Parental and carer views on the use of AI in imaging for children: a national survey.

Objectives: Although the use of artificial intelligence (AI) in healthcare is increasing, stakeholder engagement remains poor, particularly relating to understanding parent/carer acceptance of AI tools in paediatric imaging. We explore these perceptions and compare them to the opinions of children and young people (CYAP).

Materials And Methods: A UK national online survey was conducted, inviting parents, carers and guardians of children to participate.

Recognising errors in AI implementation in radiology: A narrative review.

The implementation of AI can suffer from a wide variety of failures. These failures can impact the performance of AI algorithms, impede the adoption of AI solutions in clinical practice, lead to workflow delays, or create unnecessary costs. This narrative review aims to comprehensively discuss different reasons for AI failures in Radiology through the analysis of published evidence across three main components of AI implementation: (i) the AI models throughout their lifecycle, (ii) the technical infrastructure, including the hardware and software needed to develop and deploy AI models and (iii) the human factors involved.

External validation of an artificial intelligence tool for fracture detection in children with osteogenesis imperfecta: a multireader study.

Objective: To determine the performance of a commercially available AI tool for fracture detection when used in children with osteogenesis imperfecta (OI).

Materials And Methods: All appendicular and pelvic radiographs from an OI clinic at a single centre from 48 patients were included. Seven radiologists evaluated anonymised images in two rounds, first without, then with AI assistance.

Clinical experience of a fetal postmortem imaging service using microfocus computed tomography.

Background: Postmortem imaging is becoming a more acceptable part of perinatal autopsy, particularly for early gestation or small fetuses in which conventional autopsies may be challenging and rejected by parents.

Objective: This study aimed to establish the relative diagnostic yield of microfocus computed tomography as part of a less invasive autopsy in fetuses at <24 weeks of gestation.

Study Design: This was a single-center, retrospective study of 7 years of 1007 consecutive unselected perinatal autopsies (2016-2023) of fetuses with a body weight of <300 g.

The role of procurement frameworks in responsible AI innovation in the National Health Service: a multi-stakeholder perspective.

Procurement carries legal requirements across public services in the UK but, for stakeholders in clinical Artificial Intelligence (AI) innovation, it is often poorly understood. This perspective piece summarises insights from a cross-sector workshop exploring the role of procurement frameworks in supporting AI innovation in the National Health Service (NHS). The significant characteristics of AI from a procurement perspective are identified and their consequences are explored.

Artificial intelligence (AI) in radiological paediatric fracture assessment: an updated systematic review.

Background: Recognising bone injuries in children is a critical part of children's imaging, and, recently, several AI algorithms have been developed for this purpose, both in research and commercial settings. We present an updated systematic review of the literature, including the latest developments.

Methods/materials: Scopus, Web of Science, Pubmed, Embase, and Cochrane Library databases were queried for studies published between 1 January 2011 and 6 September 2024 matching search terms 'child', 'AI', 'fracture,' and 'imaging'.

Guiding AI in radiology: ESR's recommendations for effective implementation of the European AI Act.

This statement has been produced within the European Society of Radiology AI Working Group and identifies the key policies of the EU AI Act as they pertain to medical imaging. It offers specific recommendations to policymakers and the professional community for the effective implementation of the legislation, addressing potential gaps and uncertainties. Key areas include AI literacy, classification rules for high-risk AI systems, data governance, transparency, human oversight, quality management, deployer obligations, regulatory sandboxes, post-market monitoring, information sharing, and market surveillance.

Artificial intelligence (AI) for paediatric fracture detection: a multireader multicase (MRMC) study protocol.

Introduction: Paediatric fractures are common but can be easily missed on radiography leading to potentially serious implications including long-term pain, disability and missed opportunities for safeguarding in cases of inflicted injury. Artificial intelligence (AI) tools to assist fracture detection in adult patients exist, although their efficacy in children is less well known. This study aims to evaluate whether a commercially available AI tool (certified for paediatric use) improves healthcare professionals (HCPs) detection of fractures, and how this may impact patient care in a retrospective simulated study design.

Multimodality detection of tumour rupture in children with Wilms tumour.

Background And Aims: Tumour rupture (TR) signifies stage III disease and requires treatment intensification, which includes radiotherapy. We studied the associations between radiological, surgical and pathology TR in children with Wilms tumour (WT) in a United Kingdom multicentre clinical study.

Patients And Methods: The IMPORT (Improving Population Outcomes for Renal Tumours of Childhood) study registered 712 patients between 2012 and 2021.

"How I would like AI used for my imaging": children and young persons' perspectives.

Objectives: Artificial intelligence (AI) tools are becoming more available in modern healthcare, particularly in radiology, although less attention has been paid to applications for children and young people. In the development of these, it is critical their views are heard.

Materials And Methods: A national, online survey was publicised to UK schools, universities and charity partners encouraging any child or young adult to participate.

How to report perinatal and paediatric postmortem CT.

Postmortem CT (PMCT) has become increasingly accepted alongside skeletal surveys as a critical part of investigation in childhood deaths, either as part of a suite of non-invasive investigations through parental choice, or comprehensive evaluation in a forensic setting. Whilst CT image acquisition and protocols have been published and are relatively standardised, CT imaging reporting remains highly variable, largely dependent upon reporter experience and expertise. The main "risk" in PMCT is the over-interpretation of normal physiological changes on imaging as pathological, potentially leading to misdiagnosis or overdiagnosis of the disease.

Post-mortem skeletal survey (PMSS) versus post-mortem computed tomography (PMCT) for the detection of corner metaphyseal lesions (CML) in children.

Objectives: Corner metaphyseal lesions (CMLs) are specific for child abuse but challenging to detect on radiographs. The accuracy of CT for CML detection is unknown. Our aim was to compare diagnostic accuracy for CML detection on post-mortem skeletal surveys (PMSS, plain radiography) versus post-mortem CT (PMCT).

Postmortem imaging of fetuses at early gestations: A comparison of microfocus computed tomography with postmortem magnetic resonance at 9.4 T and postmortem ultrasound.

Objective: To compare the diagnostic performance of postmortem ultrasound (PMUS), 9.4 T magnetic resonance imaging (MRI) and microfocus computed tomography (micro-CT) for the examination of early gestation fetuses.

Method: Eight unselected fetuses (10-15 weeks gestational age) underwent at least 2 of the 3 listed imaging examinations.

Body weight-based iodinated contrast immersion timing for human fetal postmortem microfocus computed tomography.

Objectives: The aim of this study was to evaluate the length of time required to achieve full iodination using potassium tri-iodide as a contrast agent, prior to human fetal postmortem microfocus computed tomography (micro-CT) imaging.

Methods: Prospective assessment of optimal contrast iodination was conducted across 157 human fetuses (postmortem weight range 2-298 g; gestational age range 12-37 weeks), following micro-CT imaging. Simple linear regression was conducted to analyse which fetal demographic factors could produce the most accurate estimate for optimal iodination time.

Commercially available artificial intelligence tools for fracture detection: the evidence.

Missed fractures are a costly healthcare issue, not only negatively impacting patient lives, leading to potential long-term disability and time off work, but also responsible for high medicolegal disbursements that could otherwise be used to improve other healthcare services. When fractures are overlooked in children, they are particularly concerning as opportunities for safeguarding may be missed. Assistance from artificial intelligence (AI) in interpreting medical images may offer a possible solution for improving patient care, and several commercial AI tools are now available for radiology workflow implementation.

Pre- and post-COVID-19 gender trends in authorship for paediatric radiology articles worldwide: a systematic review.

Background: Gender inequalities in academic medicine persist despite progress over the past decade. Evidence-based targeted interventions are needed to reduce gender inequalities.

Objective: This systematic review aimed to investigate the impact of COVID-19 on gender trends in authorship of paediatric radiology research worldwide.

The unintended consequences of artificial intelligence in paediatric radiology.

Over the past decade, there has been a dramatic rise in the interest relating to the application of artificial intelligence (AI) in radiology. Originally only 'narrow' AI tasks were possible; however, with increasing availability of data, teamed with ease of access to powerful computer processing capabilities, we are becoming more able to generate complex and nuanced prediction models and elaborate solutions for healthcare. Nevertheless, these AI models are not without their failings, and sometimes the intended use for these solutions may not lead to predictable impacts for patients, society or those working within the healthcare profession.

Less-invasive autopsy for early pregnancy loss.

Autopsy investigations provide valuable information regarding fetal death that can assist in the parental bereavement process, and influence future pregnancies, but conventional autopsy is often declined by parents because of its invasive approach. This has led to the development of less-invasive autopsy investigations based on imaging technology to provide a more accessible and acceptable choice for parents when investigating their loss. Whilst the development and use of more conventional clinical imaging techniques (radiographs, CT, MRI, US) are well described in the literature for fetuses over 20 weeks of gestational age, these investigations have limited diagnostic accuracy in imaging smaller fetuses.