Surgery

LungSurg: A Generative AI System for Segmentation and Phase Classification in Thoracoscopic Lobectomy.

The integration of artificial intelligence (AI) into surgical practices is advancing towards greater intelligence and precision. This study assesses the potential of AI in video-assisted thoracoscopic surgery (VATS) lobectomy for lung cancer by developing an AI system named LungSurg. LungSurg comprises two interconnected networks: a segmentation network for identifying intrathoracic anatomy and su...

Semantic segmentation deep learning model boosts surgeons' organ recognition in minimally invasive hysterectomy - a prospective multi-center reader performance study using pre-selected video clips.

BACKGROUND: Injury to the ureter and bladder during minimally invasive hysterectomy remains a serious complication, often resulting from insufficient intraoperative organ recognition. Artificial intelligence (AI)-based support systems may improve anatomical recognition and reduce such risks. We aimed to evaluate whether an AI-based anatomical recognition system for the ureter and bladder improves ...

A clinical-radiomics nomogram based on multisequence MRI for predicting intraoperative vertebral artery injury in patients with primary cervical spine tumor: a diagnostic study.

BACKGROUND: Currently, reliable preoperative methods for predicting vertebral artery (VA) invasion are lacking. The authors develop a novel model base...

Integrated Ultrasound Device for Precision Bladder Volume Monitoring via Acoustic Focusing and Machine Learning.

Bladder volume monitoring is critical for managing lower urinary tract dysfunctions, yet existing methods remain invasive or operator-dependent and ar...

Pathomics Signature for Prognosis and CA19-9 Interception in Pancreatic Ductal Adenocarcinoma: A Real-Life, Multi-Center Study.

Histopathological hematoxylin and eosin (H&E) slides contain valuable prognostic information for pancreatic ductal adenocarcinoma (PDAC), yet systemat...

Goals of Care Transitions following Postoperative Undertriage.

BACKGROUND: This study tests the hypothesis that postoperative undertriage of high-acuity patients to hospital floor units is associated with new post...

Intratumoral and Peritumoral Fat CT‑Based Radiomics for Predicting Recurrence Risk in Non-Muscle-Invasive Bladder Cancer: A Two-Center Study.

BACKGROUND: Non-muscle-invasive bladder cancer (NMIBC) has a high risk of recurrence, and multiple surgeries increase the disease burden on patients. ...

A Guided Sampling Enhanced Rapidly-Exploring Random Tree Path Planning Algorithm for Robot-Assisted Flexible Needle Insertion.

Percutaneous puncture techniques have been widely adopted across various domains of modern clinical interventional therapy due to their high diagnosti...

TSMS-SAM2: Multi-scale Temporal Sampling Augmentation and Memory-Splitting Pruning for Promptable Video Object Segmentation and Tracking in Surgical Scenarios.

Promptable video object segmentation and tracking (VOST) has seen significant advances with the emergence of foundation models like Segment Anything M...

Development of prediction models for perioperative opioid needs in laparoscopic cholecystectomy patients: A machine-learning approach.

BACKGROUND: Changes in opioid prescribing practices have evolved, including perioperative settings. However, computerized clinical decision support sy...

Interpretable machine learning using CT radiomics predicts pathological upgrading after secondary resection in non-muscle-invasive bladder cancer.

BACKGROUND AND PURPOSE: Repeat transurethral resection (ReTUR) is essential for reducing residual and recurrent non-muscle-invasive bladder cancer (NM...

Spectral entropy variability of intraoperative electrocorticography predicts outcome after epilepsy surgery in people with focal cortical dysplasia.

OBJECTIVE: Epilepsy surgery in people with focal cortical dysplasia (FCD) requires accurate removal of all epileptogenic tissue, and outcome is diffic...

Artificial intelligence analysis of minimally invasive surgery data.

Machine learning-based risk modeling for safety-focused learning curve assessment in robotic left-sided colorectal cancer surgery.

The transition from laparoscopic to robotic surgery for left-sided colorectal cancer raises safety concerns during the learning curve, particularly wh...

Resolution of generalized tonic seizures following focal ablative or resective surgery.

OBJECTIVE: Focal brain lesions may underlie generalized tonic seizures, as seen in Lennox-Gastaut syndrome, by engaging bilateral neural networks. How...

Machine learning integrating MRI and clinical features predicts early recurrence of hepatocellular carcinoma after resection.

This study aims to construct a robust artificial intelligence (AI) model to predict early recurrence of hepatocellular carcinoma (HCC) following surgi...

Development and validation of an interpretable machine learning model for non-invasive screening of precancerous gastric lesions using symptom and lifestyle data: a multicentre cohort study.

BACKGROUND: Precancerous gastric lesions (PLGC) are a critical stage in gastric cancer progression, where timely intervention can substantially reduce...

Deep learning-based automatic adenoid segmentation and a novel volume-based index for adenoid hypertrophy assessment.

BACKGROUND: Adenoid hypertrophy is a common cause of pediatric obstructive sleep apnea (OSA), which can impair cognitive development and affect cranio...

Diagnostic performance of radiomics for detecting and characterising upper tract urothelial carcinoma (UTUC): a systematic review.

PURPOSE: Upper tract urothelial carcinoma (UTUC) is a rare but aggressive malignancy where accurate preoperative assessment of tumour grade and stage ...

A randomized controlled trial of a WeChat-based artificial intelligence agent for postoperative care in orthopedic patients.

Effective postoperative management in orthopedic surgery is often hindered by challenges such as poor patient adherence to rehabilitation protocols, i...