Fujitsu AI retrieves similar disease cases from CT database
- 저자:Ella Cai
- 에 출시:2017-06-26
Technologies already exist to retrieve similar cases based on CT images for such diseases as early-stage lung cancer, in which abnormal shadows are concentrated in one place.
For diffuse lung diseases like pneumonia, however, in which abnormal shadows are spread throughout the organ in all directions, it has been necessary for doctors to reconfirm three-dimensional similarities, increasing the time needed to reach a conclusion.
Now Fujitsu Laboratories has developed an AI-based technology that can accurately retrieve similar cases in which abnormal shadows have spread in three dimensions.
The technology automatically separates the complex interior of the organ into areas through image analysis, and uses machine learning to recognize abnormal shadow candidates in each area.
By dividing up the organ spatially into periphery, core, top, bottom, left and right, and focusing on the spread of the abnormal shadows in each area, it becomes possible to view things in the same way doctors do when determining similarities for diagnosis.
In joint research with Professor Kazuo Awai of the Department of Diagnostic Radiology, Institute and Graduate School of Biomedical Sciences, Hiroshima University, this technology was tested using real-world data, and the result was an accuracy rate of 85% in the top five retrievals among correct answers predetermined by doctors.
This technology is expected to lead to increased efficiency in diagnostic tasks for doctors, and could reduce the time required to identify the correct diagnosis for cases in which identification previously took a great deal of time.
Going forward, Fujitsu Laboratories will conduct numerous field trials using CT images for a variety of cases, while additionally aiming to contribute to the increased efficiency of medical care by deploying this technology with related solutions from Fujitsu
For diffuse lung diseases like pneumonia, however, in which abnormal shadows are spread throughout the organ in all directions, it has been necessary for doctors to reconfirm three-dimensional similarities, increasing the time needed to reach a conclusion.
Now Fujitsu Laboratories has developed an AI-based technology that can accurately retrieve similar cases in which abnormal shadows have spread in three dimensions.
The technology automatically separates the complex interior of the organ into areas through image analysis, and uses machine learning to recognize abnormal shadow candidates in each area.
By dividing up the organ spatially into periphery, core, top, bottom, left and right, and focusing on the spread of the abnormal shadows in each area, it becomes possible to view things in the same way doctors do when determining similarities for diagnosis.
In joint research with Professor Kazuo Awai of the Department of Diagnostic Radiology, Institute and Graduate School of Biomedical Sciences, Hiroshima University, this technology was tested using real-world data, and the result was an accuracy rate of 85% in the top five retrievals among correct answers predetermined by doctors.
This technology is expected to lead to increased efficiency in diagnostic tasks for doctors, and could reduce the time required to identify the correct diagnosis for cases in which identification previously took a great deal of time.
Going forward, Fujitsu Laboratories will conduct numerous field trials using CT images for a variety of cases, while additionally aiming to contribute to the increased efficiency of medical care by deploying this technology with related solutions from Fujitsu
