PidPort

AI-Driven Pathological Image Diagnosis
for all medical practitioners around the world

Digital Pathology AI Analysis Solution 「PidPort」

Vison of PidPort

To shape a more comfortable medical environment for all healthcare and medical professionals around the world.

Through our proprietary image processing technology driven by Deep Learning/AI, PidPort provides highly accurate and rapid pathological screening, as well as telepathology function. By leveraging the power of technology, we can create a medical environment that enables a more efficient and accurate pathological diagnosis and better management of the ever-increasing burden of medical sites with an AI-driven pathological image analysis.

Read our development story

Features

PidPort’s three features. Eliminate concerns about physical space needed for the storage and management of pathology specimen slides, alleviate diagnosis workflow, and achieve efficiency through remote diagnosis.(※)

PidPort’s three features:Optimal storage space for pathologic specimens/AI-based pathological screening/Globally-linked remote pathological diagnosis PidPort’s three features:Optimal storage space for pathologic specimens/AI-based pathological screening/Globally-linked remote pathological diagnosis
Cloud Storage

Optimal storage space for pathologic specimens

Digitization enables cloud-based management and storage of pathologic image data and case histories without the need to worry about deterioration or physical storage space.

Telepathology

Globally-linked remote pathological diagnosis

PidPort’s telepathology (remote pathological diagnosis) function enables optimal, timely pathological diagnosis requests and support via a network of medical practitioners around the world.

AI Analysis

AI-based pathological screening

PidPort enables highly precise and instantaneous screening of pathologic tissue and cells using AI independently developed by Medmain. As an assistant to medical practitioners, the system eases one’s daily workflow.

※It is depending on the country and region, all or a certain feature are available for use. Please contact us for details.

Medmain’s Digital Pathology Solution Service

Once pathological specimens are digitized, AI screening analysis, telepathology, and image data storage services are all available as one.
(It is possible to provide only necessary features according to your needs, also It is depending on the country and region,
all or a certain feature are available for use. Please contact us for details. )

Case1

Digitize Histopathological Specimen

PidPort:Image of Digitize Histopathological Specimen PidPort:Image of Digitize Histopathological Specimen

Pros

Instantly access specimen information / Eliminate physical storage space / Smoothly manage and store pathology image data and case information

Case2

Run AI Analysis/Screening on Digital Pathology Images

PidPort:Image of run AI Analysis/Screening on Digital Pathology Images PidPort:Image of run AI Analysis/Screening on Digital Pathology Images

Pros

Efficient pathological diagnosis work / Smoothly manage and store pathology image data and case information

Case3

Share digital pathology images and
request diagnosis
to remote pathologists.

PidPort:Image of share digital pathology images and request diagnosis to remote pathologists. PidPort:Image of share digital pathology images and request diagnosis to remote pathologists.

Pros

No need to physically move or send pathological specimen / Timely information sharing between pathologists

Research & Development

Below are the types of organs and evaluation methods of the models we have developed.
We plan to proceed with R&D on other organs, such as pancreas (histology).

Stomach Histology
Colon Histology
Lung Histology
Breast Histology
Uterine Cervix Cytology
Urine Cytology

Publications

Deep learning models for histopathological classification of gastric
and colonic epithelial tumors

Osamu Iizuka*1, Fahdi Kanavati*1, Kei Kato*1, Michael Rambeau*1, Koji Arihiro*2 & Masayuki Tsuneki*1
1)Medmain Inc. 2) Department of Anatomical Pathology, Hiroshima University Hospital

Published: 30 Jan, 2020 Scientific Reports