Surgery

Multi-Branch CNN-LSTM Fusion Network-Driven System With BERT Semantic Evaluator for Radiology Reporting in Emergency Head CTs.

The high volume of emergency room patients often necessitates head CT examinations to rule out ischemic, hemorrhagic, or other organic pathologies. A system that enhances the diagnostic efficacy of head CT imaging in emergency settings through structured reporting would significantly improve clinical decision making. Currently, no AI solutions address this need. Thus, our research aims to develop ...

Prediction of the Serial Alignment Change after Opening-Wedge High Tibial Osteotomy Based on Coronal Plane Alignment of the Knee Using Machine Learning Algorithm.

Categorization of alignment into phenotypes can be useful for predicting and analyzing postoperative alignment changes after opening-wedge high tibial osteotomy (OWHTO). The purposes of this study were to (1) develop a machine learning model for the predicting the Coronal Plane Alignment of the Knee (CPAK) phenotypes of final alignment after OWHTO, and (2) analyze predictive factors for final alig...

Robot-Assisted Endodontic Retreatment: A Case Report with Clinical Considerations.

Fiber posts present significant challenges for nonsurgical endodontic retreatment, as improper removal may result in iatrogenic root perforation or ev...

Using artificial intelligence to evaluate adherence to best practices in one anastomosis gastric bypass: first steps in a real-world setting.

BACKGROUND: Safety in one anastomosis gastric bypass (OAGB) is judged by outcomes, but it seems reasonable to utilize best practices for safety, whose...

Automatic segmentation and volumetric analysis of intracranial hemorrhages in brain CT images.

BACKGROUND: Intracranial hemorrhages (ICH) are life-threatening conditions that require rapid detection and precise subtype classification. Automated ...

Multimodal Pain Recognition in Postoperative Patients: Machine Learning Approach.

BACKGROUND: Acute pain management is critical in postoperative care, especially in vulnerable patient populations that may be unable to self-report pa...

Deep learning based decision-making and outcome prediction for adolescent idiopathic scoliosis patients with posterior surgery.

With the emergence of numerous classifications, surgical treatment for adolescent idiopathic scoliosis (AIS) can be guided more effectively. However, ...

Statistical models versus machine learning approach for competing risks in proctological surgery.

Clinical risk prediction models are ubiquitous in many surgical domains. The traditional approach to develop these models involves the use of regressi...

Development of a Clinically Applicable Deep Learning System Based on Sparse Training Data to Accurately Detect Acute Intracranial Hemorrhage from Non-enhanced Head Computed Tomography.

Non-enhanced head computed tomography is widely used for patients presenting with head trauma or stroke, given acute intracranial hemorrhage significa...

Prediction of facial nerve outcomes after surgery for vestibular schwannoma using machine learning-based models: a systematic review and meta-analysis.

Postoperative facial nerve (FN) dysfunction is associated with a significant impact on the quality of life of patients and can result in psychological...

The global convergence of some self-scaling conjugate gradient methods for monotone nonlinear equations with application to 3DOF arm robot model.

Conjugate Gradient (CG) methods are widely used for solving large-scale nonlinear systems of equations arising in various real-life applications due t...

Quantitative assessment of the generalizability of a brain tumor Raman spectroscopy machine learning model to various tumor types including astrocytoma and oligodendroglioma.

SIGNIFICANCE: Maximal safe resection of brain tumors can be performed by neurosurgeons through the use of accurate and practical guidance tools that p...

Current update on surgical management for spinal tuberculosis: a scientific mapping of worldwide publications.

INTRODUCTION: Spinal tuberculosis (TB), or Pott's disease, remains a significant global health issue, particularly in regions with high TB prevalence....

An interpretable multi-scale convolutional attention residual neural network for glioma grading with Raman spectroscopy.

Since the malignancy of gliomas varies with their grade, classifying gliomas of different grades can assist doctors in developing personalized surgica...

Development and validation of a novel artificial intelligence algorithm for precise prediction the postoperative prognosis of esophageal squamous cell carcinoma.

BACKGROUND: Esophageal squamous cell carcinoma (ESCC) is a highly aggressive malignancy, and current postoperative prognostic assessment methods remai...

A comparative analysis and survival analysis of open versus minimally invasive radical antegrade modular pancreatosplenectomy for pancreatic cancer: a systematic review and meta-analysis.

BACKGROUND: Pancreatic ductal adenocarcinoma (PDAC) is a major public health concern, ranking as the fourth leading cause of cancer-related mortality ...

SAC-BL: A hypothesis testing framework for unsupervised visual anomaly detection and location.

Reconstruction-based methods achieve promising performance for visual anomaly detection (AD), relying on the underlying assumption that the anomalies ...

Predicting Postoperative Infection After Cytoreductive Surgery and Hyperthermic Intraperitoneal Chemotherapy with Splenectomy.

BACKGROUND: Hematologic changes after splenectomy and hyperthermic intraperitoneal chemotherapy (HIPEC) can complicate postoperative assessment of inf...

SAF-IS: A spatial annotation free framework for instance segmentation of surgical tools.

Instance segmentation of surgical instruments is a long-standing research problem, crucial for the development of many applications for computer-assis...

Deep Learning-Based Assessment of Lip Symmetry for Patients With Repaired Cleft Lip.

ObjectivePost-surgical lip symmetry assessment is a key indicator of cleft repair success. Traditional methods rely on distances between anatomical la...