Artificial-Intelligence Diagnostic


Artificial-Intelligence Diagnostic


AI can automatically diagnose enormous amount of CT and other medical images. Thus, by detecting abnormalities, AI can contribute to reduction of radiologists' workload.









Overview

In recent years, the need for diagnosis using medical images from CT and MRI has been increasing. However, in medical fields around the world including Japan, there is a shortage of radiologists who are image diagnostic specialists, despite the prevalence of the machines. Due to this, depending on the region, the difficulty in quickly providing advanced image diagnosis has become a social issue. Also, with recent sophistication in diagnostic imaging equipment, which has rapidly increased the number of images that can be taken in a single scan, the growing diagnostic workload of radiologists has become an issue.

On the other hand, image diagnosis is extremely effective for early detection of serious diseases such as cancer, heart disease and cerebrovascular disease. It is an indispensable diagnostic method in improved preventive medicine. Therefore, the meaning of streamlining the workflow from producing images with the machines to diagnosing the images and creating an AI solution which can support high quality image diagnosis by radiologists, making it penetrate the medical field, is extremely significant.

Context/Issues

  • With performance improvement of diagnostic imaging equipment such as CT and MRI, radiologists must carefully diagnose thousands of images taken from a single scan. This increases the burden in terms of time spent to create a diagnosis report for each patient.

  • There is a demand for definite detection of abnormalities from thousands of images.

Effects

  • For detecting abnormalities in kidneys - Through continuous improvements in the future, it is expected that the diagnostic workload of radiologists will be further reduced, and to have the effect of supporting high quality image diagnosis.

  • Because it can detect various abnormalities, not limited to cancer, it is highly effective in preventing the overlooking of lesions through human error.

Services to appear in this article
AI image diagnostic support solution

Through analysis of a patient's medical images with AI technology, possible disease areas are shown on a screen of a PACS system which is used for diagnosis, supporting accurate diagnoses by doctors. By providing AI diagnostic support information through interfaces used in the existing diagnostic processes, smooth collaboration between doctors and AI can be expected.



Special features

  • Can detect not only specific diseases but also various abnormalities in organs.

  • No reliance on CT imaging conditions such as differences in CT machine manufacturers and presence of contrast agents.

  • Can be introduced without major changes in existing hospital systems and workflow.



From these points, in addition to quick and accurate image diagnosis for highly urgent conditions, it can be expected to reduce the burden of radiologists and support diagnosis, even in preventive medicine including health checks.

Example of AI image diagnostic support solution

Example of AI image diagnostic support solution

Background and issues
Demand from radiologists for diagnostic support system

Recently, at University of Miyazaki Hospital, there has been a sudden increase in the number of image diagnostic cases, regardless of department. Also, with progress in diagnostic imaging equipment, there has been a great increase in the number of images taken in a single scan compared with the past.

Dr. Minako Azuma at Department of Radiology comments, "Because the CT images are reconstructed as easy-to-see 3D images, the number of image data that we radiologists have to diagnose is huge. Due to this, I feel that the time burden on doctors who have to create image diagnosis reports for each patient has greatly increased from the past."

Due to such actual conditions of image diagnosis, it is suggested that there is a potential risk of overlooking lesions through human error. Also, if a lesion unrelated to the main purpose of test appears on the images, radiologists must also discover it quickly and identify its nature and type.

"I often wondered if we could use AI to create a system that can detect lesions in images from a wider viewpoint, and prevent the slightest possibility of overlooking in advance." In order to realize Dr. Azuma's vision, the hospital looked into the possibility of image diagnostic support using cutting edge digital technology.

Dr. Minako Azuma University of Miyazaki Hospital

Dr. Minako Azuma
University of Miyazaki Hospital

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