Radiology artificial intelligence: a systematic review and evaluation of methods (RAISE).

OBJECTIVE: There has been a large amount of research in the field of artificial intelligence (AI) as applied to clinical radiology. However, these studies vary in design and quality and systematic reviews of the entire field are lacking.This systematic review aimed to identify all papers that used deep learning in radiology to survey the literature and to evaluate their methods. We aimed to identify the key questions being addressed in the literature and to identify the most effective methods employed. METHODS: We followed the PRISMA guidelines and performed a systematic review of studies of AI in radiology published from 2015 to 2019. Our published protocol was prospectively registered. RESULTS: Our search yielded 11,083 results. Seven hundred sixty-seven full texts were reviewed, and 535 articles were included. Ninety-eight percent were retrospective cohort studies. The median number of patients included was 460. Most studies involved MRI (37%). Neuroradiology was the most common subspecialty. Eighty-eight percent used supervised learning. The majority of studies undertook a segmentation task (39%). Performance comparison was with a state-of-the-art model in 37%. The most used established architecture was UNet (14%). The median performance for the most utilised evaluation metrics was Dice of 0.89 (range .49-.99), AUC of 0.903 (range 1.00-0.61) and Accuracy of 89.4 (range 70.2-100). Of the 77 studies that externally validated their results and allowed for direct comparison, performance on average decreased by 6% at external validation (range increase of 4% to decrease 44%). CONCLUSION: This systematic review has surveyed the major advances in AI as applied to clinical radiology. KEY POINTS: • While there are many papers reporting expert-level results by using deep learning in radiology, most apply only a narrow range of techniques to a narrow selection of use cases. • The literature is dominated by retrospective cohort studies with limited external validation with high potential for bias. • The recent advent of AI extensions to systematic reporting guidelines and prospective trial registration along with a focus on external validation and explanations show potential for translation of the hype surrounding AI from code to clinic.

Idiopathic Carpal Spasm (Trousseau's Sign) After Intraoperative Arm Tourniquet Inflation in an Intubated Patient.

We present a case of a 38-year-old male who sustained a laceration from a knife to the volar aspect of his left index and middle fingers. He had clinical injury to his flexor digitorum profundus tendons to both digits. He underwent operative exploration and repair of the tendons under general anaesthetic. An arm tourniquet was inflated to allow for haemostasis in the operative field. A few minutes after inflation, the patient's hand went into carpal spasm. The tourniquet was deflated and the spasm resolved. Intraoperative serum calcium and carbon dioxide levels were normal. The operation proceeded with the tourniquet deflated. Postoperatively serum calcium and magnesium levels were within normal limits, as was serum vitamin D and parathyroid hormone levels. It has been reported that carpal spasm can occur with tourniquet use in the anxious patient due to hyperventilation and resultant metabolic alkalosis. This however is the first reported case of carpal spasm in the setting of tourniquet use and normal serum electrolytes and respiratory parameters in an intubated patient.

Circulating Melanoma-Derived Extracellular Vesicles: Impact on Melanoma Diagnosis, Progression Monitoring, and Treatment Response.

Malignant melanoma, one of the most aggressive human malignancies, is responsible for 80% of skin cancer deaths. Whilst early detection of disease progression or metastasis can improve patient survival, this remains a challenge due to the lack of reliable biomarkers. Importantly, these clinical challenges are not unique to humans, as melanoma affects many other species, including companion animals, such as the dog and horse. Extracellular vesicles (EVs) are tiny nanoparticles involved in cell-to-cell communication. Several protein and genomic EV markers have been described in the literature, as well as a wide variety of methods for isolating EVs from body fluids. As such, they may be valuable biomarkers in cancer and may address some clinical challenges in the management melanoma. This review aimed to explore the translational applications of EVs as biomarkers in melanoma, as well as their role in the clinical setting in humans and animals. A summary of melanoma-specific protein and genomic EV markers is presented, followed by a discussion of the role EVs in monitoring disease progression and treatment response. Finally, herein, we reviewed the advantages and disadvantages of methods utilised to isolate EVs from bodily fluids in melanoma patients (human and animals) and describe some of the challenges that will need to be addressed before EVs can be introduced in the clinical setting.

Radiology artificial intelligence, a systematic evaluation of methods (RAISE): a systematic review protocol.

INTRODUCTION: There has been a recent explosion of research into the field of artificial intelligence as applied to clinical radiology with the advent of highly accurate computer vision technology. These studies, however, vary significantly in design and quality. While recent guidelines have been established to advise on ethics, data management and the potential directions of future research, systematic reviews of the entire field are lacking. We aim to investigate the use of artificial intelligence as applied to radiology, to identify the clinical questions being asked, which methodological approaches are applied to these questions and trends in use over time. METHODS AND ANALYSIS: We will follow the Preferred Reporting Items for Systematic Review and Meta-Analysis (PRISMA) guidelines and by the Cochrane Collaboration Handbook. We will perform a literature search through MEDLINE (Pubmed), and EMBASE, a detailed data extraction of trial characteristics and a narrative synthesis of the data. There will be no language restrictions. We will take a task-centred approach rather than focusing on modality or clinical subspecialty. Sub-group analysis will be performed by segmentation tasks, identification tasks, classification tasks, pegression/prediction tasks as well as a sub-analysis for paediatric patients. ETHICS AND DISSEMINATION: Ethical approval will not be required for this study, as data will be obtained from publicly available clinical trials. We will disseminate our results in a peer-reviewed publication. Registration number PROSPERO: CRD42020154790.