3D Printing, Computational Modeling, and Artificial Intelligence for Structural Heart Disease

心理干预 计算机科学 心脏成像 医学 模式 重症监护医学 医学物理学 人工智能 放射科 社会科学 精神科 社会学
作者
Dee Dee Wang,Zhen Qian,Marija Vukicevic,Sandy Engelhardt,Arash Kheradvar,Chuck Zhang,Stephen H. Little,Johan W. Verjans,Dorin Comaniciu,William W. O'Neill,Mani A. Vannan
出处
期刊:Jacc-cardiovascular Imaging [Elsevier BV]
卷期号:14 (1): 41-60 被引量:40
标识
DOI:10.1016/j.jcmg.2019.12.022
摘要

Structural heart disease (SHD) is a new field within cardiovascular medicine. Traditional imaging modalities fall short in supporting the needs of SHD interventions, as they have been constructed around the concept of disease diagnosis. SHD interventions disrupt traditional concepts of imaging in requiring imaging to plan, simulate, and predict intraprocedural outcomes. In transcatheter SHD interventions, the absence of a gold-standard open cavity surgical field deprives physicians of the opportunity for tactile feedback and visual confirmation of cardiac anatomy. Hence, dependency on imaging in periprocedural guidance has led to evolution of a new generation of procedural skillsets, concept of a visual field, and technologies in the periprocedural planning period to accelerate preclinical device development, physician, and patient education. Adaptation of 3-dimensional (3D) printing in clinical care and procedural planning has demonstrated a reduction in early-operator learning curve for transcatheter interventions. Integration of computation modeling to 3D printing has accelerated research and development understanding of fluid mechanics within device testing. Application of 3D printing, computational modeling, and ultimately incorporation of artificial intelligence is changing the landscape of physician training and delivery of patient-centric care. Transcatheter structural heart interventions are requiring in-depth periprocedural understanding of cardiac pathophysiology and device interactions not afforded by traditional imaging metrics. • Structural heart interventions require in-depth understanding of cardiac pathophysiology. • 3D printing can decrease the early-operator learning curve for new technology adaptation. • Computational fluid modeling has potential to emulate dynamic physical and physiological properties of cardiac pathophysiology. • Application of AI has potential for patient-specific anatomic replica procedural simulation training.
最长约 10秒,即可获得该文献文件

科研通智能强力驱动
Strongly Powered by AbleSci AI
科研通是完全免费的文献互助平台,具备全网最快的应助速度,最高的求助完成率。 对每一个文献求助,科研通都将尽心尽力,给求助人一个满意的交代。
实时播报
1秒前
默默冷松完成签到,获得积分10
1秒前
刘成奥发布了新的文献求助10
1秒前
小M完成签到,获得积分10
1秒前
量子星尘发布了新的文献求助10
2秒前
2秒前
早睡早起完成签到 ,获得积分10
3秒前
呱呱完成签到 ,获得积分10
3秒前
3秒前
nanashi完成签到 ,获得积分10
3秒前
Damon发布了新的文献求助10
3秒前
666完成签到,获得积分10
4秒前
4秒前
4秒前
笨笨的从寒完成签到,获得积分20
4秒前
xiaohu发布了新的文献求助10
5秒前
青黛发布了新的文献求助10
5秒前
岁岁菌完成签到,获得积分10
6秒前
安陌煜完成签到,获得积分10
7秒前
7秒前
7秒前
666发布了新的文献求助10
7秒前
海光发布了新的文献求助10
7秒前
7秒前
鲤鱼灵阳完成签到,获得积分10
8秒前
8秒前
9秒前
9秒前
9秒前
简简单单发布了新的文献求助10
9秒前
9秒前
9秒前
华仔应助tongbuxiang采纳,获得10
10秒前
真正的man完成签到,获得积分10
10秒前
10秒前
小青完成签到,获得积分10
10秒前
koko发布了新的文献求助10
10秒前
量子星尘发布了新的文献求助10
11秒前
lunaxia完成签到,获得积分10
11秒前
英俊的铭应助清新的苑博采纳,获得10
11秒前
高分求助中
Production Logging: Theoretical and Interpretive Elements 2700
Neuromuscular and Electrodiagnostic Medicine Board Review 1000
こんなに痛いのにどうして「なんでもない」と医者にいわれてしまうのでしょうか 510
The First Nuclear Era: The Life and Times of a Technological Fixer 500
ALUMINUM STANDARDS AND DATA 500
Walter Gilbert: Selected Works 500
岡本唐貴自伝的回想画集 500
热门求助领域 (近24小时)
化学 材料科学 医学 生物 工程类 有机化学 物理 生物化学 纳米技术 计算机科学 化学工程 内科学 复合材料 物理化学 电极 遗传学 量子力学 基因 冶金 催化作用
热门帖子
关注 科研通微信公众号,转发送积分 3667657
求助须知:如何正确求助?哪些是违规求助? 3226188
关于积分的说明 9768281
捐赠科研通 2936167
什么是DOI,文献DOI怎么找? 1608152
邀请新用户注册赠送积分活动 759520
科研通“疑难数据库(出版商)”最低求助积分说明 735404