Refractive Surgery

Clinical domain knowledge-derived template improves post hoc AI explanations in pneumothorax classification.

OBJECTIVE: Pneumothorax is an acute thoracic disease caused by abnormal air collection between the l...

Cancer diagnosis in the post-coronavirus disease era: the promising role of telepathology and artificial intelligence.

Enhancing post-traumatic stress disorder patient assessment: leveraging natural language processing for research of domain criteria identification using electronic medical records.

BACKGROUND: Extracting research of domain criteria (RDoC) from high-risk populations like those with...

Artificial Intelligence in Rhinology.

Rhinology, allergy, and skull base surgery are fields primed for the integration and implementation ...

Clinicosocial determinants of hospital stay following cervical decompression: A public healthcare perspective and machine learning model.

OBJECTIVE: Post-operative length of hospital stay (LOS) is a valuable measure for monitoring quality...

Can machine learning predict late seizures after intracerebral hemorrhages? Evidence from real-world data.

INTRODUCTION: Intracerebral hemorrhage represents 15 % of all strokes and it is associated with a hi...

Interdisciplinary approach to identify language markers for post-traumatic stress disorder using machine learning and deep learning.

Post-traumatic stress disorder (PTSD) lacks clear biomarkers in clinical practice. Language as a pot...

Artificial intelligence in retinal screening using OCT images: A review of the last decade (2013-2023).

BACKGROUND AND OBJECTIVES: Optical coherence tomography (OCT) has ushered in a transformative era in...

PfgPDI: Pocket feature-enabled graph neural network for protein-drug interaction prediction.

Biomolecular interaction recognition between ligands and proteins is an essential task, which largel...

Predicting post-surgical functional status in high-grade glioma with resting state fMRI and machine learning.

PURPOSE: High-grade glioma (HGG) is the most common and deadly malignant glioma of the central nervo...

Impact Exploration of Spatiotemporal Feature Derivation and Selection on Machine Learning-Based Predictive Models for Post-Embolization Cerebral Aneurysm Recanalization.

PURPOSE: To enhance the performance of machine learning (ML) models for the post-embolization recana...

Mitigating Trunk Compensatory Movements in Post-Stroke Survivors through Visual Feedback during Robotic-Assisted Arm Reaching Exercises.

Trunk compensatory movements frequently manifest during robotic-assisted arm reaching exercises for ...

Utilizing machine learning to predict post-treatment outcomes in chronic non-specific neck pain patients undergoing cervical extension traction.

This study explored the application of machine learning in predicting post-treatment outcomes for ch...

Assessment of image quality and impact of deep learning-based software in non-contrast head CT scans.

In this retrospective study, we aimed to assess the objective and subjective image quality of differ...

Oral ketamine effects on dynamics of functional network connectivity in patients treated for chronic suicidality.

The underlying brain mechanisms of ketamine in treating chronic suicidality and the characteristics ...

A complex fuzzy decision model for analysing the post-pandemic immuno-sustainability.

The post-effects of the COronaVIrus Disease (COVID-19) vary depending on socioeconomic and biologica...

Prevalence of non-communicable chronic diseases in rural India amongst peri- and post-menopausal women: Can artificial intelligence help in early identification?

AIMS: To identify peri- and post-menopausal women at risk of non-communicable diseases in rural Indi...

Employing machine learning for enhanced abdominal fat prediction in cavitation post-treatment.

This study investigates the application of cavitation in non-invasive abdominal fat reduction and bo...

Application of Natural Language Processing in Electronic Health Record Data Extraction for Navigating Prostate Cancer Care: A Narrative Review.

Natural language processing (NLP)-based data extraction from electronic health records (EHRs) holds...

The value of linear and non-linear quantitative EEG analysis in paediatric epilepsy surgery: a machine learning approach.

Epilepsy surgery is effective for patients with medication-resistant seizures, however 20-40% of the...

Leveraging machine learning for predicting acute graft-versus-host disease grades in allogeneic hematopoietic cell transplantation for T-cell prolymphocytic leukaemia.

Orphan diseases, exemplified by T-cell prolymphocytic leukemia, present inherent challenges due to l...