Morphology-aware multi-source fusion–based intracranial aneurysms rupture prediction

Ou, Chubin; Li, Caizi; Qian, Yi; Si, Weixin; Zhang, Xin; Morgan, M.; Dou, Qi; Heng, Pheng‐Ann

doi:10.1007/s00330-022-08608-7

Cited by 12 publications

(12 citation statements)

References 40 publications

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“…They also consider other risk factors such as cancer history, exposure to chemical and types of skin ( 19 ). Including meta information and multiple sources of data has been proved to be helpful for deep learning model in some previous studies ( 20 , 21 ). In this study, we incorporated meta information into neural network and have showed that it can significantly improve the skin lesion diagnostic performance.…”

Section: Discussionmentioning

confidence: 99%

A deep learning based multimodal fusion model for skin lesion diagnosis using smartphone collected clinical images and metadata

Zhou

Yang

et al. 2022

Front. Surg.

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IntroductionSkin cancer is one of the most common types of cancer. An accessible tool to the public can help screening for malign lesion. We aimed to develop a deep learning model to classify skin lesion using clinical images and meta information collected from smartphones.MethodsA deep neural network was developed with two encoders for extracting information from image data and metadata. A multimodal fusion module with intra-modality self-attention and inter-modality cross-attention was proposed to effectively combine image features and meta features. The model was trained on tested on a public dataset and compared with other state-of-the-art methods using five-fold cross-validation.ResultsIncluding metadata is shown to significantly improve a model's performance. Our model outperformed other metadata fusion methods in terms of accuracy, balanced accuracy and area under the receiver-operating characteristic curve, with an averaged value of 0.768±0.022, 0.775±0.022 and 0.947±0.007.ConclusionA deep learning model using smartphone collected images and metadata for skin lesion diagnosis was successfully developed. The proposed model showed promising performance and could be a potential tool for skin cancer screening.

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Section: Discussionmentioning

confidence: 99%

A deep learning based multimodal fusion model for skin lesion diagnosis using smartphone collected clinical images and metadata

Zhou

Yang

et al. 2022

Front. Surg.

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“…Kim et al 9 employed a neural network on 272 scans, obtaining an AUC of 0.755, underscoring the pressing need for improved risk assessment methodologies. Utilizing radiomics from 120 aneurysms, Ou et al 10 achieved an AUC of 0.787, which increased to 0.852 with additional information. Meanwhile, Tong et al 11 manually extracted features from 105 patients with 254 aneurysms, reaching an AUC of 0.849.…”

Section: Introductionmentioning

confidence: 99%

Deep shape based intracranial aneurysm rupture prediction

Choi,

Bizjak,

Park

et al. 2024

Medical Imaging 2024: Computer-Aided Diagnosis

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Intracranial aneurysms (IAs) are a common vascular pathology and are associated with a risk of rupture, an event that is often fatal. Human evaluation of IA rupture is rather subjective, therefore we aimed to develop a deep learning based rupture event prediction model from aneurysm shape information. We used 386 CTA scans with 500 IAs that were either unruptured or ruptured (250/250). The IA rupture status was computed using bottleneck layer feature vectors sourced from two deep learning models trained for the respective auxiliary tasks of cerebral vessel labeling and aneurysm isolation from its parent vessels. The two extracted feature vectors were concatenated with 20 established features (patient sex, age, aneurysm location and morphological parameters) and used to predict aneurysm rupture status using eight different machine learning models. We achieved the best AUC of 0.851 using random forest with feature selection based on Spearman's rank correlation thresholding. The rather good performance of IA rupture status classification renders the proposed approach as a promising tool for management of rupture risk in the "no treatment" paradigm of patient follow-up imaging.

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“…For rupture risk prediction, most studies employ statistical learning algorithms such as logistic regression, support vector machine, and random forest, with features such as morphology, 19 hemodynamics, 20 radiomics, 21,22 and demographics 23 as input variables. A few studies applied deep learning on images for rupture prediction 24,25 …”

Section: Introductionmentioning

confidence: 99%

“…A few studies applied deep learning on images for rupture prediction. 24,25 A formal report of aneurysm usually includes patient demographics, aneurysm morphology (size and some other derived quantities), and location. Such information is most relevant to the rupture risk of aneurysm, 26,27 which can help neurosurgeons to determine whether immediate treatment is needed.…”

Section: Introductionmentioning

confidence: 99%

A deep learning–based automatic system for intracranial aneurysms diagnosis on three‐dimensional digital subtraction angiographic images

Qian

Chong

et al. 2022

Medical Physics

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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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Morphology-aware multi-source fusion–based intracranial aneurysms rupture prediction

Cited by 12 publications

References 40 publications

A deep learning based multimodal fusion model for skin lesion diagnosis using smartphone collected clinical images and metadata

A deep learning based multimodal fusion model for skin lesion diagnosis using smartphone collected clinical images and metadata

Deep shape based intracranial aneurysm rupture prediction

A deep learning–based automatic system for intracranial aneurysms diagnosis on three‐dimensional digital subtraction angiographic images

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