We use cookies to understand how you use our site and to improve your experience. This includes personalizing content and advertising. To learn more, click here. By continuing to use our site, you accept our use of cookies. Cookie Policy.

Features Partner Sites Information LinkXpress
Sign In
Advertise with Us

Download Mobile App




AI Pain Recognition System Detects Patients’ Pain Before, During and After Surgery

By HospiMedica International staff writers
Posted on 16 Oct 2023
Print article
Image: The automated pain recognition system analyzes facial images using AI (Photo courtesy of 123RF)
Image: The automated pain recognition system analyzes facial images using AI (Photo courtesy of 123RF)

Early identification and timely management of pain in patients can lead to shorter hospital stays and reduce the risk of long-term issues like chronic pain, anxiety, and depression. At present, medically assessing pain is largely subjective, relying on tools like the Visual Analog Scale (VAS), where patients rate their own pain levels, and the Critical-Care Pain Observation Tool (CPOT), where healthcare professionals evaluate pain through patients' facial expressions, body movements, and muscle tension. Now, an automated pain recognition system that uses artificial intelligence (AI) has the potential to detect pain in patients before, during, and after surgery in an unbiased manner.

The automated pain recognition system developed by researchers at the University of California San Diego (La Jolla, CA, USA) employs two kinds of AI: computer vision to give the computer the ability to "see," and deep learning to interpret those visual inputs and evaluate pain levels in patients. The researchers fed the AI model with 143,293 facial pictures taken from 69 patients undergoing a variety of elective surgeries, ranging from joint replacements to intricate heart procedures. These images were associated with 115 episodes of pain and 159 episodes without pain. The team trained the computer by showing it each individual facial image and labeling it as representing pain or not, allowing the machine to start recognizing patterns. Heat maps revealed that the AI focused on specific facial areas like the eyebrows, lips, and nose to assess pain.

After sufficient training, the system began making predictions of the pain levels, and its results were aligned with CPOT assessments 88% of the time and with VAS assessments 66% of the time. If these findings are validated, the technology could serve as an additional resource for doctors aiming to enhance patient care. For instance, cameras could be installed in surgical recovery rooms to continuously monitor pain levels of patients, capturing 15 images per second. This could free nurses and other healthcare professionals from having to frequently assess the patient’s pain, allowing them to concentrate on other care tasks. Future iterations of the system could incorporate additional factors like movement and sound for more accurate pain evaluation.

“Traditional pain assessment tools can be influenced by racial and cultural biases, potentially resulting in poor pain management and worse health outcomes,” said Timothy Heintz, B.S., lead author of the study and a fourth-year medical student at the University of California San Diego. “Further, there is a gap in perioperative care due to the absence of continuous observable methods for pain detection. Our proof-of-concept AI model could help improve patient care through real-time, unbiased pain detection.”

“The VAS is less accurate compared to CPOT because VAS is a subjective measurement that can be more heavily influenced by emotions and behaviors than CPOT might be,” added Heintz. “However, our models were able to predict VAS to some extent, indicating there are very subtle cues that the AI system can identify that humans cannot.”

Related Links:
University of California San Diego 

Gold Member
STI Test
Vivalytic Sexually Transmitted Infection (STI) Array
Gold Member
Solid State Kv/Dose Multi-Sensor
AGMS-DM+
Silver Member
Compact 14-Day Uninterrupted Holter ECG
NR-314P
New
Medication Cart
Avalo Udi

Print article

Channels

Surgical Techniques

view channel
Image: Real-time navigation is a useful tool for ablation procedures to destroy tumors in the liver (Photo courtesy of University of Cincinnati)

Real-Time Navigation Found To Be Useful Tool for Liver Cancer Procedures

Liver cancer, ranking as the world's fourth most common cause of cancer-related deaths, presents a significant health challenge. For certain patients, ablation offers a less invasive alternative to traditional... Read more

Patient Care

view channel
Image: The newly-launched solution can transform operating room scheduling and boost utilization rates (Photo courtesy of Fujitsu)

Surgical Capacity Optimization Solution Helps Hospitals Boost OR Utilization

An innovative solution has the capability to transform surgical capacity utilization by targeting the root cause of surgical block time inefficiencies. Fujitsu Limited’s (Tokyo, Japan) Surgical Capacity... Read more

Health IT

view channel
Image: First ever institution-specific model provides significant performance advantage over current population-derived models (Photo courtesy of Mount Sinai)

Machine Learning Model Improves Mortality Risk Prediction for Cardiac Surgery Patients

Machine learning algorithms have been deployed to create predictive models in various medical fields, with some demonstrating improved outcomes compared to their standard-of-care counterparts.... Read more

Point of Care

view channel
Image: The new eye-safe laser technology can diagnose traumatic brain injury (Photo courtesy of 123RF)

Novel Diagnostic Hand-Held Device Detects Known Biomarkers for Traumatic Brain Injury

The growing need for prompt and efficient diagnosis of traumatic brain injury (TBI), a major cause of mortality globally, has spurred the development of innovative diagnostic technologies.... Read more
Copyright © 2000-2024 Globetech Media. All rights reserved.