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Artificial Intelligence Applications in Emergency Service

Yıl 2021, Cilt: 1 Sayı: 3, 1 - 5, 26.12.2021

Öz

Research with artificial intelligence has gained importance in recent years. The main reasons for this increase are modern machine learning techniques; deep learning, the availability of large datasets, and advances in computing power and increasing success in these areas. The applicability of artificial intelligence in healthcare has been demonstrated. Algorithms can equate to or even exceed physician performances. Al- based tools have been used to predict various factors in medicine, including risk stratification, diagnosis, and treatment selection. Artificial intelligence technologies will be used more and more in the field of emergency medicine in the coming years. The purpose of this review is to provide an overview of current artificial intelligence research related to emergency medicine.

Kaynakça

  • 1. Jordan MI, Mitchell TM. Machine learning: Trends, perspectives, and prospects. Science 2015;80(349):255-60. https://doi.org/10.1126/science.aaa8415.
  • 2. Ramesh A, Kambhampati C, Monson J, Drew P. Artificial intelligence in medicine. Ann R Coll Surg Engl. 2004;86(5):334- 8.
  • 3. Lynch S. Andrew Ng: why Al is the new electricity. Stanford News. 2017. [Cited 15 Mar 2018.] Available from URL: https://news.stanford.edu/thedish/2017/03/14/andrew-ng-why- ai-is-the-new-electricity
  • 4. Obermeyer Z, Emanuel EJ. Predicting the future big data, machine learning, and clinical medicine. N Engl J Med 2016;375:1216–9. https://doi.org/10.1056/NEJMp1606181.
  • 5. Senders JT, Arnaout O, Karhade AV, et al. Natural and artificial intelligence in neurosurgery: a systemic review. Neurosurgery 2017;0:1-12. https://doi.org/10.1093 /neuros/nyx384.
  • 6. Chen MC, Ball RL, Yang L, et al. Deep learning to classify radiology free-text reports. Radiology 2017:171115. https://doi.org 10.1148/radiol.2017171115.
  • 7. Walton OB, Garoon RB, Weng CY, et al. Evaluation of automated teleretinal screening program for diabetic retinopathy. JAMA Ophthalmol 2016;134:204. https://doi.org/10.1001/jamaophthalmol.2015.5083.
  • 8. Esteva A, Kuprel B, Novoa RA, et al. Dermatologist-level classification of skin cancer with deep neural networks. Nature 2017;542:115-8. https://doi.org/10.1038/nature21056. 9. Liu N, Holcomb J, Wade C, Darrah M, Salinas J. Utility of vital signs, heart rate variability and complexity, and machine
  • learning for identifying the need for lifesaving interventions in trauma patients. Shock. 2014;42(2):108-14. 10. Houthooft R, Ruyssinck J, van der Herten J, et al.
  • Predictive modelling of survival and length of stay in critically ill patients using sequential organ failure scores. Artif Intell Med. 2015;63(3):191-207.
  • 11. Prevedello LM, Little KJ, Qian S, White RD. Automated critical test findings identification and online notification system using artificial intelligence in imaging. Radiology 2017;0:1-9. https://doi.org/10.1148/radiol.2017162664.
  • 12. Li YH, Zhang L, Hu QM, Li HW, Jia FC, Wu JH. Automatic subarachnoid space segmentation and hemorrhage detection in clinical head CT scans. Int J Comput Assist Radiol Surg 2012;7:507-16. https://doi.org/10.1007/s11548-011- 0664-3.
  • 13. Xiao F, Liao CC, Huang KC, Chiang IJ, Wong JM. Automated assessment of midline shift in head injury patients. Clin Neurol Neurosurg 2010;112:785-90. https://doi. org/10.1016/j.clineuro.2010.06.020.
  • 14. Olczak J, Fahlberg N, Maki A, et al. Artificial intelligence for analyzing orthopedic trauma radiographs: deep learning algorithms are they on par with humans for diagnosing fractures? Acta Orthop 2017;3674:1-6. https://doi.org/10. 1080/17453674.2017.1344459.
  • 15. Rajpurkar P, Irvin J, Zhu K et al. CheXNet: radiologist- level pneumonia detection on chest X-rays with deep learning. arXiv:171105225. 2017. [Cited 21 Mar 2017.] Available from URL: http://arxiv.org/abs/1711.05225
  • 16. Farzaneh N, Soroushmehr SMR, Williamson CA et al. Automated subdural hematoma segmentation for traumatic brain injured (TBI) patients. Conf. Proc. IEEE Eng. Med. Biol. Soc. 2017; 2017:3069-72.
  • 17. Liu J, Chen Y, Lan L et al. Prediction of rupture risk in anterior communicating artery aneurysms with a feed-forward artificial neural network. Eur. Radiol. 2018; 28:3268-75.
  • 18. Herweh C, Ringleb PA, Rauch G et al. Performance of e- ASPECTS software in comparison to that of stroke physicians on assessing CT scans of acute ischemic stroke patients. Int.J. Stroke 2016; 11:438-45.
  • 19. Squarcina L, Perlini C, Peruzzo D et al. The use of dynamic susceptibility contrast (DSC) MRI to automatically classify patients with first episode psychosis. Schizophr. Res. 2015; 165:38-44.
  • 20. Guggenmos M, Scheel M, Sekutowicz M et al. Decoding diagnosis and lifetime consumption in alcohol dependence from grey-matter pattern information. Acta Psychiatr. Scand. 2018; 137:252-62.
  • 21. Sjogren AR, Leo MM, Feldman J, Gwin JT. Image segmentation and machine learning for detection of abdominal free fluid in focused assessment with sonography for trauma examinations: a pilot study. J. Ultrasound Med. 2016; 35:2501-9.
  • 22. Knackstedt C, Bekkers SCAM, Schummers G et al. Fully automated versus standard tracking of left ventricular ejection fraction and longitudinal strain. J. Am. Coll. Cardiol. 2015; 66: 1456-66.
  • 23. Persson M, Fhager A, Trefna HD et al. Microwave-based stroke diagnosis making global prehospital thrombolytic treatment possible. I.Ε.Ε.Ε. Trans. Biomed. Eng. 2014; 61: 2806-17.
  • 24. Convertino VA, Grudic G, Mulligan J, Moulton S. Estimation of individual-specific progression to impending cardiovascular instability using arterial waveforms. J. Appl. Physiol. 2013; 115:1196-202.
  • 25. Johnson MC, Alarhayem A, Convertino V, et al. Compensatory reserve index: performance of a novel monitoring technology to identify the bleeding trauma patient. Shock. 2018 Mar 1;49(3):295-300.
  • 26. Convertino VA, Howard JT, Hinojosa-Laborde C et al. Individual-specific, beat-to-beat trending of significant human blood loss: the compensatory reserve. Shock 2015; 44: 27-32. 27. Muniz GW, Wampler DA, Manifold CA et al. Promoting early diagnosis of hemodynamic instability during simulated hemorrhage with the use of a real-time decision-assist algorithm. J. Trauma Acute Care Surg. 2013; 75: 184-9.
  • 28. Shashikumar SP, Stanley MD, Sadiq I et al. Early sepsis in critical care patients using multiscale blood pressure and heart rate dynamics. J. Electrocardiol. 2017; 50: 739-43.
  • 29. Mao Q, Jay M, Hoffman JL et al. Multicentre validation of a sepsis prediction algorithm using only vital sign data in the emergency department, general ward and ICU. BMJ Open 2018; 8: e017833. 30. Rajpurkar P, Hannun AY, Haghpanahi M, Bourn C, Ng AY. Cardiologist-level arrhythmia detection with convolutional neural networks. arXiv:170701836. 2017. [Cited 21 Mar 2017.] Available from URL: http://arxiv.org/abs/1707.01836
  • 31. Plesinger F, Klimes P, Halamek J, Jurak P. False alarms in intensive care unit monitors: detection of lifethreatening arrhythmias using elementary algebra, descriptive statistics and fuzzy logic. Comput. Cardiol. 2015; 42: 281-4.
  • 32. Monkaresi H, Calvo RA, Yan H. A machine learning approach to improve contactless heart rate monitoring using a webcam. IEEE J. Biomed. Heal. Informatics 2014; 18: 1153-60.
  • 33. Zhang Q, Zhou D, Zeng X. Highly wearable cuff-less blood pressure and heart rate monitoring with single-arm electrocardiogram and photoplethysmogram signals. Biomed. Eng. Online 2017; 16: 1–20.
  • 34. Simjanoska M, Gjoreski M, Gams M, Madevska Bogdanova A. Non-invasive blood pressure estimation from ECG using machine learning techniques. Sensors (Basel) 2018; 18:1160.
  • 35. Stewart J, Sprivulis P, Dwivedi G. Artificial intelligence and machine learning in emergency medicine. Emergency Medicine Australasia. 2018 Dec;30(6):870-4.
  • 36. Rajkomar A, Oren E, Chen K et al. Scalable and accurate deep learning with electronic health records. Npj Digital Medicine 2018; 1: 18.
  • 37. Levin S, Toerper M, Hamrock E et al. Machine-learning- based electronic triage more accurately differentiates patients with respect to clinical outcomes compared with the emergency severity index. Ann. Emerg. Med. 2018; 71:565-74.
  • 38. VanHouten JP, Starmer JM, Lorenzi NM, Maron DJ, Lasko TA. Machine learning for risk prediction of acute coronary syndrome. AMIA Annu. Symp. Proc. 2014; 2014: 1940-9.
  • 39. Liu N, Lee MAB, Ho AFW et al. Risk stratification for prediction of adverse coronary events in emergency department chest pain patients with a machine learning score compared with the TIMI score. Int. J. Cardiol. 2014; 177: 1095-7.
  • 40. Shouval R, Hadanny A, Shlomo N et al. Machine learning for prediction of 30-day mortality after ST elevation myocardial infraction: an acute coronary syndrome Israeli survey data mining study. Int. J. Cardiol. 2017; 246: 7-13.
  • 41. Molaei S, Korley FK, Soroushmehr SMR et al. A machine learning based approach for identifying traumatic brain injury patients for whom a head CT scan can be avoided. 38th Annu. Int. Conf. IEEE Eng. Med. Biol. Soc. 2016: 2258-61.
  • 42. Sefrioui I, Amadini R, Mauro J, Fallahi A, Gabbrielli M. Survival prediction of trauma patients: a study on US National Trauma Data Bank. Eur. J. Trauma Emerg. Surg. 2017; 43: 805-22.
  • 43. Feng C, Wang L, Chen X, et al. A novel artificial intelligence-assisted triage tool to aid in the diagnosis of suspected COVID-19 pneumonia cases in fever clinics. Annals of Translational Medicine. 2021 Feb;9(3).
  • 44. Farahmand S, Shabestari O, Pakrah M, et al. Artificial intelligence-based triage for patients with acute abdominal pain in emergency department; a diagnostic accuracy study. Advanced journal of emergency medicine. 2017;1(1).

Acil Serviste Yapay Zeka Uygulamaları

Yıl 2021, Cilt: 1 Sayı: 3, 1 - 5, 26.12.2021

Öz

Yapay zeka ile yapılan araştırmalar son günlerde önem kazanmıştır. Bunun nedenlerinin başında modern makine öğrenimi tekniklerinden; derin öğrenme, büyük veri kümelerinin kullanılabilirliği ve bilgi işlem gücündeki gelişmelerin başarısı sayesinde olmuştur. Sağlık hizmetlerinde yapay zekanın uygulanabilirliği gösterilmiştir. Algoritmalar doktor performanslarıyla eş değer olabilmektedir ve hatta onları aşabilmektedir. Yapay zeka tabanlı araçlar risk sınıflandırması, tanı ve tedavi seçimi dahil olmak üzere tıpta çeşitli faktörleri tahmin etmek için kullanılmıştır. Yapay zeka teknolojileri önümüzdeki yıllarda acil tıp alanında giderek daha fazla kullanılacaktır. Bu derlemenin amacı acil tıp ile ilgili olan mevcut yapay zeka araştırmalarına genel bakış sağlamaktır.

Kaynakça

  • 1. Jordan MI, Mitchell TM. Machine learning: Trends, perspectives, and prospects. Science 2015;80(349):255-60. https://doi.org/10.1126/science.aaa8415.
  • 2. Ramesh A, Kambhampati C, Monson J, Drew P. Artificial intelligence in medicine. Ann R Coll Surg Engl. 2004;86(5):334- 8.
  • 3. Lynch S. Andrew Ng: why Al is the new electricity. Stanford News. 2017. [Cited 15 Mar 2018.] Available from URL: https://news.stanford.edu/thedish/2017/03/14/andrew-ng-why- ai-is-the-new-electricity
  • 4. Obermeyer Z, Emanuel EJ. Predicting the future big data, machine learning, and clinical medicine. N Engl J Med 2016;375:1216–9. https://doi.org/10.1056/NEJMp1606181.
  • 5. Senders JT, Arnaout O, Karhade AV, et al. Natural and artificial intelligence in neurosurgery: a systemic review. Neurosurgery 2017;0:1-12. https://doi.org/10.1093 /neuros/nyx384.
  • 6. Chen MC, Ball RL, Yang L, et al. Deep learning to classify radiology free-text reports. Radiology 2017:171115. https://doi.org 10.1148/radiol.2017171115.
  • 7. Walton OB, Garoon RB, Weng CY, et al. Evaluation of automated teleretinal screening program for diabetic retinopathy. JAMA Ophthalmol 2016;134:204. https://doi.org/10.1001/jamaophthalmol.2015.5083.
  • 8. Esteva A, Kuprel B, Novoa RA, et al. Dermatologist-level classification of skin cancer with deep neural networks. Nature 2017;542:115-8. https://doi.org/10.1038/nature21056. 9. Liu N, Holcomb J, Wade C, Darrah M, Salinas J. Utility of vital signs, heart rate variability and complexity, and machine
  • learning for identifying the need for lifesaving interventions in trauma patients. Shock. 2014;42(2):108-14. 10. Houthooft R, Ruyssinck J, van der Herten J, et al.
  • Predictive modelling of survival and length of stay in critically ill patients using sequential organ failure scores. Artif Intell Med. 2015;63(3):191-207.
  • 11. Prevedello LM, Little KJ, Qian S, White RD. Automated critical test findings identification and online notification system using artificial intelligence in imaging. Radiology 2017;0:1-9. https://doi.org/10.1148/radiol.2017162664.
  • 12. Li YH, Zhang L, Hu QM, Li HW, Jia FC, Wu JH. Automatic subarachnoid space segmentation and hemorrhage detection in clinical head CT scans. Int J Comput Assist Radiol Surg 2012;7:507-16. https://doi.org/10.1007/s11548-011- 0664-3.
  • 13. Xiao F, Liao CC, Huang KC, Chiang IJ, Wong JM. Automated assessment of midline shift in head injury patients. Clin Neurol Neurosurg 2010;112:785-90. https://doi. org/10.1016/j.clineuro.2010.06.020.
  • 14. Olczak J, Fahlberg N, Maki A, et al. Artificial intelligence for analyzing orthopedic trauma radiographs: deep learning algorithms are they on par with humans for diagnosing fractures? Acta Orthop 2017;3674:1-6. https://doi.org/10. 1080/17453674.2017.1344459.
  • 15. Rajpurkar P, Irvin J, Zhu K et al. CheXNet: radiologist- level pneumonia detection on chest X-rays with deep learning. arXiv:171105225. 2017. [Cited 21 Mar 2017.] Available from URL: http://arxiv.org/abs/1711.05225
  • 16. Farzaneh N, Soroushmehr SMR, Williamson CA et al. Automated subdural hematoma segmentation for traumatic brain injured (TBI) patients. Conf. Proc. IEEE Eng. Med. Biol. Soc. 2017; 2017:3069-72.
  • 17. Liu J, Chen Y, Lan L et al. Prediction of rupture risk in anterior communicating artery aneurysms with a feed-forward artificial neural network. Eur. Radiol. 2018; 28:3268-75.
  • 18. Herweh C, Ringleb PA, Rauch G et al. Performance of e- ASPECTS software in comparison to that of stroke physicians on assessing CT scans of acute ischemic stroke patients. Int.J. Stroke 2016; 11:438-45.
  • 19. Squarcina L, Perlini C, Peruzzo D et al. The use of dynamic susceptibility contrast (DSC) MRI to automatically classify patients with first episode psychosis. Schizophr. Res. 2015; 165:38-44.
  • 20. Guggenmos M, Scheel M, Sekutowicz M et al. Decoding diagnosis and lifetime consumption in alcohol dependence from grey-matter pattern information. Acta Psychiatr. Scand. 2018; 137:252-62.
  • 21. Sjogren AR, Leo MM, Feldman J, Gwin JT. Image segmentation and machine learning for detection of abdominal free fluid in focused assessment with sonography for trauma examinations: a pilot study. J. Ultrasound Med. 2016; 35:2501-9.
  • 22. Knackstedt C, Bekkers SCAM, Schummers G et al. Fully automated versus standard tracking of left ventricular ejection fraction and longitudinal strain. J. Am. Coll. Cardiol. 2015; 66: 1456-66.
  • 23. Persson M, Fhager A, Trefna HD et al. Microwave-based stroke diagnosis making global prehospital thrombolytic treatment possible. I.Ε.Ε.Ε. Trans. Biomed. Eng. 2014; 61: 2806-17.
  • 24. Convertino VA, Grudic G, Mulligan J, Moulton S. Estimation of individual-specific progression to impending cardiovascular instability using arterial waveforms. J. Appl. Physiol. 2013; 115:1196-202.
  • 25. Johnson MC, Alarhayem A, Convertino V, et al. Compensatory reserve index: performance of a novel monitoring technology to identify the bleeding trauma patient. Shock. 2018 Mar 1;49(3):295-300.
  • 26. Convertino VA, Howard JT, Hinojosa-Laborde C et al. Individual-specific, beat-to-beat trending of significant human blood loss: the compensatory reserve. Shock 2015; 44: 27-32. 27. Muniz GW, Wampler DA, Manifold CA et al. Promoting early diagnosis of hemodynamic instability during simulated hemorrhage with the use of a real-time decision-assist algorithm. J. Trauma Acute Care Surg. 2013; 75: 184-9.
  • 28. Shashikumar SP, Stanley MD, Sadiq I et al. Early sepsis in critical care patients using multiscale blood pressure and heart rate dynamics. J. Electrocardiol. 2017; 50: 739-43.
  • 29. Mao Q, Jay M, Hoffman JL et al. Multicentre validation of a sepsis prediction algorithm using only vital sign data in the emergency department, general ward and ICU. BMJ Open 2018; 8: e017833. 30. Rajpurkar P, Hannun AY, Haghpanahi M, Bourn C, Ng AY. Cardiologist-level arrhythmia detection with convolutional neural networks. arXiv:170701836. 2017. [Cited 21 Mar 2017.] Available from URL: http://arxiv.org/abs/1707.01836
  • 31. Plesinger F, Klimes P, Halamek J, Jurak P. False alarms in intensive care unit monitors: detection of lifethreatening arrhythmias using elementary algebra, descriptive statistics and fuzzy logic. Comput. Cardiol. 2015; 42: 281-4.
  • 32. Monkaresi H, Calvo RA, Yan H. A machine learning approach to improve contactless heart rate monitoring using a webcam. IEEE J. Biomed. Heal. Informatics 2014; 18: 1153-60.
  • 33. Zhang Q, Zhou D, Zeng X. Highly wearable cuff-less blood pressure and heart rate monitoring with single-arm electrocardiogram and photoplethysmogram signals. Biomed. Eng. Online 2017; 16: 1–20.
  • 34. Simjanoska M, Gjoreski M, Gams M, Madevska Bogdanova A. Non-invasive blood pressure estimation from ECG using machine learning techniques. Sensors (Basel) 2018; 18:1160.
  • 35. Stewart J, Sprivulis P, Dwivedi G. Artificial intelligence and machine learning in emergency medicine. Emergency Medicine Australasia. 2018 Dec;30(6):870-4.
  • 36. Rajkomar A, Oren E, Chen K et al. Scalable and accurate deep learning with electronic health records. Npj Digital Medicine 2018; 1: 18.
  • 37. Levin S, Toerper M, Hamrock E et al. Machine-learning- based electronic triage more accurately differentiates patients with respect to clinical outcomes compared with the emergency severity index. Ann. Emerg. Med. 2018; 71:565-74.
  • 38. VanHouten JP, Starmer JM, Lorenzi NM, Maron DJ, Lasko TA. Machine learning for risk prediction of acute coronary syndrome. AMIA Annu. Symp. Proc. 2014; 2014: 1940-9.
  • 39. Liu N, Lee MAB, Ho AFW et al. Risk stratification for prediction of adverse coronary events in emergency department chest pain patients with a machine learning score compared with the TIMI score. Int. J. Cardiol. 2014; 177: 1095-7.
  • 40. Shouval R, Hadanny A, Shlomo N et al. Machine learning for prediction of 30-day mortality after ST elevation myocardial infraction: an acute coronary syndrome Israeli survey data mining study. Int. J. Cardiol. 2017; 246: 7-13.
  • 41. Molaei S, Korley FK, Soroushmehr SMR et al. A machine learning based approach for identifying traumatic brain injury patients for whom a head CT scan can be avoided. 38th Annu. Int. Conf. IEEE Eng. Med. Biol. Soc. 2016: 2258-61.
  • 42. Sefrioui I, Amadini R, Mauro J, Fallahi A, Gabbrielli M. Survival prediction of trauma patients: a study on US National Trauma Data Bank. Eur. J. Trauma Emerg. Surg. 2017; 43: 805-22.
  • 43. Feng C, Wang L, Chen X, et al. A novel artificial intelligence-assisted triage tool to aid in the diagnosis of suspected COVID-19 pneumonia cases in fever clinics. Annals of Translational Medicine. 2021 Feb;9(3).
  • 44. Farahmand S, Shabestari O, Pakrah M, et al. Artificial intelligence-based triage for patients with acute abdominal pain in emergency department; a diagnostic accuracy study. Advanced journal of emergency medicine. 2017;1(1).
Toplam 42 adet kaynakça vardır.

Ayrıntılar

Birincil Dil Türkçe
Konular Yapay Zeka (Diğer)
Bölüm Derlemeler
Yazarlar

Arife Erdoğan

Yayımlanma Tarihi 26 Aralık 2021
Yayımlandığı Sayı Yıl 2021 Cilt: 1 Sayı: 3

Kaynak Göster

Vancouver Erdoğan A. Acil Serviste Yapay Zeka Uygulamaları. JAIHS. 2021;1(3):1-5.