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1. Pain Action Unit Detection in Critically Ill Patients

   https://doi.org/10.1109/compsac51774.2021.00094  

   Nerella, Subhash; Cupka, Julie; Ruppert, Matthew; Tighe, Patrick; Bihorac, Azra; Rashidi, Parisa (July 2021, IEEE Computer Society Computers, Software, and Applications Conference (COMPSAC))

   Existing pain assessment methods in the intensive care unit rely on patient self-report or visual observation by nurses. Patient self-report is subjective and can suffer from poor recall. In the case of non-verbal patients, behavioral pain assessment methods provide limited granularity, are subjective, and put additional burden on already overworked staff. Previous studies have shown the feasibility of autonomous pain expression assessment by detecting Facial Action Units (AUs). However, previous approaches for detecting facial pain AUs are historically limited to controlled environments. In this study, for the first time, we collected and annotated a pain-related AU dataset, Pain-ICU, containing 55,085 images from critically ill adult patients. We evaluated the performance of OpenFace, an open-source facial behavior analysis tool, and the trained AU R-CNN model on our Pain-ICU dataset. Variables such as assisted breathing devices, environmental lighting, and patient orientation with respect to the camera make AU detection harder than with controlled settings. Although OpenFace has shown state-of-the-art results in general purpose AU detection tasks, it could not accurately detect AUs in our Pain-ICU dataset (F1-score 0.42). To address this problem, we trained the AU R-CNN model on our Pain-ICU dataset, resulting in a satisfactory average F1-score 0.77. In this study, we show the feasibility of detecting facial pain AUs in uncontrolled ICU settings. 

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2. The Intelligent ICU Pilot Study: Using Artificial Intelligence Technology for Autonomous Patient Monitoring

   Anis Davoudi, Kumar Rohit (September 2018, ArXivorg)

   Currently, many critical care indices are repetitively assessed and recorded by overburdened nurses, e.g. physical function or facial pain expressions of nonverbal patients. In addition, many essential information on patients and their environment are not captured at all, or are captured in a non-granular manner, e.g. sleep disturbance factors such as bright light, loud background noise, or excessive visitations. In this pilot study, we examined the feasibility of using pervasive sensing technology and artificial intelligence for autonomous and granular monitoring of critically ill patients and their environment in the Intensive Care Unit (ICU). As an exemplar prevalent condition, we also characterized delirious and non-delirious patients and their environment. We used wearable sensors, light and sound sensors, and a high-resolution camera to collected data on patients and their environment. We analyzed collected data using deep learning and statistical analysis. Our system performed face detection, face recognition, facial action unit detection, head pose detection, facial expression recognition, posture recognition, actigraphy analysis, sound pressure and light level detection, and visitation frequency detection. We were able to detect patient's face (Mean average precision (mAP)=0.94), recognize patient's face (mAP=0.80), and their postures (F1=0.94). We also found that all facial expressions, 11 activity features, visitation frequency during the day, visitation frequency during the night, light levels, and sound pressure levels during the night were significantly different between delirious and non-delirious patients (p-value<0.05). In summary, we showed that granular and autonomous monitoring of critically ill patients and their environment is feasible and can be used for characterizing critical care conditions and related environment factors. 

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3. Pain and Physical Activity Association in Critically Ill Patients ^*

   https://doi.org/10.1109/EMBC44109.2020.9176227  

   Davoudi, Anis; Ozrazgat-Baslanti, Tezcan; Tighe, Patrick J.; Bihorac, Azra; Rashidi, Parisa (July 2020, 2020 42nd Annual International Conference of the IEEE Engineering in Medicine & Biology Society (EMBC))

   null (Ed.)

   Critical care patients experience varying levels of pain during their stay in the intensive care unit, often requiring administration of analgesics and sedation. Such medications generally exacerbate the already sedentary physical activity profiles of critical care patients, contributing to delayed recovery. Thus, it is important not only to minimize pain levels, but also to optimize analgesic strategies in order to maximize mobility and activity of ICU patients. Currently, we lack an understanding of the relation between pain and physical activity on a granular level. In this study, we examined the relationship between nurse assessed pain scores and physical activity as measured using a wearable accelerometer device. We found that average, standard deviation, and maximum physical activity counts are significantly higher before high pain reports compared to before low pain reports during both daytime and nighttime, while percentage of time spent immobile was not significantly different between the two pain report groups. Clusters detected among patients using extracted physical activity features were significant in adjusted logistic regression analysis for prediction of pain report group. 

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4. Deep Multi-Modal Transfer Learning for Augmented Patient Acuity Assessment in the Intelligent ICU

   https://doi.org/10.3389/fdgth.2021.640685  

   Shickel, Benjamin; Davoudi, Anis; Ozrazgat-Baslanti, Tezcan; Ruppert, Matthew; Bihorac, Azra; Rashidi, Parisa (February 2021, Frontiers in Digital Health)

   Accurate prediction and monitoring of patient health in the intensive care unit can inform shared decisions regarding appropriateness of care delivery, risk-reduction strategies, and intensive care resource use. Traditionally, algorithmic solutions for patient outcome prediction rely solely on data available from electronic health records (EHR). In this pilot study, we explore the benefits of augmenting existing EHR data with novel measurements from wrist-worn activity sensors as part of a clinical environment known as the Intelligent ICU. We implemented temporal deep learning models based on two distinct sources of patient data: (1) routinely measured vital signs from electronic health records, and (2) activity data collected from wearable sensors. As a proxy for illness severity, our models predicted whether patients leaving the intensive care unit would be successfully or unsuccessfully discharged from the hospital. We overcome the challenge of small sample size in our prospective cohort by applying deep transfer learning using EHR data from a much larger cohort of traditional ICU patients. Our experiments quantify added utility of non-traditional measurements for predicting patient health, especially when applying a transfer learning procedure to small novel Intelligent ICU cohorts of critically ill patients. 

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5. A digital protein microarray for COVID-19 cytokine storm monitoring

   https://doi.org/10.1039/D0LC00678E  

   Song, Yujing; Ye, Yuxuan; Su, Shiuan-Haur; Stephens, Andrew; Cai, Tao; Chung, Meng-Ting; Han, Meilan K.; Newstead, Michael W.; Yessayan, Lenar; Frame, David; et al (January 2021, Lab on a Chip)

   Despite widespread concern regarding cytokine storms leading to severe morbidity in COVID-19, rapid cytokine assays are not routinely available for monitoring critically ill patients. We report the clinical application of a digital protein microarray platform for rapid multiplex quantification of cytokines from critically ill COVID-19 patients admitted to the intensive care unit (ICU) at the University of Michigan Hospital. The platform comprises two low-cost modules: (i) a semi-automated fluidic dispensing/mixing module that can be operated inside a biosafety cabinet to minimize the exposure of the technician to the virus infection and (ii) a 12–12–15 inch compact fluorescence optical scanner for the potential near-bedside readout. The platform enabled daily cytokine analysis in clinical practice with high sensitivity (<0.4 pg mL −1 ), inter-assay repeatability (∼10% CV), and rapid operation providing feedback on the progress of therapy within 4 hours. This test allowed us to perform serial monitoring of two critically ill patients with respiratory failure and to support immunomodulatory therapy using the selective cytopheretic device (SCD). We also observed clear interleukin-6 (IL-6) elevations after receiving tocilizumab (IL-6 inhibitor) while significant cytokine profile variability exists across all critically ill COVID-19 patients and to discover a weak correlation between IL-6 to clinical biomarkers, such as ferritin and C-reactive protein (CRP). Our data revealed large subject-to-subject variability in patients' response to COVID-19, reaffirming the need for a personalized strategy guided by rapid cytokine assays. 

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