Artificial intelligence is a rapidly evolving field, with modern technological advances and the growth of electronic health data opening new possibilities in diagnostic radiology. In recent years, the performance of deep learning (DL) algorithms on various medical image tasks have continually improved. DL algorithms have been proposed as a tool to detect various forms of intracranial hemorrhage on non-contrast computed tomography (NCCT) of the head. In subtle, acute cases, the capacity for DL algorithm image interpretation support might improve the diagnostic yield of CT for detection of this time-critical condition, potentially expediting treatment where appropriate and improving patient outcomes. However, there are multiple challenges to DL algorithm implementation, such as the relative scarcity of labeled datasets, the difficulties in developing algorithms capable of volumetric medical image analysis, and the complex practicalities of deployment into clinical practice. This review examines the literature and the approaches taken in the development of DL algorithms for the detection of intracranial hemorrhage on NCCT head studies. Considerations in crafting such algorithms will be discussed, as well as challenges which must be overcome to ensure effective, dependable implementations as automated tools in a clinical setting.

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Introduction: Hepatic steatosis is a common incidental finding on computed tomography (CT) in patients presenting to the emergency department (ED). The aims of our study were to assess the prevalence of hepatic steatosis in ED patients with suspected renal colic and to assess documentation in radiology reports and medical charts correlated with alanine transaminase (ALT) levels. Methods: Over 18 months from January 2016 to June 2017, all unenhanced CTs performed for suspected renal colic were reviewed. Quantitative assessment measuring hepatic and splenic attenuation in Hounsfield Units was performed. Hepatic steatosis was defined using multiple CT criteria including liver/spleen (L/S) ratio. Radiology reports, medical charts and ALT levels, if collected within 24 h of CT, were reviewed. Results: A total of 1290 patients were included with a median age 52.5 years (range 16-98) and male predominance (835 [64.7%]). A total of 336 (26%) patients had hepatic steatosis measured by L/S ratio of ≤ 1.0. Ninety-four patients (28%) had radiology reports noting steatosis. Documentation in medical charts was noted in 18 of the 94 patients (19.1%) for whom steatosis was reported. Liver enzymes were available for 704 (54.6%) patients. There was a significantly higher mean ALT level in patients with hepatic steatosis (42.2 U/L; 95% CI 38.4-46.0) compared to patients without (28.8 U/L; 95% CI 25.7-31.9) (P < 0.0001). Conclusion: Our findings highlight multiple gaps in the reporting and evaluation of hepatic steatosis among radiologists and emergency clinicians alike. Recognising and reporting this incidental finding may impact health outcomes.

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Artificial intelligence (AI) is making a profound impact in healthcare, with the number of AI applications in medicine increasing substantially over the past five years. In acute stroke, it is playing an increasingly important role in clinical decision-making. Contemporary advances have increased the amount of informationboth clinical and radiologicalwhich clinicians must consider when managing patients. In the time-critical setting of acute stroke, AI offers the tools to rapidly evaluate and consolidate available information, extracting specific predictions from rich, noisy data. It has been applied to the automatic detection of stroke lesions on imaging and can guide treatment decisions through the prediction of tissue outcomes and long-term functional outcomes. This review examines the current state of AI applications in stroke, exploring their potential to reform stroke care through clinical decision support, as well as the challenges and limitations which must be addressed to facilitate their acceptance and adoption for clinical use.