“…However, the DLM of the present study finds a considerably low number of false positives per scan (less than 1), which is unaffected by the degree of the hemorrhage [26]. This number of false-positive findings per scan is in fact lower than in other studies investigating deep learning-based detection of predominantly unruptured aneurysms on TOF-MRA (e.g., Sichtermann et al [18]), which questions their feasibility of automated detection in clinical routine. The time needed for the DLM to fully automatically segment the aneurysms is about 3 min and therefore feasible in an emergency workflow, in which the inference can be performed after acquisition and integrated with the Alongside the aforementioned studies, the DLM boosted the detection sensitivity of all three radiologists with different experience levels of neurovascular imaging for small and large aneurysms, even for reader 1 with 13 years of experience [20,25].…”
Section: Discussioncontrasting
confidence: 80%
“…Previous studies have evaluated DLMs for the detection of intracranial aneurysms on CTA [18,[20][21][22] and time-of-flight (TOF)-MRA [23][24][25] and investigated whether deep learning enhancement could increase the diagnostic performance of human readers [20,25]. In the study by Park et al, artificial intelligence assistance increased the detection sensitivity of radiologists and of a neurosurgeon (2-12 years of experience) for UIAs on CTA significantly from 83% to 89% [20].…”
Section: Discussionmentioning
confidence: 99%
“…Given that aneurysm detection on CTA proves to be challenging with misdiagnosis of aSAH eventually resulting in a poor clinical outcome, automated detection of intracranial aneurysms may be of valuable assistance to physicians [11][12][13][14]. Over the last decade, deep learning models (DLMs), in particular convolutional neural networks (CNNs), have shown great potential in performing diagnostic and analyzing tasks on medical imaging for different subspecialties [15][16][17][18]19].…”
Section: Introductionmentioning
confidence: 99%
“…Previous studies have introduced several approaches for deep learning-based detection of aneurysms on CTA [18,[20][21][22] or magnetic resonance angiography (MRA) [23][24][25], compared the accuracy of the DLM to human readers, and investigated the deep learning-augmented diagnostic performance of physicians [20,25]. However, these studies did not include patients with aSAH and focused on unruptured intracranial aneurysms (UIAs) [20,25].…”
Purpose
To evaluate whether a deep learning model (DLM) could increase the detection sensitivity of radiologists for intracranial aneurysms on CT angiography (CTA) in aneurysmal subarachnoid hemorrhage (aSAH).
Methods
Three different DLMs were trained on CTA datasets of 68 aSAH patients with 79 aneurysms with their outputs being combined applying ensemble learning (DLM-Ens). The DLM-Ens was evaluated on an independent test set of 104 aSAH patients with 126 aneuryms (mean volume 129.2 ± 185.4 mm3, 13.0% at the posterior circulation), which were determined by two radiologists and one neurosurgeon in consensus using CTA and digital subtraction angiography scans. CTA scans of the test set were then presented to three blinded radiologists (reader 1: 13, reader 2: 4, and reader 3: 3 years of experience in diagnostic neuroradiology), who assessed them individually for aneurysms. Detection sensitivities for aneurysms of the readers with and without the assistance of the DLM were compared.
Results
In the test set, the detection sensitivity of the DLM-Ens (85.7%) was comparable to the radiologists (reader 1: 91.2%, reader 2: 86.5%, and reader 3: 86.5%; Fleiss κ of 0.502). DLM-assistance significantly increased the detection sensitivity (reader 1: 97.6%, reader 2: 97.6%,and reader 3: 96.0%; overall P=.024; Fleiss κ of 0.878), especially for secondary aneurysms (88.2% of the additional aneurysms provided by the DLM).
Conclusion
Deep learning significantly improved the detection sensitivity of radiologists for aneurysms in aSAH, especially for secondary aneurysms. It therefore represents a valuable adjunct for physicians to establish an accurate diagnosis in order to optimize patient treatment.
“…However, the DLM of the present study finds a considerably low number of false positives per scan (less than 1), which is unaffected by the degree of the hemorrhage [26]. This number of false-positive findings per scan is in fact lower than in other studies investigating deep learning-based detection of predominantly unruptured aneurysms on TOF-MRA (e.g., Sichtermann et al [18]), which questions their feasibility of automated detection in clinical routine. The time needed for the DLM to fully automatically segment the aneurysms is about 3 min and therefore feasible in an emergency workflow, in which the inference can be performed after acquisition and integrated with the Alongside the aforementioned studies, the DLM boosted the detection sensitivity of all three radiologists with different experience levels of neurovascular imaging for small and large aneurysms, even for reader 1 with 13 years of experience [20,25].…”
Section: Discussioncontrasting
confidence: 80%
“…Previous studies have evaluated DLMs for the detection of intracranial aneurysms on CTA [18,[20][21][22] and time-of-flight (TOF)-MRA [23][24][25] and investigated whether deep learning enhancement could increase the diagnostic performance of human readers [20,25]. In the study by Park et al, artificial intelligence assistance increased the detection sensitivity of radiologists and of a neurosurgeon (2-12 years of experience) for UIAs on CTA significantly from 83% to 89% [20].…”
Section: Discussionmentioning
confidence: 99%
“…Given that aneurysm detection on CTA proves to be challenging with misdiagnosis of aSAH eventually resulting in a poor clinical outcome, automated detection of intracranial aneurysms may be of valuable assistance to physicians [11][12][13][14]. Over the last decade, deep learning models (DLMs), in particular convolutional neural networks (CNNs), have shown great potential in performing diagnostic and analyzing tasks on medical imaging for different subspecialties [15][16][17][18]19].…”
Section: Introductionmentioning
confidence: 99%
“…Previous studies have introduced several approaches for deep learning-based detection of aneurysms on CTA [18,[20][21][22] or magnetic resonance angiography (MRA) [23][24][25], compared the accuracy of the DLM to human readers, and investigated the deep learning-augmented diagnostic performance of physicians [20,25]. However, these studies did not include patients with aSAH and focused on unruptured intracranial aneurysms (UIAs) [20,25].…”
Purpose
To evaluate whether a deep learning model (DLM) could increase the detection sensitivity of radiologists for intracranial aneurysms on CT angiography (CTA) in aneurysmal subarachnoid hemorrhage (aSAH).
Methods
Three different DLMs were trained on CTA datasets of 68 aSAH patients with 79 aneurysms with their outputs being combined applying ensemble learning (DLM-Ens). The DLM-Ens was evaluated on an independent test set of 104 aSAH patients with 126 aneuryms (mean volume 129.2 ± 185.4 mm3, 13.0% at the posterior circulation), which were determined by two radiologists and one neurosurgeon in consensus using CTA and digital subtraction angiography scans. CTA scans of the test set were then presented to three blinded radiologists (reader 1: 13, reader 2: 4, and reader 3: 3 years of experience in diagnostic neuroradiology), who assessed them individually for aneurysms. Detection sensitivities for aneurysms of the readers with and without the assistance of the DLM were compared.
Results
In the test set, the detection sensitivity of the DLM-Ens (85.7%) was comparable to the radiologists (reader 1: 91.2%, reader 2: 86.5%, and reader 3: 86.5%; Fleiss κ of 0.502). DLM-assistance significantly increased the detection sensitivity (reader 1: 97.6%, reader 2: 97.6%,and reader 3: 96.0%; overall P=.024; Fleiss κ of 0.878), especially for secondary aneurysms (88.2% of the additional aneurysms provided by the DLM).
Conclusion
Deep learning significantly improved the detection sensitivity of radiologists for aneurysms in aSAH, especially for secondary aneurysms. It therefore represents a valuable adjunct for physicians to establish an accurate diagnosis in order to optimize patient treatment.
“…Image from [ 173 ] with permission from Elsevier Fig. 19 Aneurysm detection network proposed in [ 200 ]. Reproduced with permission from The Radiological Society of North America.…”
The use of deep learning in medical imaging has increased rapidly over the past few years, finding applications throughout the entire radiology pipeline, from improved scanner performance to automatic disease detection and diagnosis. These advancements have resulted in a wide variety of deep learning approaches being developed, solving unique challenges for various imaging modalities. This paper provides a review on these developments from a technical point of view, categorizing the different methodologies and summarizing their implementation. We provide an introduction to the design of neural networks and their training procedure, after which we take an extended look at their uses in medical imaging. We cover the different sections of the radiology pipeline, highlighting some influential works and discussing the merits and limitations of deep learning approaches compared to other traditional methods. As such, this review is intended to provide a broad yet concise overview for the interested reader, facilitating adoption and interdisciplinary research of deep learning in the field of medical imaging.
Background
Intracranial aneurysms (IAs) are a life‐threatening disease. Their rupture can lead to hemorrhagic stroke. Most studies applying deep learning for the detection of aneurysms are based on angiographic images. However, critical diagnostic information such as morphology and aneurysm location are not captured by deep learning algorithms and still require manual assessments.
Purpose
Digital subtraction angiography (DSA) is the gold standard for aneurysm diagnosis. To facilitate the fully automatic diagnosis of aneurysms, we proposed a comprehensive system for the detection, morphology measurement, and location classification of aneurysms on three‐dimensional DSA images, allowing automatic diagnosis without further human input.
Methods
The system comprised three neural networks: a network for aneurysm detection, a network for morphology measurement, and a network for aneurysm location identification. A cross‐scale dual‐path transformer module was proposed to effectively fuse local and global information to capture aneurysms of varying sizes. A multitask learning approach was also proposed to allow an accurate localization of aneurysm neck for morphology measurement.
Results
The cross‐scale dual‐path transformer module was shown to outperform other state‐of‐the‐art network architectures, improving segmentation, and classification accuracy. The detection network in our system achieved an F2 score of 0.946 (recall 93%, precision 100%), better than the winning team in the Cerebral Aneurysm Detection and Analysis challenge. The measurement network achieved a relative error of less than 10% for morphology measurement, at the same level as human operators. Perfect accuracy (100%) was achieved on aneurysm location classification.
Conclusions
We have demonstrated that a comprehensive system can automatically detect, measure morphology and report the aneurysm location of aneurysms without human intervention. This can be a potential tool for the diagnosis of IAs, improving radiologists’ performance and reducing their workload.
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