FUJIFILM

AI technology to detect a suspcious rib fracture from CT images. This technology will assist prevention of overlooking of subtle rib fracture.

AI technology to identify suspicious region with COVID-19 related findings from CT images. This technology will help doctors diagnose efficiently.

This technology assists real time detection and characterization of colonic polyps from colonoscopy images with AI software.

AI technology to detect and quantify suspicious lesion from CT Images.
This will assit prevention of overlooking of nodule and generation of language of findings for radiology report.

This technology identifies various findings of interstitial pneumonia that appear on CT images, such as consolidation, reticular pattern, ground glass opacity and honeycomb, and calculates their distribution and volume.
This will assist in the diagnosis of the severity and therapeutic efficacy of interstitial pneumonia, which are conventionally performed qualitatively, by providing a quantitative value for assessment.

The technology which prepares environment on the modality to exapnd the opportuities of AI-CAD applications and improve workflow not only in hospitals, but also at point of care.

This technology extracts high-signal and low-signal areas in head CT images by comparing the left and right sides of the brain region. Generally, high-signal and low-signal areas are used to evaluate the state of hemorrhage and ischemia in the brain for stroke diagnosis. This will assist the diagnosis of head CT imaging.

The technology to extract and highlight areas of higher or lower absorption than the surrounding tissue. For example, high/low absorption in the liver, high absorption in the thoracic cavity, and low absorption in the kidney may be informative in determining the findings of each part. In the future, we aim to highlight both contrast-enhanced and non-contrast-enhanced images.

The technology detects three types of imaging findings: nodule, consolidation, and pneumothorax from chest X-ray images. It is expected to contribute to preventing oversights in various chest X-ray examinations, such as health checkups and routine medical examinations.

This technology highlights areas in the B-mode image where luminance characteristics are different from the surroundings

The technology estimates high-value threshold of the region of interest, and quantify high intensity area in lung field. For example, it is expected to offer information for quantitative analysis of partially solid nodules.

This technology automatically measures EvansIndex, corpus callosum angle, and MidlineShift from head CT. EvansIndex and corpus callosum angle are expected to support the evaluation of hydrocephalus and MidlineShift to evaluate head trauma.

The technology under development quantitative detection of slight brain volume loss and iron deposition in the early stages of dementia using hybrid analysis of QSM（Quantitative Susceptibility Mapping）and VBM（Voxel Based Morphometry）in MRI. This will assist the diagnosis of dementia.