There is always exciting research being conducted at IC3 labs. Find out more about our exciting research or read one of our many published articles below


Pain recognition

Autonomous Pain Recognition in Non-Verbal and Critically Ill Patients

The under-assessment of pain response is one of the primary barriers to the adequate treatment of pain in critically ill patients and is associated with many negative outcomes. Nonetheless, many ICU patients are unable to self-report their pain intensity. Currently, behavioral pain scales are used to assess pain in nonverbal patients. Unfortunately, these scales require repetitive manual administration by overburdened nurses and show variability in pain intensity ratings by nurses. Furthermore, manual pain assessment tools cannot monitor pain continuously and autonomously. The PIs’ long-term goal is to specify pain intensity in an autonomous and precise manner. The overall objective of this application is to build the foundation of an autonomous, clinically- available pain assessment system by developing and validating pain recognition algorithms in a fully uncontrolled ICU setting. The central hypothesis is we can autonomously assess facial pain expressions and patient activity. The rationale is that autonomous pain quantification can reduce nurse workload and can enable real-time pain monitoring. Contextualization of pain with respect to patient function can also lead to improved functional and clinical outcomes.


Intelligent Intensive Care Unit (I2CU)

Pervasive Sensing and Artificial Intelligence for Augmented Clinical Decision-making

Although close monitoring and dynamic assessment of patient acuity are key aspects of ICU care, both are limited by the time constraints imposed on healthcare providers. Currently, dynamic and precise assessment of patient acuity in ICU relies almost exclusively on physicians’ clinical judgment and vigilance. Furthermore, important visual assessment details, such as facial expressions, posture, and mobility, are captured sporadically by overburdened nurses or are not captured at all. However, these visual assessment details are associated with critical indices such as physical function, pain, and subsequent clinical deterioration. The PIs’ long-term goal is to sense, quantify, and communicate a patient’s clinical condition in an autonomous and precise manner. The overall objective of this application is to develop novel tools for sensing, quantifying, and communicating any patient’s condition in an autonomous, precise, and interpretable manner. The central hypothesis is that deep learning models will be superior to existing acuity clinical scores by predicting acuity in a dynamic, precise, and interpretable manner, using autonomous assessment of pain, emotional distress, and physical function, together with clinical and physiologic data. The hypothesis has been formulated based on preliminary data and is well-grounded in clinical care literature. The rationale is that autonomous and precise patient quantification can result in enhanced clinical workflow and early intervention.



Autonomous Delirium Monitoring and Adaptive Prevention

Recent large-scale trials have shown no significant benefit of pharmacological interventions in delirium patients, and non-pharmacological approaches remain the cornerstone of delirium prevention. Among those strategies, minimizing patient immobility and circadian desynchrony are particularly difficult to implement, as their assessment is dependent on sporadic human observations. The overall objective of this application is to develop ADAPT, the Autonomous Delirium Monitoring and Adaptive Prevention system using novel pervasive sensing and deep learning techniques. It will autonomously quantify patients’ mobility and circadian desynchrony in terms of nightly disruptions, light intensity, and sound pressure level. This will allow for the integration of these risk factors into a dynamic model for predicting delirium trajectories. It will also enable adaptive action prompts aimed at increasing patients’ mobility, reducing nightly disruptions, optimizing ambient light, and reducing noise, based on precise real-time quantification. The rationale is that the successful application of the proposed technology would augment clinical-decision making in the fast-paced ICU environment and would promote more targeted interventions.



Digital Rehabilitation Environment Augmenting Medical System

The purpose of Digital Rehabilitation Environment Augmenting Medical System (D.R.E.A.M.S.) is to research the feasibility and clinical potential of an immersive digital reality-augmenting system in reducing the occurrence of cognitive, behavioral, and emotional consequences of critical illness (e.g. pain, anxiety and insomnia) and environmental exposures (noise & light) that are risk factors for the development of delirium, a common and devastating complication in the intensive care unit (ICU).

DREAM project, AR with medical research


Automated Algorithm Identifies and Communicates Risk of Acute Kidney Injury among Health Care Providers and Patients

Acute kidney injury (AKI) is a sudden reduction in kidney function that may go unrecognized and is associated with increased adverse events. We will develop and implement an automated algorithm that feeds findings to the EHR in real-time, allowing AKI diagnosis and severity to be communicated to healthcare providers using interface on EPIC platforms for early management for AKI. We will assess its impact on knowledge, awareness and communication about AKI among healthcare providers.