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Abstract: Automatic CAD-RADS Scoring using Deep Learning [chapter]

Felix Denzinger, Michael Wels, Katharina Breininger, Mehmet A. Gülsün, Max Schöbinger, Florian André, Sebastian Buß, Johannes Görich, Michael Sühling, Andreas Maier
2021 Informatik aktuell  
The CAD-RADS score is determined by manual assessment of all coronary vessels and the grading of lesions within the coronary artery tree.  ...  On the task of identifying patients with a CAD-RADS score indicating the need for further invasive investigation our approach reaches an area under curve (AUC) of 0.923 and an AUC of 0.914 for determining  ...  The CAD-RADS score is determined by manual assessment of all coronary vessels and the grading of lesions within the coronary artery tree.  ... 
doi:10.1007/978-3-658-33198-6_24 fatcat:vq2fhbxuh5cmddr4ssxt62wy3u

A Computer-Aided Diagnosis System and Thyroid Imaging Reporting and Data System for Dual Validation of Ultrasound-Guided Fine-Needle Aspiration of Indeterminate Thyroid Nodules

Xiaowen Liang, Yingmin Huang, Yongyi Cai, Jianyi Liao, Zhiyi Chen
2021 Frontiers in Oncology  
The CAD system based on deep learning had better accuracy and feasibility for the diagnosis of thyroid nodules, and was useful to avoid unnecessary FNA.  ...  The aim of this study was to investigate the efficiency of the AI-Sonic CAD system with the use of a deep learning algorithm to improve the diagnostic accuracy of ultrasound-guided fine-needle aspiration  ...  AI-SONIC is a fully automatic diagnosis system based on deep learning, which includes a training set consisting of more than 60,000 US images of the thyroid.  ... 
doi:10.3389/fonc.2021.611436 pmid:34692466 pmcid:PMC8529148 fatcat:pmq7gk47v5gwvpqefdgo4cphry

Deep learning-based computer-aided diagnosis in screening breast ultrasound to reduce false-positive diagnoses

Soo -Yeon Kim, Yunhee Choi, Eun -Kyung Kim, Boo-Kyung Han, Jung Hyun Yoon, Ji Soo Choi, Jung Min Chang
2021 Scientific Reports  
This study aimed to develop a deep learning-based computer-aided diagnosis (DL-CAD)-based diagnostic model to improve the differential diagnosis of screening US-detected breast masses and reduce false-positive  ...  Among the quantitative morphologic features extracted from DL-CAD, a higher irregular shape score (P = .018) and lower parallel orientation score (P = .007) were associated with malignancy.  ...  In these studies, the diagnostic performances of the commercial DL-CAD software were tested without using the original DL-CAD-extracted quantitative morphologic scores that was used in this study.  ... 
doi:10.1038/s41598-020-79880-0 pmid:33432076 fatcat:his732xx5fhmbgpcvnratyfofa

Deep Learning Predicts Cardiovascular Disease Risks from Lung Cancer Screening Low Dose Computed Tomography [article]

Hanqing Chao, Hongming Shan, Fatemeh Homayounieh, Ramandeep Singh, Ruhani Doda Khera, Hengtao Guo, Timothy Su, Ge Wang, Mannudeep K. Kalra, Pingkun Yan
2020 arXiv   pre-print
Our deep model was further calibrated against the clinical gold standard CVD risk scores from ECG-gated dedicated cardiac CT, including coronary artery calcification (CAC) score, CAD-RADS score and MESA  ...  In this validation study, our model achieved AUC of 0.942, 0.809 and 0.817 for CAC, CAD-RADS and MESA scores, respectively.  ...  , CAD-RADS score 4 and MESA 10-year CHD risk score 5 .  ... 
arXiv:2008.06997v1 fatcat:ojelmuunbrfsjpdxuhfh2y4hq4

Effect of a Deep Learning Framework-Based Computer-Aided Diagnosis System on the Diagnostic Performance of Radiologists in Differentiating between Malignant and Benign Masses on Breast Ultrasonography

Ji Soo Choi, Boo-Kyung Han, Eun Sook Ko, Jung Min Bae, Eun Young Ko, So Hee Song, Mi-ri Kwon, Jung Hee Shin, Soo Yeon Hahn
2019 Korean Journal of Radiology  
To investigate whether a computer-aided diagnosis (CAD) system based on a deep learning framework (deep learning-based CAD) improves the diagnostic performance of radiologists in differentiating between  ...  malignant and benign masses on breast ultrasound (US).  ...  Based on the given ROI, the deep learning-based CAD system automatically analyzed the US features and provided a final assessment of the mass displayed on the screen.  ... 
doi:10.3348/kjr.2018.0530 pmid:30993926 pmcid:PMC6470083 fatcat:dujdt5tdavc37h4h3e674pxkha

The correlation of deep learning-based CAD-RADS evaluated by coronary computed tomography angiography with breast arterial calcification on mammography

Zengfa Huang, Jianwei Xiao, Yuanliang Xie, Yun Hu, Shutong Zhang, Xiang Li, Zheng Wang, Zuoqin Li, Xiang Wang
2020 Scientific Reports  
CAD-RADS was scored based on Deep Learning (DL). Mammograms were assessed visually for the presence of BAC. A total of 213 patients were included in the analysis.  ...  BAC may be used as an additional diagnostic tool to predict the severity of CAD in this population.  ...  A previous study demonstrated that automatic calculating CAD-RADS score using structured reporting platform might play an important role in improving data quality and supporting standardization of clinical  ... 
doi:10.1038/s41598-020-68378-4 pmid:32661231 fatcat:x6bocg6spjcybd3jna62awt5nm

A deep learning pipeline for localization, differentiation, and uncertainty estimation of liver lesions using multi-phasic and multi-sequence MRI [article]

Peng Wang, Yuhsuan Wu, Bolin Lai, Xiao-Yun Zhou, Le Lu, Wendi Liu, Huabang Zhou, Lingyun Huang, Jing Xiao, Adam P. Harrison, Ningyang Jia, Heping Hu
2021 arXiv   pre-print
We propose a fully-automatic deep CAD pipeline that localizes lesions from 3D MRI studies using key-slice parsing and provides a confidence measure for its diagnoses.  ...  Objectives: to propose a fully-automatic computer-aided diagnosis (CAD) solution for liver lesion characterization, with uncertainty estimation.  ...  Deep Learning Model for Lesion Characterization After localization, we use a deep 2D Densent121 [17] CNN to classify ROIs into three types: HCC, ICC, or metastasis.  ... 
arXiv:2110.08817v1 fatcat:c54xlq7f5nbo5ptu5golhq6gg4

Application of machine learning and deep learning to thyroid imaging: where do we stand?

Eun Ju Ha, Jung Hwan Baek
2020 Ultrasonography  
Artificial intelligence (AI)-based computer-aided diagnosis (CAD) systems have been introduced to help with the accurate and consistent interpretation of US features, ultimately leading to a decrease in  ...  This review provides a developmental overview of the current AI-based CAD system used for thyroid nodules and describes the future developmental direction of this system for the personalized and optimized  ...  Artificial intelligence (AI)-based computer-aided diagnosis (CAD) systems, based on machine learning (ML) and deep learning (DL) techniques, have been introduced for thyroid cancer diagnosis Ultrasonography  ... 
doi:10.14366/usg.20068 pmid:32660203 pmcid:PMC7758100 fatcat:orm4e3pdpngrldi7txo4bxgfku

Machine Learning and Deep Neural Networks Applications in Computed Tomography for Coronary Artery Disease and Myocardial Perfusion

Caterina B. Monti, Marina Codari, Marly van Assen, Carlo N. De Cecco, Rozemarijn Vliegenthart
2020 Journal of thoracic imaging  
In fact, ML allows the efficient handling of big volumes of data, allowing to tackle issues that were unfeasible before, especially with deep learning, which utilizes multilayered neural networks.  ...  Moreover, cardiac CT presents some fields wherein ML may be pivotal, such as coronary calcium scoring, CT angiography, and perfusion.  ...  Deep learning represents a technique of ML that exploits artificial neural networks.  ... 
doi:10.1097/rti.0000000000000490 pmid:32195886 fatcat:unh46rzy6fa7hiq4beppuvdkfq

CAD-RADS Scoring using Deep Learning and Task-Specific Centerline Labeling [article]

Felix Denzinger, Michael Wels, Oliver Taubmann, Mehmet A. Gülsün, Max Schöbinger, Florian André, Sebastian J. Buss, Johannes Görich, Michael Sühling, Andreas Maier, Katharina Breininger
2022 arXiv   pre-print
In this work, we reach new state-of-the-art performance for automatic CAD-RADS scoring.  ...  We propose using severity-based label encoding, test time augmentation (TTA) and model ensembling for a task-specific deep learning architecture.  ...  Conclusion In this paper, we improve the automatic deep learning-based assessment of patients regarding the CAD-RADS score.  ... 
arXiv:2202.03671v1 fatcat:aecpt3zyxfdezftxf7obqf35xa

Artificial Intelligence in Coronary Computed Tomography Angiography: From Anatomy to Prognosis

Giuseppe Muscogiuri, Marly Van Assen, Christian Tesche, Carlo N. De Cecco, Mattia Chiesa, Stefano Scafuri, Marco Guglielmo, Andrea Baggiano, Laura Fusini, Andrea I. Guaricci, Mark G. Rabbat, Gianluca Pontone (+1 others)
2020 BioMed Research International  
Cardiac computed tomography angiography (CCTA) is widely used as a diagnostic tool for evaluation of coronary artery disease (CAD).  ...  Furthermore, in comparison with existing risk scores, machine-learning algorithms can better predict the outcome utilizing both imaging findings and clinical parameters.  ...  The authors classified CAD-RADS using a deep learning algorithm and subsequently correlated the results with the presence of arterial breast calcification.  ... 
doi:10.1155/2020/6649410 pmid:33381570 pmcid:PMC7762640 fatcat:tjkwakhp6zaa3mmldwc2zm2lc4

Deep learning predicts cardiovascular disease risks from lung cancer screening low dose computed tomography

Hanqing Chao, Hongming Shan, Fatemeh Homayounieh, Ramandeep Singh, Ruhani Doda Khera, Hengtao Guo, Timothy Su, Ge Wang, Mannudeep K. Kalra, Pingkun Yan
2021 Nature Communications  
We validate our model against ECG-gated cardiac CT based markers, including coronary artery calcification (CAC) score, CAD-RADS score, and MESA 10-year risk score from an independent dataset of 335 subjects  ...  Our work shows that, in high-risk patients, deep learning can convert LDCT for lung cancer screening into a dual-screening quantitative tool for CVD risk estimation.  ...  Code availability The code used for training the models and the parameters of the pretrained deep learning networks has been made publicly available 53 at  ... 
doi:10.1038/s41467-021-23235-4 pmid:34017001 fatcat:cwi6j4vpu5fvjf3qqajxtine6u

Multiparametric MRI and Radiomics in Prostate Cancer: A Review of the Current Literature

Federico Midiri, Federica Vernuccio, Pierpaolo Purpura, Pierpaolo Alongi, Tommaso Vincenzo Bartolotta
2021 Diagnostics  
Multiparametric MRI (mp-MRI) has high sensitivity and specificity in the detection of PCa, and it is currently the most widely used imaging technique for tumor localization and cancer staging. mp-MRI plays  ...  Deep learning networks for automated prostate segmentation through CAD using atlas-based and model-based methods could facilitate the radiomic analysis of the tissue and reduce the time needed for segmentation  ...  In the last few years, deep learning has been applied to prostate cancer with promising results, although it is not yet used in the clinical routine.  ... 
doi:10.3390/diagnostics11101829 pmid:34679527 fatcat:5f4mtvpe6zbj3fjifb2gn5r7nm

Thyroid Nodule Classification in Ultrasound Images by Fine-Tuning Deep Convolutional Neural Network

Jianning Chi, Ekta Walia, Paul Babyn, Jimmy Wang, Gary Groot, Mark Eramian
2017 Journal of digital imaging  
This paper presents a computer-aided diagnosis (CAD) system for classifying thyroid nodules in ultrasound images. We use deep learning approach to extract features from thyroid ultrasound images.  ...  A pre-trained GoogLeNet model is then fine-tuned using the pre-processed image samples which leads to superior feature extraction.  ...  Therefore, an automatic or semi-automatic classification system based on image features would be possible and very helpful to report TI-RADS scores and classify the thyroid nodule ultrasound images.  ... 
doi:10.1007/s10278-017-9997-y pmid:28695342 pmcid:PMC5537102 fatcat:ulxpbj3zx5eslgtfx7yjvd5bse

Two-Stage Deep Learning Method for Breast Cancer Detection Using High-Resolution Mammogram Images

Bunyodbek Ibrokhimov, Justin-Youngwook Kang
2022 Applied Sciences  
In this work, we propose a new two-stage deep learning method.  ...  In the second stage, breast masses are detected and classified into BI-RADS categories.  ...  Conclusions In this paper, we propose a two-stage deep learning method for breast cancer detection using high-resolution mammograms.  ... 
doi:10.3390/app12094616 fatcat:ujjxg7gwgvhp3huipg6tpc3k5e
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