Research and Development
LPIXEL puts focus on a various array of
diseases to optimize doctor’s workloads.

EIRL’s goals are to serve as an aid to sustainable development for medical and to maximize people’s well-being. LPIXEL puts focus on developing medical image diagnosis support technology with our distinctive algorithm powered by AI, leveraging a numerous amount of medical big data which revolves around medical images such as CTs, MRIs, and pathological images.

The following software under research and development has not yet been granted an approval under the Pharmaceutical Affairs Act. Hence, this can be used for neither medical use nor single application but can be used for situations such as researches, examinations, and education that don’t have any ill effect on a human body, and the explanation for research subjects.

Powered by a spirit of venture and a cutting-edge technology, LPIXEL challenges itself to create the future of medical.

LPIXEL aims to expand its expertise into the world and provide a prosperous future and revolutionary products as a hub for open innovation with its various partners. With this belief in mind, LPIXEL is on a continuous journey to make the world a happier place for everyone.

Brain Disease

Brain Intracranial Stenosis Detection

In efforts to reduce the number of misdiagnosed cases of intracranial stenosis, our technology conducts a careful analysis of MRA scans to detect patterns that are similar to narrowed arteries.

Lung Disease

Lung Cancer

This cutting edge technology targets lung cancer CT scans to automatically identify the type of lung cancer and gene mutation. Our goal is to reduce the need for highly invasive biopsies and open doors to imaging-based treatment options for patients.

 *Currently under development with the National Cancer Center in Japan.

Mammary Gland Disease

Breast Cancer

This technology conducts a time-intensity curve (TIC) analysis for MRI scans of the breasts. Our goal is to be able to augment machine learning technology to identify the degree of malignancy of the tumor.

Colonic Disease

Colon Cancer

With aims to decrease the number of overlooked lesions, this technology analyzes the examination results of the colonoscopy images in real time and displays the areas in the colon where the prevalence of lesions, such as tumors, are high.

*Image provided by The Jikei University School of Medicine