Clinical Lead: Ryan Stidham

Project Team: Lingrui Cai, Cristian Minoccheri, Flora Rajaei, Lara King, Dian Jiao, Sophia Tesic

Inactive Project Team: Heming Yao, Reza Soroushmehr, Jonathan Gryak, Jie Gui, Shuyang Cheng

Colonoscopy is a standard medical examination used to inspect the mucosal surface and detect abnormalities of the colon. Objective assessment and scoring of disease features in the colon are important in conditions such as colorectal cancer and inflammatory bowel disease. However, human disease assessment and measurement is hampered by interobserver variation and several biases.

In this project, a computer-aided system for colonoscopy video analysis is under development, which will facilitate disease severity measurement and aid in treatment selection and clinical outcome prediction. The proposed system consists of non-informativeness classification, Mayo severity estimation, image-wise biopsy forceps detection, image-wise location estimation, image segmentation and camera’s motion tracking. By analyzing image features and camera motion, we could derive the contextual understanding of the colonoscopy video such as severity and surgical regions distribution, which can provide richer information for patient’s condition evaluation and outcome prediction.

Project Lead: Yufeng Zhang, Oviyan Anbarasu

Clinical Leads: Jessie Golbus,  Keith Aaronson

Inactive Project Team: Heming Yao, Jonathan Gryak, Renaid Kim, Harm Derksen, Shuyang Cheng, Cristian Minoccheri

Determining the appropriate timing of mechanical circulatory support (MCS) device implantation or heart transplantation (HT) for patients with advanced heart failure is essential as there may be a mortality cost to delayed care. Recognizing patients eligible for an HT/MCS requires expertise from providers trained in transplant cardiology. In this project, we aim to develop an automated decision-making system that can identify patients eligible for an HT/MCS that would facilitate primary care physicians or general cardiologists referring those patients for consideration of advanced therapies. We have developed a hybrid of fuzzy logic and neural networks with a genetic algorithm to identify patients that are eligible for HT/LVAD. Our model achieved better classification performance than other traditional ML algorithms. To overcome the optimization difficulty and computational burden in the existing model, we are developing a novel soft computing architecture with tropical geometry. The proposed network can be not only optimized by gradient-based methods but also close to piecewise linear to ensure the model’s interpretability.

Project Lead: Emily Wittrup

Clinical Leads: Erica Stein, Craig Williamson

Project Team: Haoyuan Ma, Christine Geng, Ethan Schnathorst

This research project focuses on improving how doctors diagnose and treat severe brain injuries, which can be caused by accidents such as car crashes. When someone sustains a brain injury, it is crucial for the doctor to know the pressure inside their skull within the first few hours after an injury occurred. High pressure inside the skull can cause additional harm, so measuring it accurately is important and can help doctors make decisions about how to treat the patient. Right now, doctors use invasive methods that are risky and time-consuming. The researchers want to find a safer and quicker way to measure the pressure inside the skill using medical scans of the head that are already done when a patient arrives at the hospital with a brain injury. The researchers are developing a computer program that can automatically analyze these scans and measure a specific part of the eye that swells when there is high pressure in the skull. This new method aims to give doctors a fast and reliable estimate of the skull pressure, helping them make better decisions about the patient's treatment. It can also help predict negative outcomes so that the doctor can prioritize interventions leading to a better recovery. 

Project Lead: Matthew Hodgman

Clinical Lead: Daniel E. Ehrmann

Fluid overload is extremely harmful to patients and health systems and is associated with morbidity, mortality, and long-term sequalae that extend beyond discharge from the cardiac intensive care unit. Each day of failure to achieve net negative fluid balance after congenital cardiac surgery prolongs length of mechanical ventilation and hospital stay, which significantly increases hospital costs. The cornerstone of therapy for post-operative fluid overload is initiation and titration of diuretics (e.g., furosemide). However, this practice is imprecise and varies significantly between providers and heart centers, commonly leading to either under- or over-diuresis. Our multidisciplinary team proposes to solve this problem by building a system that improves how diuretic infusions are managed after congenital cardiac surgery. Specifically, we will leverage innovative computational techniques to develop a physiologic closed loop control system capable of automatically titrating a furosemide infusion to manage fluid overload. By using a PCLCS to titrate a diuretic infusion consistently and precisely, we aim to manage fluid overload more safely, effectively, and efficiently than the current standard of care. 

Project Lead: Morgen Henry

Clinical Lead: Kayvan Najarian

Oncogenic drug therapies are often combined to harness the synergistic effects of multiple medications while minimizing drug toxicity and resistance. However, clinically testing every possible drug combination is highly inefficient due to the significant time and resources required. To prioritize the most promising combinations for testing, computational methods can be employed to predict which combinations are likely to be most effective. Building on our previous work, a Feed-Forward Neural Network (FNN) that leveraged drug and tissue similarity within the National Cancer Institute’s drug combination efficacy dataset, this project proposes an enhanced approach using coupled tensor-tensor completion (CTTC). This method advances the FNN framework by retaining its foundational data (drug/tissue similarity) while improving prediction accuracy and computational scalability for identifying optimal drug combinations.