“Just Accepted” papers have undergone full peer review and have been accepted for publication in Radiology: Artificial Intelligence. This article will undergo copyediting, layout, and proof review ...

Weikert T, et al. Korean J Radiol. 2020 Jul;21(7):891-899. https://doi.org/10.3348/kjr.2019.0653

Objective: The aim of this study was to test the diagnostic performance of a deep learning-based triage system for the detection of acute findings in abdominal computed tomography (CT) examinations. Materials and Methods: Using a RIS/PACS (Radiology Information System/Picture Archiving and Communication System) search engine, we obtained 100 consecutive abdominal CTs with at least one of the following findings: free-gas, free-fluid, or fat-stranding and 100 control cases with absence of these findings. The CT data were analyzed using a convolutional neural network algorithm previously trained for detection of these findings on an independent sample. The validation of the results was performed on a Web-based feedback system by a radiologist with 1 year of experience in abdominal imaging without prior knowledge of image findings through both visual confirmation and comparison with the clinically approved, written report as the standard of reference. All cases were included in the final analysis, except those in which the whole dataset could not be processed by the detection software. Measures of diagnostic accuracy were then calculated. Results: A total of 194 cases were included in the analysis, 6 excluded because of technical problems during the extraction of the DICOM datasets from the local PACS. Overall, the algorithm achieved a 93% sensitivity (91/98, 7 false-negative) and 97% specificity (93/96, 3 false-positive) in the detection of acute abdominal findings. Intra-abdominal free gas was detected with a 92% sensitivity (54/59) and 93% specificity (39/42), free fluid with a 85% sensitivity (68/80) and 95% specificity (20/21), and fat stranding with a 81% sensitivity (42/50) and 98% specificity (48/49). False-positive results were due to streak artifacts, partial volume effects, and a misidentification of a diverticulum (each n = 1). Conclusions: The algorithm's autonomous detection of acute pathological abdominal findings demonstrated a high diagnostic performance, enabling guidance of the radiology workflow toward prioritization of abdominal CT examinations with acute conditions.

Rapid communication of CT exams positive for pulmonary embolism (PE) is crucial for timely initiation of anticoagulation and patient outcome. It is unknown if deep learning automated detection of PE on CT Pulmonary Angiograms (CTPA) in combination with worklist prioritization and an electronic notification system (ENS) can improve communication times and patient turnaround in the Emergency Department (ED). In 01/2019, an ENS allowing direct communication between radiology and ED was installed. Starting in 10/2019, CTPAs were processed by a deep learning (DL)-powered algorithm for detection of PE. CTPAs acquired between 04/2018 and 06/2020 (n = 1808) were analysed. To assess the impact of the ENS and the DL-algorithm, radiology report reading times (RRT), radiology report communication time (RCT), time to anticoagulation (TTA), and patient turnaround times (TAT) in the ED were compared for three consecutive time periods. Performance measures of the algorithm were calculated on a per exam level (sensitivity, specificity, PPV, NPV, F1-score), with written reports and exam review as ground truth. Sensitivity of the algorithm was 79.6 % (95 %CI:70.8-87.2%), specificity 95.0 % (95 %CI:92.0-97.1%), PPV 82.2 % (95 %CI:73.9-88.3), and NPV 94.1 % (95 %CI:91.4-96 %). There was no statistically significant reduction of any of the observed times (RRT, RCT, TTA, TAT). DL-assisted detection of PE in CTPAs and ENS-assisted communication of results to referring physicians technically work. However, the mere clinical introduction of these tools, even if they exhibit a good performance, is not sufficient to achieve significant effects on clinical performance measures.

The use of artificial intelligence (AI) is a powerful tool for image analysis that is increasingly being evaluated by radiology professionals. However, due to the fact that these methods have been developed for the analysis of nonmedical image data and data structure in radiology departments is not “AI ready”, implementing AI in radiology is not straightforward. The purpose of this review is to guide the reader through the pipeline of an AI project for automated image analysis in radiology and thereby encourage its implementation in radiology departments. At the same time, this review aims to enable readers to critically appraise articles on AI-based software in radiology.