Understanding the factors that influence CT utilization for mild traumatic brain injury in a low resource setting - a qualitative study using the Theoretical Domains Framework.

Introduction: In low resource settings (LRS), utilization of Computed Tomography scan (CTS) for mild traumatic brain injuries (mTBIs) presents unique challenges and considerations given the limited infrastructure, financial resources, and trained personnel. The Theoretical Domains Framework (TDF) offers a comprehensive theoretical lens to explore factors influencing the decision-making to order CTS for mTBI by imaging referrers (IRs). Objectives: The primary objective was to explore IRs' beliefs about factors influencing CT utilization in mTBIs using TDF in Uganda.Differences in the factors influencing CTS ordering behavior across specialties, levels of experience, and hospital category were also explored. Materials and Methods: In-depth semi-structured interviews guided by TDF were conducted among purposively selected IRs from 6 tertiary public and private hospitals with functional CTS services. A thematic analysis was performed with codes and emerging themes developed based on the TDF. Results: Eleven IRs including medical officers, non-neurosurgeon specialists and neurosurgeons aged on average 42 years (SD+/-12.3 years) participated.Identified factors within skills domain involved IRs' clinical assessment and decision-making abilities, while beliefs about capabilities and consequences encompassed their confidence in diagnostic abilities and perceptions of CTS risks and benefits. The environmental context and resources domain addressed the availability of CT scanners and financial constraints. The knowledge domain elicited IRs' understanding of clinical guidelines and evidence-based practices while social influences considered peer influence and institutional culture. For memory, attention & decision processes domain, IRs adherence to guidelines and intentions to order CT scans were cited. Conclusion: Using TDF, IRs identified several factors believed to influence decision making to order CTS in mTBI in a LRS. The findings can inform stakeholders to develop targeted strategies and evidence-based interventions to optimize CT utilization in mTBI such as; educational programs, workflow modifications, decision support tools, and infrastructure improvements, among others.

Generalisability of fetal ultrasound deep learning models to low-resource imaging settings in five African countries.

Most artificial intelligence (AI) research and innovations have concentrated in high-income countries, where imaging data, IT infrastructures and clinical expertise are plentiful. However, slower progress has been made in limited-resource environments where medical imaging is needed. For example, in Sub-Saharan Africa, the rate of perinatal mortality is very high due to limited access to antenatal screening. In these countries, AI models could be implemented to help clinicians acquire fetal ultrasound planes for the diagnosis of fetal abnormalities. So far, deep learning models have been proposed to identify standard fetal planes, but there is no evidence of their ability to generalise in centres with low resources, i.e. with limited access to high-end ultrasound equipment and ultrasound data. This work investigates for the first time different strategies to reduce the domain-shift effect arising from a fetal plane classification model trained on one clinical centre with high-resource settings and transferred to a new centre with low-resource settings. To that end, a classifier trained with 1792 patients from Spain is first evaluated on a new centre in Denmark in optimal conditions with 1008 patients and is later optimised to reach the same performance in five African centres (Egypt, Algeria, Uganda, Ghana and Malawi) with 25 patients each. The results show that a transfer learning approach for domain adaptation can be a solution to integrate small-size African samples with existing large-scale databases in developed countries. In particular, the model can be re-aligned and optimised to boost the performance on African populations by increasing the recall to [Formula: see text] and at the same time maintaining a high precision across centres. This framework shows promise for building new AI models generalisable across clinical centres with limited data acquired in challenging and heterogeneous conditions and calls for further research to develop new solutions for the usability of AI in countries with fewer resources and, consequently, in higher need of clinical support.

An Africa point of view on quality and safety in imaging.

Africa has seen an upsurge in diagnostic imaging utilization, with benefits of efficient and accurate diagnosis, but these could easily be offset by undesirable effects attributed to unjustified, unoptimized imaging and poor quality examinations. This paper aims to present Africa's position regarding quality and safety in imaging, give reasons for the rising interest in quality and safety, define quality and safety from an African context, list drivers for quality and safety in Africa, discuss the impact of COVID-19 on quality and safety, and review Africa's progress using the Bonn Call for Action framework while proposing a way forward for imaging quality and safety in Africa. In spite of a healthcare setting characterized by meagre financial, human and technology resources, a rapidly widening disease-burden spectrum, growing proportion of non-communicable diseases and resurgence of tropical and global infections, Africa has over the last ten years made significant strides in quality and safety for imaging. These include raising radiation-safety awareness, interest and application of evidence-based radiation safety recommendations and guidance tools, establishing facility and national diagnostic reference levels (DRLs) and strengthening end-user education and training. Major challenges are: limited human resource, low prioritization of imaging in relation to other health services, low level of integration of imaging into the entire health service delivery, insufficient awareness for radiation safety awareness, a radiation safety culture which is emerging, insufficient facilities and opportunities for education and training. Solutions to these challenges should target the entire hierarchy of health service delivery from prioritization, policy, planning, processes to procedures.

Prospective verification of sonographic fetal weight estimators among term parturients in Uganda.

BACKGROUND: Accuracy of fetal weight estimation by ultrasound is essential in making decisions on the time and mode of delivery. There are many proposed formulas for fetal weight estimation such as Hadlock 1, Hadlock 2, Hadlock 3, Hadlock 4 and Shepard. What best applies to the Ugandan population is not known since no verification of any of the formulas has been done before. The primary aim of this study was to determine the accuracy of sonographic estimation of fetal weight using five most commonly used formulas, and analyze formula variations for different weight ranges. METHODS: This was a hospital based prospective cohort study at Mulago National Referral Hospital, Kampala, Uganda. A total of 356 pregnant women who consented and were within 3 days of birth were enrolled. Prenatal ultrasound fetal weight determined by measuring the biparietal diameter, head circumference, abdominal circumference, femoral length, and then was compared with actual birth weight. RESULTS: The overall accuracy of Hadlock 1, Hadlock 2, Hadlock 3, Hadlock 4 and Shepard formula were 66.9, 73.3, 77.3, 78.4 and 69.7% respectively. All Hadlocks showed significant mean difference between weight estimates and actual birth weight (p < 0.01) whereas Shepard formula did not [p - 0.2], when no stratification of fetal weights was done. However, all Hadlocks showed a none significant (p-values > 0.05) mean difference between weight estimates and actual birth weight when the actual birth weight was ≥4000.0 g. Shepard weight estimates showed a none significant mean difference when actual birth weight was < 4000 g. Bland-Altman graphs also showed a better agreement of weight estimated by Shepard formula and actual birth weights. CONCLUSION: All the five formulas were accurate at estimating actual birth weights within 10% accuracy. However, this accuracy varied with the fetal birth weight. Shepard was more accurate in estimating actual birth weights < 4000 g whereas all Hadlocks were more accurate when the actual birthweight was ≥4000 g.

Barriers and facilitators to implementing clinical imaging guidelines by healthcare professionals using theoretical domains framework: a mixed-methods systematic review protocol.

Objectives: To identify, categorize, and develop an aggregated synthesis of evidence using the theoretical domains framework (TDF) on barriers and facilitators that influence implementation of clinical imaging guidelines (CIGs) by healthcare professionals (HCPs) in diagnostic imaging. Methods: The protocol will be guided by the Joanna Briggs Institute Reviewers' Manual 2014. Methodology for JBI Mixed Methods Systematic Reviews and will adhere to Preferred Reporting Items for Systematic Reviews and Meta-Analyses guidelines (PRISMA-P). Information source will include databases (MEDLINE, EMBASE and The Cochrane Library), internet search (https://www.google.com/scholar), experts' opinion, professional societies/organizations websites and government bodies strategies/recommendations, and reference lists of included studies. Articles of any study design published in English from 1990 to date, having investigated factors operating as barriers and/or facilitators to the implementation CIGs by HCPs will be eligible. Selecting, appraising, and extracting data from the included studies will be independently performed by at least two reviewers using validated tools and Rayyan - Systematic Review web application. Disagreements will be resolved by consensus and a third reviewer as a tie breaker. The aggregated studies will be synthesized using thematic analysis guided by TDF. Results: Identified barriers will be defined a priori and mapped into 7 TDF domains including knowledge, awareness, effectiveness, time, litigationand financial incentives. Conclusion: The results will provide an insight into a theory-based approach to predict behavior-related determinants for implementing CIGs and develop strategies/interventions to target the elicited behaviors. Recommendations will be made if the level of evidence is sufficient. Advances in knowledge: Resource-constrained settings that are in the process of adopting CIGs may opt for this strategy to predict in advance likely impediments to achieving the goal of CIG implementation and develop tailored interventions during the planning phase.Systematic review Registration: PROSPERO ID = CRD42020136372 (https://www.crd.york.ac.uk/PROSPERO).