Surgery

Automation of multiplex biochemical assays to enhance productivity and reduce cycle time using a modular robotic platform.

Pharmaceutical and biotechnology companies are increasingly being challenged to shorten the cycle time between design, make, test, and analyze (DMTA) compounds. Automation of multiplex assays to obtain multiparameter data on the same robotic run is instrumental in reducing cycle time. Consequently, an increasing need in automated systems to streamline laboratory workflows with the goal to expedite...

Computed Tomography Scan and Clinical-based Complete Response Prediction in Locally Advanced Rectal Cancer after Neoadjuvant Chemoradiotherapy: A Machine Learning Approach.

BACKGROUND: Treatment of locally advanced rectal cancer (LARC) involves neoadjuvant chemoradiotherapy (nCRT), followed by total mesorectal excision. Examining the response to treatment is one of the most important factors in the follow-up of patients; therefore, in this study, radiomics patterns derived from pretreatment computed tomography images in rectal cancer and its relationship with treatme...

Bioinspired design and validation of a soft robotic end-effector with integrated shape memory alloy-driven suction capabilities.

The exploration of adaptive robotic systems capable of performing complex tasks in unstructured environments, such as underwater salvage operations, p...

Predefined-Time Convergent Kinematic Control of Robotic Manipulators With Unknown Models Based on Hybrid Neural Dynamics and Human Behaviors.

This article proposes a model-free kinematic control method with predefined-time convergence for robotic manipulators with unknown models. The predefi...

Development and validation of a deep learning model for predicting gastric cancer recurrence based on CT imaging: a multicenter study.

INTRODUCTION: The postoperative recurrence of gastric cancer (GC) has a significant impact on the overall prognosis of patients. Therefore, accurately...

Impact of an AI-based laparoscopic cholecystectomy coaching program on the surgical performance: a randomized controlled trial.

BACKGROUND: Laparoscopic cholecystectomy (LC) is the gold standard for treating symptomatic gallstones but carries inherent risks like bile duct injur...

A versatile attention-based neural network for chemical perturbation analysis and its potential to aid surgical treatment: an experimental study.

Deep learning models have emerged as rapid, accurate, and effective approaches for clinical decisions. Through a combination of drug screening and dee...

Surgical Insight-guided Deep Learning for Colorectal Lesion Management.

BACKGROUND: Colonoscopy stands as a pivotal diagnostic tool in identifying gastrointestinal diseases, including potentially malignant tumors. The proc...

Toward automated detection of microbleeds with anatomical scale localization using deep learning.

Cerebral Microbleeds (CMBs) are chronic deposits of small blood products in the brain tissues, which have explicit relation to various cerebrovascular...

Predictive model for abdominal liposuction volume in patients with obesity using machine learning in a longitudinal multi-center study in Korea.

This study aimed to develop and validate a machine learning (ML)-based model for predicting liposuction volumes in patients with obesity. This study u...

Development and validation of an artificial intelligence system for surgical case length prediction.

BACKGROUND: Accurate case length estimation is a vital part of optimizing operating room use; however, significant inaccuracies exist with current sol...

Development of a visuo-tactile sensor for non-destructive peach firmness and contact force measurement suitable for robotic arm applications.

Precise measurement of firmness was crucial for determining optimal harvesting times, implementing rational storage strategies and minimizing avoidabl...

The Role and Future of Artificial Intelligence in Robotic Image-Guided Interventions.

Artificial intelligence and robotics are transforming interventional radiology, driven by advancements in computer vision, robotics and procedural aut...

Machine learning-based diagnostic model for stroke in non-neurological intensive care unit patients with acute neurological manifestations.

Stroke is a neurological complication that can occur in patients admitted to the intensive care unit (ICU) for non-neurological conditions, leading to...

External validation and performance analysis of a deep learning-based model for the detection of intracranial hemorrhage.

PurposeWe aimed to investigate the external validation and performance of an FDA-approved deep learning model in labeling intracranial hemorrhage (ICH...

Activities of Daily Living Object Dataset: Advancing Assistive Robotic Manipulation with a Tailored Dataset.

The increasing number of individuals with disabilities-over 61 million adults in the United States alone-underscores the urgent need for technologies ...

Precision Opioid Prescription in ICU Surgery: Insights from an Interpretable Deep Learning Framework.

PURPOSE: Appropriate opioid management is crucial to reduce opioid overdose risk for ICU surgical patients, which can lead to severe complications. Ac...

Advancements in microsurgery: A comprehensive systematic review of artificial intelligence applications.

Machine learning (ML) is a branch of artificial intelligence (AI) that enables computers to learn from data and discern patterns without direct instru...

Interpretable machine learning model based on CT semantic features and radiomics features to preoperatively predict Ki-67 expression in gastrointestinal stromal tumors.

To develop and validate a machine learning (ML) model which combined computed tomography (CT) semantic and radiomics features to preoperatively predic...

Comprehensive Management of Intracranial Aneurysms Using Artificial Intelligence: An Overview.

Intracranial aneurysms (IAs), an asymptomatic vascular lesion, are becoming increasingly common as imaging technology progresses. Subarachnoid hemorrh...