Laser Surgery

New Directions for Ophthalmic OCT - Handhelds, Surgery, and Robotics.

The introduction of optical coherence tomography (OCT) in the 1990s revolutionized diagnostic ophthalmic imaging. Initially, OCT's role was primarily in the adult ambulatory ophthalmic clinics. Subsequent advances in handheld form factors, integration into surgical microscopes, and robotic assistance have expanded OCT's utility and impact outside of its initial environment in the adult outpatient ...

TCUP – An Open Access Tool to Predict Tissue of Origin and Cancer of Unknown Primary (CUP)

Cancer of unknown primary (CUP) remains a major diagnostic hurdle, compromising therapies that depend on accurately identifying tissue of origin. We present TCUP, an ensemble learning framework that combines Contrastive Autoencoders (CAE) and Siamese Neural Networks (SNN) with base classifiers and a meta-learning layer to classify and interpret CUP, adding biological insight through Monte-Carlo ab...

Performance of DeepSeek-R1 in Ophthalmology: An Evaluation of Clinical Decision-Making and Cost-Effectiveness

To compare the performance and cost-effectiveness of DeepSeek-R1 with OpenAI o1 in diagnosing and managing ophthalmology clinical cases. Cross-section...

OphthUS-GPT: Multimodal AI for Automated Reporting in Ophthalmic B-Scan Ultrasound

The rapid advancement of AI in ophthalmology is transforming diagnostics, especially in resource-limited settings. The shortage of ophthalmologists an...

RFA-U-Net: A Foundation Model-Driven Approach for Accurate Choroid Segmentation in OCT Imaging

The choroid layer plays a critical role in maintaining outer retinal health and is implicated in numerous vision-threatening diseases such as diabetic...

OCT-based Visual Field Estimation via Segmentation-free 3D CNNs Shows Lower Longitudinal Variability than Standard Automated Perimetry

To train and evaluate segmentation-free 3D convolutional neural network (3DCNN) models for estimating visual field (VF) from optical coherence tomogra...

Assessing the Potential of AI-Driven Drug Repurposing in Ophthalmology: An Analysis of ChatGPT’s Therapeutic Recommendations

This study aimed to evaluate the novelty and potential value of therapeutic suggestions made by an artificial intelligence large language model for tr...

AI-GUIDED ENDPOINT SELECTION FOR NEUROPROTECTION TRIALS IN GLAUCOMA

Standard Automated Perimetry (SAP) is the mainstay for monitoring glaucoma progression and has been accepted by the U.S. Food and Drug Administration ...

Automated Assessment of Choroidal Mass Dimensions Using Static and Dynamic Ultrasonographic Imaging

To develop and validate an artificial intelligence (AI)-based model that automatically measures choroidal mass dimensions on B□scan ophthalmic ultraso...

Premature Ventricular Contraction-Mediated Ventricular Fibrillation: Clinical characteristics, Application of Machine-Learning Algorithm and Outcomes of Catheter Ablation: Multicentric Case Series

Premature ventricular contractions (PVCs) are common in patients with and without structural heart disease. In a subset of patients, PVCs are associat...

Explainable Deep Learning for Lesion-Level Detection of Diabetic Retinopathy: A Segmentation Approach Using Fundus Images Graded as Mild-to-Moderate Nonproliferative Diabetic Retinopathy

Deep learning has shown promise in diabetic retinopathy screening using fundus images. However, many existing models operate as “black boxes,” providi...

Multi-branch convolutional neural network using intracranial EEG high frequency oscillation features for predicting post-surgical seizure outcomes

Pathological high-frequency oscillations (HFOs 80-600 Hz) in intracranial EEG distinguish epileptogenic cortex. However, it is uncertain whether utili...

Prediction of Pulmonary Vein Isolation and Gap Recurrence on 12-Lead ECG Using Deep Learning

Pulmonary vein isolation (PVI) is key to atrial fibrillation (AF) ablation, but arrhythmia often recurs due to conduction gaps permitting pulmonary ve...

Rule-out test for autism using machine-learning analysis of molecular temporal dynamics in hair - a multicenter study

Early intervention can improve autism-related outcomes. However, no valid biosignature test exists yet for detecting or excluding autism. In addition,...

Mobile Objective Diagnostics of Macular Degeneration using Dark-Adapted Visual Evoked Potentials

Delayed Dark-Adapted vision Recovery (DAR) is a known biomarker for Age-related Macular Degeneration (AMD); however, its measurement is often cumberso...

Continuous Multimodal AI with Wearable Vital Signs Predicts Postoperative Complications in the General Ward

Surgery is inherently associated with complications, making early detection the cornerstone of timely intervention and improved outcomes. Artificial i...

Deep Learning Driven Field Dose Prediction for Head and Neck Cancer Treated with Spot Scanning Proton Therapy

Accurate dose prediction is essential for automating radiotherapy planning. In spot scanning proton therapy (SSPT), dose evaluation is required at bot...

Identification of Risk Factors for Glaucoma Progression in Free-Text Clinical Notes using a Local Large Language Model

To evaluate the performance of a large language model (LLM) in identifying medication non-adherence, visit non-adherence, and family history of glauco...

Glaucoma Detection Using Deep Learning and Prompt-Based Explainable Report Generation

Glaucoma is a leading cause of irreversible blindness and requires early detection to prevent vision loss. This study proposes a novel framework for a...

Can Intra-Operative Ablation-Specific Features Based on Ultrasound Fusion Imaging be Used to Predict Early Recurrence of Hepatocellular Carcinoma After Microwave Ablation: A Proof-of-Concept Study.

PURPOSE: Intra-operative factors are crucial to early recurrence of hepatocellular carcinoma (HCC) after microwave ablation (MWA), but few models have...