End-to-end lung cancer screening with three-dimensional deep learning on low-dose chest computed tomography

By Diego Ardila, A. Kiraly, Sujeeth Bharadwaj, Bokyung Choi, J. Reicher, Lily H. Peng, Daniel Tse, M. Etemadi, Wenxing Ye, Greg S. Corrado and 2 more. Nature Medicine, 2019

1,861 citations recorded by the source corpus.

Subfields: Pulmonary and Respiratory Medicine, Radiology, Nuclear Medicine and Imaging

View Paper: https://www.semanticscholar.org/paper/45aa47293d70bbed4d93725ed9858bf6dfcd81a1

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In Pulmonary and Respiratory Medicine

In Radiology, Nuclear Medicine and Imaging

Citations in the corpus

The Emergence of AI-Based Wearable Sensors for Digital Health Technology: A Review (2023, methodology): “…lab-based diagnostic tools for disease diagnostics, including precision screening of prostate cancer [203], breast cancer [204], lung cancer [205], brain lesion [206], coronary artery disease [207], diabetes [208], esophageal adenocarcinoma [209], COVID-19 [210], heart failure [211], skin…”
Harnessing Explanations to Bridge AI and Humans (2020, background): “Model performance has begun to meet or exceed expert human performance in numerous areas such as recidivism prediction [8] and medical diagnosis [1].”
Harnessing multimodal data integration to advance precision oncology (2021, methodology): “For example, RetinaNet, an object detection CNN, has been used to localize lung nodules on CT, enabling use of 42,290 CT cases for training 97 .”
Deep learning-based model for detecting 2019 novel coronavirus pneumonia on high-resolution computed tomography: a prospective study (2020, methodology): “4%.[27] 289 Chae KJ, et al successfully used the convolutional neural network to classify small (≤2 cm) 290 pulmonary nodules on CT scan images.”
BEHRT: Transformer for Electronic Health Records (2019, methodology): “For instance, [1] introduced a DL model that can predict the risk of lung cancer from a patient’s tomography images with a striking 94.”
Predicting Deep Learning Based Multi-Omics Parallel Integration Survival Subtypes in Lung Cancer Using Reverse Phase Protein Array Data (2020, result): “In other words, one type of data may not be able to appropriately describe all the characteristics of tumor even though some of the reported analyses showed achievements [9,10].”
Blockchain-Federated-Learning and Deep Learning Models for COVID-19 Detection Using CT Imaging (2020, background): “markable results for lung cancer screening in 2019 [59].”
Predicting cancer outcomes with radiomics and artificial intelligence in radiology (2021, background): “In several high-profile studies, CNN-based models have even surpassed the performance of expert human readers in interpreting chest radiography 84 and CT 85 , and digital mammography 6,86 .”
How Machine Learning will Transform Biomedicine (2020, neutral): “Machine learning systems will have a key role by automating routine diagnosis, flagging challenging cases that require more human input, and providing additional information useful in making diagnoses (e.g., (Ardila et al., 2019)).”