中国科技核心期刊(中国科技论文统计源期刊)收录证书

近年来,人工智能在心律失常领域展现出巨大的潜力。智能研究和心律失常的结合给疾病的预防、诊断和治疗带来巨大变革。本文将就人工智能分别在心律失常诊断、挖掘疾病新特征、建立疾病预测模型、局限性及展望四个方面作一阐述。     

人工智能在心脏影像诊断中的研究进展

人工智能(AI)中的传统机器学习和深度学习依赖于数据的训练来实现对疾病精确诊断及分类,在心脏影像诊断中各具优势。近年AI在心脏超声、CT、MRI、单光子发射体层成像(SPECT)和正电子发射体层成像(PET)中的应用以心脏分割和快速成像为核心,不断深入对低辐射剂量扫描、精确的自动化数据测量以及准确的预后评估的研究。就AI基本原理及基于AI的不同影像检查方法在心脏成像中的研究进展予以综述。     

人工智能在腹部影像的研究现状与展望

【摘要】随着计算机计算能力的显著提高、深度学习算法的更新以及大型数据集的可用性,迅速地推动着人工智能在医学影像中的应用。人工智能可从影像图像中提取人眼无法辨别的有价值信息,并且为分析图像数据提供了有前途的新方法,使得放射科医生有可能将人工智能纳入临床实践中。本文就人工智能在腹部影像的研究现状做简要介绍,并展望人工智能在腹部影像学中应用的前景。     

扩张型心肌病心律失常与心脏结构改变分析

目的探讨扩张型心肌病（DCM）心律失常的发生与心脏结构改变的关系。方法98例DCM患者均行常规心电图、24h动态心电图检查及心脏超声检查。分析心律失常与心脏结构改变的相关性。结果DCM心房颤动的发生与左房扩大程度密切相关,传导阻滞的发生与左室扩大程度有关,室性心律失常的发生与心室腔的扩大无明显关系。结论DCM患者心房颤动、传导阻滞的发生与心脏结构改变有关,通过改善心脏结构的重塑可延缓本病的自然进程。     

人工智能在介入放射学中的运用前景及挑战

随着人工智能在医学领域的研究逐步深入,未来将极大地改变介入诊疗的工作流程。本文围绕人工智能在介入诊疗中的应用展开详述,涵盖了使用预测模型对高危患者的筛查、治疗方案的选择、通过增强现实技术改善手术治疗、对年轻医生的培养等方面,同时也分析了目前人工智能在临床实际应用中遇到的困难与挑战。     

运动性心律失常研究现状与展望

运动性心律失常一直是体育科学领域十分关注的问题,由于其影响到运动员的身体健康、系统训练以及比赛成绩,尤其耐力项目运动员和从事过大强度与大运动量训练的运动员可出现严重的心律失常,其结果影响自身健康,甚至发生运动性猝死。本文结合作者自身近年研究成果,从运动性心律失常流行病学调查和实验研究诸方面综述运动性心律失常研究现状、病理改变及其发生机制,尤其分析了心脏传导系统中细胞骨架、离子通道、能量代谢以及炎性反应相关因子对运动性心律失常发生的影响。     

慢性阻塞性肺疾病的人工智能研究进展

人工智能（AI）已广泛应用于慢性阻塞性肺疾病（COPD）的研究,在COPD的临床筛查、预诊分级、风险评估、医学成像和远程监护等方面均取得一定进展。AI不仅可以对未患病人群进行早筛、早诊及早治,也可为COPD病人的治疗和管控提供众多的可行方案。重点就AI在肺气肿量化、CT纹理分析、解剖结构分割等影像诊断应用进展予以综述。     

心脏病介入诊疗术中心律失常的防治与体会

本文对我院心血管病介入检查及治疗１８００例、术中心律失常的发生及处理进行了分析探讨。１病例与方法本组病例均为１９８２～１９９６住院病人行心血管介入诊治者，男性１０２０例，女性７８０例；年龄２～８８岁，平均４６岁。其中右心导管检查８７０例，右心室造影１...     

基于深度学习的人工智能在肿瘤诊断中的应用进展

随着大数据时代的到来,人工智能得以在医疗领域崭露头角并实现了飞速发展,尤其在肿瘤诊断方面存在巨大潜能。人工智能利用自动化图像分割及提取等关键技术,在实现短时间内对大量肿瘤信息汇总分析的同时,还可以反映现实环境中成像数据的分布,使肿瘤诊断从主观感知转向客观科学,从而高效精确地协助医师的诊断,为诊疗计划的制订和预后的判断提供坚实的基础。笔者拟对人工智能在肿瘤诊断中的关键技术及当前的应用进行综述。     

人工智能在医学影像CAD中的应用

深度学习是目前人工智能领域备受关注和极具应用前景的机器学习算法，有望革新传统计算机辅助诊断系统，在精准影像诊断中发挥重要作用。本文就人工智能、机器学习、深度学习、卷积神经网络、迁移学习的基本概念以及基于深度学习的计算机辅助诊断系统在肺、乳腺、心脏、颅脑、肝脏、前列腺、骨骼影像领域及病理领域的研究现状予以综述。     

Artificial intelligence in breast ultrasound

Artificial intelligence (AI) is gaining extensive attention for its excellent performance in image-recognition tasks and increasingly applied in breast ultrasound. AI can conduct a quantitative assessment by recognizing imaging information automatically and make more accurate and reproductive imaging diagnosis. Breast cancer is the most commonly diagnosed cancer in women, severely threatening women’s health, the early screening of which is closely related to the prognosis of patients. Therefore, utilization of AI in breast cancer screening and detection is of great significance, which can not only save time for radiologists, but also make up for experience and skill deficiency on some beginners. This article illustrates the basic technical knowledge regarding AI in breast ultrasound, including early machine learning algorithms and deep learning algorithms, and their application in the differential diagnosis of benign and malignant masses. At last, we talk about the future perspectives of AI in breast ultrasound.     

Attitude of Brazilian dentists and dental students regarding the future role of artificial intelligence in oral radiology: a multicenter survey

Objectives:The aim of this study was to assess the attitude of dentists and dental students in Brazil regarding the impact of artificial intelligence (AI) in oral radiology, and to evaluate the effect of an introductory AI lecture on their attitude.Methods:A questionnaire was prepared, comprising statements regarding the future role of AI in oral radiology and dentistry. A lecture of approx. 1 h was prepared, comprising the basic principles of AI and a non-exhaustive overview of AI research in medicine and dentistry. Participants filled in the questionnaire prior to the lecture. After the lecture, the questionnaire was repeated.Results:Throughout 7 sessions at 6 locations, 293 questionnaires were collected. The majority of participants were undergraduate dental students (57%). Prior to the lecture, there was a strong agreement regarding the various future roles and expected impact of AI in oral radiology. Approximately, one-third of participants was concerned about AI. After the lecture, agreement regarding the different roles of AI in oral radiology increased, overall excitement regarding AI increased, and concerns regarding the potential replacement of oral radiologists decreased.Conclusions:A generally positive attitude towards AI was found; an introductory lecture was beneficial towards this attitude and alleviated concerns regarding the effect of AI on the oral radiology profession. Given the unprecedented, ongoing revolution of AI-augmented radiology, it is pivotal to incorporate AI topics in dental training curricula.     

基于深度学习的人工智能在肌骨影像中的研究进展

人工智能(AI)已成为当今社会信息技术领域最重要的技术革命,随着深度学习算法的进步及硬件的升级,人工智能发展迅猛.基于深度学习的人工智能在医学影像的图像分割、图像分类识别和计算机辅助诊断方面都有较大的发展,本文主要讲述人工智能在肌骨影像中的研究进展.     

机器学习在放射治疗中的临床应用

放射治疗是癌症的主要治疗手段之一,以机器学习为代表的人工智能飞速发展,可应用于放射治疗临床实践的各个环节,包括临床决策支持、自动勾画靶区、预测疗效和副反应等,提高准确性与效率.尽管面临着结构化数据缺乏、模型可解释性差等挑战,机器学习在放射治疗中的应用将日趋深刻而广泛.本文从机器学习简介、在放射治疗中的临床应用研究进展和...     

Artificial intelligence in cardiovascular CT: Current status and future implications

Artificial intelligence (AI) refers to the use of computational techniques to mimic human thought processes and learning capacity. The past decade has seen a rapid proliferation of AI developments for cardiovascular computed tomography (CT). These algorithms aim to increase efficiency, objectivity, and performance in clinical tasks such as image quality improvement, structure segmentation, quantitative measurements, and outcome prediction. By doing so, AI has the potential to streamline clinical workflow, increase interpretative speed and accuracy, and inform subsequent clinical pathways. This review covers state-of-the-art AI techniques in cardiovascular CT and the future role of AI as a clinical support tool.