PFN’s deep learning-based image analysis tool indicates chest x-ray abnormalities

(The images above may differ from the actual tool)

The diagnostic assistance tool uses a model based on PFN’s unique deep learning algorithms and was trained with a large number of actual chest X-ray images paired with lung cancer diagnosis. The model analyzes the test takers’ chest X-ray images and automatically indicates abnormalities that may represent lung cancer, which is expected to allow physicians to interpret them quickly and accurately. During the trial, two physicians will interpret each chest X-ray image as recommended by Japan’s Ministry of Health, Labour and Welfare (MHLW) while they refer to the analysis results. The test data will be securely managed on NOBORI, Nobori’s cloud-based platform that allows medical institutions to store and use anonymized medical information.

PFN demonstrated its medical image analysis technology when the company ranked sixth out of 1,499 teams from around the world at Kaggle RSNA Pneumonia Detection Challenge co-hosted by Kaggle and Radiological Society of North America in 2018, in which the teams competed on accuracy to detect potential pneumonia cases from chest X-ray images.

The shortage and workload of physicians who are able to interpret numerous X-ray images each day are known issues in Japan. Although X-ray interpretation requires extensive training and experience for physicians, it is still the most common method, compared to the more expensive and lengthier alternatives. According to the Japan’s National Cancer Center statistics, lung cancer had the highest cancer mortality rate for males and second highest for females in 2018. The top cause of death in Japan in 2018 was cancer according to MHLW’s data.

投稿 Preferred Networks Uses Deep Learning to Help Kyoto Physicians Diagnose Lung Cancer from Chest X-Ray Images は Preferred Networks, Inc. に最初に表示されました。

■ Initiative for practical applications of the sebum RNA monitoring technology

Initially, Kao and PFN aim to develop a beauty counseling service committed to skin conditions by applying AI technologies, such as machine learning and deep learning,^*2 to data obtained from RNA in sebum.
In this initiative, PFN will utilize the information obtained from Kao’s sebum RNA monitoring technology to develop a highly sophisticated prediction algorithm based on PFN’s machine learning and deep learning technologies. This will enable a better understanding of the internal conditions of the skin, and also assess future risks of skin damage. Further, this will pave the way for improvement and preventive measures of skin conditions by providing personalized beauty advice or skincare, based on genetic information. Some of these features are slated to begin in 2020 on a trial basis, with further improvements implemented based on user feedback.
In addition, Kao and PFN plan to conduct joint research into technology for improving early diagnosis of intractable diseases, including Parkinson’s disease, which has been increasing amid the aging society.

■ Structure of this project

【Kao】will collect approximately 13,000 types of RNA per person, measure RNA expression levels through Kao’s sebum RNA monitoring technology. In addition, Kao will obtain data concerning skin and other health conditions.
【PFN】will apply machine learning and deep learning technologies to train and build a prediction model using the sebum RNA expression and other data that can infer the skin conditions and biologic factors within the body.

*1   Sebum RNA monitoring technology is used to isolate RNA, which reflects day-to-day changes in conditions inside the body, from sebum, and analyze it. About 13,000 types of RNA’s expression levels can be obtained from a sebum sample in a non-invasive test (less stress on the body) using an oil blotting film.

*2   Deep learning technology allows machines to automatically extract features or patterns from a large amount of data. Deep learning has significantly increased the accuracy in certain tasks such as image recognition and voice recognition.

■Expectations for this collaborative project

Michitaka Sawada, President and CEO, Kao Corporation

“Kao is aiming to provide a practical method to accurately monitor biological information by applying our sebum RNA monitoring technology. To achieve this goal, we have high expectations that collaborating with PFN will greatly improve the accuracy and speed, thanks to PFN’s considerable experience with the utilization of AI technologies in the bio-healthcare field. Kao is focusing on social innovations that contribute to improved QOL under an ESG-driven management strategy. This project is part of that initiative, and we will contribute to the future society in collaboration with PFN.”

Toru Nishikawa, President and CEO, Preferred Networks, Inc.

“By combining PFN’s machine learning and deep learning technologies and accumulated know-how in RNA analysis with Kao’s research achievements in dermatology, productization expertise, and marketing capabilities, we expect to accelerate practical applications of products and services that utilize PFN’s technologies.”

投稿 Kao and Preferred Networks Launch a Collaborative Project for Practical Applications of Sebum RNA Monitoring Technology は Preferred Networks, Inc. に最初に表示されました。

Oct. 29, 2018, Tokyo Japan – Preferred Networks, Inc. and PFDeNA Inc. will start research and development to create a blood test system that utilizes deep learning technology to detect 14 types of cancers^*1 in their early stages.

In this R&D initiative, PFN and PFDeNA will use blood samples (DNA repository samples) and clinical information, both collected by the National Cancer Center Japan (NCC) for research purposes with donor consent. PFDeNA will measure the expression levels of ExRNA^*2 in the DNA repository samples by using a next-generation sequencer^*3 in a manner that does not identify individuals. PFN will apply deep learning technology to learn, evaluate, and analyze the measurements together with clinical data. The aim is to put the resulting system to practical use, which will be able to accurately determine the presence or absence and kind of cancer based on ExRNA expression levels in the blood.

Social background

Cancer is the leading cause of death among Japanese people, with about one in two developing cancer in their lifetimes. The number of Japanese who died from cancer is more than 370,000 a year and continuing to rise. This amounts to one out of every 3.6 deaths being caused by cancer *.

Even though it is critical to detect cancer at an early stage, screening rates for various types of cancers remain at roughly 30%, one of the lowest among developed countries. Each type of cancer has its own screening methods and requires different areas and organs in our bodies to be tested. The level of accuracy differs from one test to another. The burden of taking these tests need to be reduced both physically and financially in order to improve the screening rates.

Against this backdrop, many studies have been reported recently on gene expression of ExRNAs which include miRNAs^*4, bringing to light miRNA expressions that are unique biomarkers of cancer in each organ. Because the types or numbers of miRNAs expressed in bodily fluids will change once a person has cancer, researchers have high expectations that it will become easier to diagnose cancers using easily-collectible bodily fluids, such as blood.

Going forward

After PMDA’s^*5 review and approval, PFN and PFDeNA aim to develop the results of this research into a business by 2021 and promote its widespread use in Japan.

The high-precision, low-impact screening system will require only a small amount of blood to detect 14 types of cancers in their early stages and is expected to become a common cancer test in the future. Through early cancer detection, PFN and PFDeNA will contribute to efforts to decrease the mortality rate, to reduce medical costs, to extending healthy life expectancy and increasing cancer screening rates in Japan.

*1 The 14 types of cancers covered in this research are stomach cancer, colon cancer, esophageal cancer, pancreatic cancer, liver cancer, bile duct cancer, lung cancer, breast cancer, ovarian cancer, cervical cancer, uterine cancer, prostate cancer, bladder cancer, and kidney cancer.

*2 ExRNA is an RNA existent in the blood and other bodily fluids, mainly miRNA (microRNA) in this research. miRNA helps regulate a variety of biological activities and is expected to be used as a diagnostic biomarker.

*3 A next-generation sequencer is a piece of equipment used to sequence the base pairs of human genes in parallel at high speed.

*4 miRNA is a ribonucleic acid that is about 20 bases long and plays a role in regulating gene expression.

*5 PMDA is an acronym for the Pharmaceuticals and Medical Devices Agency of Japan, which is an organization that conducts the scientific review for quality, efficacy, and safety of pharmaceuticals and medical equipment. https://www.pmda.go.jp/english/about-pmda/outline/0005.html

*Source: “Summary of Vital Statistics for 2017” (Ministry of Health, Labour and Welfare)

投稿 Preferred Networks and PFDeNA launch joint research project to develop a deep learning-based system to detect 14 types of cancers with a small amount of blood は Preferred Networks, Inc. に最初に表示されました。

PFN will use the raised capital to strengthen its financial base, improve computing resources, and continue recruiting talented people.

PFN and Chugai have established a comprehensive partnership to develop innovative drugs and services that create new value. PFN and Chugai will collaborate on joint projects to solve open problems in drug research and development as well as those for more exploratory initiatives by utilizing deep learning technology. Also, PFN and TEL have begun joint research on applications of deep learning to such areas as optimization and automation in semiconductor manufacturing.

PFN will strive to drive innovation not only in the fields of transportation system, manufacturing, and bio/healthcare but in a broad range of business areas to increase its corporate value.

We have received the following comments from Chugai and TEL:

“The fusion of existing and new technologies such as IoT and AI will become essential in all value-chain activities including research and development in the domain of healthcare and life science. By applying PFN’s cutting-edge data analysis techniques, such as machine learning and deep learning, to Chugai’s overall business operations centering on “technology-driven drug discovery,” we are aiming to deliver innovative drugs and services that address high unmet medical needs and contribute to the medical community and human health around the world.”

Osamu Okuda

Executive Vice President of Chugai in charge of Project & Lifecycle Management (Marketing) and Corporate Planning

“I have high expectations that PFN and TEL will be able to fuse the world’s most advanced technologies of deep learning and chipmaking to produce an epoch-making result that leads to innovation in semiconductor manufacturing.”

Toshihiko Nishigaki

Deputy General Manager, Corporate Innovation Division, Corporate Marketing, Information Technology

Related link:

Chugai Enters into Comprehensive Partnership Agreement with Preferred Network
https://www.chugai-pharm.co.jp/english/news/detail/20180726153001.html

*Company names and product names written in this release are the trademarks or the registered trademarks of each company.

投稿 Preferred Networks raises a total of about 900 million yen in capital from Chugai Pharmaceutical and Tokyo Electron は Preferred Networks, Inc. に最初に表示されました。