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Creation of Patient-Specific Silicone Cardiac Models with Applications in Pre-surgical Plans and Hands-on Training
Published on: February 10, 2022
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Big Data in cardiac surgery: real world and perspectives
Andrea Montisci1, Vittorio Palmieri2, Maria Teresa Vietri3
1Division of Cardiothoracic Intensive Care, Cardiothoracic Department, ASST Spedali Civili, 25123, Brescia, Italy. montisci.andrea@yahoo.it.
Journal of Cardiothoracic Surgery
|October 30, 2022
Summary
Big Data and AI are revolutionizing medicine, especially cardiac surgery, by enabling personalized risk prediction beyond traditional methods. Their full impact on clinical practice and guidelines is still under evaluation.
Area of Science:
- Medicine
- Data Science
- Cardiology
Background:
- Big Data and advanced analytics like artificial intelligence (AI) and machine learning (ML) are transforming medical practice.
- Cardiac surgery is exploring these tools to develop personalized predictive models for patient risk and prognosis.
Approach:
- Utilizing diverse data sources including electronic health records, clinical studies, imaging, registries, and OMICS profiles.
- Focusing on identifying hidden patterns and associations to improve upon classical surgical risk scores.
Key Points:
- Big Data analytics offer potential for personalized medicine in cardiac surgery.
- These methods highlight limitations of traditional randomized controlled trials (RCTs) in reflecting real-world scenarios.
- Predictive models aim for more accurate individual risk and prognosis assessment.
Conclusions:
- While Big Data and AI show promise in cardiac surgery, their revolutionary extent and integration into clinical guidelines require further investigation.
- Randomized controlled trials and meta-analyses remain primary evidence sources for current medical guidelines.
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