دورية أكاديمية
أعرض في EDS

A scoping review of artificial intelligence applications in thoracic surgery.

التفاصيل البيبلوغرافية
العنوان: A scoping review of artificial intelligence applications in thoracic surgery.
المؤلفون: Seastedt KP; Department of Surgery, Beth Israel Deaconess Medical Center, Harvard Medical School, Boston, MA, USA., Moukheiber D; Laboratory for Computational Physiology, Massachusetts Institute of Technology, Cambridge, MA, USA., Mahindre SA; Institute for Computational and Data Sciences, University at Buffalo, State University of New York, Buffalo, NY, USA., Thammineni C; HILS Laboratory, University at Buffalo, State University of New York, Buffalo, NY, USA., Rosen DT; Department of Medicine, Beth Israel Deaconess Medical Center, Harvard Medical School, Boston, MA, USA., Watkins AA; Department of Surgery, Beth Israel Deaconess Medical Center, Harvard Medical School, Boston, MA, USA., Hashimoto DA; Surgical AI & Innovation Laboratory, Department of Surgery, Massachusetts General Hospital, Harvard Medical School, Boston, MA, USA., Hoang CD; Thoracic Surgery Branch, National Cancer Institute, National Institutes of Health, Bethesda, MD, USA., Kpodonu J; Division of Cardiac Surgery, Department of Surgery, Beth Israel Deaconess Medical Center, Harvard Medical School, Boston, MA, USA., Celi LA; Division of Pulmonary, Critical Care and Sleep Medicine, Beth Israel Deaconess Medical Center, Harvard Medical School, Boston, MA, USA.
المصدر: European journal of cardio-thoracic surgery : official journal of the European Association for Cardio-thoracic Surgery [Eur J Cardiothorac Surg] 2022 Jan 24; Vol. 61 (2), pp. 239-248.
نوع المنشور: Journal Article; Review
اللغة: English
بيانات الدورية: Publisher: Oxford University Press Country of Publication: Germany NLM ID: 8804069 Publication Model: Print Cited Medium: Internet ISSN: 1873-734X (Electronic) Linking ISSN: 10107940 NLM ISO Abbreviation: Eur J Cardiothorac Surg Subsets: MEDLINE
أسماء مطبوعة: Publication: 2012-: Oxford, England : Oxford University Press
Original Publication: [Berlin] : Springer International ; [Secaucus, NJ, USA : Springer-Verlag New York Inc., distributor, c1987-
مواضيع طبية MeSH: Thoracic Surgery* , Thoracic Surgical Procedures*/adverse effects, Algorithms ; Artificial Intelligence ; Humans ; Reproducibility of Results
مستخلص: Objectives: Machine learning (ML) has great potential, but there are few examples of its implementation improving outcomes. The thoracic surgeon must be aware of pertinent ML literature and how to evaluate this field for the safe translation to patient care. This scoping review provides an introduction to ML applications specific to the thoracic surgeon. We review current applications, limitations and future directions.
Methods: A search of the PubMed database was conducted with inclusion requirements being the use of an ML algorithm to analyse patient information relevant to a thoracic surgeon and contain sufficient details on the data used, ML methods and results. Twenty-two papers met the criteria and were reviewed using a methodological quality rubric.
Results: ML demonstrated enhanced preoperative test accuracy, earlier pathological diagnosis, therapies to maximize survival and predictions of adverse events and survival after surgery. However, only 4 performed external validation. One demonstrated improved patient outcomes, nearly all failed to perform model calibration and one addressed fairness and bias with most not generalizable to different populations. There was a considerable variation to allow for reproducibility.
Conclusions: There is promise but also challenges for ML in thoracic surgery. The transparency of data and algorithm design and the systemic bias on which models are dependent remain issues to be addressed. Although there has yet to be widespread use in thoracic surgery, it is essential thoracic surgeons be at the forefront of the eventual safe introduction of ML to the clinic and operating room.
(© The Author(s) 2021. Published by Oxford University Press on behalf of the European Association for Cardio-Thoracic Surgery. All rights reserved.)
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معلومات مُعتمدة: R01 EB017205 United States EB NIBIB NIH HHS
فهرسة مساهمة: Keywords: Algorithm*; Artificial intelligence*; Complications*; Machine learning*; Prediction*; Survival*
تواريخ الأحداث: Date Created: 20211003 Date Completed: 20220406 Latest Revision: 20240825
رمز التحديث: 20240826
مُعرف محوري في PubMed: PMC8932394
DOI: 10.1093/ejcts/ezab422
PMID: 34601587
قاعدة البيانات: MEDLINE
الوصف
تدمد:1873-734X
DOI:10.1093/ejcts/ezab422