Acta Scientific Medical Sciences (ISSN: 2582-0931)

Research Article Volume 4 Issue 1

From Coronary Arteriography to Stenosis Flow Reserve to FMTVDM. The Sequential Evolution of Artificial Intelligence in Cardiology and Oncology-Removing the Human Error Element

Richard M Fleming1*, Matthew R Fleming1, William C Dooley2 and Tapan K Chaudhuri3

1 FHHI-Omnificimaging-Camelot, Los Angeles, CA, USA
2 Oklahoma University Health Science Center, Oklahoma City, Oklahoma
3 Eastern Virginia Medical School, Norfolk, VA, USA

*Corresponding Author: Richard M Fleming, FHHI-Omnificimaging-Camelot, Los Angeles, CA, USA.

Received: November 26, 2019; Published: December 24, 2019

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Abstract

Background: Efforts to enhance results obtained from cardiology and oncology imaging has resulted in the development of true quantification of regional blood flow and metabolic differences. The purpose of this study was to enhance that quantification and remove the human error.

Methods: Proprietary quantitative equations provided the first machine-to-machine (M2M) exchange of data. Following first generational artificial intelligence from these proprietary equations, M2M exchange of data continued to provide machine learning (ML) and an artificial intelligence (AI) used to measure coronary artery disease (CAD) and cancer.

Results: M2M learning eliminated the erroneous human input, further modifying the proprietary equations, developing R2 values of 1.0 for percent diameter stenosis (% DS) to stenosis flow reserve (SFR) and 0.99 for SFR to% DS.

Conclusion: M2M learning removed human introduced error to diagnosis and decision making for CAD and Cancer, evolving *FMTVDM AI.

Keywords: FMTVDM; Artificial Intelligence (AI); Machine Learning (ML); Machine-To-Machine (M2M); Cardiology; Oncology

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References

  1. Fleming RM. “Chapter 64. The Pathogenesis of Vascular Disease”. Textbook of Angiology. John C. Chang Editor, Springer-Verlag New York, NY (1999): 787-798. 
  2. Fleming RM. “Chapter 29. Atherosclerosis: Understanding the relationship between coronary artery disease and stenosis flow reserve. Textbook of Angiology”. John C. Chang Editor, Springer-Verlag, New York, NY (1999): 381-387. 
  3. Fleming RM, Kirkeeide RL, Smalling RW., et al. “Patterns in Visual Interpretation of Coronary Arteriograms as Detected by Quantitative Coronary Arteriography”. Journal of the American College of Cardiology 18 (1991): 945- 951. 
  4. Fleming RM and Gaede R.” Teaching Physicians and Health Care Providers to Accurately Read Coronary Arteriograms”. Angiology 47.4 (1996): 349-359. 
  5. Fleming RM. “Shortcomings of coronary angiography. Letter to the Editor”. Cleveland Clinic Journal of Medicine 67 (2000): 450. 
  6. Fleming RM. “Coronary Artery Disease is More than Just Coronary Lumen Disease”. The American Journal of Cardiology 88 (2001): 599-600. 
  7. Fleming RM. “Angina and coronary Ischemia are the result of coronary regional Blood Flow Differences”. Journal of the American College of Cardiology 1 (2003): 127-142. 
  8. Fleming RM and Harrington GM. “Quantitative Coronary Arteriography and its Assessment of Atherosclerosis. Part 1. Examining the Independent Variables”. Angiology 45.10 (1994): 829-833. 
  9. Fleming RM and Harrington GM. “Quantitative Coronary Arteriography and its Assessment of Atherosclerosis. Part 2. Calculating Stenosis Flow Reserve Directly from Percent Diameter Stenosis”. Angiology 45.10 (1994): 835-840. 
  10. Wüsten B, Flameng W, Schaper W. “The distribution of myocardial flow. Part I: Effects of experimental coronary occlusion”. Basic Research in Cardiology 69.4 (1974): 422-434.
  11. Demer L, Gould KL, Kirkeeide R. “Assessing Stenosis Severity: Coronary Flow Reserve, Collateral Function, Quantitative Coronary Arteriography, Positron Imaging, and Digital Subtraction Angiography. A Review and Analysis”. Progress in Cardiovascular Diseases 30.5 (1988): 307-322.
  12. Fleming RM and Fleming MR. “The Importance of Thinking about and Quantifying Disease like Cancer and Heart Disease on a “Health-Spectrum” Continuum”. Journal of Comprehensive Cancer Representative 3.1 (2019): 1-3.
  13. The Fleming Method for Tissue and Vascular Differentiation and Metabolism (FMTVDM) using same state single or sequential quantification comparisons (2017).
  14. Fleming RM, Fleming MR, McKusick A., et al. “FMTVDM-TFM©℗: True Quantification requires Standardization of the tool being used to Measure, with a Known, Unchanging Standard to produce accurate, consistent and reproducible Quantified Measurements”. Journal of Nuclear Cardiology (2018). 
  15. Fleming RM, Fleming MR, McKusick A., et al. “Semi-quantification limitations: FMTVDM©℗ demonstrates quantified tumor response to treatment with both regional blood flow and metabolic changes”. The Journal of Nuclear Medicine 59.10 (2018): 1643-1644. 
  16. Fleming RM, Fleming MR, McKusick A., et al. “FMTVDM©℗ Nuclear Imaging Artificial (AI) Intelligence but first we need to clarify the use of (1) Stress, (2) Rest, (3) Redistribution and (4) Quantification”. Biomedical Journal of Scientific and Technical Research 7.2 (2018): 1-4. 
  17. Fleming RM, Fleming MR, Dooley WC., et al. “FMTVDM©℗ Provides the First Nuclear Quantitative Method for Nuclear Cardiology and Introduces a New Era for Nuclear Cardiology”. Journal of Nuclear Cardiology 25.4 (2018): 1453. 
  18. Fleming RM, Fleming MR, McKusick A., et al. “The Nuclear Imaging Uncertainty Principle. Do Our Nuclear Cameras Really Work?” Open Access Journal of Oncology and Medicine 3.1 (2019): 233-237. 
  19. Fleming RM, Fleming MR, Chaudhuri TK. “True AI Implementation Through FMTVDM Proprietary Equations and QCA”. Biomedical Journal of Scientific and Technical Research 20.4 (2019): 15154 -15160. 
  20. Fleming RM, Fleming MR, Chaudhuri TK., et al. “Machine Learning through FMTVDM Proprietary QCA Equations”. Journal of Angiology and Vascular Surgery 4 (2019): 026. 
  21. Fleming RM, Fleming MR, Chaudhuri TK., et al. “First Patented Quantitative Molecular Imaging Method for Detection and Measurement of CAD and Cancer”. Acta Scientific Pharmaceutical Sciences 3.9 (2019): 30-32. 
  22. Fleming RM, Fleming MR, Chaudhuri TK. “The Need to Actually Measure What We’re Talking about before We Put it All Together”. International Journal of Nuclear Medicine and Radioactive Substances 2.1 (2019): 000114. 
  23. Fleming RM, Fleming MR, Chaudhuri TK., et al. “FMTVDM Quantitative Imaging Replaces Current Qualitative Imaging for Coronary Artery Disease and Cancer, Increasing Diagnostic Accuracy and Providing Patient- Specific, Patient-Directed Treatment”. Cardio Open 4.3 (2019). 
  24. Sheikh A. “Evolution of Quantification in Clinical Nuclear Medicine: A Brief Overview of Salient Uses and Upcoming Trends”. Journal of Nuclear Medicine and Radiation Therapy 9.4 (2018): 375. 
  25. ACC/AHA Task Force. “Cholesterol Clinical Practice Guidelines”. Circulation 139 (2019): e10820e1143.
  26. Lamy JB, Sekar B, Gueaennec G., et al. “Explainable artificial intelligence for breast cancer: A visual case-based reasoning approach”. Artificial Intelligence in Medicine 94 (2019): 42-53.
  27. Davenport TH and Glover WJ. “Artificial Intelligence and the Augmentation of Health Care Decision-Making”. NEJM Catalyst (2018). 
  28. Fleming RM, Fleming MR, Chaudhuri TK. “How Beneficial are Statins and PCSK9-Inhibitors?” Scholarly Journal of Food and Nutrition 2.3 (2019): 213-218. 
  29. Fleming RM, Fleming MR, Chaudhuri TK., et al. “Theranostic Information Provided by FMTVDM©℗; B.E.S.T. ©℗ Imaging”. Advances Hema Oncology Research 2.1 (2019): 1-3.
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Citation

Citation: Richard M Fleming, Matthew R Fleming, William C Dooley and Tapan K Chaudhuri. “From Coronary Arteriography to Stenosis Flow Reserve to FMTVDM. The Sequential Evolution of Artificial Intelligence in Cardiology and Oncology-Removing the Human Error Element". Acta Scientific Medical Sciences 4.1 (2020): 114-118.



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