Dataset Information

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

ABSTRACT: 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.

SUBMITTER: Shi Z

PROVIDER: S-EPMC7705757 | biostudies-literature | 2020 Nov

REPOSITORIES: biostudies-literature

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

Shi Zhao Z Miao Chongchang C Schoepf U Joseph UJ Savage Rock H RH Dargis Danielle M DM Pan Chengwei C Chai Xue X Li Xiu Li XL Xia Shuang S Zhang Xin X Gu Yan Y Zhang Yonggang Y Hu Bin B Xu Wenda W Zhou Changsheng C Luo Song S Wang Hao H Mao Li L Liang Kongming K Wen Lili L Zhou Longjiang L Yu Yizhou Y Lu Guang Ming GM Zhang Long Jiang LJ

Nature communications 20201130 1

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 consecut ...[more]

PMID: 33257700

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Project description:BackgroundAs infundibular dilation (ID) is less likely to cause hemorrhage or other clinical sequelae than an intracranial aneurysm (IA) and treating infundibulum itself may put the patient at unnecessary risk for stroke, it is important to distinguish between the ID and IA. Given the limitations of conventional single-phase computed tomography angiography (sCTA) to show small branches of intracranial arteries, the application of multiphase computed tomography angiography (mCTA) for identification seems promising. Our main objective was to evaluate whether using mCTA derived from computed tomography perfusion (CTP) data can improve distinction between IA and ID.MethodsA total of 35 patients diagnosed with IA or ID of the posterior communicating artery at its junction with the internal carotid artery junction (ICA-PComA) by sCTA at the 8th Medical Center of Chinese PLA General Hospital between January 2019 and May 2022 were retrospectively selected. All patients underwent CTP. The simulated mCTA was reconstructed from 0.75 mm CTP data for assessment of vascular branches. All data were processed separately by 2 CTA post-processors; 2 observers diagnosed IA and ID by source and volume rendering (VR) images of sCTA and VR images of mCTA, and compared the diagnostic efficacy of source and VR images of sCTA with VR images of mCTA.ResultsThe quality of the reconstructed images was more consistent between the 2 post-processors mCTA (K=0.856) than sCTA (K=0.648). The sensitivity, specificity, positive predictive value (PPV), negative predictive value (NPV), and accuracy of the source image for ID identification were 78.9%, 86.7%, 84.2%, 81.3%, and 80.0% for sCTA, 73.7%, 81.2%, 82.3%, 72.2%, and 77.2% for the VR image of sCTA, and 94.7%, 87.5%, 90.0%, 93.3%, and 91.4% for the VR image based on mCTA, respectively. The net reclassification index (NRI) of mCTA for VR and the source image of sCTA was 0.273 and 0.220, respectively. VR base on mCTA was on average better than VR and the source image of sCTA at differentiating ID from IA (P=0.005 and P=0.001, respectively).ConclusionsCompared to sCTA, mCTA is more helpful in improving the distinction of ID and IA.

Project description:Study designRetrospective diagnostic study.ObjectiveTo automatically detect osteolytic bone metastasis lesions in the thoracolumbar region using conventional computed tomography (CT) scans, we developed a new deep learning (DL)-based computer-aided detection model.Summary of background dataRadiographic detection of bone metastasis is often difficult, even for orthopedic surgeons and diagnostic radiologists, with a consequent risk for pathologic fracture or spinal cord injury. If we can improve detection rates, we will be able to prevent the deterioration of patients' quality of life at the end stage of cancer.Materials and methodsThis study included CT scans acquired at Tokyo Medical and Dental University (TMDU) Hospital between 2016 and 2022. A total of 263 positive CT scans that included at least one osteolytic bone metastasis lesion in the thoracolumbar spine and 172 negative CT scans without bone metastasis were collected for the datasets to train and validate the DL algorithm. As a test data set, 20 positive and 20 negative CT scans were separately collected from the training and validation datasets. To evaluate the performance of the established artificial intelligence (AI) model, sensitivity, precision, F1-score, and specificity were calculated. The clinical utility of our AI model was also evaluated through observer studies involving six orthopaedic surgeons and six radiologists.ResultsOur AI model showed a sensitivity, precision, and F1-score of 0.78, 0.68, and 0.72 (per slice) and 0.75, 0.36, and 0.48 (per lesion), respectively. The observer studies revealed that our AI model had comparable sensitivity to orthopaedic or radiology experts and improved the sensitivity and F1-score of residents.ConclusionWe developed a novel DL-based AI model for detecting osteolytic bone metastases in the thoracolumbar spine. Although further improvement in accuracy is needed, the current AI model may be applied to current clinical practice.Level of evidenceLevel III.

Project description:ObjectivesDeep-learning image reconstruction (DLIR) offers unique opportunities for reducing image noise without degrading image quality or diagnostic accuracy in coronary CT angiography (CCTA). The present study aimed at exploiting the capabilities of DLIR to reduce radiation dose and assess its impact on stenosis severity, plaque composition analysis, and plaque volume quantification.MethodsThis prospective study includes 50 patients who underwent two sequential CCTA scans at normal-dose (ND) and lower-dose (LD). ND scans were reconstructed with Adaptive Statistical Iterative Reconstruction-Veo (ASiR-V) 100%, and LD scans with DLIR. Image noise (in Hounsfield units, HU) and quantitative plaque volumes (in mm3) were assessed quantitatively. Stenosis severity was visually categorized into no stenosis (0%), stenosis (< 20%, 20-50%, 51-70%, 71-90%, 91-99%), and occlusion (100%). Plaque composition was classified as calcified, non-calcified, or mixed.ResultsReduction of radiation dose from ND scans with ASiR-V 100% to LD scans with DLIR at the highest level (DLIR-H; 1.4 mSv vs. 0.8 mSv, p < 0.001) had no impact on image noise (28 vs. 27 HU, p = 0.598). Reliability of stenosis severity and plaque composition was excellent between ND scans with ASiR-V 100% and LD scans with DLIR-H (intraclass correlation coefficients of 0.995 and 0.974, respectively). Comparison of plaque volumes using Bland-Altman analysis revealed a mean difference of - 0.8 mm3 (± 2.5 mm3) and limits of agreement between - 5.8 and + 4.1 mm3.ConclusionDLIR enables a reduction in radiation dose from CCTA by 43% without significant impact on image noise, stenosis severity, plaque composition, and quantitative plaque volume.Key points•Deep-learning image reconstruction (DLIR) enables radiation dose reduction by over 40% for coronary computed tomography angiography (CCTA). •Image noise remains unchanged between a normal-dose CCTA reconstructed by ASiR-V and a lower-dose CCTA reconstructed by DLIR. •There is no impact on the assessment of stenosis severity, plaque composition, and quantitative plaque volume between the two scans.

Dataset Information

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

Publications

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

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