Clinical decision support systems in myocardial perfusion imaging

详细信息    查看全文

• 作者：Ernest V. Garcia PhD (1)
  J. Larry Klein MD (2)
  Andrew T. Taylor MD (1)
  
• 关键词：Coronary artery disease ; image processing ; myocardial perfusion ; decision support systems
• 刊名：Journal of Nuclear Cardiology
• 出版年：2014
• 出版时间：June 2014
• 年：2014
• 卷：21
• 期：3
• 页码：427-439
• 全文大小：
• 参考文献：1. Kochanek KD, Xu JQ, Murphy SL, Mini?o AM, Kung HC. Deaths: final data for 2009. National vital statistics reports, vol. 60, 2011, pp. 1-16.
  2. IMV Medical Information Division, Inc. 2008 Nuclear medicine market summary report. 2008. vision-Inc-v229/" class="a-plus-plus">http://www.marketresearch.com/IMV-Medical-Information-Division-Inc-v229/. Accessed 15 Nov 2011.
  3. Knickman JR, Snell EK. The 2030 problem: Caring for aging baby boomers. Health Serv Res 2002;37:849-84. CrossRef
  4. Fye WB. Cardiology’s workforce shortage: Implication for patient care and research. Circulation 2004;109:813-6. CrossRef
  5. Nelson N. Principles of artificial intelligence. Palo Alto: Toga Publishing; 1980.
  6. Matsumoto N, Berman DS, Kavanagh PB, Gerlach J, Hayes SW, Lewin HC, et al. Quantitative assessment of motion artifacts and validation of a new motion-correction program for myocardial perfusion SPECT. J Nucl Med 2001;42:687-94.
  7. Fitzgerald J, Danias PG. Effect of motion on cardiac SPECT imaging: Recognition and motion correction. J Nucl Cardiol 2001;8:701-6. CrossRef
  8. Germano G, Kavanagh PB, Slomka PJ, Van Kriekinge SD, Pollard G, Berman DS, et al. Quantitation in gated perfusion SPECT imaging: The Cedars-Sinai approach. J Nucl Cardiol 2007;14:433-54. CrossRef
  9. Germano G, Kavanagh PB, Su HT, Mazzanti M, Kiat H, Hachamovitch R, et al. Automatic reorientation of 3-dimensional transaxial myocardial perfusion SPECT images. J Nucl Med 1995;36:1107-14.
  10. Mullick R, Ezquerra NF. Automatic determination of left ventricular orientation from SPECT data. IEEE Trans Med Imaging 1995;14:88-99. CrossRef
  11. Ficaro EP, Lee BC, Kritzman JN, Corbett JR. Corridor4DM: The Michigan method for quantitative nuclear cardiology. J Nucl Cardiol 2007;14:455-65. CrossRef
  12. Garcia EV, Faber TL, Cooke CD, Folks RD, Chen J, Santana C. The increasing role of quantification in nuclear cardiology: The Emory approach. J Nucl Cardiol 2007;14:420-32. CrossRef
  13. Liu YH, Sinusa AJ, DeMan P, Zaret BL, Wackers FJT. Quantification of SPECT myocardial perfusion images: Methodology and validation of the Yale-CQ method. J Nucl Cardiol 1999;6:190-203. CrossRef
  14. Johansson L, Edenbrandt L, Nakajima K, Lomsky M, Svensson S-E, Tr?g?rdh E, et al. Computer-aided diagnosis system outperforms scoring analysis in myocardial perfusion imaging. J Nucl Cardiol 2014. doi:10.1007/s12350-013-9815-y .
  15. Garcia EV, Van Train K, Maddahi J, Prigent F, Friedman J, Areeda J, et al. Quantification of rotational thallium-201 myocardial tomography. J Nucl Med 1985;26:17-26.
  16. Van Train KF, Maddahi J, Berman DS, Kiat H, Areeda J, Prigent F, et al. Quantitative analysis of tomographic stress thallium-201 myocardial scintigrams: A multicenter trial. J Nucl Med 1990;31:1168-79.
  17. Sharir T, Germano G, Waechter PB, Kavanagh PB, Areeda JS, Gerlach J, et al. A new algorithm for the quantitation of myocardial perfusion SPECT. II: Validation and diagnostic yield. J Nucl Med 2000;41:720-7.
  18. Slomka PJ, Hurwitz GA, Stephenson J, Cradduck T. Automated alignment and sizing of myocardial stress and rest scans to three-dimensional normal templates using an image registration algorithm. J Nucl Med 1995;36:1115-22.
  19. Slomka PJ, Nishina H, Berman D, Akincioglu C, Abidov A, Friedman JD, et al. Automated quantification of myocardial perfusion SPECT using simplified normal limits. J Nucl Cardiol 2005;12:66-77. CrossRef
  20. Liu YH. Quantification of nuclear cardiac images: The Yale method. J Nucl Cardiol 2007;14:483-91. CrossRef
  21. DePasquale E, Nody A, DePuey G, Garcia EV, Pilcher G, Bredleau C, et al. Quantitative rotational thallium-201 tomography for identifying and localizing coronary artery disease. Circulation 1988;77:316-27. CrossRef
  22. Faber TL, Cooke CD, Folks RD, Vansant JP, Nichols KJ, DePuey EG, et al. Left ventricular function and perfusion from gated SPECT perfusion images: An integrated method. J Nucl Med 1999;40:650-9.
  23. Garcia E, Depuey EG, Sonnemaker RE, Neely HR, DePasquale EE, Robbins WL, et al. Quantification of the reversibility of stress induced SPECT thallium-201 myocardial perfusion defects: A multicenter trial using Bull’s-eye polar maps and standard normal limits. J Nucl Med 1990;31:1761-5.
  24. Van Train KF, Areeda J, Garcia EV, Cooke CD, Maddahi J, Kiat H, et al. Quantitative same-day rest-stress technetium-99?m-sestamibi SPECT: Definition and validation of stress normal limits and criteria for abnormality. J Nucl Med 1993;34:1494-502.
  25. Van Train KF, Garcia EV, Maddahi J, Areeda J, Cooke CD, Kiat H, et al. Multicenter trial validation for quantitative analysis of same-day rest-stress technetium-99?m-sestamibi myocardial tomograms. J Nucl Med 1994;35:168-609.
  26. Wolak A, Slomka PJ, Fish MB, Lorenzo S, Acampa W, Berman DS, et al. Quantitative myocardial-perfusion SPECT: Comparison of three state-of-the-art software packages. J Nucl Cardiol 2008;15:27-34. CrossRef
  27. Wackers FJT. Science, art, and artifacts: How important is quantification for the practicing physician interpreting myocardial perfusion studies? J Nucl Cardiol 1994;1:S109-17. CrossRef
  28. Berman DS, Abidov A, Kang X, et al. Prognostic validation of a 17-segment score derived from a 20-segment score for myocardial perfusion SPECT interpretation. J Nucl Cardiol 2004;11:414-23. CrossRef
  29. Berman DS, Hachamovich R, Germano G. Risk stratification and patient management. In: Dilsizian V, Narula J, editors. Atlas of nuclear cardiology, Chapter 1. 3rd ed. Hoboken: Current Medicine LLC; 2009. p. 141-68.
  30. Gorry G. Computer-assisted clinical decision making. Methods Inf Med 1973;12:45-51.
  31. Shortliffe EH. Computer-based medical consultations: MYCIN. Amsterdam: Elsevier; 1976. p. 264.
  32. Fujita H, Katafuchi T, Uehara T, Nishimura T. Application of neural network to computer-aided diagnosis of coronary artery disease in myocardial SPECT Bull’s-eye images. J Nucl Med 1992;33:272-6.
  33. Porenta G, Dorffner G, Kundrat S, Petta P, Duit-Schedlmayer J, Sochor H. Automated interpretation of planar thallium-201-dipyridamole stress-redistribution scintigrams using artificial neural networks. J Nucl Med 1994;35:2041-7.
  34. Hamilton D, Riley PJ, Miola UJ, Amro AA. A feed forward neural network for classification of bull’s-eye myocardial perfusion images. Eur J Nucl Med 1995;22:108-15. CrossRef
  35. Lindahl D, Palmer J, Ohlsson M, Peterson C, Lundin A, Edenbrandt L. Automated interpretation of myocardial SPECT perfusion images using artificial neural networks. J Nucl Med 1997;38:1870-5.
  36. Lindahl D, Palmer J, Pettersson J, White T, Lundin A, Edenbrandt L. Scintigraphic diagnosis of coronary artery disease: Myocardial bull’s-eye images contain the important information. Clin Physiol 1998;18:554-61. CrossRef
  37. Lindahl D, Lanke J, Lundin A, Palmer J, Edenbradt L. Improved classifications of myocardial bull’s-eye scintigrams with computer-based decision support system. J Nucl Med 1999;40:96-101.
  38. Lindahl D, Palmer J, Edenbrandt L. Myocardial SPET: Artificial neural networks describe extent and severity of perfusion defects. Clin Physiol 1999;19:497-503. CrossRef
  39. Lindahl D, Toft J, Hesse B, Palmer J, Ali S, Lundin A, et al. Scandinavian test of artificial neural network for classification of myocardial perfusion images. Clin Physiol 2000;20:253-61. CrossRef
  40. Allison JS, Heo J, Iskandrian AE. Artificial neural network modeling of stress single-photon emission computed tomographic imaging for detecting extensive coronary artery disease. Am J Cardiol 2005;95:178-81. CrossRef
  41. Haddad M, Moertl D, Porenta G. SCINA: A case-based reasoning system for the interpretation of myocardial perfusion scintigrams. Vienna: Computers in Cardiology; 1995. ISBN: 0-7803-3053-6. pp. 761-764.
  42. http://en.wikipedia.org/wiki/Clinical_decision_support_system. Accessed 11 Nov 2013.
  43. Osheroff JA, Teich JM, Middleton BF, Steen EB, Wright A, Detmer DE, et al. A roadmap for national action on clinical decision support, national strategic plan by the American College of Medical Informatics (ACMI). 2006. http://www.amia.org/public-policy/reports-and-fact-sheets/a-roadmap-for-national-action-on-clinical-decision-support. Accessed 11 Nov 2013.
  44. Osheroff JA, Pifer EA, Teich JM, Sittig DF, Jenders RA. Improving outcomes with clinical decision support: An implementer’s guide. Chicago: Healthcare Information and Management Systems Society Press; 2005.
  45. Ezquerra NF, Garcia EV. Artificial intelligence in nuclear medicine imaging. Am J Cardiac Imaging 1989;3:130-41.
  46. Garcia EV, Cooke CD, Folks RD, Santana CA, Krawczynska EG, De Braal L, et al. Diagnostic performance of an expert system for the interpretation of myocardial perfusion SPECT studies. J Nucl Med 2001;42:1185-91.
  47. Khorsand A, Haddad M, Graf S, Moertl D, Sochor H, Porenta G. Automated assessment of dipyridamole Tl-201 myocardial SPECT perfusion scintigraphy by case-based reasoning. J Nucl Med 2001;42:189-93.
  48. Haddad M, Adlassnig KP, Porenta G. Feasibility analysis of a case-based reasoning system for automated detection of coronary heart disease from myocardial scintigrams. Artif Intell Med 1997;9:61-78. CrossRef
  49. Ordonez C, Omiecinski E, de Braal L, Santana CA, Ezquerra N, Taboada JA, et al. Mining constrained association rules to predict heart disease. In: Proceedings of the 2001 IEEE international conference on data mining (ICDM-1). ISBN: 0-7695-1119-8.
  50. Ordonez C, Ezquerra N, Santana CA. Constraining and summarizing association rules in medical data. Knowl Inf Syst 2006;9:1-2. CrossRef
  51. Liu Y, Navathe SB, Pivosheko A, Civera J, Dasigi V, Ram A, et al. Text mining biomedical literature for discovering gene-gene relationships. IEEE/ACM Trans Comput Biol Bioinform 2005;2:62-76. CrossRef
  52. Li B, Sugandh N, Garcia EV, Ram A. 2007. Adapting associative classification to text categorization. In: Proceedings of the 2007 ACM symposium on document engineering (ACM DocEng 2007). pp. 205-208.
  53. Sahay S, Li B, Garcia EV, Agichtein E, Ram A. Domain ontology construction from biomedical text. In: Proceedings of the 2007 international conference on artificial intelligence (IC-AI-7: June 25-28, 2007); 2007. pp. 28-34.
  54. Kurgan LA, Cios KJ, Tadeusiewicz R, Ogiela M, Goodenday LS. Knowledge discovery approach to automated cardiac SPECT diagnosis. Artif Intell Med 2001;23:149-69. CrossRef
  55. Sacha JP, Goodenday L, Cios KJ. Bayesian learning for cardiac SPECT image interpretation. Artif Intell Med 2002;26:109-43. CrossRef
  56. Douglas PS, Hendel RC, Cummings JE, Dent JM, Hodgson JM, Hoffmann U, et al. ACCF/ACR/AHA/ASE/ASNC/HRS/NASCI/RSNA/SAIP/SCAI/SCCT/SCMR 2008 health policy statement on structured reporting in cardiovascular imaging. JACC 2009;53:76-90. CrossRef
  57. Tilkemeier PL, Cooke CD, Grossman GB, McCallister BJ, Ward RP. Standardized reporting of myocardial perfusion and function. J Nucl Cardiol 2009. doi:10.1007/s12350-009-9095-8 .
  58. Garcia EV. Novel web decision support system for cardiac image interpretation and reporting. NIH Reporter Website, 5R42HL 106818-03. v/project_info_description.cfm?aid=8427296&icde=18340126&ddparam=&ddvalue=&ddsub=&cr=2&csb=default&cs=ASC" class="a-plus-plus">http://projectreporter.nih.gov/project_info_description.cfm?aid=8427296&icde=18340126&ddparam=&ddvalue=&ddsub=&cr=2&csb=default&cs=ASC. Accessed 14 Nov 2013.
  59. Petretta M, Soricelli A, Storto G, Cuocolo A. Assessment of coronary flow reserve using single photon emission computed tomography with technetium 99?m labeled tracers. J Nucl Cardiol 2008;15:456-65. CrossRef
  60. Hillman BJ, Neiman HL. Radiology 2012: Radiology and radiologists a decade hence—A strategic analysis for radiology from the Second Annual American College of Radiology FORUM. Radiology 2003;227:9-14. CrossRef
  
• 作者单位：Ernest V. Garcia PhD (1)
  J. Larry Klein MD (2)
  Andrew T. Taylor MD (1)
  
  1. Department of Radiology and Imaging Sciences, Emory University, 101 Woodruff Circle, Room 1203, Atlanta, GA, 30322, USA
  2. Division of Cardiology, Department of Medicine, UNC Healthcare System, Chapel Hill, NC, USA
  
• ISSN：1532-6551

文摘

Diagnostic imaging is becoming more complicated, physicians are also required to master an ever-expanding knowledge base and take into account an ever increasing amount of patient-specific clinical information while the time available to master this knowledge base, assemble the relevant clinical data, and apply it to specific tasks is steadily shrinking. Compounding these problems, there is an ever increasing number of aging “Baby Boomers-who are becoming patients coupled with a declining number of cardiac diagnosticians experienced in interpreting these studies. Hence, it is crucial that decision support tools be developed and implemented to assist physicians in interpreting studies at a faster rate and at the highest level of up-to-date expertise. Such tools will minimize subjectivity and intra- and inter-observer variation in image interpretation, help achieve a standardized high level of performance, and reduce healthcare costs. Presently, there are many decision support systems and approaches being developed and implemented to provide greater automation and to further objectify and standardize analysis, display, integration, interpretation, and reporting of myocardial perfusion SPECT and PET studies. This review focuses on these systems and approaches.