Clinician and Visitor Activity Patterns in an Intensive Care Unit Room: A Study to Examine How Ambient Monitoring Can Inform the Measurement of Delirium Severity and Escalation of Care
The early detection of the acute deterioration of escalating illness severity is crucial for effective patient management and can significantly impact patient outcomes. Ambient sensing technology, such as computer vision, may provide real-time information that could impact early recognition and resp...
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| Format: | Article |
| Language: | English |
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MDPI AG
2024-10-01
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| Series: | Journal of Imaging |
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| Online Access: | https://www.mdpi.com/2313-433X/10/10/253 |
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| author | Keivan Nalaie Vitaly Herasevich Laura M. Heier Brian W. Pickering Daniel Diedrich Heidi Lindroth |
| author_facet | Keivan Nalaie Vitaly Herasevich Laura M. Heier Brian W. Pickering Daniel Diedrich Heidi Lindroth |
| author_sort | Keivan Nalaie |
| collection | DOAJ |
| description | The early detection of the acute deterioration of escalating illness severity is crucial for effective patient management and can significantly impact patient outcomes. Ambient sensing technology, such as computer vision, may provide real-time information that could impact early recognition and response. This study aimed to develop a computer vision model to quantify the number and type (clinician vs. visitor) of people in an intensive care unit (ICU) room, study the trajectory of their movement, and preliminarily explore its relationship with delirium as a marker of illness severity. To quantify the number of people present, we implemented a counting-by-detection supervised strategy using images from ICU rooms. This was accomplished through developing three methods: single-frame, multi-frame, and tracking-to-count. We then explored how the type of person and distribution in the room corresponded to the presence of delirium. Our designed pipeline was tested with a different set of detection models. We report model performance statistics and preliminary insights into the relationship between the number and type of persons in the ICU room and delirium. We evaluated our method and compared it with other approaches, including density estimation, counting by detection, regression methods, and their adaptability to ICU environments. |
| format | Article |
| id | doaj-art-ac2641ef02e0488695d1ffb47cad4ea6 |
| institution | OA Journals |
| issn | 2313-433X |
| language | English |
| publishDate | 2024-10-01 |
| publisher | MDPI AG |
| record_format | Article |
| series | Journal of Imaging |
| spelling | doaj-art-ac2641ef02e0488695d1ffb47cad4ea62025-08-20T02:11:04ZengMDPI AGJournal of Imaging2313-433X2024-10-01101025310.3390/jimaging10100253Clinician and Visitor Activity Patterns in an Intensive Care Unit Room: A Study to Examine How Ambient Monitoring Can Inform the Measurement of Delirium Severity and Escalation of CareKeivan Nalaie0Vitaly Herasevich1Laura M. Heier2Brian W. Pickering3Daniel Diedrich4Heidi Lindroth5Division of Nursing Research, Department of Nursing, Mayo Clinic, Rochester, MN 55905, USADepartment of Anesthesiology and Perioperative Medicine, Mayo Clinic, Rochester, MN 55905, USADivision of Nursing Research, Department of Nursing, Mayo Clinic, Rochester, MN 55905, USADepartment of Anesthesiology and Perioperative Medicine, Mayo Clinic, Rochester, MN 55905, USADepartment of Anesthesiology and Perioperative Medicine, Mayo Clinic, Rochester, MN 55905, USADivision of Nursing Research, Department of Nursing, Mayo Clinic, Rochester, MN 55905, USAThe early detection of the acute deterioration of escalating illness severity is crucial for effective patient management and can significantly impact patient outcomes. Ambient sensing technology, such as computer vision, may provide real-time information that could impact early recognition and response. This study aimed to develop a computer vision model to quantify the number and type (clinician vs. visitor) of people in an intensive care unit (ICU) room, study the trajectory of their movement, and preliminarily explore its relationship with delirium as a marker of illness severity. To quantify the number of people present, we implemented a counting-by-detection supervised strategy using images from ICU rooms. This was accomplished through developing three methods: single-frame, multi-frame, and tracking-to-count. We then explored how the type of person and distribution in the room corresponded to the presence of delirium. Our designed pipeline was tested with a different set of detection models. We report model performance statistics and preliminary insights into the relationship between the number and type of persons in the ICU room and delirium. We evaluated our method and compared it with other approaches, including density estimation, counting by detection, regression methods, and their adaptability to ICU environments.https://www.mdpi.com/2313-433X/10/10/253people countingintensive careobject detectioncomputer visionhealth carehospital |
| spellingShingle | Keivan Nalaie Vitaly Herasevich Laura M. Heier Brian W. Pickering Daniel Diedrich Heidi Lindroth Clinician and Visitor Activity Patterns in an Intensive Care Unit Room: A Study to Examine How Ambient Monitoring Can Inform the Measurement of Delirium Severity and Escalation of Care Journal of Imaging people counting intensive care object detection computer vision health care hospital |
| title | Clinician and Visitor Activity Patterns in an Intensive Care Unit Room: A Study to Examine How Ambient Monitoring Can Inform the Measurement of Delirium Severity and Escalation of Care |
| title_full | Clinician and Visitor Activity Patterns in an Intensive Care Unit Room: A Study to Examine How Ambient Monitoring Can Inform the Measurement of Delirium Severity and Escalation of Care |
| title_fullStr | Clinician and Visitor Activity Patterns in an Intensive Care Unit Room: A Study to Examine How Ambient Monitoring Can Inform the Measurement of Delirium Severity and Escalation of Care |
| title_full_unstemmed | Clinician and Visitor Activity Patterns in an Intensive Care Unit Room: A Study to Examine How Ambient Monitoring Can Inform the Measurement of Delirium Severity and Escalation of Care |
| title_short | Clinician and Visitor Activity Patterns in an Intensive Care Unit Room: A Study to Examine How Ambient Monitoring Can Inform the Measurement of Delirium Severity and Escalation of Care |
| title_sort | clinician and visitor activity patterns in an intensive care unit room a study to examine how ambient monitoring can inform the measurement of delirium severity and escalation of care |
| topic | people counting intensive care object detection computer vision health care hospital |
| url | https://www.mdpi.com/2313-433X/10/10/253 |
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