Automated data extraction and report analysis in computer-aided radiology audit: practice implications from post-mortem paediatric imaging

Artificial intelligence applied to fetal MRI: A scoping review of current research

Artificial intelligence (AI) is defined as the development of computer systems to perform tasks normally requiring human intelligence. A subset of AI, known as machine learning (ML), takes this further by drawing inferences from patterns in data to 'learn' and 'adapt' without explicit instructions meaning that computer systems can 'evolve' and hopefully improve without necessarily requiring external human influences. The potential for this novel technology has resulted in great interest from the medical community regarding how it can be applied in healthcare. Within radiology, the focus has mostly been for applications in oncological imaging, although new roles in other subspecialty fields are slowly emerging.In this scoping review, we performed a literature search of the current state-of-the-art and emerging trends for the use of artificial intelligence as applied to fetal magnetic resonance imaging (MRI). Our search yielded several publications covering AI tools for anatomical organ segmentation, improved imaging sequences and aiding in diagnostic applications such as automated biometric fetal measurements and the detection of congenital and acquired abnormalities. We highlight our own perceived gaps in this literature and suggest future avenues for further research. It is our hope that the information presented highlights the varied ways and potential that novel digital technology could make an impact to future clinical practice with regards to fetal MRI.

Post-mortem perinatal imaging: what is the evidence?

Post-mortem imaging for the investigation of perinatal deaths is an acceptable tool amongst parents and religious groups, enabling a less invasive autopsy examination. Nevertheless, availability is scarce nationwide, and there is some debate amongst radiologists regarding the best practice and optimal protocols for performing such studies. Much of the published literature to date focusses on single centre experiences or interesting case reports. Diagnostic accuracy studies are available for a variety of individual imaging modalities (e.g. post-mortem CT, MRI, ultrasound and micro-CT), however, assimilating this information is important when attempting to start a local service.In this article, we present a comprehensive review summarising the latest research, recently published international guidelines, and describe which imaging modalities are best suited for specific indications. When the antenatal clinical findings are not supported by the post-mortem imaging, we also suggest how and when an invasive autopsy may be considered. In general, a collaborative working relationship within a multidisciplinary team (consisting of radiologists, radiographers, the local pathology department, mortuary staff, foetal medicine specialists, obstetricians and bereavement midwives) is vital for a successful service.

A pragmatic evidence-based approach to post-mortem perinatal imaging

Post-mortem imaging has a high acceptance rate amongst parents and healthcare professionals as a non-invasive method for investigating perinatal deaths. Previously viewed as a 'niche' subspecialty, it is becoming increasingly requested, with general radiologists now more frequently asked to oversee and advise on appropriate imaging protocols. Much of the current literature to date has focussed on diagnostic accuracy and clinical experiences of individual centres and their imaging techniques (e.g. post-mortem CT, MRI, ultrasound and micro-CT), and pragmatic, evidence-based guidance for how to approach such referrals in real-world practice is lacking. In this review, we summarise the latest research and provide an approach and flowchart to aid decision-making for perinatal post-mortem imaging. We highlight key aspects of the maternal and antenatal history that radiologists should consider when protocolling studies (e.g. antenatal imaging findings and history), and emphasise important factors that could impact the diagnostic quality of post-mortem imaging examinations (e.g. post-mortem weight and time interval). Considerations regarding when ancillary post-mortem image-guided biopsy tests are beneficial are also addressed, and we provide key references for imaging protocols for a variety of cross-sectional imaging modalities.

A systematic review of natural language processing applied to radiology reports

BACKGROUND

Natural language processing (NLP) has a significant role in advancing healthcare and has been found to be key in extracting structured information from radiology reports. Understanding recent developments in NLP application to radiology is of significance but recent reviews on this are limited. This study systematically assesses and quantifies recent literature in NLP applied to radiology reports.

METHODS

We conduct an automated literature search yielding 4836 results using automated filtering, metadata enriching steps and citation search combined with manual review. Our analysis is based on 21 variables including radiology characteristics, NLP methodology, performance, study, and clinical application characteristics.

RESULTS

We present a comprehensive analysis of the 164 publications retrieved with publications in 2019 almost triple those in 2015. Each publication is categorised into one of 6 clinical application categories. Deep learning use increases in the period but conventional machine learning approaches are still prevalent. Deep learning remains challenged when data is scarce and there is little evidence of adoption into clinical practice. Despite 17% of studies reporting greater than 0.85 F1 scores, it is hard to comparatively evaluate these approaches given that most of them use different datasets. Only 14 studies made their data and 15 their code available with 10 externally validating results.

CONCLUSIONS

Automated understanding of clinical narratives of the radiology reports has the potential to enhance the healthcare process and we show that research in this field continues to grow. Reproducibility and explainability of models are important if the domain is to move applications into clinical use. More could be done to share code enabling validation of methods on different institutional data and to reduce heterogeneity in reporting of study properties allowing inter-study comparisons. Our results have significance for researchers in the field providing a systematic synthesis of existing work to build on, identify gaps, opportunities for collaboration and avoid duplication.

[Postmortem sonography helpful in death of unknown origin]

Postmortem imaging has been used primarily in forensic medicine since 1895. Conventional x‑ray, computed tomography (CT), and magnetic resonance imaging (MRI) are used. In studies, sonography is not considered to be of particular value, especially because of postmortem gas formation in adults. We report three cases in which postmortem sonography within three hours of death allowed clarification of a previously unclear cause of death.

Artificial intelligence in paediatric radiology: Future opportunities

Artificial intelligence (AI) has received widespread and growing interest in healthcare, as a method to save time, cost and improve efficiencies. The high-performance statistics and diagnostic accuracies reported by using AI algorithms (with respect to predefined reference standards), particularly from image pattern recognition studies, have resulted in extensive applications proposed for clinical radiology, especially for enhanced image interpretation. Whilst certain sub-speciality areas in radiology, such as those relating to cancer screening, have received wide-spread attention in the media and scientific community, children's imaging has been hitherto neglected.In this article, we discuss a variety of possible 'use cases' in paediatric radiology from a patient pathway perspective where AI has either been implemented or shown early-stage feasibility, while also taking inspiration from the adult literature to propose potential areas for future development. We aim to demonstrate how a 'future, enhanced paediatric radiology service' could operate and to stimulate further discussion with avenues for research.