine, full clinical ultrasound examination that served as the comparator. We describe the development of a simple-to-use clinical protocol designed for nonexpert users to assess fetal viability, detect the presence of multiple pregnancies, evaluate placental location, assess amniotic fluid volume, determine fetal presentation, and perform basic fetal biometry. The CALOPUS protocol was designed using the smallest number of steps to minimize redundant information, while maximizing diagnostic information. Here, we describe how ultrasound videos and annotations are captured for machine learning. RESULTS: Over 5571 scans have been acquired, from which 1,541,751 label annotations have been performed. An adapted protocol, including a low pelvic brim sweep and a well-filled maternal bladder, improved visualization of the cervix from 28% to 91% and classification of placental location from 82% to 94%. Excellent levels of intra- and interannotator agreement are achievable following training and standardization. CONCLUSIONS: The CALOPUS study is a unique study that uses obstetric ultrasound videos and annotations from pregnancies dated from 11 weeks and followed up until birth using novel ultrasound and annotation protocols. The data from this study are being used to develop and test several different machine learning algorithms to address key clinical diagnostic questions pertaining to obstetric risk management. We also highlight some of the challenges and potential solutions to interdisciplinary multinational imaging collaboration. INTERNATIONAL REGISTERED REPORT IDENTIFIER (IRRID): RR1-10.2196/37374.

Ultrasound Diagnosis of the Small and Large Fetus.

Antenatal imaging is crucial in the management of high-risk pregnancies. Accurate dating relies on acquisition of reliable and reproducible ultrasound images and measurements. Quality image acquisition is necessary for assessing fetal growth and performing Doppler measurements to help diagnose pregnancy complications, stratify risk, and guide management. Further research is needed to ascertain whether current methods for estimating fetal weight can be improved with 3-dimensional ultrasound or magnetic resonance imaging; optimize dating with late initiation of prenatal care; minimize under-diagnosis of fetal growth restriction; and identify the best strategies to make ultrasound more available in low-income and middle-income countries.

Toward point-of-care ultrasound estimation of fetal gestational age from the trans-cerebellar diameter using CNN-based ultrasound image analysis.

Obstetric ultrasound is a fundamental ingredient of modern prenatal care with many applications including accurate dating of a pregnancy, identifying pregnancy-related complications, and diagnosis of fetal abnormalities. However, despite its many benefits, two factors currently prevent wide-scale uptake of this technology for point-of-care clinical decision-making in low- and middle-income country (LMIC) settings. First, there is a steep learning curve for scan proficiency, and second, there has been a lack of easy-to-use, affordable, and portable ultrasound devices. We introduce a framework toward addressing these barriers, enabled by recent advances in machine learning applied to medical imaging. The framework is designed to be realizable as a point-of-care ultrasound (POCUS) solution with an affordable wireless ultrasound probe, a smartphone or tablet, and automated machine-learning-based image processing. Specifically, we propose a machine-learning-based algorithm pipeline designed to automatically estimate the gestational age of a fetus from a short fetal ultrasound scan. We present proof-of-concept evaluation of accuracy of the key image analysis algorithms for automatic head transcerebellar plane detection, automatic transcerebellar diameter measurement, and estimation of gestational age on conventional ultrasound data simulating the POCUS task and discuss next steps toward translation via a first application on clinical ultrasound video from a low-cost ultrasound probe.

Why are women dying when they reach hospital on time? A systematic review of the 'third delay'.

BACKGROUND: The 'three delays model' attempts to explain delays in women accessing emergency obstetric care as the result of: 1) decision-making, 2) accessing services and 3) receipt of appropriate care once a health facility is reached. The third delay, although under-researched, is likely to be a source of considerable inequity in access to emergency obstetric care in developing countries. The aim of this systematic review was to identify and categorise specific facility-level barriers to the provision of evidence-based maternal health care in developing countries. METHODS AND FINDINGS: Five electronic databases were systematically searched using a 4-way strategy that combined search terms related to: 1) maternal health care; 2) maternity units; 3) barriers, and 4) developing countries. Forty-three original research articles were eligible to be included in the review. Thirty-two barriers to the receipt of timely and appropriate obstetric care at the facility level were identified and categorised into six emerging themes (Drugs and equipment; Policy and guidelines; Human resources; Facility infrastructure; Patient-related and Referral-related). Two investigators independently recorded the frequency with which barriers relating to the third delay were reported in the literature. The most commonly cited barriers were inadequate training/skills mix (86%); drug procurement/logistics problems (65%); staff shortages (60%); lack of equipment (51%) and low staff motivation (44%). CONCLUSIONS: This review highlights how a focus on patient-side delays in the decision to seek care can conceal the fact that many health facilities in the developing world are still chronically under-resourced and unable to cope effectively with serious obstetric complications. We stress the importance of addressing supply-side barriers alongside demand-side factors if further reductions in maternal mortality are to be achieved.