AI Medical Compendium Topic

Explore the latest research on artificial intelligence and machine learning in medicine.

Treatment Outcome

Showing 231 to 240 of 3071 articles

Clear Filters

Machine learning approaches to evaluate heterogeneous treatment effects in randomized controlled trials: a scoping review.

Journal of clinical epidemiology
BACKGROUND AND OBJECTIVES: Estimating heterogeneous treatment effects (HTEs) in randomized controlled trials (RCTs) has received substantial attention recently. This has led to the development of several statistical and machine learning (ML) algorith...

OxcarNet: sinc convolutional network with temporal and channel attention for prediction of oxcarbazepine monotherapy responses in patients with newly diagnosed epilepsy.

Journal of neural engineering
Monotherapy with antiepileptic drugs (AEDs) is the preferred strategy for the initial treatment of epilepsy. However, an inadequate response to the initially prescribed AED is a significant indicator of a poor long-term prognosis, emphasizing the imp...

Machine learning algorithm for predicting seizure control after temporal lobe resection using peri-ictal electroencephalography.

Scientific reports
Brain resection is curative for a subset of patients with drug resistant epilepsy but up to half will fail to achieve sustained seizure freedom in the long term. There is a critical need for accurate prediction tools to identify patients likely to ha...

Sensory Stimulation and Robot-Assisted Arm Training After Stroke: A Randomized Controlled Trial.

Journal of neurologic physical therapy : JNPT
BACKGROUND AND PURPOSE: Functional recovery after stroke is often limited, despite various treatment methods such as robot-assisted therapy. Repetitive sensory stimulation (RSS) might be a promising add-on therapy that is thought to directly drive pl...

The emerging role of AI in enhancing intratumoral immunotherapy care.

Oncotarget
The emergence of immunotherapy (IO), and more recently intratumoral IO presents a novel approach to cancer treatment which can enhance immune responses while allowing combination therapy and reducing systemic adverse events. These techniques are inte...

Automated biventricular quantification in patients with repaired tetralogy of Fallot using a three-dimensional deep learning segmentation model.

Journal of cardiovascular magnetic resonance : official journal of the Society for Cardiovascular Magnetic Resonance
BACKGROUND: Deep learning is the state-of-the-art approach for automated segmentation of the left ventricle (LV) and right ventricle (RV) in cardiovascular magnetic resonance (CMR) images. However, these models have been mostly trained and validated ...

Preoperative treatment response prediction for pancreatic cancer by multiple microRNAs in plasma exosomes: Optimization using machine learning and network analysis.

Pancreatology : official journal of the International Association of Pancreatology (IAP) ... [et al.]
BACKGROUND/OBJECTIVES: MicroRNAs (miRNAs) are involved in chemosensitivity through their biological activities in various malignancies, including pancreatic cancer (PC). However, single-miRNA models offer limited predictability of treatment response....

Deep learning Radiomics Based on Two-Dimensional Ultrasound for Predicting the Efficacy of Neoadjuvant Chemotherapy in Breast Cancer.

Ultrasonic imaging
We investigate the predictive value of a comprehensive model based on preoperative ultrasound radiomics, deep learning, and clinical features for pathological complete response (pCR) after neoadjuvant chemotherapy (NAC) for the breast cancer. We enro...