Our Projects

This is a federal award from the United States Defense Advanced Research Projects Agency (DARPA) to develop a quantum computing benchmarking framework for quantum computing architectures that can overcome any failures in hardware or software. It will lead to the development and analysis of various computational analysis problems to test and evaluate scenarios relevant to a variety of fields — including genomics, computational biology, protein conformational analysis, protein-drug interaction, radiation therapy planning, healthcare process optimization, computational biology and computational chemistry.

This project will explore the benefits of quantum computing to study the risk factors for cardiovascular complications following non-cardiac surgery. Major adverse cardiac events are a leading cause of non-cardiac morbidity and mortality around the time of surgery. By applying quantum machine learning techniques, we can enhance our understanding of the risk factors, improve patient outcomes and potentially reduce healthcare costs and utilization by facilitating clinical decision-making. The results of this project can also help determine if quantum computing-based machine learning algorithms can improve patient outcomes in other clinical settings.

Cleveland Clinic and IBM are combining artificial intelligence and quantum computing to advance a key biomedical task in cancer immunotherapy design. The project’s goal is to engineer optimal immune T-cells and immune T-cell receptors that are highly efficient at killing cancer cells. The team will achieve this by combining knowledge of classical and quantum computing algorithms with Cleveland Clinic’s novel experimental data sets.

This project aims to leverage the power of quantum computing algorithms to optimize feature identification and feature selection in healthcare and life sciences (HCLS) data sets. Cleveland Clinic and IBM will identify optimal ways to represent HCLS datasets using fewer features. This will help to represent noisy datasets that are common bottlenecks for performance. By creating a better understanding of this problem in the context of quantum computing, we will help guide future research efforts, as well as hardware and software development. A deeper understanding of this field will also help with the improvement of classical AI algorithms.

This project aims to perform quantum simulations to study chemical reactions in important biological processes. Through simulations, we may develop a deeper understanding of biomedical reactions that can lead to human disease. Currently, performing these types of simulations is quite limited by existing computing technology. Cleveland Clinic and IBM plan to address this by developing new quantum simulation algorithms as the first of their kind to be demonstrated on a quantum computer. One of the most significant benefits of this work is that it will allow researchers to stop relying on low-accuracy methods and instead make use of more precise quantum simulations that can potentially lead to new therapies.

Cleveland Clinic and IBM will develop a quantum computing method to screen and optimize drugs targeted to specific proteins. This joint research project will utilize quantum computing and AI tools in a hybrid quantum/classical computing approach to investigate protein and drug interactions. Project results will advance our knowledge of drug development and potentially lead to creating more effective drugs for preventing and treating disease.

This project aims to improve the apnea-hypopnea index (AHI), which is a diagnostic tool for determining the presence and severity of obstructive sleep apnea. Currently, the AHI depends on manual scoring that can result in different findings based on the observer and is subject to night-to-night variability. The study's goal is to create an alternative approach to obstructive sleep apnea diagnosis using modern self-supervised deep learning models. These models will be more efficient, reliable and informative. They may also be extended to gather data from non-medical grade sleep sensors like smartwatches, smart rings and others.

This project will study the cellular malfunctions that contribute to Inflammatory Bowel Disease (IBD), Ulcerative Colitis (UC) and Crohn’s Disease (CD). Using advanced computing methods, including Biomedical Foundational Models (BMFM), the team will identify targets that are potential drivers of these diseases. This model will be trained to identify individual cells as either healthy, disease or pre-diseased. The findings from this will help provide new insights into target discovery and cell-to-cell interactions.

Physicians and researchers are struggling to stay up to date with the vast amount of scientific information being published each year. There is a need to identify and summarize what really matters from the data so that new information can be incorporated into clinical practice and drive new research avenues. Cleveland Clinic and IBM aim to use artificial intelligence to enhance the way in which researchers and physicians search and review large amounts of documents from multiple sources, such as scientific literature or clinical trial protocols. The team will build tools to help identify knowledge gaps, generate new hypotheses and extract hidden factors (e.g., clinical, social, biological) that may be important for the way in which physicians study and treat IBD.

A longitudinal study of disease phenotypes and signatures of diverse Crohn’s -IBM and Cleveland Clinic are conducting a digital health study to understand how social determinants of health, such as education and neighborhood, impact Crohn’s disease (CD) progression. The study will focus on diverse populations that are experiencing an increase in CD diagnosis. To better understand patients’ symptoms, the IBM Accelerated Discovery Platform will monitor patients’ symptoms daily and combine the results with their clinical assessments. Additionally, an AI-powered data collection technology will be created to monitor other patient and environmental factors. This will provide a better understanding of how social determinants of health affect disease.

Two major forms of inflammatory bowel disease (IBD) are Crohn’s Disease and ulcerative colitis. While they are similar, their symptoms differ among patients based on race, ethnicity, disease location at onset and diagnosis, as well as varying treatment responses. There is an urgent need for more accurate identification of the IBD sub-types to effectively provide therapeutic interventions and guide the development of new therapies. To achieve this, the team aimed to: (1) Generate hypotheses about novel disease progression sub-groups and (2) Define characteristics of those who respond to existing drug therapeutics while focusing on specific clinical outcomes. The data-driven approach provided novel insights and generated new hypotheses for future IBD drug discovery and clinical trial enhancement, potentially using data from EMR.

This project will explore a very large national data set that contains information from insurance claims to see if certain medications that reduce inflammation have also reduced the occurrence of seizures in epilepsy patients after brain surgery. The team will then confirm any information discovered by studying a smaller patient population from Cleveland Clinic where detailed clinical information is available.

Severe congenital heart disease (CHD) and prematurity can cause physiological instability in infants. In an outpatient setting, these high-risk infants can suffer from clinical deterioration leading to hospital readmission, morbidity and mortality. Wearable biosensors (wearables) have been developed to monitor infants, creating an opportunity to collect continuous physiological data (CPD) from home. Cleveland Clinic and IBM will explore the use of AI to predict low cardiac output syndrome and other relevant outcomes through wearable-derived analytics for high-risk infants.

This project builds on a previous study that detailed how the immune system recognizes immunotherapy targets through artificial intelligence. The team will apply biomedical foundation models (BMFM) to expand on this work, identifying potential vaccine targets and generating therapeutic T-cell receptors. The goal of the project is to develop the next generation of immunotherapies through in silico evolution.

IBM and Cleveland Clinic aim to identify existing drugs that have the potential of improving management, slowing deterioration and/or delaying the onset for neurodegenerative diseases including Alzheimer’s disease. This study will leverage network medicine analysis technologies of Cleveland Clinic and IBM’s prototype toolset called the drug repurposing engine (IBM DRE) which will be applied to a large data set. The team will focus on Alzheimer’s disease as the first instance to show acceleration of discovery with the combined tools.

This project will be a unique opportunity for Cleveland Clinic to work with IBM’s computational experts, advanced computing platforms and molecular modeling tools for small molecule discovery/optimization in the areas of COVID antiviral drug development and inhibitors of a protein implicated in cancer development. The project will have chemists, structural biologists and computational scientists working together as a closely aligned team. The effort, if successful, will result in discoveries in chemistry that can ultimately lead towards novel drug development candidates for clinical testing.

Patients with congenital heart disease receive a variety of diagnostic tests to help determine the next steps for their treatment. Tests include imaging studies, electrocardiograms(ECGs) and exercise testing. Analyzing the results is a challenging task generally done in the clinician’s head. This project will combine various test results including cardiovascular MRI and ECGs with novel machine learning techniques to improve predictions of outcomes for patients with congenital heart disease, initially focusing on a repaired tetralogy of Fallot (rTOF).

This project will explore how hybrid cloud technologies can accelerate Cleveland Clinic’s research. The team will take one of Cleveland important workloads and conduct a series of computing experiments to identify the benefits of using various hybrid cloud techniques. These computational workflow experiments will help to demonstrate the benefits and challenges of each technique, allowing researchers to find which one is best suited to address the workload. The findings from this study will be used to develop other scientific research use cases for hybrid cloud technologies.

IBM will provide Cleveland Clinic with IBM Deep Search technology with the aim of enabling researchers to search for, and within, medical studies and publications. Additionally, IBM aims to further enhance and develop Deep Search to improve its functionalities to allow Cleveland Clinic to more efficiently utilize published literature and datasets to design clinical trials, develop clinical prediction tools, and identify patterns/associations.

Gated Sodium Channel Disorders-IBM and Cleveland Clinic researchers are developing new techniques to perform larger capacity molecular simulations using quantum computing. Currently, simulations can only be run on small molecules, but the project aims to overcome present limitations and enhance the ability to analyze complex systems. Performing quantum simulations on larger molecular systems can improve the understanding of cellular mechanisms and potential therapeutic targets, which could have important applications in drug discovery.

This project will find new insights and develop new technologies that accelerate next-generation cancer immunotherapy discovery. Cleveland Clinic and IBM will use artificial intelligence and molecular simulations to develop better ways to identify targets for cancer vaccines. By creating and leveraging a combination of mechanistic simulations and AI models, we will expand our knowledge related to immunotherapy, vaccine design and clinical response to cancer immunotherapies.

This project will identify the core computational technology capabilities of the Discovery Accelerator Platform through a series of discussions with key stakeholders. These discussions will provide detailed insights on Cleveland Clinic’s unique research computing and analytics needs. The findings will provide the required infrastructure planning needed to deploy IBM’s forthcoming Discovery Accelerator technologies.

This project will develop pre-trained models as the basis of a foundation model for medical image analysis. The foundation model will be able to identify various features and patterns to make diagnosis assessments. The team will focus on knee MRIs in the first stage, before expanding to multiple body parts (neural, cardiac, breast, musculoskeletal, etc.) and multiple modalities (X-ray, CT etc). The resulting models will significantly impact clinical analysis and enable downstream applications for automated medical image analysis.

IBM and Cleveland Clinic will launch and evaluate a self-paced online learning journey focused on AI and Data Science for a group of medical students, PhD candidates, nursing students and postdoctoral researchers. This program will utilize IBM-developed courses and educational and training assets. The goal is to evaluate the relevance of IBM’s content on AI and data science for student and researcher skills training and to benefit their research and future goals.

IBM and Cleveland Clinic will develop educational resources to instruct Cleveland Clinic’s key researchers and technical personnel about the fundamental aspects of quantum computing and enable them to utilize IBM’s quantum computers via the Quantum Information Science Kit framework (Qiskit), a programming interface to utilize quantum computers. The goal of this initiative is to prepare researchers to do state-of-the-art research on quantum computing applications in healthcare.