Laser Surgery

Boosting foundation models for rare eye disease diagnosis via a multimodal text-to-image generative framework.

The rising prevalence of vision-threatening retinal diseases poses a significant burden on the global healthcare systems. Though deep learning (DL) techniques offer promising avenues for improving diagnostic efficiency, data scarcity and imbalance issues persist in training robust diagnostic models, particularly for rare eye diseases. Here, we introduce EyeDiff, a generative foundation model capab...

Exploring Deep Learning Models for Small Histopathology Datasets: Segmentation and Classification of Glomerular Crescent Lesions with Ablation, Interpretability, and Calibration Analyses.

Glomerular crescent lesions are critical indicators of severe kidney injury and are closely associated with disease progression. However, their automated identification remains challenging due to limited annotated data, class imbalance, and subtle morphological variations. This study proposes a comprehensive deep learning (DL) framework for segmentation and classification of glomerular crescent le...

Comprehensive Evaluation of ChatGPT's Diagnostic Accuracy on Image-based Ophthalmic Case Interpretations.

OBJECTIVE: To evaluate the diagnostic and treatment accuracy of Chat Generative Pre-trained Transformer (GPT-4.o) in ophthalmology, comparing performa...

Fuzzy logic for preanesthetic risk assessment in cataract surgery patients.

We aimed to build a fuzzy logic preanaesthetic risk score tailored to cataract surgery. By fusing systemic comorbidities with key patient attributes i...

Discrimination of vascular proliferation in eyelid surgery using multimodal hyperspectral imaging technology.

BACKGROUND AND OBJECTIVE: Vascular proliferation is a common skin reaction following eyelid surgery, and early diagnosis is crucial for improving trea...

Artificial Intelligence Enabled Noninvasive Mapping of Cardiac Arrhythmia Origins Using the 12-Lead Electrocardiogram.

Artificial intelligence (AI) is reshaping cardiac electrophysiology by extracting information from electrocardiograms that exceeds human visual interp...

Study of bladder cancer detection in standard white light versus AI-supported endoscopy-01 (RAISE-01) - Development and validation of an AI-based support tool.

BACKGROUND: Diagnosis and surveillance of bladder cancer rely on white-light cystoscopy (WLC). However, this modality is operator-dependent and associ...

Routine blood tests and machine learning identify complications in high myopia.

High myopia can lead to cataract, glaucoma, retinal detachment, choroidal neovascularisation, and macular degeneration, causing irreversible vision lo...

Integrating traditional statistics and explainable machine learning for risk stratification of atrial fibrillation recurrence after pulsed field ablation: a hypothesis-generating single-center study.

BACKGROUND: Predicting recurrence after pulsed field ablation (PFA) for paroxysmal atrial fibrillation (AF) remains challenging, particularly in early...

Why nonlinear models matter: unified analysis of cognitive load, stress, and exercise using wearable physiological signals.

\textbf{Objective:} Physiological measurements obtained from wearable devices reflect complex autonomic nervous system dynamics that are often ass...

Real world practice of Artificial intelligence Diagnostic System for Diabetic Retinopathy in Taiwan.

PURPOSE: We evaluated the alterations of applying artificial intelligence (AI) diagnostic system for diabetic retinopathy (DR) screening in real-world...

Multimodal MRI radiomics for predicting HIFU ablation efficacy in uterine fibroids: a machine learning study.

OBJECTIVE: To explore the predictive value of machine learning-based multimodal MRI radiomics combined with clinical features in the efficacy of high-...

Toward Long-Term Visual Field Appearance Forecasting Using Artificial Intelligence for Ophthalmic Education and Diagnosis.

OBJECTIVE: To provide state-of-the-art, post hoc-explainable visual field (VF) forecasts to aid in training ophthalmic residents to characterize glauc...

Simulated Patient Queries Reveal Limitations of ChatGPT in Dermatologic Image Classification.

[Application of artificial intelligence in pathological diagnosis of ocular tumors].

Ocular tumors encompass ocular surface tumors, orbital tumors, and intraocular tumors, characterized by high heterogeneity and complex classifications...

Latent Supervision: A Method for Improved Performance and Calibration of Machine Learning Classification Models in Ophthalmology.

PURPOSE: Standard supervised learning assumes deterministic labels (e.g., positive or negative, present or absent), neglecting the diagnostic uncertai...

Bridging Population Patterns and Individual Prediction: Framework for Prospective Multimorbidity Study.

BACKGROUND: Multimorbidity has become a major global public health challenge. However, existing research primarily emphasizes the identification of di...

Synthetic skin image generation using a physics-based, object-to-image computational pipeline.

PURPOSE: We describe an extended overview and evaluation of the S-SYNTH skin simulation approach (Kim et al. in MICCAI, Springer, pp 734-744, 2024), a...

Human-in-the-loop validation of an artificial intelligence-driven real-time prostatic capsule recognition model for nerve-sparing robot-assisted radical prostatectomy.

BACKGROUND: Nerve-sparing robot-assisted radical prostatectomy (NS-RARP) requires precise prostatic capsule identification to balance oncological cont...

Funders' Role in Ophthalmic Artificial Intelligence Innovations.