Black box no more: A cross-sectional multi-disciplinary survey for exploring governance and guiding adoption of AI in medical imaging and radiotherapy in the UK

BACKGROUND

Medical Imaging and radiotherapy (MIRT) are at the forefront of artificial intelligence applications. The exponential increase of these applications has made governance frameworks necessary to uphold safe and effective clinical adoption. There is little information about how healthcare practitioners in MIRT in the UK use AI tools, their governance and associated challenges, opportunities and priorities for the future.

METHODS

This cross-sectional survey was open from November to December 2022 to MIRT professionals who had knowledge or made use of AI tools, as an attempt to map out current policy and practice and to identify future needs. The survey was electronically distributed to the participants. Statistical analysis included descriptive statistics and inferential statistics on the SPSS statistical software. Content analysis was employed for the open-ended questions.

RESULTS

Among the 245 responses, the following were emphasised as central to AI adoption: governance frameworks, practitioner training, leadership, and teamwork within the AI ecosystem. Prior training was strongly correlated with increased knowledge about AI tools and frameworks. However, knowledge of related frameworks remained low, with different professionals showing different affinity to certain frameworks related to their respective roles. Common challenges and opportunities of AI adoption were also highlighted, with recommendations for future practice.

AI implementation in the UK landscape: Knowledge of AI governance, perceived challenges and opportunities, and ways forward for radiographers

INTRODUCTION

Despite the rapid increase of AI-enabled applications deployed in clinical practice, many challenges exist around AI implementation, including the clarity of governance frameworks, usability of validation of AI models, and customisation of training for radiographers. This study aimed to explore the perceptions of diagnostic and therapeutic radiographers, with existing theoretical and/or practical knowledge of AI, on issues of relevance to the field, such as AI implementation, including knowledge of AI governance and procurement, perceptions about enablers and challenges and future priorities for AI adoption.

METHODS

An online survey was designed and distributed to UK-based qualified radiographers who work in medical imaging and/or radiotherapy and have some previous theoretical and/or practical knowledge of working with AI. Participants were recruited through the researchers' professional networks on social media with support from the AI advisory group of the Society and College of Radiographers. Survey questions related to AI training/education, knowledge of AI governance frameworks, data privacy procedures, AI implementation considerations, and priorities for AI adoption. Descriptive statistics were employed to analyse the data, and chi-square tests were used to explore significant relationships between variables.

RESULTS

In total, 88 valid responses were received. Most radiographers (56.6 %) had not received any AI-related training. Also, although approximately 63 % of them used an evaluation framework to assess AI models' performance before implementation, many (36.9 %) were still unsure about suitable evaluation methods. Radiographers requested clearer guidance on AI governance, ample time to implement AI in their practice safely, adequate funding, effective leadership, and targeted support from AI champions. AI training, robust governance frameworks, and patient and public involvement were seen as priorities for the successful implementation of AI by radiographers.

CONCLUSION

AI implementation is progressing within radiography, but without customised training, clearer governance, key stakeholder engagement and suitable new roles created, it will be hard to harness its benefits and minimise related risks.

IMPLICATIONS FOR PRACTICE

The results of this study highlight some of the priorities and challenges for radiographers in relation to AI adoption, namely the need for developing robust AI governance frameworks and providing optimal AI training.

Strategies to improve the magnetic resonance imaging experience for autistic individuals: a cross-sectional study exploring parents and carers' experiences

BACKGROUND

Autistic individuals encounter numerous barriers in accessing healthcare, including communication difficulties, sensory sensitivities, and a lack of appropriate adjustments. These issues are particularly acute during MRI scans, which involve confined spaces, loud noises, and the necessity to remain still. There remains no unified approach to preparing autistic individuals for MRI procedures.

METHODS

A cross-sectional online survey was conducted with parents and carers of autistic individuals in the UK to explore their experiences, barriers, and recommendations concerning MRI scans. The survey collected demographic information and experiential accounts of previous MRI procedures. Quantitative data were analysed descriptively, while key themes were identified within the qualitative data through inductive thematic analysis.

RESULTS

Sixteen parents/carers participated. The majority reported difficulties with communication, inadequate pre-scan preparation, and insufficient adjustments during MRI scans for their autistic children. Key barriers included an overwhelming sensory environment, radiographers' limited understanding of autism, and anxiety stemming from uncertainties about the procedure. Recommended improvements encompassed accessible communication, pre-visit familiarisation, noise-reduction and sensory adaptations, staff training on autism, and greater flexibility to meet individual needs.

CONCLUSIONS

There is an urgent need to enhance MRI experiences for autistic individuals. This can be achieved through improved staff knowledge, effective communication strategies, thorough pre-scan preparation, and tailored reasonable adjustments. Co-producing clear MRI guidelines with the autism community could standardise sensitive practices. An individualised approach is crucial for reducing anxiety and facilitating participation. Empowering radiographers through autism-specific education and incorporating insights from autistic individuals and their families could transform MRI experiences and outcomes.

Black box no more: a scoping review of AI governance frameworks to guide procurement and adoption of AI in medical imaging and radiotherapy in the UK

Technological advancements in computer science have started to bring artificial intelligence (AI) from the bench closer to the bedside. While there is still lots to do and improve, AI models in medical imaging and radiotherapy are rapidly being developed and increasingly deployed in clinical practice. At the same time, AI governance frameworks are still under development. Clinical practitioners involved with procuring, deploying, and adopting AI tools in the UK should be well-informed about these AI governance frameworks. This scoping review aimed to map out available literature on AI governance in the UK, focusing on medical imaging and radiotherapy. Searches were performed on Google Scholar, Pubmed, and the Cochrane Library, between June and July 2022. Of 4225 initially identified sources, 35 were finally included in this review. A comprehensive conceptual AI governance framework was proposed, guided by the need for rigorous AI validation and evaluation procedures, the accreditation rules and standards, and the fundamental ethical principles of AI. Fairness, transparency, trustworthiness, and explainability should be drivers of all AI models deployed in clinical practice. Appropriate staff education is also mandatory to ensure AI's safe and responsible use. Multidisciplinary teams under robust leadership will facilitate AI adoption, and it is crucial to involve patients, the public, and practitioners in decision-making. Collaborative research should be encouraged to enhance and promote innovation, while caution should be paid to the ongoing auditing of AI tools to ensure safety and clinical effectiveness.

Toward Autism-Friendly Magnetic Resonance Imaging: Exploring Autistic Individuals' Experiences of Magnetic Resonance Imaging Scans in the United Kingdom, a Cross-Sectional Survey

Background

Autistic individuals might undergo a magnetic resonance imaging (MRI) examination for clinical concerns or research. Increased sensory stimulation, lack of appropriate environmental adjustments, or lack of streamlined communication in the MRI suite may pose challenges to autistic patients and render MRI scans inaccessible. This study aimed at (i) exploring the MRI scan experiences of autistic adults in the United Kingdom; (ii) identifying barriers and enablers toward successful and safe MRI examinations; (iii) assessing autistic individuals' satisfaction with MRI service; and (iv) informing future recommendations for practice improvement.

Methods

We distributed an online survey to the autistic community on social media, using snowball sampling. Inclusion criteria were: being older than 16, have an autism diagnosis or self-diagnosis, self-reported capacity to consent, and having had an MRI scan in the United Kingdom. We used descriptive statistics for demographics, inferential statistics for group comparisons/correlations, and content analysis for qualitative data.

Results

We received 112 responses. A total of 29.6% of the respondents reported not being sent any information before the scan. Most participants (68%) confirmed that radiographers provided detailed information on the day of the examination, but only 17.1% reported that radiographers offered some reasonable environmental adjustments. Only 23.2% of them confirmed they disclosed their autistic identity when booking MRI scanning. We found that quality of communication, physical environment, patient emotions, staff training, and confounding societal factors impacted their MRI experiences. Autistic individuals rated their overall MRI experience as neutral and reported high levels of claustrophobia (44.8%).

Conclusion

This study highlighted a lack of effective communication and coordination of care, either between health care services or between patients and radiographers, and lack of reasonable adjustments as vital for more accessible and person-centered MRI scanning for autistic individuals. Enablers of successful scans included effective communication, adjusted MRI environment, scans tailored to individuals' needs/preferences, and well-trained staff.

Exploring radiographers' perceptions and knowledge about patient lead shielding: a cross-sectional study in Greece and Cyprus

The present study aimed to explore radiographers' knowledge, clinical practice and perceptions regarding the use of patient lead shielding in Greece and Cyprus. Qualitative data were analyzed using conceptual content analysis and through the classification of findings into themes and categories. A total of 216 valid responses were received. Most respondents reported not being aware of the patient shielding recommendations issued by the American Association of Physicists in Medicine (67%) or the guidance issued by the British Institute of Radiology (69%). Shielding-related training was generally not provided by radiography departments (74%). Most of them (85%) reported that they need specific guidance on lead shielding practices. Also, 82% of the respondents said that lead shielding should continue to be used outside the pelvic area when imaging pregnant patients. Pediatric patients are the most common patient category to which lead shielding was applied. Significant gaps in relevant training have been identified among radiographers in Greece and Cyprus, highlighting the need for new protocols and provision of adequate training on lead shielding practices. Radiography departments should invest in appropriate shielding equipment and adequately train their staff.

Responsible AI practice and AI education are central to AI implementation: a rapid review for all medical imaging professionals in Europe

Artificial intelligence (AI) has transitioned from the lab to the bedside, and it is increasingly being used in healthcare. Radiology and Radiography are on the frontline of AI implementation, because of the use of big data for medical imaging and diagnosis for different patient groups. Safe and effective AI implementation requires that responsible and ethical practices are upheld by all key stakeholders, that there is harmonious collaboration between different professional groups, and customised educational provisions for all involved. This paper outlines key principles of ethical and responsible AI, highlights recent educational initiatives for clinical practitioners and discusses the synergies between all medical imaging professionals as they prepare for the digital future in Europe. Responsible and ethical AI is vital to enhance a culture of safety and trust for healthcare professionals and patients alike. Educational and training provisions for medical imaging professionals on AI is central to the understanding of basic AI principles and applications and there are many offerings currently in Europe. Education can facilitate the transparency of AI tools, but more formalised, university-led training is needed to ensure the academic scrutiny, appropriate pedagogy, multidisciplinarity and customisation to the learners' unique needs are being adhered to. As radiographers and radiologists work together and with other professionals to understand and harness the benefits of AI in medical imaging, it becomes clear that they are faced with the same challenges and that they have the same needs. The digital future belongs to multidisciplinary teams that work seamlessly together, learn together, manage risk collectively and collaborate for the benefit of the patients they serve.

A cross-sectional study of explainable machine learning in Alzheimer's disease: diagnostic classification using MR radiomic features

Introduction

Alzheimer's disease (AD) even nowadays remains a complex neurodegenerative disease and its diagnosis relies mainly on cognitive tests which have many limitations. On the other hand, qualitative imaging will not provide an early diagnosis because the radiologist will perceive brain atrophy on a late disease stage. Therefore, the main objective of this study is to investigate the necessity of quantitative imaging in the assessment of AD by using machine learning (ML) methods. Nowadays, ML methods are used to address high dimensional data, integrate data from different sources, model the etiological and clinical heterogeneity, and discover new biomarkers in the assessment of AD.

Methods

In this study radiomic features from both entorhinal cortex and hippocampus were extracted from 194 normal controls (NC), 284 mild cognitive impairment (MCI) and 130 AD subjects. Texture analysis evaluates statistical properties of the image intensities which might represent changes in MRI image pixel intensity due to the pathophysiology of a disease. Therefore, this quantitative method could detect smaller-scale changes of neurodegeneration. Then the radiomics signatures extracted by texture analysis and baseline neuropsychological scales, were used to build an XGBoost integrated model which has been trained and integrated.

Results

The model was explained by using the Shapley values produced by the SHAP (SHapley Additive exPlanations) method. XGBoost produced a f1-score of 0.949, 0.818, and 0.810 between NC vs. AD, MC vs. MCI, and MCI vs. AD, respectively.

Discussion

These directions have the potential to help to the earlier diagnosis and to a better manage of the disease progression and therefore, develop novel treatment strategies. This study clearly showed the importance of explainable ML approach in the assessment of AD.

Analysis of radiomic features derived from post-contrast T1-weighted images and apparent diffusion coefficient (ADC) maps for breast lesion evaluation: A retrospective study

INTRODUCTION

Breast cancer is the most common malignancy among women, and its diagnosis relies on medical imaging and the invasive, uncomforted biopsy. Recent advances in quantitative imaging and specifically the application of radiomics has proved to be a very promising technique, facilitating both diagnosis and therapy. The purpose of this study is to assess radiomic features derived from post-contrast T1w Magnetic Resonance Imaging (MRI) sequences and Apparent Diffusion Coefficient (ADC) maps for the evaluation of breast pathologies.

METHODS

MRI data from 52 women were retrospectively reviewed, involving 54 breast lesions, both malignant and benign. Diffusion Weighted Imaging (DWI) was applied as a standard MRΙ protocol, including dynamic contrast-enhanced (DCE) MRΙ in all cases. All patients were examined on a 1.5T MRI scanner, and 216 features were initially extracted from DCE-MRI images. Histological analysis of the breast lesions was performed, and a comparative analysis of the results was carried out to assess the accuracy of the method.

RESULTS

Following surgery and histological analysis, 30 lesions were found to be malignant and 24 benign. Implementation of a Machine Learning (ML) classification algorithm with 5-fold cross-validation resulted in a sensitivity of 70%, specificity of 66%, Negative Predictive Value of 82% and overall accuracy of 67% in differentiating malignancy from benevolence.

CONCLUSION

Texture analysis and ML methodology based on the first post-contrast dynamic sequences and ADC maps may be employed to differentiate between malignant and benign breast lesions, offering a promising new tool for diagnostic analysis.

IMPLICATIONS FOR PRACTICE

The results of this study will enhance knowledge around application and performance of radiomics in breast MRI, thus helping MRI radiographers who use AI-enabled technologies to better delineate the pros and cons of these procedures.

Radiomics for Alzheimer's Disease: Fundamental Principles and Clinical Applications

Alzheimer's disease is a neurodegenerative disease with a huge impact on people's quality of life, life expectancy, and morbidity. The ongoing prevalence of the disease, in conjunction with an increased financial burden to healthcare services, necessitates the development of new technologies to be employed in this field. Hence, advanced computational methods have been developed to facilitate early and accurate diagnosis of the disease and improve all health outcomes. Artificial intelligence is now deeply involved in the fight against this disease, with many clinical applications in the field of medical imaging. Deep learning approaches have been tested for use in this domain, while radiomics, an emerging quantitative method, are already being evaluated to be used in various medical imaging modalities. This chapter aims to provide an insight into the fundamental principles behind radiomics, discuss the most common techniques alongside their strengths and weaknesses, and suggest ways forward for future research standardization and reproducibility.

A systematic review of person-centred adjustments to facilitate magnetic resonance imaging for autistic patients without the use of sedation or anaesthesia

LAY ABSTRACT

Autistic patients often undergo magnetic resonance imaging examinations. Within this environment, it is usual to feel anxious and overwhelmed by noises, lights or other people. The narrow scanners, the loud noises and the long examination time can easily cause panic attacks. This review aims to identify any adaptations for autistic individuals to have a magnetic resonance imaging scan without sedation or anaesthesia. Out of 4442 articles screened, 53 more relevant were evaluated and 21 were finally included in this study. Customising communication, different techniques to improve the environment, using technology for familiarisation and distraction have been used in previous studies. The results of this study can be used to make suggestions on how to improve magnetic resonance imaging practice and the autistic patient experience. They can also be used to create training for the healthcare professionals using the magnetic resonance imaging scanners.

Identifying cardiomegaly in chest X-rays: a cross-sectional study of evaluation and comparison between different transfer learning methods

BACKGROUND

Cardiomegaly is a relatively common incidental finding on chest X-rays; if left untreated, it can result in significant complications. Using Artificial Intelligence for diagnosing cardiomegaly could be beneficial, as this pathology may be underreported, or overlooked, especially in busy or under-staffed settings.

PURPOSE

To explore the feasibility of applying four different transfer learning methods to identify the presence of cardiomegaly in chest X-rays and to compare their diagnostic performance using the radiologists' report as the gold standard.

MATERIAL AND METHODS

Two thousand chest X-rays were utilized in the current study: 1000 were normal and 1000 had confirmed cardiomegaly. Of these exams, 80% were used for training and 20% as a holdout test dataset. A total of 2048 deep features were extracted using Google's Inception V3, VGG16, VGG19, and SqueezeNet networks. A logistic regression algorithm optimized in regularization terms was used to classify chest X-rays into those with presence or absence of cardiomegaly.

RESULTS

Diagnostic accuracy is reported by means of sensitivity, specificity, positive predictive value (PPV), and negative predictive value (NPV), with the VGG19 network providing the best values of sensitivity (84%), specificity (83%), PPV (83%), NPV (84%), and overall accuracy (84,5%). The other networks presented sensitivity at 64.1%-82%, specificity at 77.1%-81.1%, PPV at 74%-81.4%, NPV at 68%-82%, and overall accuracy at 71%-81.3%.

CONCLUSION

Deep learning using transfer learning methods based on VGG19 network can be used for the automatic detection of cardiomegaly on chest X-ray images. However, further validation and training of each method is required before application to clinical cases.

COVID-19 in the radiology department: What radiographers need to know

OBJECTIVES

The aim is to review current literature related to the diagnosis, management, and follow-up of suspected and confirmed Covid-19 cases.

KEY FINDINGS

Medical Imaging plays an important auxiliary role in the diagnosis of Covid-19 patients, mainly those most seriously affected. Practice differs widely among different countries, mainly due to the variability of access to resources (viral testing and imaging equipment, specialised staff, protective equipment). It has been now well-documented that chest radiographs should be the first-line imaging tool and chest CT should only be reserved for critically ill patients, or when chest radiograph and clinical presentation may be inconclusive.

CONCLUSION

As radiographers work on the frontline, they should be aware of the potential risks associated with Covid-19 and engage in optimal strategies to reduce these. Their role in vetting, conducting and often reporting the imaging examinations is vital, as well as their contribution in patient safety and care. Medical Imaging should be limited to critically ill patients, and where it may have an impact on the patient management plan.

IMPLICATIONS FOR PRACTICE

At the time of publication, this review offers the most up-to-date recommendations for clinical practitioners in radiology departments, including radiographers. Radiography practice has to significantly adjust to these new requirements to support optimal and safe imaging practices for the diagnosis of Covid-19. The adoption of low dose CT, rigorous infection control protocols and optimal use of personal protective equipment may reduce the potential risks of radiation exposure and infection, respectively, within Radiology departments.