Deep learning technology for improving cancer care in society: New directions in cancer imaging driven by artificial intelligence |
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| Affiliation: | 1. Department of Health Research Methods, Evidence and Impact, McMaster University, Hamilton, Ontario, Canada;2. Division of Thoracic Surgery, Department of Surgery, St Joseph’s Healthcare Hamilton, Hamilton, Ontario, Canada;3. NeuralSeg Ltd, Hamilton, Ontario, Canada;4. Division of Gastroenterology and Farncombe Family Digestive Health Research Institute, Department of Medicine, McMaster University, Hamilton, Ontario, Canada;1. Odette Cancer Program, Sunnybrook Health Sciences Centre, Toronto, Canada;2. Faculty of Medicine, Department Radiation Oncology, University of Toronto, Toronto, Canada;3. Faculty of Health and Wellbeing, Sheffield Hallam University, Sheffield, United Kingdom;4. Radiogenomics Laboratory, Sunnybrook Health Sciences Centre, Toronto, Canada;5. Division of Medical Oncology, Sunnybrook Health Sciences Centre, Toronto, Canada;6. Faculty of Medicine, Department of Medicine, University of Toronto, Toronto, Canada;7. Anatomic Pathology, Sunnybrook Health Sciences Centre, Toronto, Canada;8. Department of Radiation Oncology, Albert Einstein College of Medicine, Bronx, USA;9. Instituto de Fisica, Universidad Nacional Autónoma de México, Mexico City, Mexico;10. Department of Electrical Engineering and Computer Science, Lassonde School of Engineering, York University, Toronto, Canada;1. Department of Medical Imaging, Sunnybrook Health Sciences Centre, University of Toronto, Canada;2. Department of Radiology, Stanford University, CA, USA;1. CNR – National Research Council of Italy, Italy;2. Arizona State University, USA;1. Department of Gastroenterology, Cancer Institute Hospital, Japanese Foundation for Cancer Research, Tokyo, Japan;2. Division of Gastroenterology and Hepatology, Department of Internal Medicine, Toho University Ohashi Medical Center, Tokyo, Japan;3. Tada Tomohiro Institute of Gastroenterology and Proctology, Saitama, Japan;4. AI Medical Service Inc, Tokyo, Japan;5. Surgery Department, Sanno Hospital, International University of Health and Welfare, Tokyo, Japan;6. Department of Digestive Tract and General Surgery, Saitama Medical Center, Saitama Medical University, Saitama, Japan;7. Department of Gastroenterology, Graduate School of Medicine, The University of Tokyo, Tokyo, Japan;8. Department of Surgical Oncology, Graduate School of Medicine, The University of Tokyo, Tokyo, Japan |
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| Abstract: | The goal of this study is to show emerging applications of deep learning technology in cancer imaging. Deep learning technology is a family of computational methods that allow an algorithm to program itself by learning from a large set of examples that demonstrate the desired behavior. Applications of deep learning technology to cancer imaging can assist pathologists in the detection and classification of cancer in the early stages of its development to allow patients to have appropriate treatments that can increase their survival. Statistical analyses and other analytical approaches, based on data of ScienceDirect (a source for scientific research), suggest that the sharp increase of the studies of deep learning technology in cancer imaging seems to be driven by high rates of mortality of some types of cancer (e.g., lung and breast) in order to solve consequential problems of a more accurate detection and characterization of cancer types to apply efficient anti-cancer therapies. Moreover, this study also shows sources of the trajectories of deep learning technology in cancer imaging at level of scientific subject areas, universities and countries with the highest scientific production in these research fields. This new technology, in accordance with Amara's law, can generate a shift of technological paradigm for diagnostic assessment of any cancer type and disease. This new technology can also generate socioeconomic benefits for poor regions because they can send digital images to labs of other developed regions to have diagnosis of cancer types, reducing as far as possible current gap in healthcare sector among different regions. |
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| Keywords: | Deep learning Cancer imaging Artificial intelligence Lung cancer Breast cancer Technological paradigm Amara's law Gartner hype cycle Emerging technology New technology O32 O33 |
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