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Acute Pain

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Naturalistic acute pain states decoded from neural and facial dynamics.

Nature communications
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...

Acute pain intensity monitoring with the classification of multiple physiological parameters.

Journal of clinical monitoring and computing
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...

Differentiating kidney stones from phleboliths in unenhanced low-dose computed tomography using radiomics and machine learning.

European radiology
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...

The feasibility of deep learning-based synthetic contrast-enhanced CT from nonenhanced CT in emergency department patients with acute abdominal pain.

Scientific reports
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...

Design and Evaluation of Deep Learning Models for Continuous Acute Pain Detection Based on Phasic Electrodermal Activity.

IEEE journal of biomedical and health informatics
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...

Fully automated deep learning models with smartphone applicability for prediction of pain using the Feline Grimace Scale.

Scientific reports
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...

Automated acute pain prediction in domestic goats using deep learning-based models on video-recordings.

Scientific reports
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...

Comparison between AI and human expert performance in acute pain assessment in sheep.

Scientific reports
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...

Proposing a short version of the Unesp-Botucatu pig acute pain scale using a novel application of machine learning technique.

Scientific reports
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...

Towards artificial intelligence application in pain medicine.

Recenti progressi in medicina
Pain is a complex, multidimensional experience involving significant challenges in both diagnosis and management. While acute pain serves as a critical warning mechanism, chronic pain encompasses intricate biological, psychological, and social compon...