The automaton as a surgeon: the future of artificial intelligence in emergency and general surgery.
Artificial intelligence (AI)
Big data
Emergency surgery (ES)
Machine learning
Journal
European journal of trauma and emergency surgery : official publication of the European Trauma Society
ISSN: 1863-9941
Titre abrégé: Eur J Trauma Emerg Surg
Pays: Germany
ID NLM: 101313350
Informations de publication
Date de publication:
Jun 2021
Jun 2021
Historique:
received:
13
05
2020
accepted:
16
07
2020
pubmed:
28
7
2020
medline:
12
10
2021
entrez:
28
7
2020
Statut:
ppublish
Résumé
Artificial intelligence (AI) is a field involving computational simulation of human intelligence processes; these applications of deep learning could have implications in the specialty of emergency surgery (ES). ES is a rapidly advancing area, and this review will outline the most recent advances. A literature search encompassing the uses of AI in surgery was conducted across large databases (Pubmed, OVID, SCOPUS). Two doctors (LR, CH) both collated relevant papers and appraised them. Papers included were published within the last 5 years, and a "snowball effect" used to collate further relevant literature. AI has been shown to provide value in predicting surgical outcomes and giving personalised patient risks based on inputted data. Further to this, image recognition technology within AI has showed success in fracture identification and breast cancer diagnosis. Regarding theatre presence, supervised robots have carried out suturing and anastomosis of bowel in controlled environments to a high standard. AI has potential for integration across surgical services, from diagnosis to treatment, and aiding the surgeon in key decision-making for risks per patient. Fully automated surgery may be the future, but at present, AI needs human supervision.
Sections du résumé
BACKGROUND
BACKGROUND
Artificial intelligence (AI) is a field involving computational simulation of human intelligence processes; these applications of deep learning could have implications in the specialty of emergency surgery (ES). ES is a rapidly advancing area, and this review will outline the most recent advances.
METHODS
METHODS
A literature search encompassing the uses of AI in surgery was conducted across large databases (Pubmed, OVID, SCOPUS). Two doctors (LR, CH) both collated relevant papers and appraised them. Papers included were published within the last 5 years, and a "snowball effect" used to collate further relevant literature.
RESULTS
RESULTS
AI has been shown to provide value in predicting surgical outcomes and giving personalised patient risks based on inputted data. Further to this, image recognition technology within AI has showed success in fracture identification and breast cancer diagnosis. Regarding theatre presence, supervised robots have carried out suturing and anastomosis of bowel in controlled environments to a high standard.
CONCLUSION
CONCLUSIONS
AI has potential for integration across surgical services, from diagnosis to treatment, and aiding the surgeon in key decision-making for risks per patient. Fully automated surgery may be the future, but at present, AI needs human supervision.
Identifiants
pubmed: 32715331
doi: 10.1007/s00068-020-01444-8
pii: 10.1007/s00068-020-01444-8
doi:
Types de publication
Journal Article
Review
Langues
eng
Sous-ensembles de citation
IM
Pagination
757-762Références
Bashir M, Harky A. Artificial intelligence in aortic surgery: the rise of the machine. Semin Thorac Cardiovasc Surg. 2019;31:635–7.
pubmed: 31279913
doi: 10.1053/j.semtcvs.2019.05.040
Maddox TM, Rumsfeld JS, Payne PRO. Questions for artificial intelligence in health care. JAMA J Am MedAssoc. 2019;321(1):31–2.
doi: 10.1001/jama.2018.18932
Hashimoto DA, Rosman G, Rus D, Meireles OR. Artificial intelligence in surgery: promises and perils. Ann Surg. 2018;268(1):70–6.
pubmed: 29389679
pmcid: 5995666
doi: 10.1097/SLA.0000000000002693
Schmidhuber J. Deep Learning in neural networks: an overview. Neural Netw. 2015;61:85–117.
pubmed: 25462637
doi: 10.1016/j.neunet.2014.09.003
D. Silver et al., “A general reinforcement learning algorithm that masters chess, shogi, and Go through self-play,” Science (80-.)., vol. 362, no. 6419, pp. 1140–1144, 2018.
Raghupathi W, Raghupathi V. Big data analytics in healthcare: promise and potential. Health Inf Sci Syst. 2014;2:3.
pubmed: 25825667
pmcid: 4341817
doi: 10.1186/2047-2501-2-3
Loftus T, et al. Artificial intelligence and Surgical decision-making. JAMA Surg. 2019;155(2):148–58.
doi: 10.1001/jamasurg.2019.4917
Farahmand S, Shabestari O, Pakrah M, Hossein-Nejad H, Arbab M, Bagheri-Hariri S. Artificial intelligence-based triage for patients with acute abdominal pain in emergency department; a diagnostic accuracy study. Adv J Emerg Med. 2017;1(1):5.
Zho S, Greenspan H, Shen D. Deep learning for medical image analysis. 2017.
Esteva A, Kuprel B, Novoa R. Dermatologist-level classification of skin cancer with deep neural networks. Nature. 2017;542(7639):115–8.
pubmed: 28117445
doi: 10.1038/nature21056
Lakhani P, Sundaram B. Deep learning at chest radiography: automated classification of pulmonary tuberculosis by using convolutional neural networks. Radiology. 2017;284(2):574–82.
pubmed: 28436741
doi: 10.1148/radiol.2017162326
Ehteshami B, Veta M, Johannes van Diest P, van Ginneken B, Karssemeijer N, Litjens G. Diagnostic assessment of deep learning algorithms for detection of lymph node metastases in women with breast cancer. JAMA. 2017;318(22):2199–210.
doi: 10.1001/jama.2017.14585
K. Yasaka, H. Akai, O. Abe, and S. Kiryu, “Deep learning with convolutional neural network for differentiation of liver masses at dynamic contrast-enhanced CT: a preliminary study.,” Radiology, 2017.
Gulshan V, Peng L, Coram M. Development and validation of a deep learning algorithm for detection of diabetic retinopathy in retinal fundus photographs. JAMA. 2016;316(22):2402–10.
pubmed: 27898976
doi: 10.1001/jama.2016.17216
Reismann J, et al. Diagnosis and classification of pediatric acute appendicitis by artificial intelligence methods: an investigator-independent approach. PLoS ONE. 2019;14(9):1–11.
doi: 10.1371/journal.pone.0222030
Sato Y, Asamoto T, Ono Y, Goto R, Kitamura A, Honda S. A computer-aided diagnosis system using artificial intelligence for proximal femoral fractures enables residents to achieve a diagnostic rate equivalent to orthopedic surgeons - multi -institutional joint development research. Medicine. 2019. https://doi.org/10.1097/MD.0000000000014146 .
pubmed: 31764824
pmcid: 6882617
doi: 10.1097/MD.0000000000014146
Olczak J, et al. Artificial intelligence for analyzing orthopedic trauma radiographs: deep learning algorithms—are they on par with humans for diagnosing fractures? Acta Orthop. 2017;88(6):581–6.
pubmed: 28681679
pmcid: 5694800
doi: 10.1080/17453674.2017.1344459
Cheng PM, Tejura TK, Tran KN, Whang G. Detection of high-grade small bowel obstruction on conventional radiography with convolutional neural networks. Abdom Radiol. 2018;43(5):1120–7.
doi: 10.1007/s00261-017-1294-1
Cheng PM, Tran KN, Whang G, Tejura TK. Refining convolutional neural network detection of small-bowel obstruction in conventional radiography. Am J Roentgenol. 2019;212(2):342–50.
doi: 10.2214/AJR.18.20362
Bilimoria KY, et al. Surgical risk calculator : a decision aide and informed consent tool for patients and surgeons. J Am Coll Surg. 2013;217(5):833–42.
pubmed: 24055383
pmcid: 3805776
doi: 10.1016/j.jamcollsurg.2013.07.385
Bagnall NM, et al. Perioperative risk prediction in the era of enhanced recovery: a comparison of POSSUM, ACPGBI, and E-PASS scoring systems in major surgical procedures of the colorectal surgeon. Int J Colorectal Dis. 2018;33(11):1627–34.
pubmed: 30078107
pmcid: 6208691
doi: 10.1007/s00384-018-3141-4
Shortliffe EH, Sepúlveda MJ. Clinical decision support in the era of artificial intelligence. J Am Med Assoc. 2018;320(21):2199–200.
doi: 10.1001/jama.2018.17163
Fritz B, et al. Deep-learning model for predicting 30-day postoperative mortality. Br J Anaesth. 2019;123(5):688–95.
pubmed: 31558311
pmcid: 6993109
doi: 10.1016/j.bja.2019.07.025
Hill B, et al. An automated machine learning-based model predicts postoperative mortality using readily-extractable preoperative electronic health record data. Br J Anaesth. 2019;123(6):877–86.
pubmed: 31627890
pmcid: 6883494
doi: 10.1016/j.bja.2019.07.030
Bertsimas D, Dunn J, Velmahos GC, Kaafarani HMA. Surgical risk is not linear: derivation and validation of a novel, user-friendly, and machine-learning-based predictive OpTimal Trees in Emergency Surgery Risk (POTTER) Calculator. Ann Surg. 2018;268(4):574–83.
pubmed: 30124479
doi: 10.1097/SLA.0000000000002956
Corey KM, et al. Development and validation of machine learning models to identify high-risk surgical patients using automatically curated electronic health record data (Pythia): a retrospective, single-site study. PLoS Med. 2018;15(11):1–19.
doi: 10.1371/journal.pmed.1002701
Bihorac A, et al. MySurgeryRisk: development and validation of a machine-learning risk algorithm for major complications and death after surgery. Ann Surg. 2019;269(4):652–62.
pubmed: 29489489
pmcid: 6110979
doi: 10.1097/SLA.0000000000002706
Hofer IS, Lee C, Gabel E, Baldi P, Cannesson M. Development and validation of a deep neural network model to predict postoperative mortality, acute kidney injury, and reintubation using a single feature set. Digit Med. 2020;3:1.
Lei VJ, et al. Risk stratification for postoperative acute kidney injury in major noncardiac surgery using preoperative and intraoperative data. JAMA Netw Open. 2019;2(12):e1916921.
pubmed: 31808922
pmcid: 6902769
doi: 10.1001/jamanetworkopen.2019.16921
Parreco J, Hidalgo A, Parks JJ, Kozol R, Rattan R. Using artificial intelligence to predict prolonged mechanical ventilation and tracheostomy placement. J Surg Res. 2018;228:179–87.
pubmed: 29907209
doi: 10.1016/j.jss.2018.03.028
Kose E, Ozturk NN, Karahan SR. Artificial intelligence in surgery. Eur Arch Med Res. 2018;34(Suppl 1):4–6.
doi: 10.5152/eamr.2018.43043
Panesar S, Cagle Y, Chander D, Morey J, Fernandez-Miranda J, Kliot M. Artificial intelligence and the future of surgical robotics. Ann Surg. 2019;270(2):223–6.
pubmed: 30907754
doi: 10.1097/SLA.0000000000003262
Aruni G, Amit G, Dasgupta P. New surgical robots on the horizon and the potential role of artificial intelligence. Investig Clin Urol. 2018;59(4):221–2.
pubmed: 29984335
pmcid: 6028471
doi: 10.4111/icu.2018.59.4.221
Saeidi H, Opfermann JD, Kam M, Raghunathan S, Leonard S, Krieger A. A confidence-based shared control strategy for the smart tissue autonomous robot (STAR). In: IEEE international conference intelligence robotics system. 2018. pp. 1268–1275.
Kaan HL, Ho KY. Robot-assisted endoscopic resection: current status and future directions. Gut Liver. 2020;14(2):150–2.
pubmed: 31158954
doi: 10.5009/gnl19047
Lin Y, Lin C. The application of artificial intelligence technology in the diagnosis of acute pancreatitis. In: Progn. Syst. Health Manag. Conf. 2019. pp. 244–8.
Knoops PGM, et al. A machine learning framework for automated diagnosis and computer-assisted planning in plastic and reconstructive surgery. Sci Rep. 2019;9(1):1–12.
doi: 10.1038/s41598-019-49506-1
Damian DD. Regenerative robotics. Birth Defects Res. 2020;112(2):131–6.
pubmed: 31187605
doi: 10.1002/bdr2.1533
Hung A, et al. Experts vs super-experts: differences in automated performance metrics and clinical outcomes for robot-assisted radical prostatectomy. BJU Int. 2018;123:5.
Verghese A, Shah NH, Harrington RA. What this computer needs is a physician humanism and artificial intelligence. JAMA J Am Med Assoc. 2018;319(1):19–20.
doi: 10.1001/jama.2017.19198
O’Sullivan S, et al. Legal, regulatory, and ethical frameworks for development of standards in artificial intelligence (AI) and autonomous robotic surgery. Int J Med Robot Comput Assist Surg. 2019;15(1):1–12.
Kelly CJ, Karthikesalingam A, Suleyman M, Corrado G, King D. Key challenges for delivering clinical impact with artificial intelligence. BMC Med. 2019;17(1):1–9.
doi: 10.1186/s12916-019-1426-2
Badgeley MA, et al. Deep learning predicts hip fracture using confounding patient and healthcare variables. Digit Med. 2019;2:1.
Pesapane F, Volonté C, Codari M, Sardanelli F. Artificial intelligence as a medical device in radiology: ethical and regulatory issues in Europe and the United States. Insights Imaging. 2018;9(5):745–53.
pubmed: 30112675
pmcid: 6206380
doi: 10.1007/s13244-018-0645-y
King TC, Aggarwal N, Taddeo M, Floridi L. Artificial intelligence crime: an interdisciplinary analysis of foreseeable threats and solutions, vol. 26. Netherlands: Springer; 2020.