Pain Management

Application of AI in Multilevel Pain Assessment Using Facial Images: Systematic Review and Meta-Analysis.

BACKGROUND: The continuous monitoring and recording of patients' pain status is a major problem in current research on postoperative pain management. In the large number of original or review articles focusing on different approaches for pain assessment, many researchers have investigated how computer vision (CV) can help by capturing facial expressions. However, there is a lack of proper comparis...

Artificial intelligence-enhanced electrocardiogram analysis for identifying cardiac autonomic neuropathy in patients with diabetes.

AIM: To develop and employ machine learning (ML) algorithms to analyse electrocardiograms (ECGs) for the diagnosis of cardiac autonomic neuropathy (CAN).

Mo-fi-disc scoring system: Towards understanding the radiological tools to better delineate the disease process and enhancing our solutions for low back pain in artificial intelligence era.

BACKGROUND: 'Mo-fi-disc' is a new scoring system that quantifies degeneration of the lumbar spine and predicts the intensity of low back pain (LBP). H...

Explainable artificial intelligence models for predicting risk of suicide using health administrative data in Quebec.

Suicide is a complex, multidimensional event, and a significant challenge for prevention globally. Artificial intelligence (AI) and machine learning (...

Operating under the influence: the effect of alcohol on operative performance using a virtual robotic training platform-an experimental comparative cohort study.

An elevated percentage of medical personnel reports using alcohol to relieve stress. Levels of alcohol addiction are almost double that of the general...

Wearable Movement Exploration Device with Machine Learning Algorithm for Screening and Tracking Diabetic Neuropathy-A Cross-Sectional, Diagnostic, Comparative Study.

BACKGROUND: Diabetic neuropathy is one of the most common complications of diabetes mellitus. The aim of this study is to evaluate the Moveo device, a...

Diagnostic and prognostic performance of artificial intelligence-based fully-automated on-site CT-FFR in patients with CAD.

Currently, clinically available coronary CT angiography (CCTA) derived fractional flow reserve (CT-FFR) is time-consuming and complex. We propose a no...

A large language model-based generative natural language processing framework fine-tuned on clinical notes accurately extracts headache frequency from electronic health records.

OBJECTIVE: To develop a natural language processing (NLP) algorithm that can accurately extract headache frequency from free-text clinical notes.

Machine learning models help differentiate between causes of recurrent spontaneous vertigo.

BACKGROUND: Vestibular migraine (VM) and Menière's disease (MD) are two common causes of recurrent spontaneous vertigo. Using history, video-nystagmog...

Early prediction of ventricular peritoneal shunt dependency in aneurysmal subarachnoid haemorrhage patients by recurrent neural network-based machine learning using routine intensive care unit data.

Aneurysmal subarachnoid haemorrhage (aSAH) can lead to complications such as acute hydrocephalic congestion. Treatment of this acute condition often i...

Cracking the Chronic Pain code: A scoping review of Artificial Intelligence in Chronic Pain research.

OBJECTIVE: The aim of this review is to identify gaps and provide a direction for future research in the utilization of Artificial Intelligence (AI) i...

Toxicity prediction and classification of Gunqile-7 with small sample based on transfer learning method.

Drug-induced diseases are the most important component of iatrogenic disease. It is the duty of doctors to provide a reasonable and safe dose of medic...

Differential Diagnosis of Diabetic Foot Osteomyelitis and Charcot Neuropathic Osteoarthropathy with Deep Learning Methods.

Our study aims to evaluate the potential of a deep learning (DL) algorithm for differentiating the signal intensity of bone marrow between osteomyelit...

An accurately supervised motion-aware deep network for non-contact pain assessment of trigeminal neuralgia mouse model.

Pain assessment in trigeminal neuralgia (TN) mouse models is essential for exploring its pathophysiology and developing effective analgesics. However,...

Chinese text dual attention network for aspect-level sentiment classification.

English text has a clear and compact subject structure, which makes it easy to find dependency relationships between words. However, Chinese text ofte...

Defining suffering in pain: a systematic review on pain-related suffering using natural language processing.

Understanding, measuring, and mitigating pain-related suffering is a key challenge for both clinical care and pain research. However, there is no cons...

Robot-assisted laparoscopic nephrectomy: early outcome measures with the implementation of multimodal analgesia and intrathecal morphine via the acute pain service.

PURPOSE: The objective of this study was to perform a retrospective cohort analysis, in which we measured the association of an acute pain service (AP...

Migraine headache (MH) classification using machine learning methods with data augmentation.

Migraine headache, a prevalent and intricate neurovascular disease, presents significant challenges in its clinical identification. Existing technique...

Deep Learning for Face Detection and Pain Assessment in Japanese macaques ().

Facial expressions have increasingly been used to assess emotional states in mammals. The recognition of pain in research animals is essential for the...

Development of a deep learning model to distinguish the cause of optic disc atrophy using retinal fundus photography.

The differential diagnosis for optic atrophy can be challenging and requires expensive, time-consuming ancillary testing to determine the cause. While...