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Related Concept Videos

Glaucoma: Overview01:25

Glaucoma: Overview

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Glaucoma is an eye condition characterized by increased intraocular pressure that damages the retina and optic nerve, leading to irreversible blindness if left untreated. The human eye has various components, including the cornea, iris, pupil, lens, and optic nerve. Aqueous humor is secreted by the epithelium of the ciliary body in the posterior chamber and flows through the trabecular meshwork and canal of Schlemm, maintaining normal intraocular pressure. The trabecular meshwork and the canal...
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Open Angle Glaucoma: Treatment01:27

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In open-angle glaucoma, the iridocorneal angle remains open, but the trabecular meshwork becomes stiff, slowing down the outflow of aqueous humor. This causes a buildup of aqueous humor in the anterior chamber, leading to a sudden increase in intraocular pressure. The treatment for open-angle glaucoma focuses on reducing the elevated intraocular pressure by either decreasing the secretion of aqueous humor or increasing its outflow.
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Angle Closure Glaucoma: Treatment01:28

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Angle-closure glaucoma, or closed-angle glaucoma, is an eye condition where the iris bulges out and blocks the iridocorneal angle, resulting in a buildup of aqueous humor and increased intraocular pressure. Immediate medical attention is necessary due to the sudden onset of symptoms. The treatment for angle-closure glaucoma includes short-term and long-term approaches. Short-term treatment involves using eye drops like pilocarpine to lower intraocular pressure by increasing aqueous humor...
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Base complementarity between the three base pairs of mRNA codon and the tRNA anticodon is not a failsafe mechanism. Inaccuracies can range from a single mismatch to no correct base pairing at all. The free energy difference between the correct and nearly correct base pairs can be as small as 3 kcal/ mol. With complementarity being the only proofreading step, the estimated error frequency would be one wrong amino acid in every 100 amino acids incorporated. However, error frequencies observed in...
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Related Experiment Video

Updated: Sep 28, 2025

Predicting Treatment Response to Image-Guided Therapies Using Machine Learning: An Example for Trans-Arterial Treatment of Hepatocellular Carcinoma
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Predicting Glaucoma Progression Requiring Surgery Using Clinical Free-Text Notes and Transfer Learning With

Wendeng Hu1, Sophia Y Wang1

  • 1Byers Eye Institute, Department of Ophthalmology, Stanford University School of Medicine, Palo Alto, CA, USA.

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Summary

Massive transformer models accurately predict glaucoma surgery needs from clinical notes. These artificial intelligence tools outperform human review, aiding in personalized glaucoma treatment strategies.

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Area of Science:

  • Ophthalmology
  • Artificial Intelligence
  • Natural Language Processing

Background:

  • Glaucoma progression requiring surgery is a significant clinical challenge.
  • Electronic health records (EHRs) contain vast amounts of unstructured clinical information.
  • Predicting glaucoma surgery needs aids in timely and tailored patient management.

Purpose of the Study:

  • To evaluate transformer-based language models for predicting glaucoma progression requiring surgery.
  • To leverage ophthalmology clinical notes from EHRs for predictive modeling.
  • To assess the performance of Bidirectional Encoder Representations from Transformers (BERT)-based models in this task.

Main Methods:

  • Utilized clinical notes from 4512 glaucoma patients (2008-2020).
  • Fine-tuned four pre-trained BERT-based models on early follow-up notes (120 days).
  • Evaluated models using area under the receiver operating characteristic curve (AUROC) and F1 score.

Main Results:

  • The original BERT model achieved the highest AUROC (73.4%) and F1 score (45.0%).
  • All evaluated models demonstrated superior F1 scores compared to manual ophthalmologist review (29.9%).
  • RoBERTa, DistilBERT, and BioBERT also showed strong predictive capabilities.

Conclusions:

  • Massively pre-trained BERT models effectively utilize clinical notes for glaucoma progression prediction.
  • Transfer learning offers a powerful approach for extracting clinical insights from EHRs.
  • Future research should explore integrating structured data and domain-specific model adaptations.