Background. Coronary artery calcium (CAC) is a powerful predictor of major adverse cardiovascular events (MACE). Traditional Agatston score simply sums the calcium, albeit in a non-linear way, leaving room for improved calcification assessments that will more fully capture the extent of disease. Objective. To determine if AI methods using detailed calcification features (i.e., calcium-omics) can improve MACE prediction. Methods. We investigated additional features of calcification including assessment of mass, volume, density, spatial distribution, territory, etc. We used a Cox model with elastic-net regularization on 2457 CT calcium score (CTCS) enriched for MACE events obtained from a large no-cost CLARIFY program (ClinicalTri-als.gov Identifier: NCT04075162). We employed sampling techniques to enhance model training. We also investigated Cox models with selected features to identify explainable high-risk characteristics. Results. Our proposed calcium-omics model with modified synthetic down sampling and up sampling gave C-index (80.5%/71.6%) and two-year AUC (82.4%/74.8%) for (80:20, training/testing), respectively (sampling was applied to the training set only). Results compared favorably to Agatston which gave C-index (71.3%/70.3%) and AUC (71.8%/68.8%), respectively. Among calcium-omics features, numbers of calcifications, LAD mass, and diffusivity (a measure of spatial distribution) were important determinants of increased risk, with dense calcification (>1000HU) associated with lower risk. The calcium-omics model reclassified 63% of MACE patients to the high risk group in a held-out test. The categorical net-reclassification index was NRI=0.153. Conclusions. AI analysis of coronary calcification can lead to improved results as compared to Agatston scoring. Our findings suggest the utility of calcium-omics in improved prediction of risk.

For construction companies, the usage of data science techniques provides a huge opportunity to stand out from the competition and reinvent their business. There is a vast amount of continuously changing construction data which creates a necessity for engaging machine learning and artificial intelligent tools into different aspects of the business. Architecture is still a key place for technology and innovation to shake things up, especially with the increase of urbanization and the influx of more concentrated human populations around metropolitan areas. Realizing the difficulties within the domain of residential construction, Octett decided to deploy this initiative with the intention to solve simple problems that hold complex issues if not managed appropriately. These major inconsistencies within the sectors, left most construction specialists with little to no solutions.

Our nation has been embroiled in social unrest for several years, reaching a boiling point in May 2020 with the death of George Floyd. Health care is not immune to such inequalities. The United States is facing a crisis in health disparity -- the unequal burden of illness, injury or mortality experienced among population groups. Research by the W.K. Kellogg Foundation and Altarum, a nonprofit organization dedicated to advancing health among at-risk and disenfranchised populations, estimates that disparities lead to approximately $93 billion in excess medical care costs and $42 billion in lost productivity per year. Close to home, Cleveland rightfully boasts its role as a leading medical destination.

Artificial intelligence holds great promise for improving human health by helping doctors make accurate diagnoses and treatment decisions. It can also lead to discrimination that can harm minorities, women and economically disadvantaged people. The question is, when health care algorithms discriminate, what recourse do people have? A prominent example of this kind of discrimination is an algorithm used to refer chronically ill patients to programs that care for high-risk patients. A study in 2019 found that the algorithm favored whites over sicker African Americans in selecting patients for these beneficial services.

Other authors on the paper were: Germán Corredor, Mehdi Alilou and Kaustav Bera from Biomedical Engineering, Case Western Reserve University; Pingfu Fu from Population and Quantitative Health Sciences, Case Western Reserve University; Amit Gupta of University Hospitals Cleveland Medical Center; Pradnya Patil of Cleveland Clinic; Priya D. Velu of Weill Cornell Medicine; Rajat Thawani of Maimonides Medical Center; Michael Feldman from Perelman School of Medicine of the University of Pennsylvania; and Vamsidhar Velcheti from NYU-Langone Medical Center.

CLEVELAND--Scientists from the Case Western Reserve University digital imaging lab, already pioneering the use of Artificial Intelligence (AI) to predict whether chemotherapy will be successful, can now determine which lung-cancer patients will benefit from expensive immunotherapy. And, once again, they're doing it by teaching a computer to find previously unseen changes in patterns in CT scans taken when the lung cancer is first diagnosed compared to scans taken after the first 2-3 cycles of immunotherapy treatment. And, as with previous work, those changes have been discovered both inside--and outside--the tumor, a signature of the lab's recent research. "This is no flash in the pan--this research really seems to be reflecting something about the very biology of the disease, about which is the more aggressive phenotype, and that's information oncologists do not currently have," said Anant Madabhushi, whose Center for Computational Imaging and Personalized Diagnostics (CCIPD) has become a global leader in the detection, diagnosis and characterization of various cancers and other diseases by meshing medical imaging, machine learning and AI. Currently, only about 20% of all cancer patients will actually benefit from immunotherapy, a treatment that differs from chemotherapy in that it uses drugs to help your immune system fight cancer, while chemotherapy uses drugs to directly kill cancer cells, according to the National Cancer Institute.

Madabhushi, along with 11 additional winners, will be recognized at the second annual cleveland.com/The HomeGrown Heroes celebrates the unsung heroes of our community who are working tirelessly on their start-ups, businesses, innovations and social organizations to fuel the economic development of our region. See Madabhushi's story in the video feature by John Pana at the top of this post. A Case Western Reserve University biomedical engineering researcher, Madabhushi is making award-winning gains in how artificial intelligence can contribute significantly not only to diagnosing cancer, but also giving physicians personalized guidance on the best treatment options for each patient. His work on how computers can more accurately predict which lung cancer patients would benefit from chemotherapy was named one of the Top 10 medical breakthroughs of 2018 by Prevention.

An estimated 17.5 million people died from a cardiovascular disease in 2012, representing 31 percent of all global deaths. Most acute coronary events result from rupture of the protective fibrous cap overlying an atherosclerotic plaque. The task of early identification of plaque types that can potentially rupture is, therefore, of great importance. The state-of-the-art approach to imaging blood vessels is intravascular optical coherence tomography (IVOCT). However, currently, this is an offline approach where the images are first collected and then manually analyzed an image at a time to identify regions at risk of thrombosis. This process is extremely laborious, time consuming and prone to human error. We are building a system that, when complete, will provide interactive 3D visualization of a blood vessel as an IVOCT is in progress. The visualization will highlight different plaque types and enable quick identification of regions at risk for thrombosis. In this paper, we describe our approach, focusing on machine learning methods that are a key enabling technology. Our empirical results using real OCT data show that our approach can identify different plaque types efficiently with high accuracy across multiple patients.

William Kochevar of Cleveland can slowly move his right arm and hand. No big deal--except that the 56-year-old had been paralyzed from the shoulders down since a bicycling accident ten years ago. The setup that is allowing Kochevar to move his arm again is a "neuroprosthetic" involving two tiny recording chips implanted in his motor cortex and another 36 electrodes embedded in his right arm. Now, during visits he makes to an Ohio lab each week, signals collected in his brain are being captured and sent to his arm so he can make some simple voluntary movements. "I was completely amazed," says Kochevar.

The Modular Prosthetic Limb will help patients to feel and manipulate objects just as they would with a native hand. Sitting motionless in her wheelchair, paralysed from the neck down by a stroke, Cathy Hutchinson seems to take no notice of the cable rising from the top of her head through her curly dark hair. Her gaze never wavers as she mentally guides a robot arm beside her to reach across the table, close its grippers around the bottle, then slowly lift the vessel towards her mouth. Only when she finally manages to take a sip does her face relax into a luminous smile. This video of 58-year-old Hutchinson illustrates the strides being taken in brain-controlled prosthetics1.