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简介：AbstractDiabetic retinopathy (DR) is an important cause of blindness globally, and its prevalence is increasing. Early detection and intervention can help change the outcomes of the disease. The rapid development of artificial intelligence (AI) in recent years has led to new possibilities for the screening and diagnosis of DR. An AI-based diagnostic system for the detection of DR has significant advantages, such as high efficiency, high accuracy, and lower demand for human resources. At the same time, there are shortcomings, such as the lack of standards for development and evaluation and the limited scope of application. This article demonstrates the current applications of AI in the field of DR, existing problems, and possible future development directions.

简介：AbstractArtificial intelligence (AI) is now a trendy subject in clinical medicine and especially in gastrointestinal (GI) endoscopy. AI has the potential to improve the quality of GI endoscopy at all levels. It will compensate for humans’ errors and limited capabilities by bringing more accuracy, consistency, and higher speed, making endoscopic procedures more efficient and of higher quality. AI showed great results in diagnostic and therapeutic endoscopy in all parts of the GI tract. More studies are still needed before the introduction of this new technology in our daily practice and clinical guidelines. Furthermore, ethical clearance and new legislations might be needed. In conclusion, the introduction of AI will be a big breakthrough in the field of GI endoscopy in the upcoming years. It has the potential to bring major improvements to GI endoscopy at all levels.

简介：AbstractThe wide use and abuse of antibiotics could make antimicrobial resistance (AMR) an increasingly serious issue that threatens global health and imposes an enormous burden on society and the economy. To avoid the crisis of AMR, we have to fundamentally change our approach. Artificial intelligence (AI) represents a new paradigm to combat AMR. Thus, various AI approaches to this problem have sprung up, some of which may be considered successful cases of domain-specific AI applications in AMR. However, to the best of our knowledge, there is no systematic review illustrating the use of these AI-based applications for AMR. Therefore, this review briefly introduces how to employ AI technology against AMR by using the predictive AMR model, the rational use of antibiotics, antimicrobial peptides (AMPs) and antibiotic combinations, as well as future research directions.

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简介：AbstractArtificial intelligence (AI) has proven time and time again to be a game-changer innovation in every walk of life, including medicine. Introduced by Dr. Gunn in 1976 to accurately diagnose acute abdominal pain and list potential differentials, AI has since come a long way. In particular, AI has been aiding in radiological diagnoses with good sensitivity and specificity by using machine learning algorithms. With the coronavirus disease 2019 pandemic, AI has proven to be more than just a tool to facilitate healthcare workers in decision making and limiting physician-patient contact during the pandemic. It has guided governments and key policymakers in formulating and implementing laws, such as lockdowns and travel restrictions, to curb the spread of this viral disease. This has been made possible by the use of social media to map severe acute respiratory syndrome coronavirus 2 hotspots, laying the basis of the "smart lockdown" strategy that has been adopted globally. However, these benefits might be accompanied with concerns regarding privacy and unconsented surveillance, necessitating authorities to develop sincere and ethical government–public relations.

简介：AbstractCurrently, the diagnosis of tuberculosis (TB) is mainly based on the comprehensive consideration of the patient's symptoms and signs, laboratory examinations and chest radiography (CXR). CXR plays a pivotal role to support the early diagnosis of TB, especially when used for TB screening and differential diagnosis. However, high cost of CXR hardware and shortage of certified radiologists poses a major challenge for CXR application in TB screening in resource limited settings. The latest development of artificial intelligence (AI) combined with the accumulation of a large number of medical images provides new opportunities for the establishment of computer-aided detection (CAD) systems in the medical applications, especially in the era of deep learning (DL) technology. Several CAD solutions are now commercially available and there is growing evidence demonstrate their value in imaging diagnosis. Recently, WHO published a rapid communication which stated that CAD may be used as an alternative to human reader interpretation of plain digital CXRs for screening and triage of TB.

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简介：AbstractObjective:This study investigates the efficacy of analyzing fetal heart rate (FHR) signals based on Artificial Intelligence to obtain a baseline calculation and identify accelerations/decelerations in the FHR through electronic fetal monitoring during labor.Methods:A total of 43,888 cardiotocograph(CTG) records of female patients in labor from January 2012 to December 2020 were collected from the NanFang Hospital of Southern Medical University. After filtering the data, 2341 FHR records were used for the study. The ObVue fetal monitoring system, manufactured by Lian-Med Technology Co. Ltd., was used to monitor the FHR signals for these pregnant women from the beginning of the first stage of labor to the end of delivery. Two obstetric experts together annotated the FHR signals in the system to determine the baseline as well as accelerations/decelerations of the FHR. Our cardiotocograph network (CTGNet) as well as traditional methods were then used to automatically analyze the baseline and acceleration/deceleration of the FHR signals. The results of calculations were compared with the annotations provided by the obstetric experts, and ten-fold cross-validation was applied to evaluate them. The root-mean-square difference (RMSD) between the baselines, acceleration F-measure (Acc.F-measure), deceleration F-measure (Dec.F-measure), coefficient of synthetic inconsistency (SI) and the morphological analysis discordance index (MADI) were used as evaluation metrics. The data were analyzed by using a paired t-test.Results:The proposed CTGNet was superior to the best traditional method, proposed by Mantel, in terms of the RMSD.BL (1.7935 ± 0.8099 vs. 2.0293 ± 0.9267, t=-3.55 , P=0.004), Acc.F-measure (86.8562 ± 10.9422 vs. 72.2367 ± 14.2096, t= 12.43, P <0.001), Dec.F-measure (72.1038 ± 33.2592 vs. 58.5040 ± 38.0276, t= 4.10, P <0.001), SI (34.8277±20.9595 vs. 54.8049 ± 25.0265, t=-9.39, P <0.001), and MADI (3.1741 ± 1.9901 vs. 3.7289 ± 2.7253, t= -2.74, P= 0.012). The proposed CTGNet thus had significant advantages over the best traditional method on all evaluation metrics.Conclusion:The proposed Artificial Intelligence-based method CTGNet delivers good performance in terms of the automatic analysis of FHR based on cardiotocograph data. It promises to be a key component of smart obstetrics systems of the future.

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简介：AbstractBackground:Youzhi artificial intelligence (AI) software is the AI-assisted decision-making system for diagnosing skin tumors. The high diagnostic accuracy of Youzhi AI software was previously validated in specific datasets. The objective of this study was to compare the performance of diagnostic capacity between Youzhi AI software and dermatologists in real-world clinical settings.Methods:A total of 106 patients who underwent skin tumor resection in the Dermatology Department of China-Japan Friendship Hospital from July 2017 to June 2019 and were confirmed as skin tumors by pathological biopsy were selected. Dermoscopy and clinical images of 106 patients were diagnosed by Youzhi AI software and dermatologists at different dermoscopy diagnostic levels. The primary outcome was to compare the diagnostic accuracy of the Youzhi AI software with that of dermatologists and that measured in the laboratory using specific data sets. The secondary results included the sensitivity, specificity, positive predictive value, negative predictive value, F-measure, and Matthews correlation coefficient of Youzhi AI software in the real-world.Results:The diagnostic accuracy of Youzhi AI software in real-world clinical settings was lower than that of the laboratory data (P < 0.001). The output result of Youzhi AI software has good stability after several tests. Youzhi AI software diagnosed benign and malignant diseases by recognizing dermoscopic images and diagnosed disease types with higher diagnostic accuracy than by recognizing clinical images (P = 0.008, P = 0.016, respectively). Compared with dermatologists, Youzhi AI software was more accurate in the diagnosis of skin tumor types through the recognition of dermoscopic images (P = 0.01). By evaluating the diagnostic performance of dermatologists under different modes, the diagnostic accuracy of dermatologists in diagnosing disease types by matching dermoscopic and clinical images was significantly higher than that by identifying dermoscopic and clinical images in random sequence (P = 0.022). The diagnostic accuracy of dermatologists in the diagnosis of benign and malignant diseases by recognizing dermoscopic images was significantly higher than that by recognizing clinical images (P = 0.010).Conclusion:The diagnostic accuracy of Youzhi AI software for skin tumors in real-world clinical settings was not as high as that of using special data sets in the laboratory. However, there was no significant difference between the diagnostic capacity of Youzhi AI software and the average diagnostic capacity of dermatologists. It can provide assistant diagnostic decisions for dermatologists in the current state.

简介：AbstractBackground:Excessive exposure to fluoride can reduce intelligence. Methylenetetrahydrofolate dehydrogenase, cyclohydrolase, and formyltetrahydrofolate synthetase 1 (MTHFD1) polymorphisms have important roles in neurodevelopment. However, the association of MTHFD1 polymorphisms with children’s intelligence changes in endemic fluorosis areas has been rarely explored.Methods:A cross-sectional study was conducted in four randomly selected primary schools in Tongxu County, Henan Province, from April to May in 2017. A total of 694 children aged 8 to 12 years were included in the study with the recruitment by the cluster sampling method. Urinary fluoride (UF) and urinary creatinine were separately determined using the fluoride ion-selective electrode and creatinine assay kit. Children were classified as the high fluoride group and control group according to the median of urinary creatinine-adjusted urinary fluoride (UFCr) level. Four loci of MTHFD1 were genotyped, and the Combined Raven’s Test was used to evaluate children’s intelligence quotient (IQ). Generalized linear model and multinomial logistic regression model were performed to analyze the associations between children’s UFCr level, MTHFD1 polymorphisms, and intelligence. The general linear model was used to explore the effects of gene-environment and gene-gene interaction on intelligence.Results:In the high fluoride group, children’s IQ scores decreased by 2.502 when the UFCr level increased by 1.0 mg/L (β= –2.502, 95% confidence interval [CI]: –4.411, –0.593), and the possibility for having "excellent" intelligence decreased by 46.3% (odds ratio = 0.537, 95% CI: 0.290, 0.994). Children with the GG genotype showed increased IQ scores than those with the AA genotype of rs11627387 locus in the high fluoride group (P < 0.05). Interactions between fluoride exposure and MTHFD1 polymorphisms on intelligence were observed (Pinteraction < 0.05).Conclusion:Our findings suggest that excessive fluoride exposure may have adverse effects on children’s intelligence, and changes in children’s intelligence may be associated with the interaction between fluoride and MTHFD1 polymorphisms.

简介：摘要CT已成为颅脑损伤、脑出血、脑梗死、脑肿瘤等疾病诊断不可或缺的必备仪器。然而，大型CT体积及质量庞大，只能固定在放射科使用，对于不宜转运的危重症患者，有可能因失去CT检查而延误救治。因此，小型化移动扫描是CT发展的新方向。本文围绕CT是数字化影像诊断仪器、16排移动CT性能特点、移动CT数字化成像、16排移动CT床旁应用及移动CT在战创伤的应用等5个方面进行阐述。

简介：AbstractPurpose:The triage and initial care of injured patients and a subsequent right level of care is paramount for an overall outcome after traumatic injury. Early recognition of patients is an important case of such decision-making with risk of worse prognosis. This article is to answer if clinical and paraclinical signs can predict the critical conditions of injured patients after traumatic injury resuscitation.Methods:The study included 1107 trauma patients, 16 years and older. The patients were trauma victims of Levels I and II triage and admitted to the Rajaee (Emtiaz) Trauma Hospital, Shiraz, in 2014-2015. The cross-industry process for data mining methodology and modeling was used for assessing the best early clinical and paraclinical variables to predict the patients' prognosis. Five modeling methods including the support vector machine, K-nearest neighbor algorithms, Bagging and Adaboost, and the neural network were compared by some evaluation criteria.Results:Learning algorithms can predict the deterioration of injured patients by monitoring the Bagging and SVM models with 99% accuracy. The most-fitted variables were Glasgow Coma Scale score, base deficit, and diastolic blood pressure especially after initial resuscitation in the algorithms for overall outcome predictions.Conclusion:Data mining could help in triage, initial treatment, and further decision-making for outcome measures in trauma patients. Clinical and paraclinical variables after resuscitation could predict short-term outcomes much better than variables on arrival. With artificial intelligence modeling system, diastolic blood pressure after resuscitation has a greater association with predicting early mortality rather than systolic blood pressure after resuscitation. Artificial intelligence monitoring may have a role in trauma care and should be further investigated.

简介：摘要在无人驾驶舱面临技术和公众信心问题短期内无法推行的情况下，单人制驾驶舱无论在技术还是公众信心方面都更加成熟，这是一项足以影响甚至颠覆当前民航运行模式的变革。

简介：摘要《干预试验方案报告标准》（SPIRIT）2013年版旨在通过提供基于证据的最低报告标准，来提高临床试验方案报告的完整性，并有助于提高评估新干预方法实施过程中的透明度。近年来，人们逐渐认识到，涉及人工智能（AI）的干预措施需要经过严格的前瞻性评估，以确认其对健康结局的影响。《人工智能干预试验方案报告标准》（SPIRIT-AI）扩展版是与配套指南《人工智能试验报告统一标准》（CONSORT-AI）同步编制的新的报告指南，适用于人工智能干预试验。这两部指南的制订均通过了多阶段的共识过程，首先基于文献回顾和专家访谈制订26个候选条目，进而由国际多方利益相关小组（103位利益相关者）在两轮Delphi调查中对这些条目进行投票，然后在31位利益相关者参与的共识会议上对最终条目达成共识。最后由34位参与者帮助优化。SPIRIT-AI扩展版包含15条在人工智能干预试验方案中重要且特有的新条目。除应报告SPIRIT 2013所有条目外，新条目也应该被报告。SPIRIT-AI要求研究者应详细描述将要评估的人工智能干预措施，包括操作指南和使用者需要具备的技能、人工智能干预运行需要的环境、管理输入和输出数据的注意事项、人机交互方式以及错误案例如何分析与处理。SPIRIT-AI有利于提高人工智能干预试验方案的透明度和完整性，且有助于期刊编辑、评审专家和普通民众读懂、理解和批判性评估临床试验设计以及可能存在的偏倚风险。

简介：摘要目的分析基于5G+AI的远程动态实时心电监护技术在老年慢性疾病患者中的应用价值。方法描述性研究。选取2019年1月至2020年12月于湖南省人民医院佩戴远程动态实时监护的年龄≥65岁的老年慢性疾病患者，观察佩戴期间危急值和心电信号采集传输预警的频次、正确率，并分析危急值心电事件的临床影响因素。结果共监测6 662例老年患者，预警2 024例(30.4%)，拨出电话2 223个，预警2 291次事件。其中，危急值心电事件共预警827例，预警事件1 044次，预警不准确4次，危急值预警准确率99.6%；心电信号采集传输预警告知1 247次，电极脱落预警616次(实时处置成功351次)。阵发性或持续性心房颤动、心房扑动会增加老年患者危急值心电事件的发生风险(OR＝2.236，95%CI：1.892~2.643，P<0.001)，慢性心功能不全也增加老年患者危急值心电事件的发生风险(OR＝1.871，95%CI：1.591~2.202，P<0.001)。结论远程动态实时心电监护技术在老年慢性疾病患者中的有效应用，可提高其佩戴动态心电监护的依从性、提升监护的敏感性与时效性。阵发性或持续性心房颤动、心房扑动和慢性心功能不全是老年患者出现危急值心电事件的影响因素。