Two projects sponsored by Amazon Web Services (AWS) and the Pittsburgh Health Data Alliance (PHDA) have generated solid use cases for machine learning in clinical care.

Amazon Web Services (AWS) and the Pittsburgh Health Data Alliance (PHDA) collaborated in August 2019 to advance innovation in areas including cancer diagnostics, precision medicine, electronic health records, and medical imaging. 

Through the collaboration, researchers from the University of Pittsburgh Medical Center (UPMC), University of Pittsburgh, and Carnegie Mellon University (CMU) received support from Amazon Search Awards on top of existing support from PHDA to use machine learning to dive into various projects.

One of those projects examined machine learning techniques to help experts study breast cancer risk and understand what drives tumor growth. 

Led by Shandong Wu, an associate professor at the University of Pittsburgh department of radiology, a research team analyzed 452 normal mammograms from 226 patients in order to predict the short-term risk of developing breast cancer. 

Wu and his team, who included experts in computer vision, deep learning, bioinformatics, and breast cancer imaging, used two machine learning models and found that both models consistently outperformed in the area of breast density.

Specifically, the team’s model demonstrated between 33 percent and 35 percent improvement over the existing models, researchers highlighted. 

“This preliminary work demonstrates the feasibility and promise of applying deep-learning methodologies for in-depth interpretation of mammogram images to enhance breast cancer risk assessment,” Wu said in the announcement. 

“Identifying additional risk factors for breast cancer, including those that can lead to a more personalized approach to screening, may help patients and providers take more appropriate preventive measures to reduce the likelihood of developing the disease or catching it early on when interventions are most effective.” 

Another project led by Eva Szigethy, clinical researcher at UPMC and Louis-Phillippe Morency, associate professor of computer science at CMU, used machine learning to measure changes in an individual’s behavior to diagnosis depression.

Their machine learning models are trained on tens of thousands of language, acoustic, and visual modalities to identify biomarkers for depression. The biomarkers will be compared to results from traditional clinical assessments to determine the accuracy of the machine learning models with identifying depression.

“New insights to increase the accuracy, efficiency, and adoption of depression screening have the potential to impact millions of patients, their families, and the healthcare system as a whole,” Morency stated. 

AWS and PHDA noted that the projects on breast cancer and depression are just the start when it comes to research collaboration to improve patient care. 

Teams of researchers, healthcare professionals, and machine learning experts will continue to work to understand the risk of aneurysms, predict how cancer cells progress, and aim to improve the electronic health records system. 

“Amazon is excited and encouraged by the progress these researchers are making and how machine learning is central to their work,” said An Luo, senior technical program manager for academic programs at Amazon AI. 

“We look forward to continuing to share how this unique collaboration between the PHDA and AWS is enabling new discoveries to help patients on a global scale.”

For example, David Vorp, PhD, associate dean for research at UPMA, and his research team employed AWS cloud resources to boost the diagnosis and therapy of abdominal aortic aneurysms.

And a CMU research team led by Russell Schwartz, PhD, and Jian Ma, PhD, used machine learning to develop algorithms and software tools to better understand cell origin and evolution. 

“With the latest advances in machine learning, we are developing an algorithm that will provide clinicians with an objective, predictive tool to guide surgical interventions before symptoms appear, improving patient outcomes,” Vorp said in the August announcement.

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September 11, 2020 – A machine learning model was able to detect different clusters of gut bacteria that could potentially identify individuals with existing cardiovascular disease (CVD), according to a study published in the journal Hypertension.

Recent studies have found a link between gut microbiota, the microorganisms in human digestive tracts, and CVD, the leading cause of mortality worldwide. Gut microbiota is highly variable between individuals, and differences in gut microbial compositions between people with and without CVD have been reported.

“Based on our previous research linking gut microbiota to CVD in animal models, we designed this study to test whether it is possible to screen for CVD in humans using artificial intelligence screening of stool samples,” said Bina Joe, PhD, FAHA, the study director, Distinguished University Professor and Chairwoman of the department of physiology and pharmacology at the University of Toledo in Toledo, Ohio.

“Gut microbiota has a profound effect on cardiovascular function, and this could be a potential new strategy for evaluation of cardiovascular health.”

Researchers used data from the American Gut Project, an open platform for microbiome research based in the US. The team leveraged innovative machine learning methods to analyze microbial composition of nearly 1,000 stool samples. Approximately half of the samples were from people with CVD.

Researchers found that the model was able to identify different clusters of gut bacteria that could potentially help identify individuals with and without CVD. The results show the potential for machine learning and artificial intelligence to improve CVD screening.

“Despite the fact that gut microbiomes are highly variable among individuals, we were surprised by the promising level of accuracy obtained from these preliminary results, which indicate fecal microbiota composition could potentially serve as a convenient diagnostic screening method for CVD,” Joe said.

“It is conceivable that one day, maybe without even assessing detailed cardiovascular function, clinicians could analyze the gut microbiome of patients’ stool samples with an artificial machine learning method to screen patients for heart and vascular diseases.”

Researchers have previously used machine learning to improve cardiovascular disease treatment and detection. A study published in Cardiovascular Research showed that machine learning tools could predict patients’ long-term risk of heart attacks and cardiac deaths better than standard methods used by cardiologists.

In that study, researchers found that subjects’ predicted machine learning scores aligned accurately with the actual distribution of observed events.

“In this prospective trial, machine learning demonstrated high performance in risk assessment for myocardial infarction and cardiac death in asymptomatic subjects,” the researchers stated.  

“By objectively combining clinical data and quantitative CT measures, machine learning provided significantly superior risk prediction compared with the coronary artery calcium score. These promising results suggest that machine learning has a potential for clinical implementation to improve risk assessment.”

In a separate study published in 2018, a team from Stanford University were able to create a personal health management tool that combined EHR data and machine learning algorithms to accurately diagnose the heart condition known as abdominal aortic aneurysm (AAA).

Researchers used the hierarchical estimate from agnostic learning (HEAL) health management tool to analyze patients’ genome sequences and EHRs to identify individuals with the cardiovascular disease.

“For each individual, HEAL accurately predicted his/her AAA risk using personal genome and EHR data. On the other hand, for the same individual with newly adopted lifestyles resulting in physiological changes (e.g., from a high cholesterol to a low cholesterol diet), HEAL can immediately update his/her AAA risk upon corresponding changes conditioned on the person’s genome baseline,” Stanford researchers said.

“This allows us to further investigate the interplay between personal genomes and lifestyles underlying disease predisposition.”

As machine learning and AI become more widely used in healthcare, these tools could make the transition from research to clinical care for improved patient health and outcomes. 

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