Artificial Intelligence in Fracture Detection: A Systematic Review and Meta-Analysis.

Background Patients with fractures are a common emergency presentation and may be misdiagnosed at radiologic imaging. An increasing number of studies apply artificial intelligence (AI) techniques to fracture detection as an adjunct to clinician diagnosis. Purpose To perform a systematic review and meta-analysis comparing the diagnostic performance in fracture detection between AI and clinicians in peer-reviewed publications and the gray literature (ie, articles published on preprint repositories). Materials and Methods A search of multiple electronic databases between January 2018 and July 2020 (updated June 2021) was performed that included any primary research studies that developed and/or validated AI for the purposes of fracture detection at any imaging modality and excluded studies that evaluated image segmentation algorithms. Meta-analysis with a hierarchical model to calculate pooled sensitivity and specificity was used. Risk of bias was assessed by using a modified Prediction Model Study Risk of Bias Assessment Tool, or PROBAST, checklist. Results Included for analysis were 42 studies, with 115 contingency tables extracted from 32 studies (55 061 images). Thirty-seven studies identified fractures on radiographs and five studies identified fractures on CT images. For internal validation test sets, the pooled sensitivity was 92% (95% CI: 88, 93) for AI and 91% (95% CI: 85, 95) for clinicians, and the pooled specificity was 91% (95% CI: 88, 93) for AI and 92% (95% CI: 89, 92) for clinicians. For external validation test sets, the pooled sensitivity was 91% (95% CI: 84, 95) for AI and 94% (95% CI: 90, 96) for clinicians, and the pooled specificity was 91% (95% CI: 81, 95) for AI and 94% (95% CI: 91, 95) for clinicians. There were no statistically significant differences between clinician and AI performance. There were 22 of 42 (52%) studies that were judged to have high risk of bias. Meta-regression identified multiple sources of heterogeneity in the data, including risk of bias and fracture type. Conclusion Artificial intelligence (AI) and clinicians had comparable reported diagnostic performance in fracture detection, suggesting that AI technology holds promise as a diagnostic adjunct in future clinical practice. Clinical trial registration no. CRD42020186641 © RSNA, 2022 Online supplemental material is available for this article. See also the editorial by Cohen and McInnes in this issue.

Dupuytren Disease: Prevalence, Incidence, and Lifetime Risk of Surgical Intervention. A Population-Based Cohort Analysis.

BACKGROUND: Health care burden attributable to Dupuytren disease (DD) is largely unknown. The authors determined (1) the prevalence and incidence of DD, (2) the incidence of first surgical intervention, and (3) the lifetime risk of surgical intervention in the United Kingdom National Healthcare Service. METHODS: In this population-based dynamic cohort analysis, data of the Clinical Practice Research Datalink was linked to Hospital Episode Statistics, to characterize the diagnosis and surgical treatment of DD. Secular trends of incidence of DD diagnosis and first surgical treatment were calculated for 2000 to 2013. A multistate Markov model was designed to estimate the lifetime risk of first surgical intervention. RESULTS: A total of 10,553,454 subjects were included in the analyses, 5,502,879 (52%) of whom were women. Of these, 38,707 DD patients were identified. Point prevalence in 2013 was 0.67% (99% CI, 0.66 to 0.68). The incidence of DD almost doubled from 0.30 (99% CI, 0.28 to 0.33) per 1000 person-years in 2000, to 0.59 (99% CI, 0.56 to 0.62) per 1000 person-years in 2013. The incidence of first surgical intervention similarly increased from 0.29 (99% CI, 0.23 to 0.37) to 0.88 (99% CI, 0.77 to 1.00) in the same period. A man or woman newly diagnosed with DD at age 65 has a lifetime risk of surgical intervention of 23% and 13%, respectively, showing only a very subtle decrease when diagnosed later in life. CONCLUSIONS: DD is an important health condition in the older population, because prevalence and incidence rates have almost doubled in the past decade. Estimated lifetime risk of surgical treatment is relatively low, but almost twice in men compared with women. CLINICAL QUESTION/LEVEL OF EVIDENCE: Risk, III.

Perspectives: A surgeon's guide to machine learning.

The exponential increase in the volume and complexity of healthcare data presents new challenges to researchers and clinicians in analysis and interpretation. The requirement for new strategies to extract meaningful information from large, noisy datasets has led to the development of the field of big data analytics. Artificial intelligence (AI) is a general-purpose technology in which machines carry out tasks traditionally thought to be only achievable by humans. Machine learning (ML) is an approach to AI in which machines can "learn" to perform tasks in an automated process, rather than being explicitly programmed by a human. Research aiming to apply ML techniques to classification, prediction and decision-making problems in healthcare has increased 61-fold from 2005 to 2019, mirroring this sense of early promise. The field of healthcare ML is relatively young, and many critical steps are needed before adoption into clinical practice, including transparent, unbiased development and reporting of algorithms. Articles claiming that machines can outperform, or replace, doctors in high-level tasks, such as diagnosis or prognostication, must be carefully appraised. It is critical that surgeons have an understanding of the principles and terminology of AI and ML to evaluate these claims and to take an active role in directing research. This article is an up-to-date review and primer for surgeons covering the core tenets of ML applied to surgical problems, including algorithm types and selection, model training and validation, interpretation of common outcome metrics, current and future reporting guidelines and discussion of the challenges and limitations in this field.

Nongenetic Factors Associated with Dupuytren's Disease: A Systematic Review.

BACKGROUND: Dupuytren's disease is a common complex disease caused by genetic and nongenetic factors. The role of many nongenetic risk factors is still unclear and debatable. This study aimed to systematically review the association between Dupuytren's disease and nongenetic risk factors. METHODS: A search strategy was developed based on the Population, Exposure, Comparison, Outcomes and Study framework. A Preferred Reporting Items for Systematic Reviews and Meta-Analyses-compliant literature search was conducted in MEDLINE, Embase, Scopus, Web of Science, Cochrane Database of Systematic Reviews, and Cochrane Central Register of Controlled Trials from inception to November of 2018. Title and abstract and then full-text screening against eligibility criteria was performed independently by two reviewers, and consensus was achieved by a third reviewer. The Effective Public Health Practice Project and the Oxford Centre for Evidence Based Medicine tools were used to assess study quality and to evaluate the level of evidence of included studies, respectively. RESULTS: Reviewers identified 4434 studies, of which 54 were included in the analysis. There was strong evidence for the association between Dupuytren's disease and advanced age, male sex, family history of Dupuytren's disease, and diabetes mellitus. Furthermore, heavy alcohol drinking, cigarette smoking, and manual work exposure showed a significant dose-response relationship. The quality of the included studies was mainly low or moderate, and most studies were level 3 or 4 on the Oxford Centre for Evidence Based Medicine scale. CONCLUSIONS: The study results show a strong association between Dupuytren's disease and advanced age, male sex, family history of Dupuytren's disease, diabetes mellitus, heavy alcohol drinking, cigarette smoking, and manual work exposure. Further studies are required to explain the causal relationship of these associations.