Artificial intelligence (AI) is increasingly utilised in medical imaging systems and processes, and radiographers must embrace this advancement. This study aimed to investigate perceptions, knowledge, and expectations towards integrating AI into medical imaging amongst a sample of radiographers and determine the current state of AI education within the community. A cross-sectional online quantitative study targeting radiographers based in Europe was conducted over ten weeks. Captured data included demographical information, participants' perceptions and understanding of AI, expectations of AI and AI-related educational backgrounds. Both descriptive and inferential statistical techniques were used to analyse the obtained data. A total of 96 valid responses were collected. Of these, 64% correctly identified the true definition of AI from a range of options, but fewer (37%) fully understood the difference between AI, machine learning and deep learning. The majority of participants (83%) agreed they were excited about the advancement of AI, though a level of apprehensiveness remained amongst 29%. A severe lack of education on AI was noted, with only 8% of participants having received AI teachings in their pre-registration qualification. Overall positive attitudes towards AI implementation were observed. The slight apprehension may stem from the lack of technical understanding of AI technologies and AI training within the community. Greater educational programs focusing on AI principles are required to help increase European radiography workforce engagement and involvement in AI technologies. This study offers insight into the current perspectives of European based radiographers on AI in radiography to help facilitate the embracement of AI technology and convey the need for AI-focused education within the profession.

O'Brien S, et al. J Gastric Cancer. 2018 Sep;18(3):242-252. https://doi.org/10.5230/jgc.2018.18.e25

Computed tomography (CT) helps physicians locate and diagnose pathological conditions. In some conditions, having an airway segmentation method which facilitates reconstruction of the airway from chest CT images can help hugely in the assessment of lung diseases. Many efforts have been made to develop airway segmentation algorithms, but methods are usually not optimized to be reliable across different CT scan parameters.

Key Points CT is superior to pulmonary function tests and chest radiography for the assessment and monitoring of cystic fibrosis (CF)–related lung disease and, also, of pediatric bronchiectasis not caused by CF (hereafter referred to as non-CF bronchiectasis). Low-dose CT protocols that impart radiation doses similar to those used in chest radiography are feasible for the surveillance of patients with bronchiectasis. Chest radiography is still most commonly used as the first-line imaging examination of choice for the assessment of acute complications related to bronchiectasis. Pulmonary MRI, with or without the use of inhaled hyperpolarized gas, can be performed to obtain functional information, and, in dedicated centers, it may yield imaging results comparable to those obtained by CT. Gastrointestinal and pancreaticobiliary manifestations of CF are observed with greater frequency in adults, because of increased life expectancy.