AIMC Topic: Angiogenesis Inhibitors
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Improved prediction of anti-angiogenic peptides based on machine learning models and comprehensive features from peptide sequences.
Scientific reports
Jun 22, 2024
Angiogenesis is a key process for the proliferation and metastatic spread of cancer cells. Anti-angiogenic peptides (AAPs), with the capability of inhibiting angiogenesis, are promising candidates in cancer treatment. We propose AAPL, a sequence-base...
Approved AI-based fluid monitoring to identify morphological and functional treatment outcomes in neovascular age-related macular degeneration in real-world routine.
The British journal of ophthalmology
Jun 20, 2024
AIM: To predict antivascular endothelial growth factor (VEGF) treatment requirements, visual acuity and morphological outcomes in neovascular age-related macular degeneration (nAMD) using fluid quantification by artificial intelligence (AI) in a real...
Aged, 80 and over
Male
Angiogenesis Inhibitors
Treatment Outcome
Artificial Intelligence
Retrospective Studies
Female
Aged
Humans
Follow-Up Studies
Visual Acuity
Intravitreal Injections
Tomography, Optical Coherence
Vascular Endothelial Growth Factor A
Ranibizumab
Wet Macular Degeneration
Subretinal Fluid
Correlation of limited-early-response status with 12-month CST, BVA, and machine learning-quantified retinal fluid in diabetic macular oedema in routine clinical practice.
Eye (London, England)
Jun 18, 2024
BACKGROUND/OBJECTIVES: Anti-VEGF treatment response in DMO has been measured by changes in the central subfield thickness (CST) and best visual acuity (BVA) outcomes at 3 months after initial treatment, termed early or limited early response (ER/LER)...
Stack-AAgP: Computational prediction and interpretation of anti-angiogenic peptides using a meta-learning framework.
Computers in biology and medicine
Apr 9, 2024
BACKGROUND: Angiogenesis plays a vital role in the pathogenesis of several human diseases, particularly in the case of solid tumors. In the realm of cancer treatment, recent investigations into peptides with anti-angiogenic properties have yielded en...
Suitability of machine learning for atrophy and fibrosis development in neovascular age-related macular degeneration.
Acta ophthalmologica
Dec 22, 2023
PURPOSE: To assess the suitability of machine learning (ML) techniques in predicting the development of fibrosis and atrophy in patients with neovascular age-related macular degeneration (nAMD), receiving anti-VEGF treatment over a 36-month period.
Wet Macular Degeneration
Atrophy
Intravitreal Injections
Fluorescein Angiography
Ranibizumab
Male
Retrospective Studies
Angiogenesis Inhibitors
Female
Follow-Up Studies
Tomography, Optical Coherence
Aged
Humans
Visual Acuity
Fundus Oculi
Aged, 80 and over
Vascular Endothelial Growth Factor A
Machine Learning
Fibrosis
Preliminary analysis of predicting the first recurrence in patients with neovascular age-related macular degeneration using deep learning.
BMC ophthalmology
Dec 7, 2023
BACKGROUND: To predict, using deep learning, the first recurrence in patients with neovascular age-related macular degeneration (nAMD) after three monthly loading injections of intravitreal anti-vascular endothelial growth factor (anti-VEGF).
Predicting Visual Acuity Responses to Anti-VEGF Treatment in the Comparison of Age-related Macular Degeneration Treatments Trials Using Machine Learning.
Ophthalmology. Retina
Nov 24, 2023
PURPOSE: To evaluate multiple machine learning (ML) models for predicting 2-year visual acuity (VA) responses to anti-vascular endothelial growth factor (anti-VEGF) treatment in the Comparison of Age-related Macular Degeneration (AMD) Treatments Tria...
Deep-learning based automated quantification of critical optical coherence tomography features in neovascular age-related macular degeneration.
Eye (London, England)
Sep 5, 2023
PURPOSE: To validate a deep learning algorithm for automated intraretinal fluid (IRF), subretinal fluid (SRF) and neovascular pigment epithelium detachment (nPED) segmentations in neovascular age-related macular degeneration (nAMD).
Performance of retinal fluid monitoring in OCT imaging by automated deep learning versus human expert grading in neovascular AMD.
Eye (London, England)
Jun 13, 2023
PURPOSE: To evaluate the reliability of automated fluid detection in identifying retinal fluid activity in OCT scans of patients treated with anti-VEGF therapy for neovascular age-related macular degeneration by correlating human expert and automated...
KFWC: A Knowledge-Driven Deep Learning Model for Fine-grained Classification of Wet-AMD.
Computer methods and programs in biomedicine
Dec 15, 2022
BACKGROUND AND OBJECTIVES: Automated diagnosis using deep neural networks can help ophthalmologists detect the blinding eye disease wet Age-related Macular Degeneration (AMD). Wet-AMD has two similar subtypes, Neovascular AMD and Polypoidal Choroidal...
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