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Objective: Ultrasound is one of the most commonly used imaging modalities, though some populations face barriers in accessing ultrasound services, potentially resulting in disparities in utilization. The objective of this study was to assess the association between sociodemographic and geographic factors and non-obstetrical ultrasound utilization in the province of Saskatchewan, Canada. Methods: All non-obstetrical ultrasound exams performed from 2014 to 2018 in Saskatchewan, Canada were retrospectively identified from province-wide databases. Univariate and multivariate Poisson regression analyses were performed to assess the association between ultrasound utilization and sex, age, First Nations status, Charlson Comorbidity Index, urban vs. rural residence, geographic remoteness, and neighborhood income. Results: A total of 1,324,846 individuals (5,857,044 person-years) were included in the analysis. Female sex (adjusted incidence rate ratio [aIRR], 2.20; 95% confidence interval [CI], 2.19-2.22), age (aIRR, 4.97; 95% CI, 4.90-5.05 for ≥57 years vs. <11 years), comorbidities (aIRR, 4.36 for Charlson Comorbidity Index >10 vs. 0; 95% CI, 3.78-5.03), and higher neighborhood income (aIRR, 1.04; 95% CI, 1.02-1.05 for highest vs. lowest quintile) were associated with higher rates of ultrasound utilization. Individuals who were status First Nations (aIRR, 0.91; 95% CI, 0.90-0.92) or resided in geographically remote areas (aIRR, 0.87 for most vs. least remote; 95% CI, 0.83-0.91) had lower rates of ultrasound utilization. Individuals who lived in a rural area also had lower rates of ultrasound utilization (aIRR, 0.93; 95% CI, 0.92-0.94). Conclusion: Substantial disparities exist in non-obstetrical ultrasound utilization among individuals in low-income neighborhoods, status First Nations individuals, and individuals in rural and remote communities. [ABSTRACT FROM AUTHOR]

Artificial intelligence (AI) presents a key opportunity for radiologists to improve quality of care and enhance the value of radiology in patient care and population health. The potential opportunity of AI to aid in triage and interpretation of conventional radiographs (X-ray images) is particularly significant, as radiographs are the most common imaging examinations performed in most radiology departments. Substantial progress has been made in the past few years in the development of AI algorithms for analysis of chest and musculoskeletal (MSK) radiographs, with deep learning now the dominant approach for image analysis. Large public and proprietary image data sets have been compiled and have aided the development of AI algorithms for analysis of radiographs, many of which demonstrate accuracy equivalent to radiologists for specific, focused tasks. This article describes (1) the basis for the development of AI solutions for radiograph analysis, (2) current AI solutions to aid in the triage and interpretation of chest radiographs and MSK radiographs, (3) opportunities for AI to aid in noninterpretive tasks related to radiographs, and (4) considerations for radiology practices selecting AI solutions for radiograph analysis and integrating them into existing IT systems. Although comprehensive AI solutions across modalities have yet to be developed, institutions can begin to select and integrate focused solutions which increase efficiency, increase quality and patient safety, and add value for their patients. [ABSTRACT FROM AUTHOR]

Purpose: The study sought to determine the incidence of incidental findings on whole-body positron emission tomography with computed tomography (PET/CT) imaging and the average costs of investigations to follow-up or further characterize incidental findings. Methods: Imaging reports of 215 patients who underwent whole-body PET/CT imaging were retrospectively reviewed. Our provincial picture archiving and communication system was queried and patient charts were reviewed to identify all investigations performed to follow-up incidental findings within 1 year of the initial PET/CT study. Costs of follow-up imaging studies (professional and technical components) and other diagnostic tests and procedures were determined in Canadian dollars (CAD) and U.S. dollars (USD) using the 2015 Ontario Health Insurance Plan Schedule of Benefits and Fees and 2016 U.S. Medicare Physician Fee Schedule, respectively. Results: At least 1 incidental finding was reported in 161 reports (74.9%). The mean number of incidental findings ranged from 0.64 in patients <45 years of age to 2.2 in patients 75 years of age and older. Seventy-five recommendations for additional investigations were made for 64 (30%) patients undergoing PET/CT imaging, and 14 of those were carried out specifically to follow-up incidental findings. Averaged across all 215 patients, the total cost of investigations recommended to follow-up incidental findings was CAD$105.51 (USD$127.56) per PET/CT study if all recommendations were acted on, and CAD$22.77 (USD$29.14) based on investigations actually performed. Conclusions: As the incidence of incidental findings increases with age and a larger proportion of elderly patients is expected as population demographics change, it will be increasingly important to consider incidental findings on PET/CT imaging with standardized approaches to follow-up. [ABSTRACT FROM AUTHOR]