Refractive Surgery

Machine learning analysis of factors contributing to hypotension after lumbosacral epidural anaesthesia in dogs undergoing abdominal surgery.

The incidence of hypotension after a lumbosacral epidural in dogs depends on the volume of local anaesthetic administered. So far, there are no reports comparing both methods used to calculate this volume-body weight (BW) and occipito-coccygeal length (OCL)-in veterinary medicine. In this study, we evaluated the effect of these two common dosing strategies on risk of intraoperative hypotension in ...

T2I-ConBench: Text-to-Image Benchmark for Continual Post-training

Continual post-training adapts a single text-to-image diffusion model to learn new tasks without incurring the cost of separate models, but naive post-training causes forgetting of pretrained knowledge and undermines zero-shot compositionality. We observe that the absence of a standardized evaluation protocol hampers related research for continual post-training. To address this, we introduce T2I...

Machine learning in colorectal polyp surveillance: A paradigm shift in post-endoscopic mucosal resection follow-up.

Colorectal cancer remains a major health concern, with colorectal polyps as key precursors. Endoscopic mucosal resection (EMR) is a common treatment, ...

Unsupervised post-training learning in spiking neural networks.

The human brain is a dynamic system that is constantly learning. It employs a combination of various learning strategies to facilitate complex learnin...

EPIC: Explanation of Pretrained Image Classification Networks via Prototype

Explainable AI (XAI) methods generally fall into two categories. Post-hoc approaches generate explanations for pre-trained models and are compatible...

Temporal fine-tuning for early risk detection

Early Risk Detection (ERD) on the Web aims to identify promptly users facing social and health issues. Users are analyzed post-by-post, and it is ne...

Patient-specific uncertainty calibration of deep learning-based autosegmentation networks for adaptive MRI-guided lung radiotherapy.

Uncertainty assessment of deep learning autosegmentation (DLAS) models can support contour corrections in adaptive radiotherapy (ART), e.g. by utilizi...

Predicting Care Times at PACU.

Patients undergoing anesthetic surgery are treated postoperatively in a Post-Anesthesia Care Unit (PACU). Traditional planning methods often fail to a...

Controlled Intervention Study on Effects of an AI-Based App to Support Wound Care: First Results.

The KIADEKU project combines datascience and the clinical expertise of wound experts to develop and evaluate an AI-application for incontinence-associ...

Quantifying Area Back Scatter of Marine Organisms in the Arctic Ocean by Machine Learning-Based Post-Processing of Volume Back Scatter.

As the sea ice reduces in both extent and thickness and the Arctic Ocean opens, there is substantial interest in mapping the marine ecosystem in this ...

Machine-learning based classification of 2D-IR liquid biopsies enables stratification of melanoma relapse risk.

Non-linear laser spectroscopy methods such as two-dimensional infrared (2D-IR) produce large, information-rich datasets, while developments in laser t...

Automated Risk Prediction of Post-Stroke Adverse Mental Outcomes Using Deep Learning Methods and Sequential Data.

Depression and anxiety are common comorbidities of stroke. Research has shown that about 30% of stroke survivors develop depression and about 20% deve...

Personalized surveillance in colorectal cancer: Integrating circulating tumor DNA and artificial intelligence into post-treatment follow-up.

Given the growing burden of colorectal cancer (CRC) as a global health challenge, it becomes imperative to focus on strategies that can mitigate its i...

An analytic research and review of the literature on practice of artificial intelligence in healthcare.

Artificial intelligence (AI) has transformed healthcare, particularly in robot-assisted surgery, rehabilitation, medical imaging and diagnostics, virt...

Effect of Deep Learning-Based Image Reconstruction on Lesion Conspicuity of Liver Metastases in Pre- and Post-contrast Enhanced Computed Tomography.

The purpose of this study was to investigate the utility of deep learning image reconstruction at medium and high intensity levels (DLIR-M and DLIR-H,...

Risk factors and predictive models for post-operative moderate-to-severe mitral regurgitation following transcatheter aortic valve replacement: a machine learning approach.

BACKGROUND: Post-operative moderate-to-severe mitral regurgitation (MR) following transcatheter aortic valve replacement (TAVR) is associated with poo...

DPQ-HD: Post-Training Compression for Ultra-Low Power Hyperdimensional Computing

Hyperdimensional Computing (HDC) is emerging as a promising approach for edge AI, offering a balance between accuracy and efficiency. However, curre...

Development and Validation of Machine Learning Algorithms for Predicting Prolonged Postoperative Opioid Use in Spinal Metastatic Disease.

Introduction: Operative management of spinal metastatic disease is largely for symptom palliation and revolves around the expectation that postoperati...

PQS-BFL: A Post-Quantum Secure Blockchain-based Federated Learning Framework

Federated Learning (FL) enables collaborative model training while preserving data privacy, but its classical cryptographic underpinnings are vulner...

Amplifying Your Social Media Presence: Personalized Influential Content Generation with LLMs

The remarkable advancements in Large Language Models (LLMs) have revolutionized the content generation process in social media, offering significant...