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Leveraging Computer Vision in the Intensive Care Unit (ICU) for Examining Visitation and Mobility

Scott Siegel, Jiaqing Zhang, Sabyasachi Bandyopadhyay, Subhash Nerella, Brandon Silva, Tezcan Baslanti, Azra Bihorac, Parisa Rashidi

TL;DR

A state-of-the-art noninvasive computer vision system based on depth imaging is used to characterize ICU visitations and patients' mobility and finds an association between deteriorating patient acuity and the incidence of delirium with increased visitations.

Abstract

Despite the importance of closely monitoring patients in the Intensive Care Unit (ICU), many aspects are still assessed in a limited manner due to the time constraints imposed on healthcare providers. For example, although excessive visitations during rest hours can potentially exacerbate the risk of circadian rhythm disruption and delirium, it is not captured in the ICU. Likewise, while mobility can be an important indicator of recovery or deterioration in ICU patients, it is only captured sporadically or not captured at all. In the past few years, the computer vision field has found application in many domains by reducing the human burden. Using computer vision systems in the ICU can also potentially enable non-existing assessments or enhance the frequency and accuracy of existing assessments while reducing the staff workload. In this study, we leverage a state-of-the-art noninvasive computer vision system based on depth imaging to characterize ICU visitations and patients' mobility. We then examine the relationship between visitation and several patient outcomes, such as pain, acuity, and delirium. We found an association between deteriorating patient acuity and the incidence of delirium with increased visitations. In contrast, self-reported pain, reported using the Defense and Veteran Pain Rating Scale (DVPRS), was correlated with decreased visitations. Our findings highlight the feasibility and potential of using noninvasive autonomous systems to monitor ICU patients.

Leveraging Computer Vision in the Intensive Care Unit (ICU) for Examining Visitation and Mobility

TL;DR

A state-of-the-art noninvasive computer vision system based on depth imaging is used to characterize ICU visitations and patients' mobility and finds an association between deteriorating patient acuity and the incidence of delirium with increased visitations.

Abstract

Despite the importance of closely monitoring patients in the Intensive Care Unit (ICU), many aspects are still assessed in a limited manner due to the time constraints imposed on healthcare providers. For example, although excessive visitations during rest hours can potentially exacerbate the risk of circadian rhythm disruption and delirium, it is not captured in the ICU. Likewise, while mobility can be an important indicator of recovery or deterioration in ICU patients, it is only captured sporadically or not captured at all. In the past few years, the computer vision field has found application in many domains by reducing the human burden. Using computer vision systems in the ICU can also potentially enable non-existing assessments or enhance the frequency and accuracy of existing assessments while reducing the staff workload. In this study, we leverage a state-of-the-art noninvasive computer vision system based on depth imaging to characterize ICU visitations and patients' mobility. We then examine the relationship between visitation and several patient outcomes, such as pain, acuity, and delirium. We found an association between deteriorating patient acuity and the incidence of delirium with increased visitations. In contrast, self-reported pain, reported using the Defense and Veteran Pain Rating Scale (DVPRS), was correlated with decreased visitations. Our findings highlight the feasibility and potential of using noninvasive autonomous systems to monitor ICU patients.
Paper Structure (19 sections, 3 figures, 5 tables)

This paper contains 19 sections, 3 figures, 5 tables.

Table of Contents

  1. Introduction
  2. Methods
  3. Study participants
  4. Data Collection
  5. Data Annotation
  6. YOLOv8 Architecture
  7. Training Pipeline
  8. Characterizing mobility and sleep disturbances
  9. Patient Outcomes
  10. Associating model metrics with patient Outcomes
  11. Results
  12. YOLOv8 experiments
  13. Associations with Patient Outcomes
  14. Self-Reported Pain
  15. Patient Acuity
  16. ...and 4 more sections

Figures (3)

  • Figure 1: The front-end user interface to annotate depth frames. Posture classes include lying in bed, standing, sitting on a bed, and sitting on a chair. The tool also includes an assisted mobility class, which is intended to capture instances when someone is physically assisting the patient with their mobility.
  • Figure 2: Relationship between pain levels and the average number of people in the room during the night versus day.
  • Figure 3: Relationship between patient acuity and the average number of people in the room during the night versus day.