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

Lung function tests are commonly used to monitor lung disease in cystic fibrosis (CF). While practical, they cannot locate the exact origin of functional impairment. Contemporary magnetic resonance imaging (MRI) techniques provide information on the location of disease but the need for contrast agents constrains their repeated application. We examined the correlation between functional MRI, performed without administration of contrast agent, and lung clearance index (LCI) from nitrogen multiple-breath washout (N₂-MBW).40 children with CF (median (range) age 12.0 (6–18) years) and 12 healthy age-matched controls underwent functional and structural MRI and lung function tests on the same day. Functional MRI provided semiquantitative measures of perfusion (R_Q) and ventilation (R_FV) impairment as percentages of affected lung volume. Morphological MRI was evaluated using CF-specific scores. LCI measured global ventilation inhomogeneity.MRI detected functional impairment in CF:R_FV19–38% andR_Q16–35%.R_FVandR_Qcorrelated strongly with LCI (r=0.76, p<0.0001 and r=0.85, p<0.0001, respectively), as did total morphology score (r=0.81, p<0.0001). All indices differed significantly between patients with CF and healthy controls (p<0.001).Noninvasive functional MRI is a promising method to detect and visualise perfusion and ventilation impairment in CF without the need for contrast agents.

Accurate segmentations in medical images are the foundations for various clinical applications. Advances in machine learning-based techniques show great potential for automatic image segmentation, but these techniques usually require a huge amount of accurately annotated reference segmentations for training. The guiding hypothesis of this paper was that crowd-algorithm collaboration could evolve as a key technique in large-scale medical data annotation. As an initial step toward this goal, we evaluated the performance of untrained individuals to detect and correct errors made by three-dimensional (3-D) medical segmentation algorithms. To this end, we developed a multistage segmentation pipeline incorporating a hybrid crowd-algorithm 3-D segmentation algorithm integrated into a medical imaging platform. In a pilot study of liver segmentation using a publicly available dataset of computed tomography scans, we show that the crowd is able to detect and refine inaccurate organ contours with a quality similar to that of experts (engineers with domain knowledge, medical students, and radiologists). Although the crowds need significantly more time for the annotation of a slice, the annotation rate is extremely high. This could render crowdsourcing a key tool for cost-effective large-scale medical image annotation.

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.