Artificial Intelligence and Machine Learning in Prediction of Surgical Complications: Current State, Applications, and Implications

被引:21
作者
Hassan, Abbas M. [1 ]
Rajesh, Aashish [2 ]
Asaad, Malke [3 ]
Jonas, Nelson A. [4 ]
Coert, J. Henk [5 ]
Mehrara, Babak J. [4 ]
Butler, Charles E. [1 ]
机构
[1] Univ Texas MD Anderson Canc Ctr, Dept Plast & Reconstruct Surg, Houston, TX 77030 USA
[2] Univ Texas Hlth Sci Ctr San Antonio, Dept Surg, 1400 Pressler St,Unit 1488, San Antonio, TX 77030 USA
[3] Univ Pittsburgh, Med Ctr, Dept Plast Surg, Pittsburgh, PA USA
[4] Mem Sloan Kettering Canc Ctr, Dept Plast & Reconstruct Surg, 1275 York Ave, New York, NY 10021 USA
[5] Univ Med Ctr Utrecht, Dept Plast & Reconstruct Surg, Utrecht, Netherlands
关键词
artificial intelligence; machine learning; deep learning; surgical complications; risk assessment; calculator; RECONSTRUCTION; MORTALITY; IMPLANT; BREAST;
D O I
10.1177/00031348221101488
中图分类号
R61 [外科手术学];
学科分类号
摘要
Surgical complications pose significant challenges for surgeons, patients, and health care systems as they may result in patient distress, suboptimal outcomes, and higher health care costs. Artificial intelligence (AI)-driven models have revolutionized the field of surgery by accurately identifying patients at high risk of developing surgical complications and by overcoming several limitations associated with traditional statistics-based risk calculators. This article aims to provide an overview of AI in predicting surgical complications using common machine learning and deep learning algorithms and illustrates how this can be utilized to risk stratify patients preoperatively. This can form the basis for discussions on informed consent based on individualized patient factors in the future.
引用
收藏
页码:25 / 30
页数:6
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