LareyreFLeCDBallaithA, et al.Applications of artificial intelligence in non-cardiac vascular diseases: a bibliographic analysis. Angiology2022, in process citation.
2.
JamthikarADGuptaDSabaL, et al.Artificial intelligence framework for predictive cardiovascular and stroke risk assessment models: a narrative review of integrated approaches using carotid ultrasound. Comput Biol Med2020; 126: 104043. doi: 10.1016/j.compbiomed.2020.104043.
3.
JohriAMMantellaLEJamthikarADSabaLLairdJRSuriJS. Role of artificial intelligence in cardiovascular risk prediction and outcomes: comparison of machine-learning and conventional statistical approaches for the analysis of carotid ultrasound features and intra-plaque neovascularization. Int J Cardiovasc Imag2021; 37: 3145–3156. doi: 10.1007/s10554-021-02294-0.
4.
SabaLSanagalaSSGuptaSK, et al.Multimodality carotid plaque tissue characterization and classification in the artificial intelligence paradigm: a narrative review for stroke application. Ann Transl Med2021; 9: 1206–1206.
5.
MurrayCJ. Health effects of dietary risks in 195 countries, 1990-2017: a systematic analysis for the Global Burden of Disease Study 2017. Lancet2019; 393: 1958–1972.
6.
Ofori-AsensoRMohsenpourMANouriMFaghihSLiewDMazidiM. Association of spicy chilli food consumption with cardiovascular and all-cause mortality: a meta-analysis of prospective cohort studies. Angiology2021; 72: 625–632.
7.
JamthikarAGuptaDKhannaNN, et al.A special report on changing trends in preventive stroke/cardiovascular risk assessment via b-mode ultrasonography. Curr Atheroscle Rep2019; 21: 25.
8.
WengSFRepsJKaiJGaribaldiJMQureshiN. Can machine-learning improve cardiovascular risk prediction using routine clinical data?PLoS One2017; 12: e0174944.
9.
KakadiarisIAVrigkasMYenAAKuznetsovaTBudoffMNaghaviM. Machine learning outperforms ACC/AHA CVD risk calculator in MESA. J Am Heart Assoc2018; 7: e009476.
10.
JamthikarAGuptaDSabaL, et al.Cardiovascular/stroke risk predictive calculators: a comparison between statistical and machine learning models. Cardiovasc Diagn Ther2020; 10: 919–938.
11.
JamthikarAGuptaDJohriAMMantellaLESabaLSuriJS. A machine learning framework for risk prediction of multi-label cardiovascular events based on focused carotid plaque B-Mode ultrasound: a Canadian study. Comput Biol Med2022; 140: 105102.
12.
LareyreFAdamCCarrierMRaffortJ. Prediction of abdominal aortic aneurysm growth and risk of rupture in the era of machine learning. Angiology2020; 71: 767–767.
13.
SuriJSBhagawatiMPaulS, et al.Understanding the bias in machine learning systems for cardiovascular disease risk assessment: the first of its kind review. Comput Biol Med2022; 142: 105204. doi: 10.1016/j.compbiomed.2021.105204.
14.
ShahDZhengWAllenL, et al.Using a machine learning approach to investigate factors associated with treatment-resistant depression among adults with chronic non-cancer pain conditions and major depressive disorder. Curr Med Res Opin2021; 37: 847–859.
15.
WangYJiCWangYJiMYangJJZhouCM. Predicting postoperative liver cancer death outcomes with machine learning. Curr Med Res Opin2021; 37: 629–634.
