Artificial intelligence-based imaging analytics and lung cancer diagnostics: Considerations for health system leaders

2020 ◽  
pp. 084047042097506
Author(s):  
Amy Zarzeczny ◽  
Paul Babyn ◽  
Scott J. Adams ◽  
Justin Longo
Keyword(s):  
Artificial Intelligence ◽  
Lung Cancer ◽  
Health System ◽  
Knowledge Exchange ◽  
Healthcare Spending ◽  
Cancer Diagnostics ◽  
Financial Investment ◽  
Lung Cancer Diagnosis ◽  
System Leaders ◽  
Healthcare Innovation

Lung cancer is a leading cause of cancer death in Canada, and accurate, early diagnosis are critical to improving clinical outcomes. Artificial Intelligence (AI)-based imaging analytics are a promising healthcare innovation that aim to improve the accuracy and efficiency of lung cancer diagnosis. Maximizing their clinical potential while mitigating their risks and limitations will require focused leadership informed by interdisciplinary expertise and system-wide insight. We convened a knowledge exchange workshop with diverse Saskatchewan health system leaders and stakeholders to explore issues surrounding the use of AI in diagnostic imaging for lung cancer, including implementation opportunities, challenges, and priorities. This technology is anticipated to improve patient outcomes, reduce unnecessary healthcare spending, and increase knowledge. However, health system leaders must also address the needs for robust data, financial investment, effective communication and collaboration between healthcare sectors, privacy and data protections, and continued interdisciplinary research to achieve this technology’s potential benefits.

scholarly journals AUTOMATED DETECTION SYSTEM FOR LUNG MASSES AS A STAGE OF ARTIFICIAL INTELLIGENCE DEVELOPMENT IN THE DIAGNOSTICS OF LUNG CANCER

2018 ◽  
pp. 62-68 ◽  
Author(s):  
B. M. Moiseenko ◽  
A. A. Meldo ◽  
L. V. Utkin ◽  
I. Yu. Prokhorov ◽  
M. A. Ryabinin ◽  
...  
Keyword(s):  
Artificial Intelligence ◽  
Lung Cancer ◽  
Industrial Revolution ◽  
Detection System ◽  
Lung Nodule ◽  
Cancer Diagnostics ◽  
Digital Information ◽  
Intellectual Activity ◽  
Domestic Literature ◽  
Modern Computer

In the century of the fourth industrial revolution, there is a rapid progress of technological developments in medicine. Possibilities of collecting large amounts of digital information and the modern computer capacity growth are reasons for the increased attention to artificial intelligence (AI) and its role in the diagnostics and the prediction of diseases. In the diagnostics, AI aims to model the human intellectual activity, providing assistance to a practicing doctor in the processing of big data. Development of AI can be considered as a way for implementation and ensuring of national political and economic interests in the health care improvement. Lung cancer is on the first position of cancer incidences. This implies that the development and implementation of computed-aided systems for lung cancer diagnostic is very urgent and important. The article presents the results concerning the development of a computed-aided system for the lung nodule detection, which is based on the processing of computed tomography data. Perspectives of the AI application to the lung cancer diagnostics are discussed. There is a few information about a role of Russian developments in this area in foreign and domestic literature.

Research on CT Scan Image of Lung Cancer Based on Deep Learning Method in Artificial Intelligence Field

2020 ◽  
Vol 10 (4) ◽  
pp. 934-939
Author(s):  
Xiaochen Yi ◽  
Zongze Sun ◽  
Baolong Yu ◽  
Munan Yang ◽  
Zhuo Zhang
Keyword(s):  
Artificial Intelligence ◽  
Lung Cancer ◽  
Deep Learning ◽  
Cancer Diagnosis ◽  
Lung Nodule ◽  
Three Dimensional ◽  
Image Data ◽  
Heat Map ◽  
Lung Cancer Diagnosis ◽  
Nodule Detection

Cancer is one of the diseases with high mortality in the 21st century, and lung cancer ranks first in all cancer morbidity and mortality. In recent years, with the rise of big data and artificial intelligence, lung cancer-assisted diagnosis based on deep learning has gradually become A popular research topic. Computer-aided lung cancer diagnosis technology is mainly the process of processing and analyzing the lung image data obtained by medical instrument imaging. The process is summarized into four steps: medical image data preprocessing, lung parenchymal segmentation, lung Nodule detection and segmentation, as well as lesion diagnosis. In order to solve the problem that the two-dimensional image model is not applicable to three-dimensional images, this paper proposes a three-dimensional convolutional neural network model suitable for lung cancer diagnosis. The model consists of two parts. The first part is a three-dimensional deep nodule detection network (FCN) model, which generates a heat map of the lung nodules. We can locate the locations of those malignant nodules through the heat map. According to the heat map generated in the first part, the second part selects those malignant nodules that are likely to be large, and then fuses the features of these selected nodules into one feature vector, showing the whole lung scan. Finally, we use this feature to classify and determine whether we have lung cancer.

scholarly journals Artificial Intelligence in Lung Cancer Pathology Image Analysis

Cancers ◽  
2019 ◽  
Vol 11 (11) ◽  
pp. 1673 ◽  
Author(s):  
Shidan Wang ◽  
Donghan M. Yang ◽  
Ruichen Rong ◽  
Xiaowei Zhan ◽  
Junya Fujimoto ◽  
...  
Keyword(s):  
Artificial Intelligence ◽  
Lung Cancer ◽  
Image Analysis ◽  
Deep Learning ◽  
Digital Pathology ◽  
Diagnosis And Prognosis ◽  
Model Interpretation ◽  
Lung Cancer Diagnosis ◽  
Cancer Pathology ◽  
Pathology Image

Objective: Accurate diagnosis and prognosis are essential in lung cancer treatment selection and planning. With the rapid advance of medical imaging technology, whole slide imaging (WSI) in pathology is becoming a routine clinical procedure. An interplay of needs and challenges exists for computer-aided diagnosis based on accurate and efficient analysis of pathology images. Recently, artificial intelligence, especially deep learning, has shown great potential in pathology image analysis tasks such as tumor region identification, prognosis prediction, tumor microenvironment characterization, and metastasis detection. Materials and Methods: In this review, we aim to provide an overview of current and potential applications for AI methods in pathology image analysis, with an emphasis on lung cancer. Results: We outlined the current challenges and opportunities in lung cancer pathology image analysis, discussed the recent deep learning developments that could potentially impact digital pathology in lung cancer, and summarized the existing applications of deep learning algorithms in lung cancer diagnosis and prognosis. Discussion and Conclusion: With the advance of technology, digital pathology could have great potential impacts in lung cancer patient care. We point out some promising future directions for lung cancer pathology image analysis, including multi-task learning, transfer learning, and model interpretation.

Computer-Aided Lung Cancer Diagnosis Approaches Based on Deep Learning

2018 ◽  
Vol 30 (1) ◽  
pp. 90 ◽  
Author(s):  
Peng Zhang ◽  
Xinnan Xu ◽  
Hongwei Wang ◽  
Yuanli Feng ◽  
Haozhe Feng ◽  
...  
Keyword(s):  
Lung Cancer ◽  
Deep Learning ◽  
Cancer Diagnosis ◽  
Lung Cancer Diagnosis ◽  
Computer Aided

Lung Cancer Diagnosed by Artificial Intelligence, But It Was Actually COVID-19: A Descriptive Study

2020 ◽  
Author(s):  
Jian Zhu ◽  
Er-Ping Xi ◽  
Song Zhu ◽  
Wen-Cai Huang ◽  
Jia-Ni Zou ◽  
...  
Keyword(s):  
Artificial Intelligence ◽  
Lung Cancer ◽  
Descriptive Study

Biased Lung Cancer Risk Perceptions: Smokers are Misinformed

2018 ◽  
Vol 238 (5) ◽  
pp. 395-421 ◽  
Author(s):  
Nicolas R. Ziebarth
Keyword(s):  
Lung Cancer ◽  
Cancer Risk ◽  
Cancer Diagnosis ◽  
Mortality Risk ◽  
Risk Perceptions ◽  
Lung Cancer Risk ◽  
Lifetime Risk ◽  
Lung Cancer Diagnosis ◽  
Cancer Risk Perceptions ◽  
Overall Mortality

Abstract This paper empirically investigates biased beliefs about the risks of smoking. First, it confirms the established tendency of people to overestimate the lifetime risk of a smoker to contract lung cancer. In this paper’s survey, almost half of all respondents overestimate this risk. However, 80% underestimate lung cancer deadliness. In reality, less than one in five patients survive five years after a lung cancer diagnosis. Due to the broad underestimation of the lung cancer deadliness, the lifetime risk of a smoker to die of lung cancer is underestimated by almost half of all respondents. Smokers who do not plan to quit are significantly more likely to underestimate this overall mortality risk.

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