Surgery

Patient classification and attribute assessment based on machine learning techniques in the qualification process for surgical treatment of adrenal tumours.

Adrenal gland incidentaloma is frequently identified through computed tomography and poses a common clinical challenge. Only selected cases require surgical intervention. The primary aim of this study was to compare the effectiveness of selected machine learning (ML) techniques in proper qualifying patients for adrenalectomy and to identify the most accurate algorithm, providing a valuable tool fo...

Deep Learning Model for Predicting Proliferative Hepatocellular Carcinoma Using Dynamic Contrast-Enhanced MRI: Implications for Early Recurrence Prediction Following Radical Resection.

RATIONALE AND OBJECTIVES: The proliferative nature of hepatocellular carcinoma (HCC) is closely related to early recurrence following radical resection. This study develops and validates a deep learning (DL) prediction model to distinguish between proliferative and non-proliferative HCCs using dynamic contrast-enhanced MRI (DCE-MRI), aiming to refine preoperative assessments and optimize treatment...

Applications of artificial intelligence for surgical extraction in stomatology: a systematic review.

OBJECTIVES: Artificial intelligence (AI) has been extensively used in the field of stomatology over the past several years. This study aimed to evalua...

Development and preliminary assessment of a machine learning model to predict myocardial infarction and cardiac arrest after major operations.

INTRODUCTION: Accurate prediction of complications often informs shared decision-making. Derived over 10 years ago to enhance prediction of intra/post...

Machine learning algorithm predicts urethral stricture following transurethral prostate resection.

PURPOSE: To predict the post transurethral prostate resection(TURP) urethral stricture probability by applying different machine learning algorithms u...

Predicting severe intraventricular hemorrhage or early death using machine learning algorithms in VLBWI of the Korean Neonatal Network Database.

Severe intraventricular hemorrhage (IVH) in premature infants can lead to serious neurological complications. This retrospective cohort study used the...

Usability, Acceptability, and Implementation of Artificial Intelligence (AI) and Machine Learning (ML) Techniques in Surgical Coaching and Training: A Scoping Review.

OBJECTIVE: To define the current state of peer-reviewed literature demonstrating the usability, acceptability, and implementation of artificial intell...

Identifying potential (re)hemorrhage among sporadic cerebral cavernous malformations using machine learning.

The (re)hemorrhage in patients with sporadic cerebral cavernous malformations (CCM) was the primary aim for CCM management. However, accurately identi...

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 them are not seizure free after surgery. Aim of this ...

Artificial intelligence in surgery.

Artificial intelligence (AI) is rapidly emerging in healthcare, yet applications in surgery remain relatively nascent. Here we review the integration ...

Raman spectroscopy for esophageal tumor diagnosis and delineation using machine learning and the portable Raman spectrometer.

Esophageal cancer is one of the leading causes of cancer-related deaths worldwide. The identification of residual tumor tissues in the surgical margin...

Interpretable machine learning predicts postpartum hemorrhage at time of admission.

Predicting hematoma expansion using machine learning: An exploratory analysis of the ATACH 2 trial.

INTRODUCTION: Hematoma expansion (HE) in patients with intracerebral hemorrhage (ICH) is a key predictor of poor prognosis and potentially amenable to...

Predicting early mortality and severe intraventricular hemorrhage in very-low birth weight preterm infants: a nationwide, multicenter study using machine learning.

Our aim was to develop a machine learning-based predictor for early mortality and severe intraventricular hemorrhage (IVH) in very-low birth weight (V...

Concept Recognition and Characterization of Patients Undergoing Resection of Vestibular Schwannoma Using Natural Language Processing.

 Natural language processing (NLP), a subset of artificial intelligence (AI), aims to decipher unstructured human language. This study showcases NLP'...

Artificial intelligence-powered clinical decision making within gastrointestinal surgery: A systematic review.

BACKGROUND: Clinical decision-making in gastrointestinal surgery is complex due to the unpredictability of tumoral behavior and postoperative complica...

ECG waveform generation from radar signals: A deep learning perspective.

Cardiovascular diagnostics relies heavily on the ECG (ECG), which reveals significant information about heart rhythm and function. Despite their signi...

Application of Machine Learning in Predicting Perioperative Outcomes in Patients with Cancer: A Narrative Review for Clinicians.

This narrative review explores the utilization of machine learning (ML) and artificial intelligence (AI) models to enhance perioperative cancer care. ...

Machine learning-based prediction models affecting the recovery of postoperative bowel function for patients undergoing colorectal surgeries.

PURPOSE: The debate surrounding factors influencing postoperative flatus and defecation in patients undergoing colorectal resection prompted this stud...

Predicting blood transfusion following traumatic injury using machine learning models: A systematic review and narrative synthesis.

BACKGROUND: Hemorrhage is a leading cause of preventable death in trauma. Accurately predicting a patient's blood transfusion requirement is essential...