Surgery

AI solutions for overcoming delays in telesurgery and telementoring to enhance surgical practice and education.

Artificial intelligence (AI) has emerged as a transformative tool in surgery, particularly in telesurgery and telementoring. However, its potential to enhance data transmission efficiency and reliability in these fields remains unclear. While previous reviews have explored the general applications of telesurgery and telementoring in specific surgical contexts, this review uniquely focuses on AI mo...

Impact of different nephrectomy types on M0 renal cell carcinoma outcomes in a propensity score matching and deep learning study.

There are few analyses comparing complete nephrectomy with resection of the renal parenchyma only (CN) or radical nephrectomy that includes simultaneous resection of the parenchyma, affected perirenal fascia, perirenal fat, and ureter (RN) relative to partial nephrectomy (PN) for patients with nonmetastatic (M0) renal cell carcinoma (RCC) in terms of overall survival (OS). This study aimed to eval...

Postoperative Karnofsky performance status prediction in patients with IDH wild-type glioblastoma: A multimodal approach integrating clinical and deep imaging features.

BACKGROUND AND PURPOSE: Glioblastoma is a highly aggressive brain tumor with limited survival that poses challenges in predicting patient outcomes. Th...

Machine learning adjusted sequential CUSUM-analyses are superior to cross-sectional analysis of excess mortality after surgery.

BACKGROUND: The assessment of clinical outcome quality, particularly in surgery, is crucial for healthcare improvement. Traditional cross-sectional an...

Artificial intelligence algorithms enhance urine cytology reporting confidence in postoperative follow-up for upper urinary tract urothelial carcinoma.

PURPOSE: In Taiwan, the incidence of urothelial carcinoma of the upper urinary tract (UTUC) is high and intravesical recurrence is approximately 22%-4...

Artificial Intelligence Efficacy as a Function of Trainee Interpreter Proficiency: Lessons from a Randomized Controlled Trial.

BACKGROUND AND PURPOSE: Recently, artificial intelligence tools have been deployed with increasing speed in educational and clinical settings. However...

Integrating AI into Breast Reconstruction Surgery: Exploring Opportunities, Applications, and Challenges.

Background: Artificial intelligence (AI) has significantly influenced various sectors, including healthcare, by enhancing machine capabilities in assi...

Use of Text-to-Image Artificial Intelligence Model in Preoperative Counseling for Lip-Lift Procedures.

Text-to-image models powered by artificial intelligence offer a promising tool for enhancing patients' comprehension of cosmetic surgery outcomes and ...

Artificial intelligence based assessment of minimally invasive surgical skills using standardised objective metrics - A narrative review.

INTRODUCTION: Many studies display significant heterogeneity in the reliability of artificial intelligence (AI) assessment of minimally invasive surgi...

Multi-label text classification via secondary use of large clinical real-world data sets.

Procedural coding presents a taxing challenge for clinicians. However, recent advances in natural language processing offer a promising avenue for dev...

Artificial intelligence-assisted magnetic resonance imaging technology in the differential diagnosis and prognosis prediction of endometrial cancer.

It aimed to analyze the value of deep learning algorithm combined with magnetic resonance imaging (MRI) in the risk diagnosis and prognosis of endomet...

Development of a Dual-Plane MRI-Based Deep Learning Model to Assess the 1-Year Postoperative Outcomes in Lumbar Disc Herniation After Tubular Microdiscectomy.

BACKGROUND: Tubular microdiscectomy (TMD) is a treatment for lumbar disc herniation (LDH). Although the combination of MRI and deep learning (DL) has ...

Convolutional neural network-based classification of craniosynostosis and suture lines from multi-view cranial X-rays.

Early and precise diagnosis of craniosynostosis (CSO), which involves premature fusion of cranial sutures in infants, is crucial for effective treatme...

Development and validation of a novel AI framework using NLP with LLM integration for relevant clinical data extraction through automated chart review.

The accurate extraction of surgical data from electronic health records (EHRs), particularly operative notes through manual chart review (MCR), is com...

Scalable deep learning artificial intelligence histopathology slide analysis and validation.

Deep learning involves an artificial intelligence (AI) approach and has been shown to provide superior performance for automating image recognition ta...

Revolutionizing spinal interventions: a systematic review of artificial intelligence technology applications in contemporary surgery.

Leveraging its ability to handle large and complex datasets, artificial intelligence can uncover subtle patterns and correlations that human observati...

A Multi-Task Based Deep Learning Framework With Landmark Detection for MRI Couinaud Segmentation.

To achieve precise Couinaud liver segmentation in preoperative planning for hepatic surgery, accommodating the complex anatomy and significant variati...

Efficient brain tumor grade classification using ensemble deep learning models.

Detecting brain tumors early on is critical for effective treatment and life-saving efforts. The analysis of the brain with MRI scans is fundamental t...

Active Machine Learning for Pre-procedural Prediction of Time-Varying Boundary Condition After Fontan Procedure Using Generative Adversarial Networks.

The Fontan procedure is the definitive palliation for pediatric patients born with single ventricles. Surgical planning for the Fontan procedure has e...

Deep Learning Approaches for the Assessment of Germinal Matrix Hemorrhage Using Neonatal Head Ultrasound.

Germinal matrix hemorrhage (GMH) is a critical condition affecting premature infants, commonly diagnosed through cranial ultrasound imaging. This stud...