Laser Surgery

Endoscopic Ultrasound for the Management of Pancreatic Neuroendocrine Tumors: Diagnosis, Treatment, and Future Perspectives.

Pancreatic neuroendocrine tumors (PanNETs) are increasingly diagnosed, reflecting greater clinical awareness, improved imaging, and revised classification. This review summarizes evidence on epidemiology, diagnostic workup, and endoscopic ultrasound (EUS)-guided management of PanNETs, encompassing diagnostic evaluation, tissue acquisition, and therapeutic interventions. EUS provides the highest di...

Deep Learning-Based Diagnostic Model for Ocular Surface Neoplastic Diseases.

PURPOSE: To develop a deep learning (DL) model for diagnosing ocular surface tumors and evaluating its diagnostic performance. SETTING: Development of a deep learning diagnosis algorithm. METHODS: A total of 1491 ocular surface images representing 7 diseases-nevus (28 eyes), limbal dermoid (144), MALT lymphoma (20), ocular surface squamous neoplasia (OSSN; 138), melanoma (14), pinguecula (29), and...

Controlling the fate of two triplet states: solid-state annihilator design for photon upconversion.

Photon upconversion, the process of converting low-energy light into higher-energy photons, offers transformative opportunities for energy conversion ...

Taiwan Academy of Tumor Ablation (TATA) consensus on hepatocellular carcinoma ablation.

Hepatocellular carcinoma (HCC) ranks sixth in incidence and third in mortality worldwide, underscoring its public health burden. Ablation therapy is o...

Advancements of ultrasound modalities and their clinical potential in ophthalmology.

Ultrasound imaging has played an important role in ophthalmic diagnostics due to its real-time capability, safety, and cost-effectiveness. In recent y...

Accuracy and completeness of large language models in Epidemic keratoconjunctivitis Queries: A Comparative study.

BACKGROUND: Large language models (LLMs) are increasingly applied in clinical contexts, yet their reliability in disease-specific ophthalmic domains r...

Multi-resolution adaptive channel fusion transformer encoder LSTM for accurate streamflow prediction.

Accurate streamflow prediction plays a vital role in water management and flood mitigation. However, conventional deep learning models often fail to s...

AI and Digital Tools in Dermatology: Addressing Access and Misinformation.

Digital dermatology, which is defined as the use of digital technologies that leverage individual- and population-level skin data to improve the diagn...

Skin deep: dermatologic challenges in PCOS through the female lifespan.

INTRODUCTION: Polycystic ovary syndrome (PCOS) is a lifelong endocrine-metabolic condition with prominent dermatologic manifestations such as hirsutis...

Automated Report Generation in Ophthalmology: Integrating Artificial Intelligence, Multimodal Imaging, and Clinical Data.

Artificial intelligence (AI) has emerged as a transformative force in ophthalmology, enabling automated, accurate, and efficient clinical reporting. T...

Novel Systemic Associations of Idiopathic Epiretinal Membrane Identified via Machine Learning.

PURPOSE: To discover novel systemic associations that may lead to idiopathic epiretinal membrane (iERM) using interpretable machine learning models. D...

Predicting Visual Outcomes in Congenital Posterior Lens Opacities: A Deep Learning Model Using Ultra-Widefield Fundus Images.

PURPOSE: To develop and validate a deep learning (DL) model for the automatic segmentation of lens opacity projected shadow (LOPS) on ultra-widefield ...

Sustainable Ophthalmology Applications: From the Perspective of Strabismus and Pediatric Ophthalmology.

Ophthalmology significantly contributes to the healthcare sector's carbon footprint. Despite recent increases in sustainability research in ophthalmol...

Improving Fairness and Mitigating Bias in Multicenter Electronic Health Records Models to Predict Glaucoma Outcomes.

PURPOSE: To evaluate the effectiveness and generalizability of bias mitigation methods in glaucoma progression prediction models across a multicenter ...

Explainable machine learning for risk prediction of acute cardiac tamponade during atrial fibrillation ablation.

Cardiac tamponade is a rare yet catastrophic complication during atrial fibrillation (AF) catheter ablation. Influenced by multiple procedural and pat...

A deep learning model integrating structured data and clinical text for predicting atrial fibrillation recurrence.

Multimodal perioperative data from patients undergoing atrial fibrillation (AF) ablation offer valuable insights for stratifying recurrence risk, yet ...

Research on the Prediction of Coal Workers' Pneumoconiosis Based on Easily Detectable Clinical Data: Machine Learning Model Development and Validation Study.

BACKGROUND: Coal workers' pneumoconiosis (CWP) is the most prevalent occupational disease that causes irreversible lung damage. Early prediction of CW...

An update on the developments and challenges with the diagnosis and classification of autoimmune optic neuritis.

INTRODUCTION: Autoimmune optic neuritis (ON) is a heterogeneous spectrum that includes multiple sclerosis (MS), neuromyelitis optica spectrum disorder...

From Observation to Prediction: Machine Learning Analysis of Progression of Visual loss in Nonarteritic Anterior Ischemic Optic Neuropathy.

OBJECTIVE: To determine whether combinations of modifiable clinical/systemic risk factors and structured trial variables predict early disease progres...

Can atrial fibrillation ablation outcomes be properly predicted with electrocardiography and artificial intelligence?

AIMS: The success of ablation for atrial fibrillation (AF) varies, often leading to repeat ablation. Reliable prediction of repeat ablation remains ch...