Heart disease and cancer are the leading causes of death in the United States, and it’s increasingly understood that they share common risk factors, including tobacco use, diet, blood pressure, and obesity. Thus, a diagnostic tool that could screen for cardiovascular disease while a patient is already being screened for cancer, has the potential to expedite a diagnosis, accelerate treatment, and improve patient outcomes. 

In research published today in Nature Communications, a team of engineers from Rensselaer Polytechnic Institute and clinicians from Massachusetts General Hospital developed a deep learning algorithm that can help assess a patient’s risk of cardiovascular disease with the same low-dose computerized tomography (CT) scan used to screen for lung cancer. This approach paves the way for more efficient, more cost-effective, and lower radiation diagnoses, without requiring patients to undergo a second CT scan. 

“In this paper, we demonstrate very good performance of a deep learning algorithm in identifying patients with cardiovascular diseases and predicting their mortality risks, which shows promise in converting lung cancer screening low-dose CT into a dual screening tool,” said Pingkun Yan, an assistant professor of biomedical engineering and member of the Center for Biotechnology and Interdisciplinary Studies (CBIS) at Rensselaer.

Numerous hurdles had to be overcome in order to make this dual screening possible. Low-dose CT images tend to have lower image quality and higher noise, making the features within an image harder to see. Using a large dataset from the National Lung Screening Trial (NLST), Yan and his team used data from more than 30,000 low-dose CT images to develop, train, and validate a deep learning algorithm capable of filtering out unwanted artifacts and noise, and extracting features needed for diagnosis. Researchers validated the algorithm using an additional 2,085 NLST images.

The Rensselaer team also partnered with Massachusetts General Hospital, where researchers were able to test this deep learning approach against state-of-the-art scans and the expertise of the hospital’s radiologists. The Rensselaer-developed algorithm, Yan said, not only proved to be highly effective in analyzing the risk of cardiovascular disease in high-risk patients using low-dose CT scans, but it also proved to be equally effective as radiologists in analyzing those images. In addition, the algorithm closely mimicked the performance of dedicated cardiac CT scans when it was tested on an independent dataset collected from 335 patients at Massachusetts General Hospital.

“This innovative research is a prime example of the ways in which bioimaging and artificial intelligence can be combined to improve and deliver patient care with greater precision and safety,” said Deepak Vashishth, the director of CBIS.

Yan was joined in this work by Ge Wang, an endowed chair professor of biomedical engineering at Rensselaer and fellow member of CBIS. The Rensselaer team was joined by Dr. Mannudeep K. Kalra, an attending radiologist at Massachusetts General Hospital and professor of radiology with Harvard Medical School. This research was funded by the National Institutes of Health National Heart, Lung, and Blood Institute. 

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To be a modern-day engineer, being a domain-specific expert is just not enough. Data Science helps engineers to make better decisions at work and for organisations at large.

Why Should Companies Be Wary Of Shadow Data?

Every year, India produces over 1.5 million engineering graduates. These are disproportionately high numbers especially considering the appropriate opportunities available. To say that the competition in the job market is cutthroat, would be an understatement. In such a situation, it becomes imperative that students skill up and get that extra edge. 

Data science, apart from being a booming industry, is also a field that is interesting and challenging. Being trained in data science alongside regular engineering opens several doors of opportunity. Analytics India Magazine caught up with Dr Dakshinamurthy V Kolluru, Founder and President, INSOFE to understand more about the scope and opportunities. Dr Kolluru speaks about the engineering and data science dual degree offered by INSOFE which will equip aspirants with skills of today’s world.

AIM: How relevant are core engineers with data science specialisation in modern-day organisations?

Dr Kolluru: To be a modern-day engineer, being a domain-specific expert is just not enough. Organisations expect you to also have an understanding of data-driven innovation across various phases of engineering. Data Science helps engineers to make better decisions at work and for organisations at large.

An engineer’s role involves efficient design, development, execution, and maintenance of systems. This calls for an in-depth analysis of a variety of dynamic factors. Data Science, on the other hand, relies extensively on building a rationale around engineering endeavours. It involves collecting data on several factors affecting a product/problem, assessing the trends, analysing correlations between them, and predicting how they will change over time. 

Using the principles of data correlation and change predictions, engineers can come up with superior designs, build better products, and run systems more efficiently than ever before!  

AIM: Could you give us some examples of such applications in the real world?

Dr Kolluru: Automobile Industry

Industries such as Transportation and Automobiles depend on Artificial Intelligence and Data Science to build components with precision. Industry leaders such as GE Transportation have envisaged brilliant factories that rely extensively on Machine Intelligence. They have arrived at digitising the processes and reducing the downtime in maintenance. 

From Tool design standardisation, predicting wear and tear of moving parts to visualising exceptional malfunctions, Data Science is being adopted seamlessly. In addition, Artificial Intelligence is used for state-of-the-art innovations like aerodynamic design and autonomous navigation. 

With innovations and developments in data science applications engineers have been able to design superior solutions, which couldn’t be thought of earlier.

Healthcare providers are using statistical simulations of clinical trials to pre-test the efficacy of the drugs. Machine Learning algorithms are being utilised by the healthcare industry to identify different chemical combinations that can be used in drug discovery. Adopting machine intelligence to arrive at the most suitable combinations, companies are now able to come up with efficient drugs. These principles have sped up the drug development process and enhanced its efficiency.

Hospitals and doctors are now adopting machine learning models for prognosis, diagnosis, and treatment of chronic ailments like diabetes and terminal diseases like cancer.

AIM: How did INSOFE come up with the dual specialisation master’s programs?

Dr Kolluru: INSOFE is a world-class institute that is imparting quality data science education. To help students break into the heart of Engineering through their data science expertise, INSOFE has collaborated with top universities to offer dual specialization programs. 

Case Western Reserve University, USA and University of Strathclyde, UK are two such universities that have the finest engineering departments in the world. Case Western Reserve University in particular has been at the forefront of integrating engineering with various domains. Their work in Biomedical Engineering is an apt example of such innovation. 

The curriculum of the dual master’s degree is designed to ensure that students master data science and its implementation in different engineering domains. INSOFE’s philosophy is to extend the student’s expertise to research, product development, and new-age engineering in their core discipline. 

We strongly believe that such partnerships provide an opportunity for the student to study at some of the world-class universities, enhance student learning through multi-cultural interactions, and be mentored by renowned professors. 

AIM: What dual specialisations are offered through INSOFE and can students apply to such options in other universities?

Dr Kolluru: Case Western Reserve University and INSOFE offer Dual Specialization Masters programs in three engineering disciplines. The broad specializations include Biomedical Engineering with Translational Health Technology, Mechanical Engineering with AI and Robotics in addition to Civil, and Environmental Engineering with AI and Machine Learning. 

Students will complete the data science specific credits at INSOFE campuses in India and the latter part of the course work would be completed at Case School of Engineering, Cleveland – OH, USA.  

These programs are offered only under the Case Western – INSOFE collaboration. This option is not available for students undertaking normal courses at the university. The competitive examination scores like GRE / TOEFL / IELTS are waived off for students applying to the programs under this collaboration. However, the students have to clear INSOFE Entrance Exam (INEX) as a pre-qualification to apply for these programs. Eligible students also stand a chance to receive a scholarship of up to 25% of the tuition fee.  

AIM: How are opportunities different in the US compared to in India for students graduating in such programs?

Dr Kolluru: This is a STEM graduate program, students will be eligible to work in the USA upon the completion of the degree. The research and manufacturing focus of companies in the USA provides opportunities that require data science skills in addition to engineering expertise.

The dual specialization masters help students extend their reach beyond IT companies as they are equipped to work across industries like automobile, healthcare, petroleum, pharmaceuticals, and manufacturing. They have an added advantage over a regular engineering graduate since a greater number of organisations are looking for engineers with data science skills.  

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