Evaluation of the impact of artificial intelligence-assisted image interpretation on the diagnostic performance of clinicians in identifying pneumothoraces on plain chest X-ray: a multi-case multi-reader study.

BACKGROUND: Artificial intelligence (AI)-assisted image interpretation is a fast-developing area of clinical innovation. Most research to date has focused on the performance of AI-assisted algorithms in comparison with that of radiologists rather than evaluating the algorithms' impact on the clinicians who often undertake initial image interpretation in routine clinical practice. This study assessed the impact of AI-assisted image interpretation on the diagnostic performance of frontline acute care clinicians for the detection of pneumothoraces (PTX). METHODS: A multicentre blinded multi-case multi-reader study was conducted between October 2021 and January 2022. The online study recruited 18 clinician readers from six different clinical specialties, with differing levels of seniority, across four English hospitals. The study included 395 plain CXR images, 189 positive for PTX and 206 negative. The reference standard was the consensus opinion of two thoracic radiologists with a third acting as arbitrator. General Electric Healthcare Critical Care Suite (GEHC CCS) PTX algorithm was applied to the final dataset. Readers individually interpreted the dataset without AI assistance, recording the presence or absence of a PTX and a confidence rating. Following a 'washout' period, this process was repeated including the AI output. RESULTS: Analysis of the performance of the algorithm for detecting or ruling out a PTX revealed an overall AUROC of 0.939. Overall reader sensitivity increased by 11.4% (95% CI 4.8, 18.0, p=0.002) from 66.8% (95% CI 57.3, 76.2) unaided to 78.1% aided (95% CI 72.2, 84.0, p=0.002), specificity 93.9% (95% CI 90.9, 97.0) without AI to 95.8% (95% CI 93.7, 97.9, p=0.247). The junior reader subgroup showed the largest improvement at 21.7% (95% CI 10.9, 32.6), increasing from 56.0% (95% CI 37.7, 74.3) to 77.7% (95% CI 65.8, 89.7, p<0.01). CONCLUSION: The study indicates that AI-assisted image interpretation significantly enhances the diagnostic accuracy of clinicians in detecting PTX, particularly benefiting less experienced practitioners. While overall interpretation time remained unchanged, the use of AI improved diagnostic confidence and sensitivity, especially among junior clinicians. These findings underscore the potential of AI to support less skilled clinicians in acute care settings.

Factors associated with onset, relapses or progression in multiple sclerosis: A systematic review.

Multiple sclerosis (MS) is a chronic disease of the central nervous system with an unidentified etiology. We systematically reviewed the literature on the possible risk factors associated with MS disease onset, relapses and progression from 1960 to 2012 by accessing six databases and including relevant systematic reviews, meta-analyses, case-control or cohort studies. The focus was on identifying modifiable risk factors. Fifteen systematic reviews and 169 original articles were quality assessed and integrated into a descriptive review. Best evidence, which included one or more prospective studies, suggested that lower exposure to sunlight and/or lower serum vitamin D levels were associated with an increased risk of developing MS onset and subsequent relapses, but a similar quality of evidence was lacking for disease progression. Prospective studies indicated that cigarette smoking may increase the risk of MS as well as accelerate disease progression, but whether smoking altered the risk of a relapse was largely unknown. Infections were implicated in both risk of developing MS and relapses, but data for progression were lacking. Specifically, exposure to the Epstein-Barr virus, particularly if this manifested as infectious mononucleosis during adolescence, was associated with increased MS risk. Upper respiratory tract infections were most commonly associated with an increase in relapses. Relapse rates typically dropped during pregnancy, but there was no strong evidence to suggest that pregnancy itself altered the risk of MS or affected long-term progression. Emerging research with the greatest potential to impact public health was the suggestion that obesity during adolescence may increase the risk of MS; if confirmed, this would be of major significance.

Determinants of neurological disease: Synthesis of systematic reviews.

Systematic reviews were conducted to identify risk factors associated with the onset and progression of 14 neurological conditions, prioritized as a component of the National Population Health Study of Neurological Conditions. These systematic reviews provided a basis for evaluating the weight of evidence of evidence for risk factors for the onset and progression of the 14 individual neurological conditions considered. A number of risk factors associated with an increased risk of onset for more than one condition, including exposure to pesticides (associated with an increased risk of AD, amyotrophic lateral sclerosis, brain tumours, and PD; smoking (AD, MS); and infection (MS, Tourette syndrome). Coffee and tea intake was associated with a decreased risk of onset of both dystonia and PD. Further understanding of the etiology of priority neurological conditions will be helpful in focusing future research initiatives and in the development of interventions to reduce the burden associated with neurological conditions in Canada and internationally.