Primary care provider perspectives on the value of opportunistic CT screening.

BACKGROUND: Clinical adoption of AI applications requires stakeholders see value in their use. AI-enabled opportunistic-CT-screening (OS) capitalizes on incidentally-detected findings within CTs for potential health benefit. This study evaluates primary care providers' (PCP) perspectives on OS. METHODS: A survey was distributed to US Internal and Family Medicine residencies. Assessed were familiarity with AI and OS, perspectives on potential value/costs, communication of results, and technology implementation. RESULTS: 62 % of respondents (n = 71) were in Family Medicine, 64.8 % practiced in community hospitals. Although 74.6 % of respondents had heard of AI/machine learning, 95.8 % had little-to-no familiarity with OS. The majority reported little-to-no trust in AI. Reported concerns included AI accuracy (74.6 %) and unknown liability (73.2 %). 78.9 % of respondents reported that OS applications would require radiologist oversight. 53.5 % preferred OS results be included in a separate "screening" section within the Radiology report, accompanied by condition risks and management recommendations. The majority of respondents reported results would likely affect clinical management for all queried applications, and that atherosclerotic cardiovascular disease risk, abdominal aortic aneurysm, and liver fibrosis should be included within every CT report regardless of reason for examination. 70.5 % felt that PCP practices are unlikely to pay for OS. Added costs to the patient (91.5 %), the healthcare provider (77.5 %), and unknown liability (74.6 %) were the most frequently reported concerns. CONCLUSION: PCP preferences and concerns around AI-enabled OS offer insights into clinical value and costs. As AI applications grow, feedback from end-users should be considered in the development of such technology to optimize implementation and adoption. Increasing stakeholder familiarity with AI may be a critical prerequisite first step before stakeholders consider implementation.

Artificial intelligence matches subjective severity assessment of pneumonia for prediction of patient outcome and need for mechanical ventilation: a cohort study.

To compare the performance of artificial intelligence (AI) and Radiographic Assessment of Lung Edema (RALE) scores from frontal chest radiographs (CXRs) for predicting patient outcomes and the need for mechanical ventilation in COVID-19 pneumonia. Our IRB-approved study included 1367 serial CXRs from 405 adult patients (mean age 65 ± 16 years) from two sites in the US (Site A) and South Korea (Site B). We recorded information pertaining to patient demographics (age, gender), smoking history, comorbid conditions (such as cancer, cardiovascular and other diseases), vital signs (temperature, oxygen saturation), and available laboratory data (such as WBC count and CRP). Two thoracic radiologists performed the qualitative assessment of all CXRs based on the RALE score for assessing the severity of lung involvement. All CXRs were processed with a commercial AI algorithm to obtain the percentage of the lung affected with findings related to COVID-19 (AI score). Independent t- and chi-square tests were used in addition to multiple logistic regression with Area Under the Curve (AUC) as output for predicting disease outcome and the need for mechanical ventilation. The RALE and AI scores had a strong positive correlation in CXRs from each site (r2 = 0.79-0.86; p < 0.0001). Patients who died or received mechanical ventilation had significantly higher RALE and AI scores than those with recovery or without the need for mechanical ventilation (p < 0.001). Patients with a more substantial difference in baseline and maximum RALE scores and AI scores had a higher prevalence of death and mechanical ventilation (p < 0.001). The addition of patients' age, gender, WBC count, and peripheral oxygen saturation increased the outcome prediction from 0.87 to 0.94 (95% CI 0.90-0.97) for RALE scores and from 0.82 to 0.91 (95% CI 0.87-0.95) for the AI scores. AI algorithm is as robust a predictor of adverse patient outcome (death or need for mechanical ventilation) as subjective RALE scores in patients with COVID-19 pneumonia.

Validation of a Smartphone Application in the Evaluation and Treatment of Acute Stroke in a Comprehensive Stroke Center.

Background and Purpose- The increasing demand and shortage of experts to evaluate and treat acute stroke patients has led to the development of remote communication tools to aid stroke management. We aimed to evaluate the JOIN App smartphone system-a low-cost tool for rapid clinical and neuroimaging data sharing to expedite decision-making in stroke. Methods- Consecutive acute ischemic stroke patients treated at a University Hospital in Brazil from December 2014 to December 2015 were evaluated. The analysis included all patients presenting with acute ischemic stroke who underwent initial evaluation by neurology residents followed by JOIN teleconsultation with a stroke neurologist on call for management decisions. An expert panel of stroke neurologists and neuroradiologists revised all cases using a standard Picture Archiving and Communication System imaging workstation within 24 hours and analyzed the decision made with remote assistance during the emergency setting. Results- A total of 720 stroke codes were evaluated with 442 acute ischemic stroke qualifying. Seventy-eight (18%) patients were treated with intravenous thrombolysis. The main reasons for tPA (tissue-type plasminogen activator) exclusion were symptom onset >4.5 hours (n=295; 67%) and hypodense middle cerebral artery territory area >1/3 (n=31; 7%). The agreement rates between Picture Archiving and Communication System versus JOIN-based thrombolysis decisions were 100% for the stroke (unblinded) and 99.3% for the neuroradiologist (blinded) experts. The use of the application resulted in a significant reduction in the door-to-needle times across the pre- versus postimplementation periods (median, 90 [interquartile range, 75-106] versus 63 [interquartile range, 61-117] minutes; P=0.03). The rates of 90-day excellent outcomes (modified Rankin Scale, 0-1) were 51.3%; 90-day mortality, 2.6%; and symptomatic intracranial hemorrhage, 3.8%. Conclusions- The JOIN smartphone system allows rapid sharing of clinical and imaging data to facilitate decisions for stroke treatment. The remote application-based decisions seem to be as accurate as the physical presence of stroke experts and might lead to faster times to treatment. This system represents an easily implementable low-cost telemedicine solution for centers that cannot afford the full-time presence of stroke specialists.

Breast cancer mammographic diagnosis performance in a public health institution: a retrospective cohort study.

OBJECTIVES: To evaluate the quality assurance of mammography results at a reference institution for the diagnosis and treatment of breast cancer in southern Brazil, based on the BIRADS (Breast Imaging Reporting and Data System) 5th edition recommendations for auditing purposes. MATERIALS AND METHODS: Retrospective cohort and cross-sectional study with 4502 patients (9668 mammographies)) who underwent at least one or both breast mammographies throughout 2013 at a regional public hospital, linked to a federal public university. The results were followed until 31 December 2014, including true positives (TPs), true negatives (TNs), false positives (FPs), false negatives (FNs), positive predictive values (PPVs), negative predictive value (NPV), sensitivity and specificity, with a confidence interval of 95%. RESULTS: The study showed high quality assurance, particularly regarding sensitivity (90.22%) and specificity (92.31%). The overall positive predictive value (PPV) was 65.35%, and the negative predictive value (NPV) was 98.32%. The abnormal interpretation rate (recall rate) was 12.26%. CONCLUSIONS: The results are appropriate when compared to the values proposed by the BIRADS 5th edition. Additionally, the study provided self-reflection considering our radiological practice, which is essential for improvements and collaboration regarding breast cancer detection. It may stimulate better radiological practice performance and continuing education, despite possible infrastructure and facility limitations. MAIN MESSAGES: • Accurate quality performance rates are possible despite financial and governmental limitations. • Low-income institutions should develop standardised teamwork to improve radiological practice. • Regular mammography audits may help to increase the quality of public health systems.