Pittsburgh Center for Artificial Intelligence Innovation in Medical Imaging

We propose to create a medical imaging artificial intelligence (AI) center (name: Pittsburgh Center for Artificial Intelligence Innovation in Medical Imaging). AI is the new revolutionary technique for medical research and is reshaping tomorrow’s clinical practice in medical imaging (radiology and pathology). Our long-term vision is to build a center for innovative AI in clinical translational medical imaging by combining computational expertise and clinical resources across Pitt, UPMC, and CMU. The Center concept is a formalization of a group of researchers and clinicians that are united by the common theme: “building advanced and trustworthy imaging AI for clinical applications.” Our short-term plan is to assemble dedicated members from the School of Medicine, the School of Engineering, and the School of Computing and Information. We seek a Scaling grant from the Momentum Funds to foster collaborative activities of the Center between these three Pitt schools to provide the essential components of a competitive P41 (Biomedical Technology Resource Centers) center grant in 2 years. The National Institute of Biomedical Imaging and Bioengineering (NIBIB) P41 mechanism aligns with the overall vision of this initiative to develop specific AI imaging tools and to support the dissemination and commercialization pathways that are essential to bringing AI imaging tools to clinical practice. These projects will include key components: 1) Clinical need-driven medical imaging AI development and evaluation of tools, models, systems, and informatics, 2) Core imaging AI theory, methodology, and algorithm investigation, and 3) Linking imaging phenotypes to the biological (genomics and proteomics) underpinnings. To date, we have already 35 members for the Center. The Pitt Momentum Funds will provide critical scaling support to promote communication between the three Pitt schools to develop a competitive P41 grant application and a sustainable framework to ensure the clinical impact of these AI imaging tools

Impact of errors in recorded compressed breast thickness measurements on volumetric density classification using volpara v1.5.0 software

Purpose: Mammographic density has been demonstrated to predict breast cancer risk. It has been proposed that it could be used for stratifying screening pathways and recommending additional imaging. Volumetric density tools use the recorded compressed breast thickness (CBT) of the breast measured at the x-ray unit in their calculation, however the accuracy of the recorded thickness can vary. The aim of this study was to investigate whether inaccuracies in recorded CBT impact upon volumetric density classification and to examine whether the current quality control (QC) standard is sufficient for assessing mammographic density. Methods: Raw data from 52 digital screening mammograms were included in the study. For each image, the clinically recorded CBT was artificially increased and decreased to simulate measurement error. Increments of 1mm were used up to ±15% error of recorded CBT was achieved. New images were created for each 1mm step in thickness resulting in a total of 974 images which then had Volpara Density Grade (VDG) and volumetric density percentage assigned. Results: A change in VDG was recorded in 38.5% (n= 20) of mammograms when applying ±15% error to the recorded CBT and 11.5 % (n= 6) were within the QC standard prescribed error of ±5mm. Conclusion: The current QC standard of ±5mm error in recorded CBT creates the potential for error in mammographic density measurement. This may lead to inaccurate classification of mammographic density. The current QC standard for assessing mammographic density should be reconsidered

Tailored supplemental screening for breast cancer: What now and what next

guidelines and, for the first time, advocated supplemental screening beyond annual mammography in certain high-risk women. Specifically, annual contrast-enhanced MRI was recommended in addition to mammography for women known to carry a BRCA mutation and for their untested first-degree relative

Comment on Thigpen D. et al. The Role of Ultrasound in Screening Dense Breasts—A Review of the Literature and Practical Solutions for Implementation. Diagnostics 2018, 8, 20

We read with interest the article by Thigpen et al. [1]. With 34 states now having some form of density inform legislation[...