Date of Presentation
5-2-2024 12:00 AM
College
Rowan-Virtua School of Osteopathic Medicine
Poster Abstract
INTRODUCTION
Deep learning is a method of artificial intelligence involving progressively layered neural networks to extrapolate patterns from data to provide predictions. Moreover, given the arduous nature required for examining CT scans for intracranial aneurysms, discovering ways to expedite this process is beneficial. The use of deep learning to evaluate CT angiograms for intracranial aneurysms has been sparsely studied. This literature review aims to determine the accuracy and reliability of deep learning to analyze CT angiograms in patients suspected to have intracranial aneurysms.
METHODS
A qualitative review of literature using PubMed, SCOPUS, and EMBASE was conducted. Inclusion criteria comprised articles using deep learning to analyze CT angiograms for detecting intracranial aneurysms. The search string was “deep learning”, CT angiogram”, and “intracranial aneurysm” with results limited from 2020-2024. After screening, eleven papers met the inclusion criteria.
RESULTS
Multiple studies demonstrated that deep learning models led to higher sensitivity when radiologists use them simultaneously. Additionally, deep learning has shown benefits in predicting future aneurysm growth based on pre-existing characteristics such as size and shape. Furthermore, intracranial aneurysms >3 mm in diameter had the highest levels of sensitivity and accuracy, while those with diameters lowest.
CONCLUSION
Equipped with deep learning models, radiologists have a superior capability to detect intracranial aneurysms on CT angiograms. Future studies should establish methods that deep learning can have to identify intracranial aneurysms <3 mm, as well as minimizing biases arising during the model training period.
Keywords
deep learning, CT angiogram, CT Angiography, intracranial aneurysm
Disciplines
Cardiovascular Diseases | Diagnosis | Health and Medical Administration | Investigative Techniques | Medicine and Health Sciences | Nervous System Diseases | Neurology | Pathological Conditions, Signs and Symptoms | Radiology | Translational Medical Research
Document Type
Poster
Included in
Cardiovascular Diseases Commons, Diagnosis Commons, Health and Medical Administration Commons, Investigative Techniques Commons, Nervous System Diseases Commons, Neurology Commons, Pathological Conditions, Signs and Symptoms Commons, Radiology Commons, Translational Medical Research Commons
Applications of Deep Learning With Detecting Intracranial Aneurysms on CT Angiograms: A Literature Review
INTRODUCTION
Deep learning is a method of artificial intelligence involving progressively layered neural networks to extrapolate patterns from data to provide predictions. Moreover, given the arduous nature required for examining CT scans for intracranial aneurysms, discovering ways to expedite this process is beneficial. The use of deep learning to evaluate CT angiograms for intracranial aneurysms has been sparsely studied. This literature review aims to determine the accuracy and reliability of deep learning to analyze CT angiograms in patients suspected to have intracranial aneurysms.
METHODS
A qualitative review of literature using PubMed, SCOPUS, and EMBASE was conducted. Inclusion criteria comprised articles using deep learning to analyze CT angiograms for detecting intracranial aneurysms. The search string was “deep learning”, CT angiogram”, and “intracranial aneurysm” with results limited from 2020-2024. After screening, eleven papers met the inclusion criteria.
RESULTS
Multiple studies demonstrated that deep learning models led to higher sensitivity when radiologists use them simultaneously. Additionally, deep learning has shown benefits in predicting future aneurysm growth based on pre-existing characteristics such as size and shape. Furthermore, intracranial aneurysms >3 mm in diameter had the highest levels of sensitivity and accuracy, while those with diameters lowest.
CONCLUSION
Equipped with deep learning models, radiologists have a superior capability to detect intracranial aneurysms on CT angiograms. Future studies should establish methods that deep learning can have to identify intracranial aneurysms <3 >mm, as well as minimizing biases arising during the model training period.