Background Artificial intelligence (AI) for gastric cancer diagnosis has been discussed in recent years. The role of AI in early gastric cancer is more important than in advanced gastric cancer since early gastric cancer is not easily identified in clinical practice. However, to our knowledge, past syntheses appear to have limited focus on the populations with early gastric cancer. Objective The purpose of this study is to evaluate the diagnostic accuracy of AI in the diagnosis of early gastric cancer from endoscopic images. Methods We conducted a systematic review from database inception to June 2020 of all studies assessing the performance of AI in the endoscopic diagnosis of early gastric cancer. Studies not concerning early gastric cancer were excluded. The outcome of interest was the diagnostic accuracy (comprising sensitivity, specificity, and accuracy) of AI systems. Study quality was assessed on the basis of the revised Quality Assessment of Diagnostic Accuracy Studies. Meta-analysis was primarily based on a bivariate mixed-effects model. A summary receiver operating curve and a hierarchical summary receiver operating curve were constructed, and the area under the curve was computed. Results We analyzed 12 retrospective case control studies (n=11,685) in which AI identified early gastric cancer from endoscopic images. The pooled sensitivity and specificity of AI for early gastric cancer diagnosis were 0.86 (95% CI 0.75-0.92) and 0.90 (95% CI 0.84-0.93), respectively. The area under the curve was 0.94. Sensitivity analysis of studies using support vector machines and narrow-band imaging demonstrated more consistent results. Conclusions For early gastric cancer, to our knowledge, this was the first synthesis study on the use of endoscopic images in AI in diagnosis. AI may support the diagnosis of early gastric cancer. However, the collocation of imaging techniques and optimal algorithms remain unclear. Competing models of AI for the diagnosis of early gastric cancer are worthy of future investigation. Trial Registration PROSPERO CRD42020193223; https://www.crd.york.ac.uk/prospero/display_record.php?RecordID=193223
BackgroundAlthough early dementia detection is crucial to optimize the treatment outcomes and the management of associated symptoms, the published literature is scarce regarding the effectiveness of active screening protocols in enhancing dementia awareness and increasing the rate of early detection. The present study compared the detection ratio of an active community-based survey for dementia detection with the detection ratio of passive screening during routine clinical practice. Data for passive screening were obtained from the National Health Insurance (NHI) system, which was prospectively collected during the period from 2000 to 2003.DesignA population-based cohort study with historical control.SettingTaiwan.ParticipantsA total of 183 participants aged 65 years or older were involved in a community-based survey. Data from 1,921,308 subjects aged 65 years or older were retrieved from the NHI system.MeasurementsAn adjusted detection ratio, defined as a ratio of dementia prevalence to incidence was used.ResultsThe results showed that the dementia prevalence during the 2000–2003 period was 2.91% in the elderly population, compared with a prevalence of 6.59% when the active survey was conducted. The incidence of dementia in the active survey cohort was 1.83%. Overall, the dementia detection ratio was higher using active surveys [4.23, 95% confidence interval (CI): 2.68–6.69] than using passive detection (1.45, 95% CI: 1.43–1.47) for those aged 65–79 years. Similar findings were observed for those aged 80 years and older.ConclusionThe implementation of an active community-based survey led to a 3-fold increase in the detection rate of early dementia detection compared to passive screening during routine practice.
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