Browsing by Author "Hagmann, Cornelia"

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    Acceptability of immediate CPAP for preterm infants in the delivery room to mothers, caregivers and healthcare workers in a low-resource setting: a qualitative study
    (BMC Pediatrics, 2025) Napyo, Agnes; Nakiyemba, Alice; Muduwa, Martha; Ssenkusu, M. John; Okello, Francis; Hagmann, Cornelia; Namuyonga, Judith; Hewitt-Smith, Adam; Loe, Kate; Abongo, Grace; Amorut, Denis; Wandabwa, Julius; Olupot-Olupot, Peter; Burgoine, Kathy
    Background: Preterm birth is the leading cause of childhood mortality, with respiratory distress syndrome as the predominant aetiology. Initiating continuous positive airways pressure (CPAP) immediately after birth may reduce CPAP failure, the need for ventilation, and surfactant use. In low-resource settings, without ventilation or surfactant, immediate CPAP could significantly reduce preterm mortality. We explored the experiences, perceptions, and acceptability of immediate CPAP among parents, caregivers, and healthcare workers in a Ugandan hospital. Methods: This qualitative study (April 2023–April 2024) was nested in a pilot randomised controlled trial of immediate delivery room CPAP for very low birthweight infants (VLBW, <1500 g) at a government hospital in Uganda. Data were collected through 12 key informant interviews and focus group discussions with 36 healthcare workers, and 37 parents and caregivers of enrolled infants. We applied deductive framework analysis using the Theoretical Framework of Acceptability (TFA) and coded transcripts using Nvivo 12. Results: Regarding affective attitude, healthcare workers, mothers and caregivers expressed positive feelings towards immediate CPAP. For perceived effectiveness, healthcare workers described immediate CPAP as a prophylactic intervention that reduces the severity of complications and shortens hospital stays, while mothers and caregivers believed it expands the infant’s lungs and increases chances of survival. Concerning burden, healthcare workers highlighted that successful implementation depends on a committed neonatal team, multidisciplinary team collaboration, adequate staffing, active maternal involvement, and the availability of sufficient CPAP machines. Opportunity costs were evident where limited staffing forced healthcare workers to choose between prioritising the mother or the infant. Under ethicality, cultural beliefs, religious views, and fear were identified as influential factors in decision making around immediate CPAP. Regarding intervention coherence, healthcare workers, mothers, and caregivers demonstrated a good understanding of the purpose and process of immediate CPAP. Finally, self-efficacy was linked to the availability of adequate staff, training, and necessary equipment to confidently engage in the intervention. Conclusions Immediate CPAP was found to be acceptable among healthcare workers and mothers/caregivers. Successful implementation requires adequate staff training, comprehensive health education, adequate human resources, and sufficient availability of CPAP machines. Trial registration Study is registered on Pan African Clinical Trials Registry (PACTR) PACTR202208462613789. Keywords Preterm, Very low birthweight, VLBW, Africa, Neonatal, CPAP, Respiratory distress syndrome, Low-resource setting, Acceptability, Barriers, Facilitators, Attitude
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    CLIF-Net : intersection-guided cross-view fusion network for infection detection from cranial ultrasound.
    (IEEE., 2025) Yu, Mingzhao; Peterson, R. Mallory; Burgoine, Kathy; Harbaugh, Thaddeus; Olupot-Olupot, Peter; Gladstone, Melissa; Hagmann, Cornelia; Cowan, M. Frances; Weeks, Andrew; Morton, U. Sarah; Mulondo, Ronald; Mbabazi-Kabachelor, Edith; Schiff, J. Steven; Monga, Vishal
    This paper addresses the problem of detecting possible serious bacterial infection (pSBI) of infancy, i.e. a clinical presentation consistent with bacterial sepsis in newborn infants using cranial ultrasound (cUS) images. The captured image set for each patient enables multiview imagery: coronal and sagittal, with geometric overlap. To exploit this geometric relation, we develop a new learning framework, called the intersection-guided Cross-view Local and Image-level Fusion Network (CLIF-Net). Our technique employs two distinct convolutional neural network branches to extract features from coronal and sagittal images with newly developed multi-level fusion blocks. Specifically, we leverage the spatial position of these images to locate the intersecting region. We then identify and enhance the semantic features from this region across multiple levels using cross-attention modules, facilitating the acquisition of mutually beneficial and more representative features from both views. The final enhanced features from the two views are then integrated and projected through the image-level fusion layer, outputting pSBI and non-pSBI class probabilities. We contend that our method of exploiting multi-view cUS images enables a first of its kind, robust 3D representation tailored for pSBI detection. When evaluated on a dataset of 302 cUS scans from Mbale Regional Referral Hospital in Uganda, CLIF-Net demonstrates substantially enhanced performance, surpassing the prevailing state-ofthe-art infection detection techniques. Index Terms—Cross-view fusion, deep learning, geometric prior, infection detection, ultrasound.