Pain Management

Should Peritoneal Re-Approximation Be Performed After Transperitoneal Robot-Assisted Radical Prostatectomy?

The aim of the study is to examine the effect of peritoneal re-approximation or non-approximation on the postoperative course of patients at the end of transperitoneal robot-assisted radical prostatectomy (tRARP). It is also aimed to examine the relationship between peritoneal re-approximation or non-approximation and drain removal time, need for analgesics, passage of flatus, and length of hospi...

Semi-automated tracking of pain in critical care patients using artificial intelligence: a retrospective observational study.

Monitoring the pain intensity in critically ill patients is crucial because intense pain can cause adverse events, including poor survival rates; however, continuous pain evaluation is difficult. Vital signs have traditionally been considered ineffective in pain assessment; nevertheless, the use of machine learning may automate pain assessment using vital signs. This retrospective observational st...

Clinical presentation of COVID-19 - a model derived by a machine learning algorithm.

COVID-19 pandemic has flooded all triage stations, making it difficult to carefully select those most likely infected. Data on total patients tested, ...

Assessment of Thoracic Pain Using Machine Learning: A Case Study from Baja California, Mexico.

Thoracic pain is a shared symptom among gastrointestinal diseases, muscle pain, emotional disorders, and the most deadly: Cardiovascular diseases. Due...

Prioritizing Pain-Associated Targets with Machine Learning.

While hundreds of genes have been associated with pain, much of the molecular mechanisms of pain remain unknown. As a result, current analgesics are l...

A deep learning approach for lower back-pain risk prediction during manual lifting.

Occupationally-induced back pain is a leading cause of reduced productivity in industry. Detecting when a worker is lifting incorrectly and at increas...

Global guidance network for breast lesion segmentation in ultrasound images.

Automatic breast lesion segmentation in ultrasound helps to diagnose breast cancer, which is one of the dreadful diseases that affect women globally. ...

Objective characterization of hip pain levels during walking by combining quantitative electroencephalography with machine learning.

Pain is an undesirable sensory experience that can induce depression and limit individuals' activities of daily living, in turn negatively impacting t...

Novel Analgesic Index for Postoperative Pain Assessment Based on a Photoplethysmographic Spectrogram and Convolutional Neural Network: Observational Study.

BACKGROUND: Although commercially available analgesic indices based on biosignal processing have been used to quantify nociception during general anes...

Pain Treatment Evaluation in COVID-19 Patients with Hesitant Fuzzy Linguistic Multicriteria Decision-Making.

The coronavirus disease 2019 (COVID-19) has emerged as a worldwide pandemic since March 2020. Although most patients complain of moderate or severe pa...

Deep convolutional neural networks to predict cardiovascular risk from computed tomography.

Coronary artery calcium is an accurate predictor of cardiovascular events. While it is visible on all computed tomography (CT) scans of the chest, thi...

Intracorporeal anastomosis in right hemicolectomy for colon cancer: short-term outcomes with the DaVinci Xi robot.

Intracorporeal anastomosis (IA) may improve outcomes compared with extracorporeal anastomosis (EA) in minimally invasive right colectomy. This is a pr...

Extracting Angina Symptoms from Clinical Notes Using Pre-Trained Transformer Architectures.

Anginal symptoms can connote increased cardiac risk and a need for change in cardiovascular management. In this study, a pre-trained transformer archi...

Using Natural Language Processing and Machine Learning to Identify Hospitalized Patients with Opioid Use Disorder.

Opioid use disorder (OUD) represents a global public health crisis that challenges classic clinical decision making. As existing hospital screening me...

Building an Automated Orofacial Pain, Headache and Temporomandibular Disorder Diagnosis System.

Physicians collect data in patient encounters that they use to diagnose patients. This process can fail if the needed data is not collected or if phys...

An objective structural and functional reference standard in glaucoma.

The current lack of consensus for diagnosing glaucoma makes it difficult to develop diagnostic tests derived from deep learning (DL) algorithms. In th...

Resting-state magnetoencephalography source magnitude imaging with deep-learning neural network for classification of symptomatic combat-related mild traumatic brain injury.

Combat-related mild traumatic brain injury (cmTBI) is a leading cause of sustained physical, cognitive, emotional, and behavioral disabilities in Vete...

Automation of Quantifying Axonal Loss in Patients with Peripheral Neuropathies through Deep Learning Derived Muscle Fat Fraction.

BACKGROUND: Axonal loss denervates muscle, leading to an increase of fat accumulation in the muscle. Therefore, fat fraction (FF) in whole limb muscle...

Postoperative analgesia in patients undergoing robot-assisted thoracic surgery: a comparison between thoracic epidural analgesia and intercostal nerve block combined with intravenous patient-controlled analgesia.

BACKGROUND: Recently, robot-assisted thoracic surgery (RATS) is increasingly applied to lung or mediastinal tumor surgery. However, appropriate method...

Multi-Scale Self-Guided Attention for Medical Image Segmentation.

Even though convolutional neural networks (CNNs) are driving progress in medical image segmentation, standard models still have some drawbacks. First,...