The concept of Japan Safe Radiology is summarized in Figure 1.
The first goal of Japan Safe Radiology has been the unified management of medical resources,
i.e.,
the establishment of the J-MID itself,
a national database of diagnostic imaging.
The database will be generated by collecting CT images from medical institutions all over the country.
Collected data will be shared,
analyzed and utilized as big data.
We have already started to design and develop a small-sized system using data from six medical institutions.
We expect that this system will eventually be extended to all university hospitals,
general hospitals,
and clinics in Japan.
Our goal for the distant future is to construct an environment in which the image presentation service can be used via the Cloud.
The design and development will be performed for a client's gateway system,
which can respond to every picture archiving and communication system (PACS) maker.
Figure 2 describes the J-MID schematically.
Consideration will be given to the integration of this database with other domains such as endoscopic and pathological images.
Below,
we will report the progress of the construction of the J-MID system.
In December 2017,
we established the J-MID server at Kyushu University and then established a gateway server at each of the six collaborative medical facilities (five university hospitals and one general hospital) to be connected.
These facilities were connected by a high-speed and highly secure network called the Science Information NETwork 5 (SINET 5)-virtual private network (VPN).
In March 2018,
we started sending CT images and their diagnostic report data to the J-MID server for generating the database.
In September 2018,
43,902 CT examination results and 70,093 diagnostic reports have accumulated in the J-MID.
By March 2019,
we plan to add two university hospitals and one general hospital to the J-MID system.
Although we are currently focusing only on CT,
we are now considering doing this with imaging modalities such as MRI and other X-ray images in the future.
We advanced the following six items based on the J-MID.
We describe the progress of these items at the end of each section.
①The optimal distribution of radiologists and medical equipment
The numbers of CT and MRI systems have increased more rapidly in Japan compared to other technologically advanced nations,
but the number of radiologists in Japan is inadequate.
Approximately twice the number of radiologists now employed in Japan are needed to interpret all CT and MRI findings (Table1) [1].
Medical equipment is also unevenly distributed in Japan,
and the regional gaps in radiological medical practice are large.
In order to narrow these regional gaps,
both medical equipment and radiologists should be distributed across the country more appropriately,
based on the data collected in the J-MID.
②The development of a CDS system for the best use of medical equipment
We plan to develop a clinical decision support (CDS) system for the optimal use of medical equipment.
When the CDS system is online,
a diagnostic examination that is suitable for each patient's condition will be automatically selected in clinical practice.
The guidelines for the choice of a diagnostic examination will be stored in this system.
On the occasion of an initial examination order,
a physician could call up relevant patient data from the server,
based on which a proper examination for the patient will be selected automatically.
A reduction in the number of orders of duplicated examinations,
an improvement in diagnostic accuracy,
reduced medical radiation exposure and medical expenses,
improved quality of radiologists' reports,
and overall advances in the efficiency of clinical practice are expected.
We have developed a CDS system for a limited disease set and tested the system at a small number of facilities to verify the feasibility of a CDS in Japan.
A pilot study has been started at Juntendo University Hospital for minor head injuries in children.
Another pilot study is planned for lumbar MRI examinations for lumbago at eight facilities.
③The network-type management of radiation exposure doses (the DIR)
In addition to imaging data,
radiation exposure doses will be collected in the J-MID,
with the aim of developing a system that performs automatic statistical interpretation; that is,
a dose index registry,
or DIR.
Eventually the optimization of scanning protocols and radiation exposure doses will be performed,
and improvements in medical safety will be seen.
The exposure doses for coronary CT at each national university in Japan are shown in Figures 3 and 4.
The diagnostic reference level,
which is a standard of the upper limit of radiation exposure in a CT examination,
is set at the 75th percentile of the dose distribution from a survey conducted across a broad user base in Japan.
As shown in the figures,
the exposure dose differs markedly among institutions.
The optimization of exposure doses should be considered at some institutions.
In cooperation with the Japan Network for Research and Information on Medical Exposures (J-RIME),
which is aiming to standardize radiation exposure across large-scale imaging systems,
new evidence could be obtained by the J-MID.
At this time,
the necessary hardware and software for the DIR have been introduced.
Analyses will be started immediately after approval is gained from the institutional review board (IRB) at each collaborating facility.
④The proper quantitation of medical imaging data based on the establishment of the Japan Quantitative Imaging Biomarker Alliance (J-QIBA)
An imaging biomarker is an indicator that can be used to identify a disorder noninvasively and quantitatively.
Many researchers have undertaken clinical studies of imaging biomarkers,
and various useful biomarkers have been reported globally.
However,
due to differences in medical equipment,
scanning protocols and calculation methods,
it is difficult to make direct comparisons of imaging biomarkers obtained at different institutions.
This has been an unsolved problem worldwide.
In the J-MID project,
the standardization of quantitative imaging biomarkers will be attempted based on the imaging information collected in the J-MID.
A platform for advancing accuracy and standardizing imaging biomarkers will be structured so that they do not differ among various vendors and equipment.
Currently,
as members of the Japan Quantitative Imaging Biomarker Alliance (J-QIBA),
we are attempting to standardize various imaging biomarkers related to diffusion tensor image; visualization of nerve fiber tract,
MR elastography; evaluation of liver fibrosis,
and T1 / T2 value measurement.
In the future we will expand this activity widely.
We will eventually greatly expand this standardization.
With the standardization of images,
we can build a high-quality J-MID that could enable the development of more accurate diagnostic imaging support systems using AI.
⑤The unification of diagnostic reports
Not only imaging data but also diagnostic reports have been collected,
as much as possible,
for the J-MID.
This will enable us to compare new data with a large dataset of previously obtained images and reports at individual institutions in daily practice.
By integration with the database of pathological imaging and reports,
we will be able to share rare cases,
contributing to the education of radiologists and the improvement of their diagnostic performance.
In the future,
part of this database could also be used to fine-tune the JRS registration system and to evaluate medical treatment fees and hospital evaluation systems,
for example.
We have already constructed a system delivering diagnostic CT reports from a report server at each collaborating facility to the J-MID server after the anonymization of the patient data.
We plan to construct the structured reports for the development of a diagnostic imaging support system using AI,
as described next.
⑥The clinical applications of artificial intelligence (AI)
Diagnostic imaging support using AI has been improving,
and the technology for differentiating and classifying extreme variations in imaging by deep machine learning has recently been established.
In the J-MID project,
an automatic diagnosis system based on deep learning will be constructed using accurate annotations including the type of diagnostic imaging,
the site of disease,
the final diagnosis,
and other medical data (e.g.,
findings and diagnostic information).
Various new applications or services could be developed by environmental management such as that used for big-data imaging information that has perfect anonymity.
Moreover,
this environmental management will enable the training of humans for analytics in the field of diagnostic imaging.
Appropriate image data,
teaching data,
high-performance computers,
and learning programs are needed for the development of image diagnosis systems using AI.
We have begun the development of a diagnostic imaging support system using AI.
Imaging data in the J-MID will be used for the establishment and validation of such support systems.
The overall image of J-MID and each research theme are shown in Figure5.