Surgery

An efficient deep neural network for automatic classification of acute intracranial hemorrhages in brain CT scans.

BACKGROUND: Recent advancements in deep learning models have demonstrated their potential in the field of medical imaging, achieving remarkable performance surpassing human capabilities in tasks such as classification and segmentation. However, these modern state-of-the-art network architectures often demand substantial computational resources, which limits their practical application in resource-...

Assessment of changes in vessel area during needle manipulation in microvascular anastomosis using a deep learning-based semantic segmentation algorithm: A pilot study.

Appropriate needle manipulation to avoid abrupt deformation of fragile vessels is a critical determinant of the success of microvascular anastomosis. However, no study has yet evaluated the area changes in surgical objects using surgical videos. The present study therefore aimed to develop a deep learning-based semantic segmentation algorithm to assess the area change of vessels during microvascul...

Enhancing surgical instrument segmentation: integrating vision transformer insights with adapter.

PURPOSE: In surgical image segmentation, a major challenge is the extensive time and resources required to gather large-scale annotated datasets. Give...

Performance of artificial intelligence in bariatric surgery: comparative analysis of ChatGPT-4, Bing, and Bard in the American Society for Metabolic and Bariatric Surgery textbook of bariatric surgery questions.

BACKGROUND: The American Society for Metabolic and Bariatric Surgery (ASMBS) textbook serves as a comprehensive resource for bariatric surgery, coveri...

Semi-supervised multi-modal medical image segmentation with unified translation.

The two major challenges to deep-learning-based medical image segmentation are multi-modality and a lack of expert annotations. Existing semi-supervis...

A comprehensive study on machine learning models combining with oversampling for bronchopulmonary dysplasia-associated pulmonary hypertension in very preterm infants.

BACKGROUND: Bronchopulmonary dysplasia-associated pulmonary hypertension (BPD-PH) remains a devastating clinical complication seriously affecting the ...

Predicting Postoperative Pain and Opioid Use with Machine Learning Applied to Longitudinal Electronic Health Record and Wearable Data.

BACKGROUND:  Managing acute postoperative pain and minimizing chronic opioid use are crucial for patient recovery and long-term well-being.

Evaluating Deep Learning Techniques for Detecting Aneurysmal Subarachnoid Hemorrhage: A Comparative Analysis of Convolutional Neural Network and Transfer Learning Models.

OBJECTIVE: Machine learning and deep learning techniques offer a promising multidisciplinary solution for subarachnoid hemorrhage (SAH) detection. The...

Machine learning model for prediction of permanent stoma after anterior resection of rectal cancer: A multicenter study.

BACKGROUND: The conversion from a temporary to a permanent stoma (PS) following rectal cancer surgery significantly impacts the quality of life of pat...

Enhancing physically-based hydrological modeling with an ensemble of machine-learning reservoir operation modules under heavy human regulation using easily accessible data.

Dams and reservoirs have significantly altered river flow dynamics worldwide. Accurately representing reservoir operations in hydrological models is c...

Machine learning application in otology.

This review presents a comprehensive history of Artificial Intelligence (AI) in the context of the revolutionary application of machine learning (ML) ...

Online sequential analysis of volatile and semivolatile organic compounds in water matrices by double robotic sample preparations and dual-channel mono and comprehensive two-dimensional gas chromatography coupled with time-of-flight mass spectrometry system.

The monitoring of organic compounds in aquatic matrices poses challenges due to its complexity and time-intensive nature. To address these challenges,...

Distinguishing the Uterine Artery, the Ureter, and Nerves in Laparoscopic Surgical Images Using Ensembles of Binary Semantic Segmentation Networks.

Performing a minimally invasive surgery comes with a significant advantage regarding rehabilitating the patient after the operation. But it also cause...

Evaluating the Quality of Postpartum Hemorrhage Nursing Care Plans Generated by Artificial Intelligence Models.

BACKGROUND: With the rapidly advancing technological landscape of health care, evaluating the potential use of artificial intelligence (AI) models to ...

-A machine learning model to predict surgical site infection after surgery of lower extremity fractures.

PURPOSE: This study aimed to develop machine learning algorithms for identifying predictive factors associated with the risk of postoperative surgical...

Opening the Black Box: Spatial Transcriptomics and the Relevance of Artificial Intelligence-Detected Prognostic Regions in High-Grade Serous Carcinoma.

Image-based deep learning models are used to extract new information from standard hematoxylin and eosin pathology slides; however, biological interpr...

Applications of artificial intelligence in surgery: clinical, technical, and governance considerations.

Artificial intelligence (AI) will power many of the tools in the armamentarium of digital surgeons. AI methods and surgical proof-of-concept flourish,...

Anthropomorphic Tendon-Based Hands Controlled by Agonist-Antagonist Corticospinal Neural Network.

This article presents a study on the neurobiological control of voluntary movements for anthropomorphic robotic systems. A corticospinal neural networ...

Machine-learning and mechanistic modeling of metastatic breast cancer after neoadjuvant treatment.

Clinical trials involving systemic neoadjuvant treatments in breast cancer aim to shrink tumors before surgery while simultaneously allowing for contr...

Resist-as-Needed ADL Training With SPINDLE for Patients With Tremor.

Individuals with neurological disorders often exhibit altered manual dexterity and muscle weakness in their upper limbs. These motor impairments with ...