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
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
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.
Copyright: © 2020 Richard M Fleming, Matthew R Fleming, William C Dooley and Tapan K Chaudhuri. This is an open-access article distributed under the terms of the Creative Commons Attribution License, which permits unrestricted use, distribution, and reproduction in any medium, provided the original author and source are credited.