AIMC Topic: Acute Pain
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Proposing a short version of the Unesp-Botucatu pig acute pain scale using a novel application of machine learning technique.
Scientific reports
Feb 28, 2025
Surgical castration of males is carried out on a large scale in the US swine industry and the pain resulting from this procedure can be assessed using the Unesp-Botucatu pig composite acute pain scale (UPAPS). We aim to propose a short version of UPA...
Comparison between AI and human expert performance in acute pain assessment in sheep.
Scientific reports
Jan 3, 2025
This study explores the question whether Artificial Intelligence (AI) can outperform human experts in animal pain recognition using sheep as a case study. It uses a dataset of N = 48 sheep undergoing surgery with video recordings taken before (no pai...
Automated acute pain prediction in domestic goats using deep learning-based models on video-recordings.
Scientific reports
Nov 7, 2024
Facial expressions are essential in animal communication, and facial expression-based pain scales have been developed for different species. Automated pain recognition offers a valid alternative to manual annotation with growing evidence across speci...
Fully automated deep learning models with smartphone applicability for prediction of pain using the Feline Grimace Scale.
Scientific reports
Dec 7, 2023
This study used deep neural networks and machine learning models to predict facial landmark positions and pain scores using the Feline Grimace Scale (FGS). A total of 3447 face images of cats were annotated with 37 landmarks. Convolutional neural net...
Design and Evaluation of Deep Learning Models for Continuous Acute Pain Detection Based on Phasic Electrodermal Activity.
IEEE journal of biomedical and health informatics
Sep 6, 2023
The current method for assessing pain in clinical practice is subjective and relies on self-reported scales. An objective and accurate method of pain assessment is needed for physicians to prescribe the proper medication dosage, which could reduce ad...
The feasibility of deep learning-based synthetic contrast-enhanced CT from nonenhanced CT in emergency department patients with acute abdominal pain.
Scientific reports
Oct 14, 2021
Our objective was to investigate the feasibility of deep learning-based synthetic contrast-enhanced CT (DL-SCE-CT) from nonenhanced CT (NECT) in patients who visited the emergency department (ED) with acute abdominal pain (AAP). We trained an algorit...
Differentiating kidney stones from phleboliths in unenhanced low-dose computed tomography using radiomics and machine learning.
European radiology
Feb 12, 2019
OBJECTIVES: Distinguishing between kidney stones and phleboliths can constitute a diagnostic challenge in patients undergoing unenhanced low-dose CT (LDCT) for acute flank pain. We sought to investigate the accuracy of radiomics and a machine-learnin...
Acute pain intensity monitoring with the classification of multiple physiological parameters.
Journal of clinical monitoring and computing
Jun 26, 2018
Current acute pain intensity assessment tools are mainly based on self-reporting by patients, which is impractical for non-communicative, sedated or critically ill patients. In previous studies, various physiological signals have been observed qualit...
Lidocaine 5% Patch for Treatment of Acute Pain After Robotic Cardiac Surgery and Prevention of Persistent Incisional Pain: A Randomized, Placebo-Controlled, Double-Blind Trial.
Pain medicine (Malden, Mass.)
Jul 14, 2015
OBJECTIVES: To test the hypotheses that lidocaine 5% patches decrease the severity of acute pain and incidence of persistent incisional pain after robotic cardiac valve surgery.
Naturalistic acute pain states decoded from neural and facial dynamics.
Nature communications
May 11, 2025
Pain remains poorly understood in task-free contexts, limiting our understanding of its neurobehavioral basis in naturalistic settings. Here, we use a multimodal, data-driven approach with intracranial electroencephalography, pain self-reports, and f...
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