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PURPOSE: We wished to evaluate if an open-source artificial intelligence (AI) algorithm ( https://www.childfx.com ) could improve performance of (1) subspecialized musculoskeletal radiologists, (2) radiology residents, and (3) pediatric residents in detecting pediatric and young adult upper extremity fractures. MATERIALS AND METHODS: A set of evaluation radiographs drawn from throughout the upper extremity (elbow, hand/finger, humerus/shoulder/clavicle, wrist/forearm, and clavicle) from 240 unique patients at a single hospital was constructed (mean age 11.3 years, range 0-22 years, 37.9% female). Two fellowship-trained musculoskeletal radiologists, three radiology residents, and two pediatric residents were recruited as readers. Each reader interpreted each case initially without and then subsequently 3-4 weeks later with AI assistance and recorded if/where fracture was present. RESULTS: Access to AI significantly improved area under the receiver operator curve (AUC) of radiology residents (0.768 [0.730-0.806] without AI to 0.876 [0.845-0.908] with AI, P < 0.001) and pediatric residents (0.706 [0.659-0.753] without AI to 0.844 [0.805-0.883] with AI, P < 0.001) in identifying fracture, respectively. There was no evidence of improvement for subspecialized musculoskeletal radiology attendings in identifying fracture (AUC 0.867 [0.832-0.902] to 0.890 [0.856-0.924], P = 0.093). There was no evidence of difference between overall resident AUC with AI and subspecialist AUC without AI (resident with AI 0.863, attending without AI AUC 0.867, P = 0.856). Overall physician radiograph interpretation time was significantly lower with AI (38.9 s with AI vs. 52.1 s without AI, P = 0.030). CONCLUSION: An openly accessible AI model significantly improved radiology and pediatric resident accuracy in detecting pediatric upper extremity fractures.
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INTRODUCTION: The common comorbid conditions that accompany late-life bipolar disorder (BD) have not been well studied. This is a literature review on psychiatric and medical comorbidities among elderly individuals with BD. METHODS: A focused literature review searched PubMed. Inclusion criteria were original research reports, in English, until June 2009, specifically focused on medical and psychiatric comorbidities in BD individuals over the age of 50. RESULTS: A limited number of studies were identified. Most involved small samples (n < 100). Metabolic syndrome, respiratory and cardiovascular conditions, and endocrine abnormalities are common, with patients having an average of 3 to 4 medical comorbid conditions. Approximately 4.5% to 19% of elderly individuals with BD have dementia. Rates of psychiatric comorbidity appear lower than in younger BD populations, with the most common concurrent psychiatric illnesses being anxiety and substance use disorders. Rates of comorbid medical conditions appear similar to rates among geriatric patients without BD. CONCLUSIONS: Elderly individuals with BD are burdened by multiple concomitant medical disorders. In contrast to the elevated rates of medical comorbidity, rates of psychiatric comorbidity appear lower in elderly individuals with BD than in younger populations with BD. Greater awareness of concurrent medical conditions might help inform coordinated care that considers both mental and physical health among geriatric patients with BD.
