Progress in abdominal aortic aneurysm based on artificial intelligence and radiomics_CHINESE JOURNAL OF BASES AND CLINICS IN GENERAL SURGERY

Authors：

WANG Yonggang , SHI Zhenyu ,  FU Weiguo

Department of Vascular Surgery, Zhongshan Hospital Affiliated to Fudan University, Institute of Vascular Surgery, Fudan University, National Clinical Research Center for Interventional Medicine, Shanghai 200032, P. R. China;

Corresponding author：

FU Weiguo, Email: fu.weiguo@zs-hospital.sh.cn

Keywords：

abdominal aortic aneurysm; artificial intelligence; machine learning; deep learning; radiomics

DOI：

10.7507/1007-9424.202112089

Video：

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Abstract Full text Figures/Tables Video References Cited by

Objective To review the progress of artificial intelligence (AI) and radiomics in the study of abdominal aortic aneurysm (AAA). Method The literatures related to AI, radiomics and AAA research in recent years were collected and summarized in detail. Results AI and radiomics influenced AAA research and clinical decisions in terms of feature extraction, risk prediction, patient management, simulation of stent-graft deployment, and data mining. Conclusion The application of AI and radiomics provides new ideas for AAA research and clinical decisions, and is expected to suggest personalized treatment and follow-up protocols to guide clinical practice, aiming to achieve precision medicine of AAA.

Citation： WANG Yonggang, SHI Zhenyu, FU Weiguo. Progress in abdominal aortic aneurysm based on artificial intelligence and radiomics. CHINESE JOURNAL OF BASES AND CLINICS IN GENERAL SURGERY, 2022, 29(8): 1090-1094. doi: 10.7507/1007-9424.202112089 Copy

1.	Schanzer A, Oderich GS. Management of abdominal aortic aneurysms. N Engl J Med, 2021, 385(18): 1690-1698.
2.	Golledge J. Abdominal aortic aneurysm: update on pathogenesis and medical treatments. Nat Rev Cardiol, 2019, 16(4): 225-242.
3.	Lederle FA, Kyriakides TC, Stroupe KT, et al. Open versus endovascular repair of abdominal aortic aneurysm. N Engl J Med, 2019, 380(22): 2126-2135.
4.	Dey D, Slomka PJ, Leeson P, et al. Artificial intelligence in cardiovascular imaging: JACC state-of-the-art review. J Am Coll Cardiol, 2019, 73(11): 1317-1335.
5.	Rajkomar A, Dean J, Kohane I. Machine learning in medicine. N Engl J Med, 2019, 380(14): 1347-1358.
6.	Nagendran M, Chen Y, Lovejoy CA, et al. Artificial intelligence versus clinicians: systematic review of design, reporting standards, and claims of deep learning studies. BMJ, 2020, 368: m689. doi: 10.1136/bmj.m689.
7.	Tran KA, Kondrashova O, Bradley A, et al. Deep learning in cancer diagnosis, prognosis and treatment selection. Genome Med, 2021, 13(1): 152. doi: 10.1186/s13073-021-00968-x.
8.	Bera K, Braman N, Gupta A, et al. Predicting cancer outcomes with radiomics and artificial intelligence in radiology. Nat Rev Clin Oncol, 2022, 19(2): 132-146.
9.	Oikonomou EK, Williams MC, Kotanidis CP, et al. A novel machine learning-derived radiotranscriptomic signature of perivascular fat improves cardiac risk prediction using coronary CT angiography. Eur Heart J, 2019, 40(43): 3529-3543.
10.	Campredon A, Battistella E, Martin C, et al. Using chest CT scan and unsupervised machine learning for predicting and evaluating response to lumacaftor-ivacaftor in people with cystic fibrosis. Eur Respir J, 2021, 2101344. doi: 10.1183/13993003.01344-2021.
11.	Yang W, Cai L, Wu F. Image segmentation based on gray level and local relative entropy two dimensional histogram. PLoS One, 2020, 15(3): e0229651. doi: 10.1371/journal.pone. 0229651.
12.	Khosravanian A, Rahmanimanesh M, Keshavarzi P, et al. Fast level set method for glioma brain tumor segmentation based on Superpixel fuzzy clustering and lattice Boltzmann method. Comput Methods Programs Biomed, 2021, 198: 105809. doi: 10.1016/j.cmpb.2020.105809.
13.	El-Rewaidy H, Fahmy AS, Khalifa AM, et al. Multiple two-dimensional active shape model framework for right ventricular segmentation. Magn Reson Imaging, 2022, 85: 177-185.
14.	Gui L, Ma J, Yang X. Shape prior generation and geodesic active contour interactive iterating algorithm (SPACIAL): fully automatic segmentation for 3D lumen in intravascular optical coherence tomography images. Med Phys, 2021, 48(11): 7099-7111.
15.	Siriapisith T, Kusakunniran W, Haddawy P. Pyramid graph cut: Integrating intensity and gradient information for grayscale medical image segmentation. Comput Biol Med, 2020, 126: 103997. doi: 10.1016/j.compbiomed.2020.103997.
16.	Hernandez-Fernandez M, Reguly I, Jbabdi S, et al. Using GPUs to accelerate computational diffusion MRI: From microstructure estimation to tractography and connectomes. Neuroimage, 2019, 188: 598-615.
17.	Wang D, Zhang R, Zhu J, et al. Neural network fusion: a novel CT-MR Aortic Aneurysm image segmentation method. Proc SPIE Int Soc Opt Eng, 2018, 10574: 1057424. doi: 10.1117/12.2293371.
18.	Lareyre F, Adam C, Carrier M, et al. A fully automated pipeline for mining abdominal aortic aneurysm using image segmentation. Sci Rep, 2019, 9(1): 13750. doi: 10.1038/s41598-019- 50251-8.
19.	Mohammadi S, Mohammadi M, Dehlaghi V, et al. Automatic segmentation, detection, and diagnosis of abdominal aortic aneurysm (AAA) using convolutional neural networks and hough circles algorithm. Cardiovasc Eng Technol, 2019, 10(3): 490-499.
20.	Graffy PM, Liu J, O’Connor S, et al. Automated segmentation and quantification of aortic calcification at abdominal CT: application of a deep learning-based algorithm to a longitudinal screening cohort. Abdom Radiol (NY), 2019, 44(8): 2921-2928.
21.	López-Linares K, Aranjuelo N, Kabongo L, et al. Fully automatic detection and segmentation of abdominal aortic thrombus in post-operative CTA images using deep convolutional neural networks. Med Image Anal, 2018, 46: 202-214.
22.	Caradu C, Spampinato B, Vrancianu AM, et al. Fully automatic volume segmentation of infrarenal abdominal aortic aneurysm computed tomography images with deep learning approaches versus physician controlled manual segmentation. J Vasc Surg, 2021, 74(1): 246-256. e6. doi: 10.1016/j.jvs.2020.11.036.
23.	Rauseo E, Izquierdo Morcillo C, Raisi-Estabragh Z, et al. New imaging signatures of cardiac alterations in ischaemic heart disease and cerebrovascular disease using CMR radiomics. Front Cardiovasc Med, 2021, 8: 716577. doi: 10.3389/fcvm.2021.716577.
24.	Shang J, Ma SW, Guo Y, et al. Prediction of acute coronary syndrome within 3 years using radiomics signature of pericoronary adipose tissue based on coronary computed tomography angiography. Eur Radiol, 2022, 32(2): 1256-1266.
25.	Yan J, Zhang B, Zhang S, et al. Quantitative MRI-based radiomics for noninvasively predicting molecular subtypes and survival in glioma patients. NPJ Precis Oncol, 2021, 5(1): 72.
26.	Lin Y, Li L, Yu D, et al. A novel radiomics-platelet nomogram for the prediction of gastroesophageal varices needing treatment in cirrhotic patients. Hepatol Int, 2021, 15(4): 995-1005.
27.	Elkilany A, Fehrenbach U, Auer TA, et al. A radiomics-based model to classify the etiology of liver cirrhosis using gadoxetic acid-enhanced MRI. Sci Rep, 2021, 11(1): 10778. doi: 10.1038/s41598-021-90257-9.
28.	Dai H, Wang Y, Fu R, et al. Radiomics and stacking regression model for measuring bone mineral density using abdominal computed tomography. Acta Radiol, 2021: 2841851211068149. doi: 10.1177/02841851211068149.
29.	Maiora J, Papakostas GA, Kaburlasos VG, et al. A proposal of texture features for interactive CTA segmentation by active learning. Stud Health Technol Inform. 2014, 207: 311-320.
30.	Kotze CW, Rudd JHF, Ganeshan B, et al. CT signal heterogeneity of abdominal aortic aneurysm as a possible predictive biomarker for expansion. Atherosclerosis, 2014, 233(2): 510-517.
31.	García G, Tapia A, De Blas M. Computer-supported diagnosis for endotension cases in endovascular aortic aneurysm repair evolution. Comput Methods Programs Biomed, 2014, 115(1): 11-19.
32.	García G, Maiora J, Tapia A, et al. Evaluation of texture for classification of abdominal aortic aneurysm after endovascular repair. J Digit Imaging, 2012, 25(3): 369-376.
33.	Charalambous S, Klontzas ME, Kontopodis N, et al. Radiomics and machine learning to predict aggressive type 2 endoleaks after endovascular aneurysm repair: a proof of concept. Acta Radiol, 2021 Jul 27, 2841851211032443. doi: 10.1177/02841851211032443.
34.	Chaikof EL, Dalman RL, Eskandari MK, et al. The society for vascular surgery practice guidelines on the care of patients with an abdominal aortic aneurysm. J Vasc Surg, 2018, 67(1): 2-77.
35.	Wanhainen A, Verzini F, Van Herzeele I, et al. Editor’s Choice-European Society for Vascular Surgery (ESVS) 2019 Clinical Practice Guidelines on the Management of Abdominal Aorto-iliac Artery Aneurysms. Eur J Vasc Endovasc Surg, 2019, 57(1): 8-93.
36.	Hirata K, Nakaura T, Nakagawa M, et al. Machine learning to predict the rapid growth of small abdominal aortic aneurysm. J Comput Assist Tomogr, 2020, 44(1): 37-42.
37.	Lee R, Jarchi D, Perera R, et al. Applied machine learning for the prediction of growth of abdominal aortic aneurysm in humans. EJVES Short Rep, 2018, 39: 24-28.
38.	Erhart P, Grond-Ginsbach C, Hakimi M, et al. Finite element analysis of abdominal aortic aneurysms: predicted rupture risk correlates with aortic wall histology in individual patients. J Endovasc Ther, 2014, 21(4): 556-564.
39.	Joldes GR, Miller K, Wittek A, et al. BioPARR: A software system for estimating the rupture potential index for abdominal aortic aneurysms. Sci Rep, 2017, 7(1): 4641. doi: 10.1038/s41598-017-04699-1.
40.	Canchi T, Ng EY, Narayanan S, et al. On the assessment of abdominal aortic aneurysm rupture risk in the Asian population based on geometric attributes. Proc Inst Mech Eng H, 2018, 232(9): 922-929.
41.	Jordanski M, Radovic M, Milosevic Z, et al. Machine learning approach for predicting wall shear distribution for abdominal aortic aneurysm and carotid bifurcation models. IEEE J Biomed Health Inform, 2018, 22(2): 537-544.
42.	Özen A, Yılmaz M, Yiğit G, et al. Glasgow aneurysm score: a predictor of long-term mortality following endovascular repair of abdominal aortic aneurysm? BMC Cardiovasc Disord, 2021, 21(1): 551. doi: 10.1186/s12872-021-02366-y.
43.	Eslami MH, Rybin DV, Doros G, et al. Description of a risk predictive model of 30-day postoperative mortality after elective abdominal aortic aneurysm repair. J Vasc Surg, 2017, 65(1): 65-74.
44.	Tsolakis IA, Kakkos SK, Papageorgopoulou CP, et al. Predictors of operative mortality of 928 intact aortoiliac aneurysms. Ann Vasc Surg, 2021, 71: 370-380.
45.	Cheng EL, Hong Q, Yong E, et al. Validating the use of contrast-induced nephropathy prediction models in endovascular aneurysm repairs. J Vasc Surg, 2020, 71(5): 1546-1553.
46.	Rengarajan B, Wu W, Wiedner C, et al. A comparative classification analysis of abdominal aortic aneurysms by machine learning algorithms. Ann Biomed Eng, 2020, 48(4): 1419-1429.
47.	Monsalve-Torra A, Ruiz-Fernandez D, Marin-Alonso O, et al. Using machine learning methods for predicting inhospital mortality in patients undergoing open repair of abdominal aortic aneurysm. J Biomed Inform, 2016, 62: 195-201.
48.	Wise ES, Hocking KM, Brophy CM. Prediction of in-hospital mortality after ruptured abdominal aortic aneurysm repair using an artificial neural network. J Vasc Surg, 2015, 62(1): 8-15.
49.	Karthikesalingam A, Attallah O, Ma X, et al. An artificial neural network stratifies the risks of reintervention and mortality after endovascular aneurysm repair; a retrospective observational study. PLoS One, 2015, 10(7): e0129024. doi: 10.1371/journal.pone.0129024.
50.	Kordzadeh A, Hanif MA, Ramirez MJ, et al. Prediction, pattern recognition and modelling of complications postendovascular infra renal aneurysm repair by artificial intelligence. Vascular, 2021, 29(2): 171-182.
51.	Kim DC, Herskovits EH, Johnson PT. Science to practice: IT solutions to drive standardized report recommendations for abdominal aortic aneurysm surveillance. J Am Coll Radiol, 2018, 15(6): 865-869.
52.	Perrin D, Badel P, Orgéas L, et al. Patient-specific numerical simulation of stent-graft deployment: Validation on three clinical cases. J Biomech, 2015, 48(10): 1868-1875.
53.	Perrin D, Demanget N, Badel P, et al. Deployment of stent grafts in curved aneurysmal arteries: toward a predictive numerical tool. Int J Numer Method Biomed Eng, 2015, 31(1): e02698. doi: 10.1002/cnm.2698.
54.	Perrin D, Badel P, Orgeas L, et al. Patient-specific simulation of endovascular repair surgery with tortuous aneurysms requiring flexible stent-grafts. J Mech Behav Biomed Mater, 2016, 63: 86-99.
55.	Mei H, Xu Y, Wang J, et al. Evaluation of survival outcomes of endovascular versus open aortic repair for abdominal aortic aneurysms with a big data approach. Entropy (Basel), 2020, 22(12): 1349. doi: 10.3390/e22121349.
56.	Chakshu NK, Sazonov I, Nithiarasu P. Towards enabling a cardiovascular digital twin for human systemic circulation using inverse analysis. Biomech Model Mechanobiol, 2021, 20(2): 449-465.

1. Schanzer A, Oderich GS. Management of abdominal aortic aneurysms. N Engl J Med, 2021, 385(18): 1690-1698.
2. Golledge J. Abdominal aortic aneurysm: update on pathogenesis and medical treatments. Nat Rev Cardiol, 2019, 16(4): 225-242.
3. Lederle FA, Kyriakides TC, Stroupe KT, et al. Open versus endovascular repair of abdominal aortic aneurysm. N Engl J Med, 2019, 380(22): 2126-2135.
4. Dey D, Slomka PJ, Leeson P, et al. Artificial intelligence in cardiovascular imaging: JACC state-of-the-art review. J Am Coll Cardiol, 2019, 73(11): 1317-1335.
5. Rajkomar A, Dean J, Kohane I. Machine learning in medicine. N Engl J Med, 2019, 380(14): 1347-1358.
6. Nagendran M, Chen Y, Lovejoy CA, et al. Artificial intelligence versus clinicians: systematic review of design, reporting standards, and claims of deep learning studies. BMJ, 2020, 368: m689. doi: 10.1136/bmj.m689.
7. Tran KA, Kondrashova O, Bradley A, et al. Deep learning in cancer diagnosis, prognosis and treatment selection. Genome Med, 2021, 13(1): 152. doi: 10.1186/s13073-021-00968-x.
8. Bera K, Braman N, Gupta A, et al. Predicting cancer outcomes with radiomics and artificial intelligence in radiology. Nat Rev Clin Oncol, 2022, 19(2): 132-146.
9. Oikonomou EK, Williams MC, Kotanidis CP, et al. A novel machine learning-derived radiotranscriptomic signature of perivascular fat improves cardiac risk prediction using coronary CT angiography. Eur Heart J, 2019, 40(43): 3529-3543.
10. Campredon A, Battistella E, Martin C, et al. Using chest CT scan and unsupervised machine learning for predicting and evaluating response to lumacaftor-ivacaftor in people with cystic fibrosis. Eur Respir J, 2021, 2101344. doi: 10.1183/13993003.01344-2021.
11. Yang W, Cai L, Wu F. Image segmentation based on gray level and local relative entropy two dimensional histogram. PLoS One, 2020, 15(3): e0229651. doi: 10.1371/journal.pone. 0229651.
12. Khosravanian A, Rahmanimanesh M, Keshavarzi P, et al. Fast level set method for glioma brain tumor segmentation based on Superpixel fuzzy clustering and lattice Boltzmann method. Comput Methods Programs Biomed, 2021, 198: 105809. doi: 10.1016/j.cmpb.2020.105809.
13. El-Rewaidy H, Fahmy AS, Khalifa AM, et al. Multiple two-dimensional active shape model framework for right ventricular segmentation. Magn Reson Imaging, 2022, 85: 177-185.
14. Gui L, Ma J, Yang X. Shape prior generation and geodesic active contour interactive iterating algorithm (SPACIAL): fully automatic segmentation for 3D lumen in intravascular optical coherence tomography images. Med Phys, 2021, 48(11): 7099-7111.
15. Siriapisith T, Kusakunniran W, Haddawy P. Pyramid graph cut: Integrating intensity and gradient information for grayscale medical image segmentation. Comput Biol Med, 2020, 126: 103997. doi: 10.1016/j.compbiomed.2020.103997.
16. Hernandez-Fernandez M, Reguly I, Jbabdi S, et al. Using GPUs to accelerate computational diffusion MRI: From microstructure estimation to tractography and connectomes. Neuroimage, 2019, 188: 598-615.
17. Wang D, Zhang R, Zhu J, et al. Neural network fusion: a novel CT-MR Aortic Aneurysm image segmentation method. Proc SPIE Int Soc Opt Eng, 2018, 10574: 1057424. doi: 10.1117/12.2293371.
18. Lareyre F, Adam C, Carrier M, et al. A fully automated pipeline for mining abdominal aortic aneurysm using image segmentation. Sci Rep, 2019, 9(1): 13750. doi: 10.1038/s41598-019- 50251-8.
19. Mohammadi S, Mohammadi M, Dehlaghi V, et al. Automatic segmentation, detection, and diagnosis of abdominal aortic aneurysm (AAA) using convolutional neural networks and hough circles algorithm. Cardiovasc Eng Technol, 2019, 10(3): 490-499.
20. Graffy PM, Liu J, O’Connor S, et al. Automated segmentation and quantification of aortic calcification at abdominal CT: application of a deep learning-based algorithm to a longitudinal screening cohort. Abdom Radiol (NY), 2019, 44(8): 2921-2928.
21. López-Linares K, Aranjuelo N, Kabongo L, et al. Fully automatic detection and segmentation of abdominal aortic thrombus in post-operative CTA images using deep convolutional neural networks. Med Image Anal, 2018, 46: 202-214.
22. Caradu C, Spampinato B, Vrancianu AM, et al. Fully automatic volume segmentation of infrarenal abdominal aortic aneurysm computed tomography images with deep learning approaches versus physician controlled manual segmentation. J Vasc Surg, 2021, 74(1): 246-256. e6. doi: 10.1016/j.jvs.2020.11.036.
23. Rauseo E, Izquierdo Morcillo C, Raisi-Estabragh Z, et al. New imaging signatures of cardiac alterations in ischaemic heart disease and cerebrovascular disease using CMR radiomics. Front Cardiovasc Med, 2021, 8: 716577. doi: 10.3389/fcvm.2021.716577.
24. Shang J, Ma SW, Guo Y, et al. Prediction of acute coronary syndrome within 3 years using radiomics signature of pericoronary adipose tissue based on coronary computed tomography angiography. Eur Radiol, 2022, 32(2): 1256-1266.
25. Yan J, Zhang B, Zhang S, et al. Quantitative MRI-based radiomics for noninvasively predicting molecular subtypes and survival in glioma patients. NPJ Precis Oncol, 2021, 5(1): 72.
26. Lin Y, Li L, Yu D, et al. A novel radiomics-platelet nomogram for the prediction of gastroesophageal varices needing treatment in cirrhotic patients. Hepatol Int, 2021, 15(4): 995-1005.
27. Elkilany A, Fehrenbach U, Auer TA, et al. A radiomics-based model to classify the etiology of liver cirrhosis using gadoxetic acid-enhanced MRI. Sci Rep, 2021, 11(1): 10778. doi: 10.1038/s41598-021-90257-9.
28. Dai H, Wang Y, Fu R, et al. Radiomics and stacking regression model for measuring bone mineral density using abdominal computed tomography. Acta Radiol, 2021: 2841851211068149. doi: 10.1177/02841851211068149.
29. Maiora J, Papakostas GA, Kaburlasos VG, et al. A proposal of texture features for interactive CTA segmentation by active learning. Stud Health Technol Inform. 2014, 207: 311-320.
30. Kotze CW, Rudd JHF, Ganeshan B, et al. CT signal heterogeneity of abdominal aortic aneurysm as a possible predictive biomarker for expansion. Atherosclerosis, 2014, 233(2): 510-517.
31. García G, Tapia A, De Blas M. Computer-supported diagnosis for endotension cases in endovascular aortic aneurysm repair evolution. Comput Methods Programs Biomed, 2014, 115(1): 11-19.
32. García G, Maiora J, Tapia A, et al. Evaluation of texture for classification of abdominal aortic aneurysm after endovascular repair. J Digit Imaging, 2012, 25(3): 369-376.
33. Charalambous S, Klontzas ME, Kontopodis N, et al. Radiomics and machine learning to predict aggressive type 2 endoleaks after endovascular aneurysm repair: a proof of concept. Acta Radiol, 2021 Jul 27, 2841851211032443. doi: 10.1177/02841851211032443.
34. Chaikof EL, Dalman RL, Eskandari MK, et al. The society for vascular surgery practice guidelines on the care of patients with an abdominal aortic aneurysm. J Vasc Surg, 2018, 67(1): 2-77.
35. Wanhainen A, Verzini F, Van Herzeele I, et al. Editor’s Choice-European Society for Vascular Surgery (ESVS) 2019 Clinical Practice Guidelines on the Management of Abdominal Aorto-iliac Artery Aneurysms. Eur J Vasc Endovasc Surg, 2019, 57(1): 8-93.
36. Hirata K, Nakaura T, Nakagawa M, et al. Machine learning to predict the rapid growth of small abdominal aortic aneurysm. J Comput Assist Tomogr, 2020, 44(1): 37-42.
37. Lee R, Jarchi D, Perera R, et al. Applied machine learning for the prediction of growth of abdominal aortic aneurysm in humans. EJVES Short Rep, 2018, 39: 24-28.
38. Erhart P, Grond-Ginsbach C, Hakimi M, et al. Finite element analysis of abdominal aortic aneurysms: predicted rupture risk correlates with aortic wall histology in individual patients. J Endovasc Ther, 2014, 21(4): 556-564.
39. Joldes GR, Miller K, Wittek A, et al. BioPARR: A software system for estimating the rupture potential index for abdominal aortic aneurysms. Sci Rep, 2017, 7(1): 4641. doi: 10.1038/s41598-017-04699-1.
40. Canchi T, Ng EY, Narayanan S, et al. On the assessment of abdominal aortic aneurysm rupture risk in the Asian population based on geometric attributes. Proc Inst Mech Eng H, 2018, 232(9): 922-929.
41. Jordanski M, Radovic M, Milosevic Z, et al. Machine learning approach for predicting wall shear distribution for abdominal aortic aneurysm and carotid bifurcation models. IEEE J Biomed Health Inform, 2018, 22(2): 537-544.
42. Özen A, Yılmaz M, Yiğit G, et al. Glasgow aneurysm score: a predictor of long-term mortality following endovascular repair of abdominal aortic aneurysm? BMC Cardiovasc Disord, 2021, 21(1): 551. doi: 10.1186/s12872-021-02366-y.
43. Eslami MH, Rybin DV, Doros G, et al. Description of a risk predictive model of 30-day postoperative mortality after elective abdominal aortic aneurysm repair. J Vasc Surg, 2017, 65(1): 65-74.
44. Tsolakis IA, Kakkos SK, Papageorgopoulou CP, et al. Predictors of operative mortality of 928 intact aortoiliac aneurysms. Ann Vasc Surg, 2021, 71: 370-380.
45. Cheng EL, Hong Q, Yong E, et al. Validating the use of contrast-induced nephropathy prediction models in endovascular aneurysm repairs. J Vasc Surg, 2020, 71(5): 1546-1553.
46. Rengarajan B, Wu W, Wiedner C, et al. A comparative classification analysis of abdominal aortic aneurysms by machine learning algorithms. Ann Biomed Eng, 2020, 48(4): 1419-1429.
47. Monsalve-Torra A, Ruiz-Fernandez D, Marin-Alonso O, et al. Using machine learning methods for predicting inhospital mortality in patients undergoing open repair of abdominal aortic aneurysm. J Biomed Inform, 2016, 62: 195-201.
48. Wise ES, Hocking KM, Brophy CM. Prediction of in-hospital mortality after ruptured abdominal aortic aneurysm repair using an artificial neural network. J Vasc Surg, 2015, 62(1): 8-15.
49. Karthikesalingam A, Attallah O, Ma X, et al. An artificial neural network stratifies the risks of reintervention and mortality after endovascular aneurysm repair; a retrospective observational study. PLoS One, 2015, 10(7): e0129024. doi: 10.1371/journal.pone.0129024.
50. Kordzadeh A, Hanif MA, Ramirez MJ, et al. Prediction, pattern recognition and modelling of complications postendovascular infra renal aneurysm repair by artificial intelligence. Vascular, 2021, 29(2): 171-182.
51. Kim DC, Herskovits EH, Johnson PT. Science to practice: IT solutions to drive standardized report recommendations for abdominal aortic aneurysm surveillance. J Am Coll Radiol, 2018, 15(6): 865-869.
52. Perrin D, Badel P, Orgéas L, et al. Patient-specific numerical simulation of stent-graft deployment: Validation on three clinical cases. J Biomech, 2015, 48(10): 1868-1875.
53. Perrin D, Demanget N, Badel P, et al. Deployment of stent grafts in curved aneurysmal arteries: toward a predictive numerical tool. Int J Numer Method Biomed Eng, 2015, 31(1): e02698. doi: 10.1002/cnm.2698.
54. Perrin D, Badel P, Orgeas L, et al. Patient-specific simulation of endovascular repair surgery with tortuous aneurysms requiring flexible stent-grafts. J Mech Behav Biomed Mater, 2016, 63: 86-99.
55. Mei H, Xu Y, Wang J, et al. Evaluation of survival outcomes of endovascular versus open aortic repair for abdominal aortic aneurysms with a big data approach. Entropy (Basel), 2020, 22(12): 1349. doi: 10.3390/e22121349.
56. Chakshu NK, Sazonov I, Nithiarasu P. Towards enabling a cardiovascular digital twin for human systemic circulation using inverse analysis. Biomech Model Mechanobiol, 2021, 20(2): 449-465.

CHINESE JOURNAL OF BASES AND CLINICS IN GENERAL SURGERY

Progress in abdominal aortic aneurysm based on artificial intelligence and radiomics

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