Laser Surgery

An ophthalmic video foundation model for surgical recognition and navigation with wet-lab porcine eye validation.

Foundation models in artificial intelligence are revolutionizing healthcare by utilizing large-scale unlabelled data for pretraining. However, their intraoperative applications remain underexplored owing to limited surgical data and the challenges of real-time deployment. Here we show the development of the ophthalmic video foundation model (OVFM), designed for microscopic ophthalmic surgical reco...

A test-time clinically adaptive framework for detecting multiple fundus diseases harnessing ophthalmic foundation models.

Fundus diseases are leading causes of global vision impairment, often presenting with complex comorbidities that challenge conventional artificial intelligence models. While ophthalmic foundation models (FMs) offer promising capabilities, their clinical translation for multi-disease detection remains limited by issues such as imbalanced data distribution, uncertainty in multi-label predictions, in...

Updates in Central Retinal Artery Occlusion.

BACKGROUND: Central retinal artery occlusion (CRAO) is a vision-threatening neuro-ophthalmic emergency, analogous to acute ischemic stroke. Delayed pr...

An interpretable molecular descriptor for machine learning predictions in atmospheric science.

The study of aerosol formation and chemistry using machine learning is limited by the lack of molecular descriptors suited to atmospheric compounds. I...

Domain-Shift AI Technology for Vendor-Agnostic Multiple Macular Disease Detection From 3D OCT Scans.

IMPORTANCE: A deep learning (DL) model capable of analyzing optical coherence tomography (OCT) 3-dimensional (3D) scans from various vendors is essent...

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 classifica...

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...

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...

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 ...