Surgery

Deep learning-based CT radiomics for ALK rearrangement status prediction in lung adenocarcinoma.

BACKGROUND: Current clinical guidelines mandate routine evaluation of anaplastic lymphoma kinase (ALK) rearrangement in lung adenocarcinoma prior to ALK-targeted therapy initiation. This study aimed to develop and validate a non-invasive predictive model integrating deep learning radiomic (DLR) features from pre-treatment computed tomography (CT) images with clinical data to improve pretherapeutic...

Application of artificial intelligence based on contrast-enhanced CT imaging for predicting peritoneal metastasis in patients with T3/T4 stage gastric cancer.

Gastric cancer, prevalent in East Asia, often presents with peritoneal metastasis at diagnosis, limiting surgical options and reducing survival rates. Given the low sensitivity of current diagnostic methods, this study aimed to develop and evaluate deep learning models based on preoperative contrast-enhanced computed tomography images to improve the detection of occult peritoneal metastasis in T3/...

Enhancing glioma progression prediction: Unsupervised clustering of whole-brain magnetic resonance spectroscopy data for surgical planning.

BACKGROUND: The inevitable progression of high-grade gliomas has prompted a need for data-backed identification of compromised tissue prior to detecti...

Magnetic medical microrobots with memory-capable genetic circuits.

Autonomous microrobots can reach hard-to-access regions in the human body for minimally invasive therapy. However, their microscale size limits the in...

Early prediction of intraoperative hypotension: development and validation of the HypoBridCast hybrid deep learning model.

BACKGROUND: Intraoperative hypotension (IOH) is a frequent and clinically important complication associated with adverse postoperative outcomes. Early...

Development and internal validation of a machine learning-based model for predicting postoperative complications after primary liver cancer resection.

OBJECTIVE: To identify risk factors for postoperative major complications after resection of primary liver cancer and to develop machine learning-base...

Prediction of postoperative desaturation and bradycardia in neonates undergoing laparoscopic inguinal hernia repair: a retrospective cohort study.

BACKGROUND: Ex-premature infants have a high risk of postoperative apnea and bradycardia. This study aimed to develop a predictive model for postopera...

Development and Validation of an AI-Integrated System for Automated Fracture Detection and Pedicle Puncture Planning in Lumbar Osteoporotic Vertebral Compression Fractures Based on the Nine-Grid Area Division Method.

OBJECTIVE: The clinical application of the Nine-grid Area Division Method for pedicle puncture in L-OVCF is limited by high technical thresholds and l...

Multidimensional additive manufacturing micro/nanorobots: from elaborate design to smart cargo delivery.

Micro/nanorobots have attracted much attention because of their potential to perform complex tasks with high precision under controlled actuation with...

Non-Invasive Screening of AI-Assisted DNA Aneuploidy Cytology by Image Cytometry for the Early Diagnosis of Malignant Transformation of OPMDs.

INTRODUCTION AND AIMS: Oral exfoliative DNA aneuploidy cytology by image cytometry (DNA-ICM) has been introduced for the early diagnosis of malignant ...

EEG-Based Clustering Shows Distinct Separation of Chronic Pain Patients Before Spinal Cord Stimulation Surgery.

Chronic pain is associated with disrupted cortical activity, yet individual variability in these neural patterns remains poorly understood. Electroenc...

Acceptability, deviation, and efficiency of automated artificial intelligence-based implant planning methods: A comparison with human intelligence.

PURPOSE: To evaluate the acceptability, deviation, and efficiency of two automated, artificial intelligence-driven implant planning methods compared w...

Machine learning for risk stratification of postoperative corneal edema in East Asian cataract patients: a model for precision ophthalmology.

PURPOSE: To develop and validate machine learning (ML) models for predicting early postoperative corneal edema (CE) after phacoemulsification in patie...

Automated quantification of interstitial lung abnormalities and emphysema on computed tomography: a predictive marker for postoperative pulmonary complications after esophagectomy.

BACKGROUND: Postoperative pulmonary complications (PPCs), including pneumonia, acute lung injury, and acute respiratory distress syndrome, are common ...

Two decades of robotic pediatric urology: a single-surgeon evolution from adoption to excellence.

The establishment of a Center of Excellence in robotic pediatric urology is a deliberate, phased evolution-from individual pioneering procedures to an...

Performance of large language models (GPT-5.2, Gemini 3 Pro, Claude Sonnet 4.6 and Grok 4.1) on the Fellowship of The Royal College of Surgeons Urology Part A examination.

OBJECTIVES: To assess and compare the performance of four contemporary frontier large language models (LLMs)-GPT-5.2 (OpenAI), Gemini 3 Pro (Google De...

Prediction of Oswestry Disability Index and Numeric Rating Scale scores after lumbar spine surgery: machine learning model development and fairness assessment.

BACKGROUND: One-third of patients operated for degenerative conditions in the lumbar spine do not report substantial improvement after 12 months. Most...

Personalized machine learning-guided radiation dose escalation in newly diagnosed glioblastoma: prospective pilot study.

Glioblastoma is a highly aggressive primary brain tumor with near-universal recurrence despite maximal safe resection followed by standard chemoradiat...

Robust automatic soft tissue flap segmentation using a challenging case-enriched nnU-Net in head and neck CT images.

Reconstructive surgery with a flap makes the definition of postoperative radiotherapy volumes challenging. It may also result in errors in automatic s...

Generative adversarial networks-based Image-to-Image translation allows tumor consistency prediction from standard MR images in pituitary adenomas.

Generative adversarial networks (GANs) offer potential in cross-modality image translation, but their application in pituitary adenomas remains uncert...