Deep Learning Model for Coronary Angiography

Zurück zum Zitat Khera AV, Kathiresan S. Genetics of coronary artery disease: discovery, biology and clinical translation. Nat Rev Genet. 2017;18:331–44.CrossRefPubMedPubMedCentral Khera AV, Kathiresan S. Genetics of coronary artery disease: discovery, biology and clinical translation. Nat Rev Genet. 2017;18:331–44.CrossRefPubMedPubMedCentral

Zurück zum Zitat GBD 2015 Mortality and Causes of Death Collaborators. Global, regional, and national life expectancy, all-cause mortality, and cause-specific mortality for 249 causes of death, 1980–2015: a systematic analysis for the Global Burden of Disease Study 2015. Lancet. 2016;388:1459–1544. GBD 2015 Mortality and Causes of Death Collaborators. Global, regional, and national life expectancy, all-cause mortality, and cause-specific mortality for 249 causes of death, 1980–2015: a systematic analysis for the Global Burden of Disease Study 2015. Lancet. 2016;388:1459–1544.

Zurück zum Zitat Lemkes JS. Coronary angiography after cardiac arrest: a deep dive for PEARL. Circulation. 2020;142:2013–5.CrossRefPubMed Lemkes JS. Coronary angiography after cardiac arrest: a deep dive for PEARL. Circulation. 2020;142:2013–5.CrossRefPubMed

Zurück zum Zitat Adjedj J, Xaplanteris P, Toth G, Ferrara A, Pellicano M, Ciccarelli G, Floré V, Barbato E and De Bruyne B. Visual and quantitative assessment of coronary stenoses at angiography versus fractional flow reserve: the impact of risk factors. Circ Cardiovasc Imaging. 2017;10(7):e006243. Adjedj J, Xaplanteris P, Toth G, Ferrara A, Pellicano M, Ciccarelli G, Floré V, Barbato E and De Bruyne B. Visual and quantitative assessment of coronary stenoses at angiography versus fractional flow reserve: the impact of risk factors. Circ Cardiovasc Imaging. 2017;10(7):e006243.

Zurück zum Zitat Brilakis ES, Mashayekhi K, Tsuchikane E, Abi Rafeh N, Alaswad K, Araya M, Avran A, Azzalini L, Babunashvili AM, Bayani B, Bhindi R, Boudou N, Boukhris M, Božinović N, Bryniarski L, Bufe A, Buller CE, Burke MN, Büttner HJ, Cardoso P, Carlino M, Christiansen EH, Colombo A, Croce K, Damas de Los Santos F, De Martini T, Dens J, Di Mario C, Dou K, Egred M, ElGuindy AM, Escaned J, Furkalo S, Gagnor A, Galassi AR, Garbo R, Ge J, Goel PK, Goktekin O, Grancini L, Grantham JA, Hanratty C, Harb S, Harding SA, Henriques JPS, Hill JM, Jaffer FA, Jang Y, Jussila R, Kalnins A, Kalyanasundaram A, Kandzari DE, Kao HL, Karmpaliotis D, Kassem HH, Knaapen P, Kornowski R, Krestyaninov O, Kumar AVG, Laanmets P, Lamelas P, Lee SW, Lefevre T, Li Y, Lim ST, Lo S, Lombardi W, McEntegart M, Munawar M, Navarro Lecaro JA, Ngo HM, Nicholson W, Olivecrona GK, Padilla L, Postu M, Quadros A, Quesada FH, Prakasa Rao VS, Reifart N, Saghatelyan M, Santiago R, Sianos G, Smith E, J CS, Stone GW, Strange JW, Tammam K, Ungi I, Vo M, Vu VH, Walsh S, Werner GS, Wollmuth JR, Wu EB, Wyman RM, Xu B, Yamane M, Ybarra LF, Yeh RW, Zhang Q and Rinfret S. Guiding principles for chronic total occlusion percutaneous coronary intervention. Circulation. 2019;140:420–433. Brilakis ES, Mashayekhi K, Tsuchikane E, Abi Rafeh N, Alaswad K, Araya M, Avran A, Azzalini L, Babunashvili AM, Bayani B, Bhindi R, Boudou N, Boukhris M, Božinović N, Bryniarski L, Bufe A, Buller CE, Burke MN, Büttner HJ, Cardoso P, Carlino M, Christiansen EH, Colombo A, Croce K, Damas de Los Santos F, De Martini T, Dens J, Di Mario C, Dou K, Egred M, ElGuindy AM, Escaned J, Furkalo S, Gagnor A, Galassi AR, Garbo R, Ge J, Goel PK, Goktekin O, Grancini L, Grantham JA, Hanratty C, Harb S, Harding SA, Henriques JPS, Hill JM, Jaffer FA, Jang Y, Jussila R, Kalnins A, Kalyanasundaram A, Kandzari DE, Kao HL, Karmpaliotis D, Kassem HH, Knaapen P, Kornowski R, Krestyaninov O, Kumar AVG, Laanmets P, Lamelas P, Lee SW, Lefevre T, Li Y, Lim ST, Lo S, Lombardi W, McEntegart M, Munawar M, Navarro Lecaro JA, Ngo HM, Nicholson W, Olivecrona GK, Padilla L, Postu M, Quadros A, Quesada FH, Prakasa Rao VS, Reifart N, Saghatelyan M, Santiago R, Sianos G, Smith E, J CS, Stone GW, Strange JW, Tammam K, Ungi I, Vo M, Vu VH, Walsh S, Werner GS, Wollmuth JR, Wu EB, Wyman RM, Xu B, Yamane M, Ybarra LF, Yeh RW, Zhang Q and Rinfret S. Guiding principles for chronic total occlusion percutaneous coronary intervention. Circulation. 2019;140:420–433.

Zurück zum Zitat Zhang H, Mu L, Hu S, Nallamothu BK, Lansky AJ, Xu B, Bouras G, Cohen DJ, Spertus JA, Masoudi FA, Curtis JP, Gao R, Ge J, Yang Y, Li J, Li X, Zheng X, Li Y, Krumholz HM, Jiang L. Comparison of physician visual assessment with quantitative coronary angiography in assessment of stenosis severity in China. JAMA Intern Med. 2018;178:239–47.CrossRefPubMedPubMedCentral Zhang H, Mu L, Hu S, Nallamothu BK, Lansky AJ, Xu B, Bouras G, Cohen DJ, Spertus JA, Masoudi FA, Curtis JP, Gao R, Ge J, Yang Y, Li J, Li X, Zheng X, Li Y, Krumholz HM, Jiang L. Comparison of physician visual assessment with quantitative coronary angiography in assessment of stenosis severity in China. JAMA Intern Med. 2018;178:239–47.CrossRefPubMedPubMedCentral

Zurück zum Zitat LeCun Y, Bengio Y, Hinton G. Deep learning. Nature. 2015;521:436–44.CrossRefPubMed LeCun Y, Bengio Y, Hinton G. Deep learning. Nature. 2015;521:436–44.CrossRefPubMed

Zurück zum Zitat Esteva A, Robicquet A, Ramsundar B, Kuleshov V, DePristo M, Chou K, Cui C, Corrado G, Thrun S, Dean J. A guide to deep learning in healthcare. Nat Med. 2019;25:24–9.CrossRefPubMed Esteva A, Robicquet A, Ramsundar B, Kuleshov V, DePristo M, Chou K, Cui C, Corrado G, Thrun S, Dean J. A guide to deep learning in healthcare. Nat Med. 2019;25:24–9.CrossRefPubMed

Zurück zum Zitat Isensee F, Jaeger PF, Kohl SAA, Petersen J, Maier-Hein KH. nnU-Net: a self-configuring method for deep learning-based biomedical image segmentation. Nat Methods. 2021;18:203–11.CrossRefPubMed Isensee F, Jaeger PF, Kohl SAA, Petersen J, Maier-Hein KH. nnU-Net: a self-configuring method for deep learning-based biomedical image segmentation. Nat Methods. 2021;18:203–11.CrossRefPubMed

Zurück zum Zitat Milea D, Najjar RP, Jiang Z, Ting D, Vasseneix C, Xu X, AghsaeiFard M, Fonseca P, Vanikieti K, Lagrèze WA, La Morgia C, Cheung CY, Hamann S, Chiquet C, Sanda N, Yang H, Mejico LJ, Rougier M-B, Kho R, Tran THC, Singhal S, Gohier P, Clermont-Vignal C, Cheng C-Y, Jonas JB, Yu-Wai-Man P, Fraser CL, Chen JJ, Ambika S, Miller NR, Liu Y, Newman NJ, Wong TY, Biousse V. Artificial intelligence to detect papilledema from ocular fundus photographs. N Engl J Med. 2020;382:1687–95.CrossRefPubMed Milea D, Najjar RP, Jiang Z, Ting D, Vasseneix C, Xu X, AghsaeiFard M, Fonseca P, Vanikieti K, Lagrèze WA, La Morgia C, Cheung CY, Hamann S, Chiquet C, Sanda N, Yang H, Mejico LJ, Rougier M-B, Kho R, Tran THC, Singhal S, Gohier P, Clermont-Vignal C, Cheng C-Y, Jonas JB, Yu-Wai-Man P, Fraser CL, Chen JJ, Ambika S, Miller NR, Liu Y, Newman NJ, Wong TY, Biousse V. Artificial intelligence to detect papilledema from ocular fundus photographs. N Engl J Med. 2020;382:1687–95.CrossRefPubMed

Zurück zum Zitat SippensGroenewegen A, Spekhorst H, Hemel NMv, Kingma JH, Hauer RNW, Barker JMTd, Grimbergen CA, Janse MJ and Dunning AJ. Value of body surface mapping in localizing the site of origin of ventricular tachycardia in patients with previous myocardial infarction. J Am Coll Cardiol. 1994;24:1708–1724. SippensGroenewegen A, Spekhorst H, Hemel NMv, Kingma JH, Hauer RNW, Barker JMTd, Grimbergen CA, Janse MJ and Dunning AJ. Value of body surface mapping in localizing the site of origin of ventricular tachycardia in patients with previous myocardial infarction. J Am Coll Cardiol. 1994;24:1708–1724.

Zurück zum Zitat Gong D, Wu L, Zhang J, Mu G, Shen L, Liu J, Wang Z, Zhou W, An P, Huang X, Jiang X, Li Y, Wan X, Hu S, Chen Y, Hu X, Xu Y, Zhu X, Li S, Yao L, He X, Chen D, Huang L, Wei X, Wang X, Yu H. Detection of colorectal adenomas with a real-time computer-aided system (ENDOANGEL): a randomised controlled study. Lancet Gastroenterol Hepatol. 2020;5:352–61.CrossRefPubMed Gong D, Wu L, Zhang J, Mu G, Shen L, Liu J, Wang Z, Zhou W, An P, Huang X, Jiang X, Li Y, Wan X, Hu S, Chen Y, Hu X, Xu Y, Zhu X, Li S, Yao L, He X, Chen D, Huang L, Wei X, Wang X, Yu H. Detection of colorectal adenomas with a real-time computer-aided system (ENDOANGEL): a randomised controlled study. Lancet Gastroenterol Hepatol. 2020;5:352–61.CrossRefPubMed

Zurück zum Zitat He K, Zhang X, Ren S, Sun J. Deep residual learning for image recognition. 2016 IEEE Conf Comput Vision Pattern Recogn (CVPR). 2016;6:770–778. He K, Zhang X, Ren S, Sun J. Deep residual learning for image recognition. 2016 IEEE Conf Comput Vision Pattern Recogn (CVPR). 2016;6:770–778.

Zurück zum Zitat Zhu X, Li X, Ong K, Zhang W, Li W, Li L, Young D, Su Y, Shang B, Peng L, Xiong W, Liu Y, Liao W, Xu J, Wang F, Liao Q, Li S, Liao M, Li Y, Rao L, Lin J, Shi J, You Z, Zhong W, Liang X, Han H, Zhang Y, Tang N, Hu A, Gao H, Cheng Z, Liang L, Yu W, Ding Y. Hybrid AI-assistive diagnostic model permits rapid TBS classification of cervical liquid-based thin-layer cell smears. Nat Commun. 2021;12:3541.CrossRefPubMedPubMedCentral Zhu X, Li X, Ong K, Zhang W, Li W, Li L, Young D, Su Y, Shang B, Peng L, Xiong W, Liu Y, Liao W, Xu J, Wang F, Liao Q, Li S, Liao M, Li Y, Rao L, Lin J, Shi J, You Z, Zhong W, Liang X, Han H, Zhang Y, Tang N, Hu A, Gao H, Cheng Z, Liang L, Yu W, Ding Y. Hybrid AI-assistive diagnostic model permits rapid TBS classification of cervical liquid-based thin-layer cell smears. Nat Commun. 2021;12:3541.CrossRefPubMedPubMedCentral

Zurück zum Zitat Lin TY, Goyal P, Girshick R, He K, Dollar P. Focal loss for dense object detection. IEEE Trans Pattern Anal Mach Intell. 2020;42:318–27.CrossRefPubMed Lin TY, Goyal P, Girshick R, He K, Dollar P. Focal loss for dense object detection. IEEE Trans Pattern Anal Mach Intell. 2020;42:318–27.CrossRefPubMed

Zurück zum Zitat He K, Gkioxari G, Dollar P, Girshick R. Mask R-CNN. IEEE Trans Pattern Anal Mach Intell. 2020;42:386–97.CrossRefPubMed He K, Gkioxari G, Dollar P, Girshick R. Mask R-CNN. IEEE Trans Pattern Anal Mach Intell. 2020;42:386–97.CrossRefPubMed

Zurück zum Zitat Li D, Bledsoe JR, Zeng Y, Liu W, Hu Y, Bi K, Liang A, Li S. A deep learning diagnostic platform for diffuse large B-cell lymphoma with high accuracy across multiple hospitals. Nat Commun. 2020;11:6004.CrossRefPubMedPubMedCentral Li D, Bledsoe JR, Zeng Y, Liu W, Hu Y, Bi K, Liang A, Li S. A deep learning diagnostic platform for diffuse large B-cell lymphoma with high accuracy across multiple hospitals. Nat Commun. 2020;11:6004.CrossRefPubMedPubMedCentral

Zurück zum Zitat Huang K-C, Huang C-S, Su M-Y, Hung C-L, Tu Y-CE, Lin L-C, Hwang J-J. Artificial intelligence aids cardiac image quality assessment for improving precision in strain measurements. JACC Cardiovasc Imaging. 2021;14:335–45.CrossRefPubMed Huang K-C, Huang C-S, Su M-Y, Hung C-L, Tu Y-CE, Lin L-C, Hwang J-J. Artificial intelligence aids cardiac image quality assessment for improving precision in strain measurements. JACC Cardiovasc Imaging. 2021;14:335–45.CrossRefPubMed

Zurück zum Zitat Moon JH, Lee DY, Cha WC, Chung MJ, Lee KS, Cho BH, Choi JH. Automatic stenosis recognition from coronary angiography using convolutional neural networks. Comput Methods Programs Biomed. 2021;198:105819.CrossRefPubMed Moon JH, Lee DY, Cha WC, Chung MJ, Lee KS, Cho BH, Choi JH. Automatic stenosis recognition from coronary angiography using convolutional neural networks. Comput Methods Programs Biomed. 2021;198:105819.CrossRefPubMed

Zurück zum Zitat Constantinescu L, Kim J, Feng DD. SparkMed: a framework for dynamic integration of multimedia medical data into distributed m-Health systems. IEEE Trans Inf Technol Biomed. 2012;16:40–52.CrossRefPubMed Constantinescu L, Kim J, Feng DD. SparkMed: a framework for dynamic integration of multimedia medical data into distributed m-Health systems. IEEE Trans Inf Technol Biomed. 2012;16:40–52.CrossRefPubMed

Zurück zum Zitat Cong C, Kato Y, Vasconcellos HD, Lima J and Venkatesh B. Automated stenosis detection and classification in X-ray angiography using deep neural network. 2019 IEEE Int Conf Bioinform Biomed (BIBM). 2019;11:18–21 Cong C, Kato Y, Vasconcellos HD, Lima J and Venkatesh B. Automated stenosis detection and classification in X-ray angiography using deep neural network. 2019 IEEE Int Conf Bioinform Biomed (BIBM). 2019;11:18–21

Zurück zum Zitat Au B, Shaham U, Dhruva S, Bouras G, Krumholz H. Automated characterization of stenosis in invasive coronary angiography images with convolutional neural networks. 2018;7:1–12. Au B, Shaham U, Dhruva S, Bouras G, Krumholz H. Automated characterization of stenosis in invasive coronary angiography images with convolutional neural networks. 2018;7:1–12.

Zurück zum Zitat Quan L, Wu B, Mao S, Yang C, Li H. An instance segmentation-based method to obtain the leaf age and plant centre of weeds in complex field environments. Sensors (Basel). 2021;21(10):3389–3411. Quan L, Wu B, Mao S, Yang C, Li H. An instance segmentation-based method to obtain the leaf age and plant centre of weeds in complex field environments. Sensors (Basel). 2021;21(10):3389–3411.

Zurück zum Zitat Zreik M, van Hamersvelt RW, Wolterink JM, Leiner T, Viergever MA, Isgum I. A Recurrent CNN for automatic detection and classification of coronary artery plaque and stenosis in coronary CT angiography. IEEE Trans Med Imaging. 2019;38:1588–98.CrossRefPubMed Zreik M, van Hamersvelt RW, Wolterink JM, Leiner T, Viergever MA, Isgum I. A Recurrent CNN for automatic detection and classification of coronary artery plaque and stenosis in coronary CT angiography. IEEE Trans Med Imaging. 2019;38:1588–98.CrossRefPubMed

Zurück zum Zitat Zreik M, van Hamersvelt RW, Wolterink JM, Leiner T, Viergever MA, Isgum I. Automatic detection and characterization of coronary artery plaque and stenosis using a recurrent convolutional neural network in coronary CT angiography. 2018;9:7–11. Zreik M, van Hamersvelt RW, Wolterink JM, Leiner T, Viergever MA, Isgum I. Automatic detection and characterization of coronary artery plaque and stenosis using a recurrent convolutional neural network in coronary CT angiography. 2018;9:7–11.

Zurück zum Zitat Eberhard M, Nadarevic T, Cousin A, von Spiczak J, Hinzpeter R, Euler A, Morsbach F, Manka R, Keller DI, Alkadhi H. Machine learning-based CT fractional flow reserve assessment in acute chest pain: first experience. Cardiovasc Diagn Ther. 2020;10:820–30.CrossRefPubMedPubMedCentral Eberhard M, Nadarevic T, Cousin A, von Spiczak J, Hinzpeter R, Euler A, Morsbach F, Manka R, Keller DI, Alkadhi H. Machine learning-based CT fractional flow reserve assessment in acute chest pain: first experience. Cardiovasc Diagn Ther. 2020;10:820–30.CrossRefPubMedPubMedCentral