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血管内超声指导

Abstract

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Impact of intravascular ultrasound-guided percutaneous coronary intervention on long-term clinical outcomes in a real world population First-in-man evaluation of intravascular optical frequency domain imaging (OFDI) of Terumo: a comparison with intravascular ultrasound and quantitative coronary angiography A Randomized Study of Distal Filter Protection Versus Conventional Treatment During Percutaneous Coronary Intervention in Patients With Attenuated Plaque Identified by Intravascular Ultrasound Relationship between intravascular ultrasound guidance and clinical outcomes after drug-eluting stents: the assessment of dual antiplatelet therapy with drug-eluting stents (ADAPT-DES) study Assessment of coronary atherosclerosis by IVUS and IVUS-based imaging modalities: progression and regression studies, tissue composition and beyond Comparison of intravascular ultrasound guided versus angiography guided drug eluting stent implantation: a systematic review and meta-analysis Temporal Trends in Inpatient Use of Intravascular Imaging Among Patients Undergoing Percutaneous Coronary Intervention in the United States Intravascular ultrasound predictors for edge restenosis after newer generation drug-eluting stent implantation
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Original Research

JOURNAL：ACC Article Link

Artificial Intelligence in Interventional Cardiology

Bina Ahmed, MD, FACC

Pre-reading

The following are key points to remember from this state-of-the-art review on the impact of artificial intelligence (AI) on interventional cardiology:

  1. 1. AI encompasses a broad application of mathematical algorithms to train machines to mimic human behavior. There is increasing interest in developing AI technology for application in healthcare.

  3. 2. AI operations include machine learning (ML), deep learning (DL), natural language processing (NLP), cognitive computing, computer vision, and robotics.

  5. 3. ML is an automated system that learns to perform a task or make decisions from available data sources. Once an algorithm is programmed, ML has the ability to figure large complex and heterogeneous data sets and make predictions with fewer assumptions compared to conventional statistical methods.

  7. 4. DL is a part of ML, which is based in algorithms called neural networks. DL networks use digitized inputs that work through layers of connected neurons and perform advance pattern recognition to generate an output. DL does not require continued human input. DL is currently best applied to image recognition such as during angiography or echocardiography.

  9. 5. Virtual applications of AI have the potential to enhance image reconstruction, analysis, and interpretation. This is currently being used for coronary anatomic and functional lesion analysis.

  11. 6. Clinical decision support systems apply the use of ML, NLP, and pattern recognition to assist with imitating human thought processing. IBM is currently developing Medical Sieve, an automated cognitive assistant for cardiologists and radiologists to aid in clinical decision making.

  13. 7. Virtual reality platforms are currently being used for periprocedural planning of structural heart interventions.

  15. 8. Robotics are in their initial phase of application in interventional cardiology and not likely to replace a human interventional cardiologist in the near future. Although they can provide physical assistance, they do not perform intelligence assistance at this time.

  17. 9. Challenges to integration of AI in interventional cardiology practice include complexity of its integration, inability to ‘mimic’ human touch and emotions, and how it would impact the workforce.

  19. 10. AI is poised to transform and enhance the practice of interventional cardiology. Whether we can use it intelligently to enhance patient care and outcomes remains to be determined.
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