Laser Surgery

Dental image analysis and surgical decision support using modified generative adversarial networks.

BACKGROUND: Several clinical issues have remained in dental care and risk estimation because of the insufficient integration of multimodal features, the incapacity to integrate clinical domain literatures and evidence-based principles in their analytical platforms. The methods of deep learning-based diagnosis offer the in-depth examination of complex cases synthesized based on heterogeneous source...

Identifying the Presence and Characteristics of Mid-Myocardial and Epicardial Fibrosis From Intracardiac Electrograms in Patients Undergoing Ventricular Arrhythmia Ablation Using a Transformer-Based Self-Supervised Classifier.

BACKGROUND: Catheter ablation is an essential tool for ventricular arrhythmia management, yet sustained procedural success is hindered by the limited ability to identify nonendocardial arrhythmogenic substrates during the procedure. Although delayed enhancement cardiac magnetic resonance imaging is the reference standard for detecting myocardial fibrosis, barriers including cost, workflow complexi...

OphthoChat: A HIPAA-Compliant, Artificial Intelligence-Driven Natural-Language Chatbot for Ophthalmic Electronic Medical Record Querying and Analysis.

PURPOSE: To introduce and evaluate OphthoChat, a Health Insurance Portability and Accountability Act-compliant, artificial intelligence (AI)‑powered n...

Artificial Intelligence-Assisted Screening for Patients With Diabetic Retinopathy and Age-Related Macular Degeneration in Family Medicine and Geriatric and Gerontology Care: Protocol for a Pragmatic Randomized Clinical Trial.

BACKGROUND: Diabetic retinopathy (DR) and age-related macular degeneration (AMD) are 2 of the leading causes of vision loss worldwide. As population a...

Retinal imaging for cerebrovascular risk stratification: evidence, limitations, and practical thresholds for the ophthalmologist.

BACKGROUND: The retina shares developmental origin, microvascular anatomy, and barrier physiology with the brain, making non-invasive retinal imaging ...

Dual branch fundus deep learning network as an enhanced multi classification system for ocular disease detection via hybrid feature fusion.

Ophthalmic diagnosis relies heavily on the interpretation of fundus images to identify a range of debilitating diseases. However, the presence of mult...

A-eye: Automated 3D MRI segmentation and morphometric feature extraction for eye and orbit atlas construction.

In this study we introduce automated 3D segmentation of the healthy human adult eye and orbit from Magnetic Resonance Images, to improve ophthalmic di...

Toward autonomous robotic-assisted and microrobotic surgery.

Autonomous robotic-assisted surgery (RAS) has emerged as a promising objective in biomedical technology, further enhanced by miniaturization toward mi...

Real-time patient-specific microwave ablation zone prediction via a unified bioheat solver and MRI-informed perturbation learning.

Microwave ablation is a crucial option for liver tumors, with success hinging on generating a suitably sized ablation zone for complete tumor eradicat...

AI-driven model for early failure prediction after HIFU integrating immediate post-ablation ultrasound and clinical data.

PURPOSE: High-Intensity Focused Ultrasound (HIFU) is an emerging focal therapy for localized prostate cancer, offering an alternative to radical prost...

The role of artificial intelligence in academic citation: A study on lens, cataract, and anterior segment research.

PURPOSE: To evaluate the accuracy and reliability of four artificial intelligence (AI) models-ChatGPT, Copilot, DeepSeek, and Gemini-in generating Pub...

Real-world integration of an autonomous artificial intelligence system for diabetic retinopathy screening in an endocrinology outpatient clinic.

AIM: To evaluate a real-world clinical integration of an autonomous artificial intelligence (AI) system (AEYE Diagnostic Screening (AEYE-DS), AEYE Hea...

Bacterial keratitis: a global review of current practices, challenges and innovations.

Bacterial keratitis is a major cause of corneal blindness worldwide, with marked differences in clinical presentation and risk factors across regions....

Diagnostic Accuracy of Artificial Intelligence-based Automated Detection of Diabetic Retinopathy in Routine Care Using Nonmydriatic Fundus Images: The Silesia Diabetes-Heart Project.

PURPOSE: To evaluate the diagnostic performance of a regulatory-approved (CE-marked) artificial intelligence system (RetCAD) applied to nonmydriatic c...

An attention-guided multimodal deep learning framework by integrating CT-PET imaging and clinical data for lung cancer detection.

The proposed multi-modal deep learning system for lung cancer diagnosis and characterisation uses structural (CT), functional (PET), and clinical (EHR...

Temporal deep learning for 28-day mortality prediction in critically ill ovarian cancer patients: a multicenter development and validation study using hourly vital signs.

BACKGROUND: Ovarian cancer patients requiring intensive care unit (ICU) admission face particularly grave prognosis, yet current prognostic models rel...

Cross-attention-based multimodal fusion for early diabetic retinopathy detection.

Early detection of diabetic retinopathy (DR) is crucial for preventing irreversible vision loss; however, existing automated methods often rely on sin...

Tumor ablation: emerging uses, challenges, and strategic implementation. A green paper by the Network of Expertise in Cancer (JANE-2), High Tech Medical Resources, network on Physical Methods of Tumor Ablation.

The field of oncology has witnessed remarkable progress with the integration of high-tech innovations in tumor ablation. Tumor ablation therapies, suc...

Hybrid deep learning for mental workload classification using EEG with enhanced preprocessing and interpretability.

Mental workload classification is critical in safety-sensitive fields such as healthcare and aviation. However, electroencephalography-based approache...

Performance of multimodal Large Language Models in misdiagnosed dermatologic cases: a pilot study on diagnostic accuracy and human error replication.

BACKGROUND: Multimodal Large Language Models (LLMs) are increasingly positioned as diagnostic assistants in dermatology. However, current research oft...