Laser Surgery

Applications, Ethical Considerations, and Patient Perspectives on Artificial Intelligence in Dermatologic Surgery: Focus on Mohs Surgery.

BACKGROUND: Artificial intelligence (AI) is increasingly used in health care and has the potential to revolutionize Mohs micrographic surgery (MMS) by improving diagnostic accuracy and workflow efficiency.

Unsupervised Clustering of Patients Undergoing Thoracoscopic Ablation Identifies Relevant Phenotypes for Advanced Atrial Fibrillation.

: The rate of recurrence after ablation for atrial fibrillation (AF) is considerable. Risk stratification for AF recurrence after ablation remains incompletely developed. Unsupervised clustering is a machine learning technique which might provide valuable insights in AF recurrence by identifying patient clusters using numerous clinical characteristics. We hypothesize that unsupervised clustering i...

Transfer Learning and Multi-Feature Fusion-Based Deep Learning Model for Idiopathic Macular Hole Diagnosis and Grading from Optical Coherence Tomography Images.

BACKGROUND: Idiopathic macular hole is an ophthalmic disease that seriously affects vision, and its early diagnosis and treatment have important clini...

A monocular endoscopic image depth estimation method based on a window-adaptive asymmetric dual-branch Siamese network.

Minimally invasive surgery involves entering the body through small incisions or natural orifices, using a medical endoscope for observation and clini...

Exploring the Role of Sodium Dodecyl Sulfate Surfactant in Enhancing the Fluorescence Properties of Besifloxacin Fluoroquinolone; Application to Eye Formulations and Artificial Aqueous Humor.

An ultrasensitive, selective, rapid, and cost-effective spectrofluorimetric approach is established and validated to quantify the ophthalmic fluoroqui...

Machine learning models for predicting survival in lung cancer patients undergoing microwave ablation.

OBJECTIVE: To develop and validate predictive models assessing survival outcomes in patients with non-small cell lung cancer (NSCLC) treated with micr...

AI-assisted ophthalmic imaging for early detection of neurodegenerative diseases.

BACKGROUND: Artificial intelligence (AI) plays a promising role in ophthalmic imaging by providing innovative, non-invasive tools for the early detect...

Keypoint localization and parameter measurement in ultrasound biomicroscopy anterior segment images based on deep learning.

BACKGROUND: Accurate measurement of anterior segment parameters is crucial for diagnosing and managing ophthalmic conditions, such as glaucoma, catara...

Multi-objective optimization framework to plan laser ablation procedure for prostate tumors through a genetic algorithm.

BACKGROUND AND OBJECTIVES: Prostate cancer is the most common form of cancer in the male population. While the survival rate is high, many patients un...

Predicting Visual Acuity after Retinal Vein Occlusion Anti-VEGF Treatment: Development and Validation of an Interpretable Machine Learning Model.

Accurate prediction of post-treatment visual acuity in macular edema secondary to retinal vein occlusion (RVO-ME) is critical for optimizing anti-VEGF...

Retinal Ischemic Perivascular Lesions (RIPLs) as Potential Biomarkers for Systemic Vascular Diseases: A Narrative Review of the Literature.

Retinal ischemic perivascular lesions (RIPLs) are characteristic focal thinning of the inner nuclear layer, with an upward expansion of the outer nucl...

Sustaining Ophthalmic Practices for the Future: A High-Value Care Approach to Environmental Responsibility.

Ophthalmology, like all medical specialities, is continuously evolving to adapt to emerging challenges and advancements. The evidence-based medical pr...

Leveraging advanced graph neural networks for the enhanced classification of post anesthesia states to aid surgical procedures.

Anesthesia plays a pivotal role in modern surgery by facilitating controlled states of unconsciousness. Precise control is crucial for safe and pain-f...

Development of a Low-Profile, Piezoelectric Robot for MR-Guided Abdominal Needle Interventions.

PURPOSE: Minimally invasive needle-based interventions are commonly used in cancer diagnosis and treatment, including procedures, such as biopsy, brac...

Multimodal Performance of GPT-4 in Complex Ophthalmology Cases.

The integration of multimodal capabilities into GPT-4 represents a transformative leap for artificial intelligence in ophthalmology, yet its utility ...

Improved Solubility Predictions in scCO Using Thermodynamics-Informed Machine Learning Models.

Accurate solubility prediction in supercritical carbon dioxide (scCO) is crucial for optimizing experimental design by eliminating unnecessary and cos...

Predicting the efficacy of microwave ablation of benign thyroid nodules from ultrasound images using deep convolutional neural networks.

BACKGROUND: Thyroid nodules are frequent in clinical settings, and their diagnosis in adults is growing, with some persons experiencing symptoms. Ultr...

Enhancing Ophthalmic Anesthesia Optimization with Predictive Embedding Models.

Ophthalmic anesthesia the crucial factors in success and safety of ophthalmic surgery, which involves the delicate aspects of pain control, sedation, ...

Retinal OCT image segmentation with deep learning: A review of advances, datasets, and evaluation metrics.

Optical coherence tomography (OCT) is a widely used imaging technology in ophthalmic clinical practice, providing non-invasive access to high-resoluti...

Triage of Patient Messages Sent to the Eye Clinic via the Electronic Medical Record: A Comparative Study on AI and Human Triage Performance.

: Assess the ability of ChatGPT-4 (GPT-4) to effectively triage patient messages sent to the general eye clinic at our institution. : Patient messages...