Laser Surgery

Machine Learning Based on Clinical Information and Integrated CT Radiomics to Predict Local Recurrence of Stage Ia Lung Adenocarcinoma after Microwave Ablation.

PURPOSE: To develop and compare 3 different machine learning-based models of clinical information and integrated radiomics features predicting the local recurrence of Stage Ia lung adenocarcinoma after microwave ablation (MWA) for assisting clinical decision making.

Development of oculomics artificial intelligence for cardiovascular risk factors: A case study in fundus oculomics for HbA1c assessment and clinically relevant considerations for clinicians.

Artificial Intelligence (AI) is transforming healthcare, notably in ophthalmology, where its ability to interpret images and data can significantly enhance disease diagnosis and patient care. Recent developments in oculomics, the integration of ophthalmic features to develop biomarkers for systemic diseases, have demonstrated the potential for providing rapid, non-invasive methods of screening lea...

Feasibility of Artificial Intelligence Powered Adverse Event Analysis: Using a Large Language Model to Analyze Microwave Ablation Malfunction Data.

Determine if a large language model (LLM, GPT-4) can label and consolidate and analyze interventional radiology (IR) microwave ablation device safety...

DECNet: Left Atrial Pulmonary Vein Class Imbalance Classification Network.

In clinical practice, the anatomical classification of pulmonary veins plays a crucial role in the preoperative assessment of atrial fibrillation radi...

Commentary on "Artificial Intelligence in Dermatology: A Systematic Review of Its Applications in Melanoma and Keratinocyte Carcinoma Diagnosis" and "Artificial Intelligence for Mohs and Dermatologic Surgery: A Systematic Review and Meta-Analysis".

A prognostic model for thermal ablation of benign thyroid nodules based on interpretable machine learning.

INTRODUCTION: The detection rate of benign thyroid nodules is increasing every year, with some affected patients experiencing symptoms. Ultrasound-gui...

An automatic glaucoma grading method based on attention mechanism and EfficientNet-B3 network.

Glaucoma infection is rapidly spreading globally and the number of glaucoma patients is expected to exceed 110 million by 2040. Early identification a...

Artificial intelligence applications in ophthalmic surgery.

PURPOSE OF REVIEW: Technologies in healthcare incorporating artificial intelligence tools are experiencing rapid growth in static-image-based applicat...

Deep learning-based multimodal fusion of the surface ECG and clinical features in prediction of atrial fibrillation recurrence following catheter ablation.

BACKGROUND: Despite improvement in treatment strategies for atrial fibrillation (AF), a significant proportion of patients still experience recurrence...

DEAF-Net: Detail-Enhanced Attention Feature Fusion Network for Retinal Vessel Segmentation.

Retinal vessel segmentation is crucial for the diagnosis of ophthalmic and cardiovascular diseases. However, retinal vessels are densely and irregular...

Artificial Intelligence and Ophthalmic Clinical Registries.

PURPOSE: The recent advances in artificial intelligence (AI) represent a promising solution to increasing clinical demand and ever limited health reso...

The role of saliency maps in enhancing ophthalmologists' trust in artificial intelligence models.

PURPOSE: Saliency maps (SM) allow clinicians to better understand the opaque decision-making process in artificial intelligence (AI) models by visuali...

Understanding natural language: Potential application of large language models to ophthalmology.

Large language models (LLMs), a natural language processing technology based on deep learning, are currently in the spotlight. These models closely mi...

Advancing laser ablation assessment in hyperspectral imaging through machine learning.

Hyperspectral imaging (HSI) is gaining increasing relevance in medicine, with an innovative application being the intraoperative assessment of the out...

Role of artificial intelligence, machine learning and deep learning models in corneal disorders - A narrative review.

In the last decade, artificial intelligence (AI) has significantly impacted ophthalmology, particularly in managing corneal diseases, a major reversib...

Artificial Intelligence for Mohs and Dermatologic Surgery: A Systematic Review and Meta-Analysis.

BACKGROUND: Over the past decade, several studies have shown that potential of artificial intelligence (AI) in dermatology. However, there has yet to ...

From Diagnosis to Precision Surgery: The Transformative Role of Artificial Intelligence in Urologic Imaging.

The multidisciplinary nature of artificial intelligence (AI) has allowed for rapid growth of its application in medical imaging. Artificial intelligen...

AI in Neuro-Ophthalmology: Current Practice and Future Opportunities.

BACKGROUND: Neuro-ophthalmology frequently requires a complex and multi-faceted clinical assessment supported by sophisticated imaging techniques in o...

Predicting the Prognosis of HIFU Ablation of Uterine Fibroids Using a Deep Learning-Based 3D Super-Resolution DWI Radiomics Model: A Multicenter Study.

RATIONALE AND OBJECTIVES: To assess the feasibility and efficacy of a deep learning-based three-dimensional (3D) super-resolution diffusion-weighted i...

Revolutionizing teledermatology: Exploring the integration of artificial intelligence, including Generative Pre-trained Transformer chatbots for artificial intelligence-driven anamnesis, diagnosis, and treatment plans.

The integration of teledermatology and artificial intelligence (AI) marks a significant advancement in dermatologic care. This study examines the syne...