Surgery

Advances in Predicting Postoperative Atrial Fibrillation: A Narrative Review of the Current Literature.

Postoperative atrial fibrillation (POAF) is a common adverse event after cardiac surgery. It is asso...

Artificial intelligence in the recognition of stages in live laparoscopic cholecystectomy.

Laparoscopic cholecystectomy is the gold standard for treating symptomatic gallstones. Despite estab...

Comment on "Machine learning model for predicting a high comprehensive complication index following rectal cancer surgery".

AI-assisted cryo-dose estimation and tissue injury modeling for assessing side effects in MRI-guided prostate cryoablation: a retrospective study.

Focal therapy offers a middle ground between active surveillance and whole-gland treatment for ...

Predicting Visual Outcomes in Congenital Posterior Lens Opacities: A Deep Learning Model Using Ultra-Widefield Fundus Images.

PURPOSE: To develop and validate a deep learning (DL) model for the automatic segmentation of lens o...

KRT15 identified by scRNA-Seq and machine learning as stemness regulator and prognostic biomarker in ESCC.

Postoperative recurrence and metastasis in esophageal squamous cell carcinoma (ESCC) are closely ass...

The role of artificial intelligence in pre-operative prediction of completeness of cytoreduction for peritoneal surface malignancies: a scoping review.

BACKGROUND: Complete cytoreduction is the most important prognostic factor for patients with periton...

Prediction of hemorrhagic transformation after intravenous thrombolysis in acute anterior circulation ischemic stroke through radiomics-based machine learning models.

BACKGROUND: Hemorrhagic transformation (HT) and its associated neurological deterioration remain maj...

Normal-resolution vs. super-resolution deep learning reconstruction for diagnosis of functionally significant coronary stenosis using cardiac CT.

BACKGROUND: Super-resolution deep learning reconstruction (SR-DLR) has been developed to reduce imag...

Optimization and External Validation of a Deep Learning Model for Segmentation and Quantification of Traumatic Brain Injury Lesions on Head Computed Tomography.

Current methods for radiological classification in traumatic brain injury (TBI), such as the Marshal...

Intraoperative Ultrasound-Based Displacement Mapping Through Deep Learning.

BACKGROUND AND OBJECTIVES: Brain shift during neurological surgery for brain tumors can be caused by...

Use of a Novel Intraoperative Neurocognitive Monitoring Task During Awake Craniotomy for Tumor Resection in the Language-Dominant Parietal Lobe.

BACKGROUND AND OBJECTIVES: The identification of eloquent structures in the parietal lobe during awa...

Development and internal-external validation of a machine learning model to predict the risk for postoperative adverse events in patients with peptic ulcer perforation: a secondary cohort study of the perforated peptic ulcer analyzing project study.

BACKGROUND: Morbidity and mortality rates after surgery in patients with perforated peptic ulcer (PP...

Efficacy and neural mechanisms of a vibrotactile-enhanced, brain-controlled soft robotic glove for upper limb rehabilitation after stroke: a multicentre randomised controlled trial protocol.

INTRODUCTION: Soft robotic gloves (SRGs) integrated with brain-computer interfaces (BCIs) have demon...

Clinical-grade autonomous cytopathology through whole-slide edge tomography.

Cytopathology, often abbreviated as cytology, has a central role in the early detection of cancer, s...

Global research status in the treatment of glaucoma: a systematic bibliometric analysis.

AIM: To present an overview of the research on global glaucoma treatment in the last decade in terms...

The feasibility and accuracy of real-time intra-operative confocal tissue diagnosis in brain and spine cancer surgery.

Real-time intra-operative brain tumour tissue analysis can reduce turnaround times and enable repeat...

Technological advancements for improving preoperative assessment and precision in cardiovascular surgery.

INTRODUCTION: Cardiovascular surgery including cardiac surgery and complex endovascular procedure ne...

Application of AI Models for Preventing Surgical Complications: Scoping Review of Clinical Readiness and Barriers to Implementation.

BACKGROUND: The impact of surgical complications is substantial and multifaceted, affecting patients...

STRATUM-OS: first step in the development and validation of the STRATUM tool based on multimodal data processing to assist surgery in patients affected by intra-axial brain tumours - observational study protocol.

INTRODUCTION: Integrated digital diagnostics can support complex surgeries in many anatomic sites, a...

Assessment and AI-Based Prediction of Complications in Reduction Mammoplasty: A Combined Statistical and Fuzzy Inference Approach in a 10-Year Cohort from Tehran Hospitals (2013-2023).

INTRODUCTION: Gigantomastia causes physical, psychological, and dermatological issues, often with pt...