Medical AI company
LPIXEL
Tokyo-based maker of EIRL, AI image analysis software for brain MRI/CT, chest X-ray/CT and colonoscopy.
Company overview
LPIXEL Inc. applies image analysis and AI to medical and life-science imaging. Its EIRL suite supports interpretation of brain MRA/MRI/CT, chest X-ray and CT, and colonoscopy, with several products holding Japanese approval or certification numbers. The company states it is a registered medical device manufacturer in Japan and is expanding EIRL into ASEAN markets.
- Headquarters
- Tokyo, Japan
- Founded
- 2014 source
- Company type
- AI Technology Developer, Medical Technology Manufacturer
In its own words: “EIRL is a suite of AI image analysis software that facilitates the efficient and accurate interpretation of medical imaging including radiology (X-ray, CT, MRI) and colonoscopy.” source
AI products and platforms
- EIRL Brain seriesAI-powered
AI detection of suspected cerebral aneurysms on brain MRA and quantification on brain MRI/CT
Product page ↗ - EIRL Chest XR / Chest MetryAI-powered
AI analysis and measurement on chest X-rays
Product page ↗ - EIRL ColonAI-powered
AI support for colonoscopy
Product page ↗
Technology categories
Clinical applications
- radiology reading
- colonoscopy
Markets served
- Japan
- Thailand
- Singapore
Relationships
- Partner Clairvo Technologies source
Relationships are shown as stated on the cited page; the other party may not confirm them.
Regulatory information
- EIRL products listed with Japanese approval/certification numbers, e.g. EIRL Chest XR approval number 30400BZX00285000 source
- EIRL approved/registered in Thailand (Jan 2025, Sep 2025) source
As stated by the company or the cited regulator page. Authorisation is specific to a product, version and market; confirm the current status with the company and the regulator. About medical device regulation
Official sources
Last verified 29 October 2026 against the sources above.
Questions about LPIXEL
How is radiology AI different from general diagnostic imaging software?
General diagnostic imaging software stores, displays and manipulates images, for example PACS viewers, 3D reconstruction or measurement tools operated by the user. Radiology AI adds automated analysis that produces its own output, such as a detection, triage flag or quantitative result.
The distinction matters for evaluation:
- Viewing and processing tools are judged mainly on image quality, usability and integration.
- AI analysis tools also need evidence on accuracy, populations studied, failure modes and monitoring.
Both can be regulated medical devices depending on intended use; in the US many image management and processing functions fall under device classifications. Many vendors now combine the two, so ask which functions are AI-based and which are covered by a regulatory authorization.
What is AI image reconstruction and denoising?
AI image reconstruction and denoising use deep learning to produce clearer images from raw scanner data or to reduce noise and artifacts, for example in CT or MRI. Vendors often position these tools as supporting lower-dose or faster scanning protocols.
Unlike detection software, these tools change the image the clinician reads rather than adding a finding. Questions to ask include:
- Is the function built into the scanner or a separate software product?
- Which scanners, protocols and body regions is it authorized for?
- How was diagnostic image quality evaluated, and could fine detail be lost or altered?
These functions are typically regulated as part of the imaging device or as image processing software, depending on the product and market.
What is AI triage or worklist prioritization in radiology?
AI triage software analyzes incoming imaging studies and flags those that may contain a time-sensitive finding so clinicians can review them sooner, for example by moving a study up the worklist or sending a notification.
In the US, FDA has a device classification for radiological computer-aided triage and notification software (21 CFR 892.2080). As described in that regulation, such a device notifies clinicians of time-sensitive images, does not mark the finding on the original image, does not remove cases from the reading queue, and operates in parallel with the standard of care.
Triage is therefore not a diagnosis: every study still needs to be read. When evaluating triage tools, ask about the target findings, notification workflow and how missed cases are handled.