Supporting doctors and patients with technology

Surgeries that require the removal of lesions, such as cancer surgery, place a big burden on both patients and doctors. However, incorporating advanced image recognition technology can make it possible to identify areas of resection efficiently and with high accuracy. This can help ease the time burden on doctors in preoperative simulations. Furthermore, if doctors can reduce the extent of resection, patients could also benefit because the after-effects of surgery can be reduced.

Planning for surgical procedures can be time-consuming and labor-intensive.

The world is facing a chronic shortage of doctors. According to the Institute for Health Metrics and Evaluation (IHME), there is an estimated shortage of 6.4 million doctors globally, which is likely to increase the time burden on medical care, including in the surgical field^*1.

When resection is required due to a disease such as cancer, surgeons usually do preoperative planning in order to determine the appropriate surgical procedure for the situation, to identify the location of the nerves/vessels relative to the affected area, and more. For this, surgeons would individually look through the hundreds of 2D tomographic images acquired by CT and MRI to imagine the location and form of the affected area and the positions of blood vessels. Essentially, they are spending many hours creating a 3D visual in their minds. These tasks are time consuming for surgeons.

*1 Source: Institute for Health Metrics and Evaluation (IHME), University of Washington School of Medicine

Measuring the availability of human resources for health and its relationship to universal health coverage for 204 countries and territories from 1990 to 2019: a systematic analysis for the Global Burden of Disease Study 2019 - The Lancet

A high volume of 2D images can become 3D, making it possible to identify resection areas with precision.

Listening to surgeons' needs for even better systems

――Christophe
Around 2010, 3D medical image analysis systems were uncommon in the surgical field. That’s when we invited a number of surgeons and gave a presentation about the then-current status of our system.

We had proudly considered our system to be a breakthrough solution, but we were not sure what the surgeons would think of it. However, after I finished my presentation and left the podium, one doctor came over excitedly and said, “I want to use this system as soon as possible in my hospital.”

He became the first surgeon in Europe to adopt our 3D medical image analysis system.

When the surgeon, who performs state-of-the-art surgeries, told us, “this is beneficial to not only me, but to my patients,” we felt even more confident that our solution was in the right direction. Working closely with our technicians, we are determined to keep advancing our system to support all surgeons even more.

I am not a doctor, but I believe that with technology, we can help save the future of more people.

――Christophe
Technology supports us in every aspect of society. It will play an increasingly important role in the field of medicine, in places where we do not usually see it.

I am not a doctor, so I cannot help a patient in front of me like a surgeon. However, if we can improve the efficiency of surgeries and use of time of doctors with our technology and products, I believe we can help to save the future of even more people.

And even as of today, I can say I am working with surgeons on operations, albeit from a distance.

Fujifilm began providing 3D image analysis systems in 2008. We support doctors and patients worldwide by applying to the medical field the image-processing technology that we have continued to pursue over the past 70 years. Automatic extraction from MRI data and enhanced vascular system extraction capabilities are all possible because of the data accumulated over time. We are also taking steps toward further enhancement of AI technology^*3 in 3D medical image analysis systems.

*3 Software that uses deep learning, AI technology, in its design.
The performance and accuracy of the system do not automatically change after market release.

Application of AI Technology in 3D Medical Image Analysis Systems

Deep Learning is a form of AI machine learning that uses an algorithm based on the information-processing mechanisms of the human brain. By having AI learn a large amount of data, we are able to build complex recognition algorithms. Additionally, for Fujifilm’s 3D medical image analysis system, by taking in large amounts of image data of internal organs, the system can comprehend the characteristics automatically. We are developing and operating this technology to detect and measure suspected diseases in target images and areas.

Fujifilm will continue to strive for solutions to support doctors and patients.
We will NEVER STOP taking on healthcare challenges together.

For a healthier future.
Business solutions in all areas of prevention, diagnosis, and treatment.

Since Fujifilm launched its x-ray film in 1936, we have developed a wide range of diagnostic products, including x-ray diagnostic systems, ultrasound diagnostic systems, endoscopes, MRI, and CT. We have also applied our technological expertise from producing photographic films to enter the fields of prevention and treatment by developing and manufacturing biopharmaceuticals such as antibody drugs and vaccines, as well as providing key materials essential for the development of pharmaceutical products. As a comprehensive healthcare company, we will continue to leverage our wide range of technologies and expertise to contribute to people's health and advancement of healthcare.