Deep learning for automated cerebral aneurysm detection on computed tomography images

Dai, Xilei; Huang, Libing; Qian, Yi; Xia, Shuang; Chong, Winston; Liu, Junjie; Ieva, Antonio Di; Hou, Xiaoxi; Ou, Chubin

doi:10.1007/s11548-020-02121-2

Cited by 68 publications

(45 citation statements)

References 27 publications

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“…The peculiarity of head CTA has resulted in little efforts that apply supervised learning to IA classification and detection. Because of these reasons, CAD of CTA to automatically detect IAs has been rarely reported, and only two studies exist in the literature 24,25 . The lack of a reference standard and external validation data or focusing only on non-ruptured aneurysms ≥3 mm limited the generalization and further application of their models in the clinical setting.…”

Section: Discussionmentioning

confidence: 99%

“…Another strength of our proposed model is that all CTA studies in the training and, some of the internal and external validation cohorts were demonstrated by DSA results, which guaranteed the ground truth of the cases. Although inter-grader variability is a well-known issue in many settings including IAs diagnosis in CTA 11,12,37,38 , human interpretation is still used as the reference standard in some studies [22][23][24][25] . Some structures, such as the infundibula, might otherwise be misdiagnosed as aneurysms.…”

Section: Discussionmentioning

confidence: 99%

“…Explorations on DL combined with MRA have reported decent results for IAs detection 22,23 . CTA-based CAD systems for automatically detecting IAs have been rarely reported, and only two recently published studies can be found in this field, to the best of our knowledge 24,25 . Notably, these models were trained with a small sample size, without an outside reference standard, and were not tested in different scenarios; thus, they were not adequate to be applied in real-world clinical settings and may fall into the "Artificial intelligence (AI) chasm", which can be described as divide between developing a scientifically sound algorithm and its use in any meaningful real-world applications 26 .…”

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confidence: 99%

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A clinically applicable deep-learning model for detecting intracranial aneurysm in computed tomography angiography images

et al. 2020

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Intracranial aneurysm is a common life-threatening disease. Computed tomography angiography is recommended as the standard diagnosis tool; yet, interpretation can be time-consuming and challenging. We present a specific deep-learning-based model trained on 1,177 digital subtraction angiography verified bone-removal computed tomography angiography cases. The model has good tolerance to image quality and is tested with different manufacturers. Simulated real-world studies are conducted in consecutive internal and external cohorts, in which it achieves an improved patient-level sensitivity and lesion-level sensitivity compared to that of radiologists and expert neurosurgeons. A specific cohort of suspected acute ischemic stroke is employed and it is found that 99.0% predicted-negative cases can be trusted with high confidence, leading to a potential reduction in human workload. A prospective study is warranted to determine whether the algorithm could improve patients’ care in comparison to clinicians’ assessment.

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Section: Discussionmentioning

confidence: 99%

Section: Discussionmentioning

confidence: 99%

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confidence: 99%

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A clinically applicable deep-learning model for detecting intracranial aneurysm in computed tomography angiography images

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“…Whereas, the conventional pathology lab test routine has limitations to distinguish such molecular changes. Several interesting application of Raman spectroscopy has been reported in literature, including dengue fever infection, lungs, ovarian, and nasopharyngeal cancers 11‐14 …”

Section: Introductionmentioning

confidence: 99%

Deep transfer learning based hepatitis B virus diagnosis using spectroscopic images

Ali¹,

Hassan

Saleem

et al. 2020

Int J Imaging Syst Tech

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Accurate identification of Hepatitis B virus (HBV) disease by analyzing the Raman spectroscopic images is a challenge for pathologists. To save precious human lives, an efficient technique is required with higher diagnostic accuracy at early‐stage of HBV. We proposed a novel method of HBV diagnosis using deep neural networks with the concept of transfer learning and Raman spectroscopic images. The proposed approach developed by utilizing pretrained convolutional neural networks ResNet101 by employing transfer learning on a real dataset of HBV‐infected blood plasma samples. Dataset consists of 1000 Raman images in which 600 are HBV‐infected blood plasma samples, and 400 are healthy ones. The developed model is capable to detect minute variation between infected and healthy samples and achieved enhanced performance. The proposed approach has been assessed and attained high classification accuracy, sensitivity, specificity, and AUC of 99.7%, 100%, 99.25%, and 98.7%, respectively. The proposed TL‐ResNet101 model outperformed the conventional approaches such as PCA‐SVM and PCA‐LDA and demonstrated improved accuracy more than 7%. High performance indicates that the developed TL‐ResNet101 model has potential to use for HBV diagnosis. Moreover, the developed automated approach can be extended for other disease.

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“…Explorations on DL combined with MRA have reported decent results for IA detection 23,24 . While CTA based CAD system has been rarely reported, and only two recently published studies can be found, to the best of our knowledge 25,26 . Notably, these models were trained in small sample size, without the gold standard, and were not tested in different scenarios, thus they were not adequate to apply in real-world clinical settings, which may fall into the 'AI chasm' 27 .…”

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confidence: 96%

Clinically Applicable Deep Learning for Intracranial Aneurysm Detection in Computed Tomography Angiography Images: A Comprehensive Multicohort Study

Chen

Chi

et al. 2020

Preprint

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Intracranial aneurysm is a common life-threatening disease. CTA is recommended as a standard diagnosis tool, while the interpretation is time-consuming and challenging. We presented a novel deep-learning-based framework trained on 1,177 DSA verified bone-removal CTA cases. The framework had excellent tolerance to the influence of occult cases of CTA-negative but DSA-positive aneurysms, image quality, and manufacturers. Simulated real-world studies were conducted in consecutive internal and external cohorts, achieving improved sensitivity and negative predictive value than radiologists. A specific cohort of suspected acute ischemic stroke was employed and found 96.8% predicted-negative cases can be trusted with high confidence, leading to reducing in human burden. A prospective study is warranted to determine whether the algorithm could improve patients' care in comparison to radiologists' assessment.

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Deep learning for automated cerebral aneurysm detection on computed tomography images

Cited by 68 publications

References 27 publications

A clinically applicable deep-learning model for detecting intracranial aneurysm in computed tomography angiography images

A clinically applicable deep-learning model for detecting intracranial aneurysm in computed tomography angiography images

Deep transfer learning based hepatitis B virus diagnosis using spectroscopic images

Clinically Applicable Deep Learning for Intracranial Aneurysm Detection in Computed Tomography Angiography Images: A Comprehensive Multicohort Study

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