Fujifilm and IU School of Medicine to Study AI in Diagnostic Imaging
|
By MedImaging International staff writers Posted on 12 Sep 2018 |

Image: Researchers are working to develop the application of AI in medical imaging diagnostics (Photo courtesy of Digital Health).
Fujifilm Corporation (Tokyo, Japan) has entered into a joint research agreement with Indiana University School of Medicine (Indianapolis, IN, USA) to develop the application of artificial intelligence (AI) in medical imaging diagnostic support systems.
Going forward, as clinical information is increasingly viewed in the context of big data, AI technology will be applied to develop products that meet the different and challenging needs of the healthcare industry. For instance, recent technological advancements in diagnostic imaging system capabilities, such as multi-slice CT, have created the need for an efficient solution to read and interpret the increased number of images being generated. The application of AI technology to support physicians by detecting suspicious lesions in images, comparing results with prior studies and the implementation of semi-automated reporting is expected to significantly increase the efficiency of diagnostic medical imaging in patient care.
Fujifilm is using AI technology to develop image diagnosis support systems, which will support the overall diagnostic workflow of physicians. In addition to undertaking various in-house development projects, Fujifilm is also entering into a partnership with leading AI technology vendors to expand the disease coverage of its systems. The Indiana University School of Medicine is affiliated with Indiana University Health (IU Health), a medical healthcare system with 17 hospitals and about 33,000 employees in the US.
The collaboration aims to combine Fujifilm’s image processing and AI technology with the Indiana University School of Medicine’s rich diagnostic and clinical expertise to develop medical AI technology, while searching for a system optimized to support diagnosis workflow. The research will initially utilize Fujifilm’s AI technology to segment and quantify muscle atrophy (sarcopenia) in body images, as well as detect and quantify brain lesions in neuroradiology imaging exams.
Going forward, as clinical information is increasingly viewed in the context of big data, AI technology will be applied to develop products that meet the different and challenging needs of the healthcare industry. For instance, recent technological advancements in diagnostic imaging system capabilities, such as multi-slice CT, have created the need for an efficient solution to read and interpret the increased number of images being generated. The application of AI technology to support physicians by detecting suspicious lesions in images, comparing results with prior studies and the implementation of semi-automated reporting is expected to significantly increase the efficiency of diagnostic medical imaging in patient care.
Fujifilm is using AI technology to develop image diagnosis support systems, which will support the overall diagnostic workflow of physicians. In addition to undertaking various in-house development projects, Fujifilm is also entering into a partnership with leading AI technology vendors to expand the disease coverage of its systems. The Indiana University School of Medicine is affiliated with Indiana University Health (IU Health), a medical healthcare system with 17 hospitals and about 33,000 employees in the US.
The collaboration aims to combine Fujifilm’s image processing and AI technology with the Indiana University School of Medicine’s rich diagnostic and clinical expertise to develop medical AI technology, while searching for a system optimized to support diagnosis workflow. The research will initially utilize Fujifilm’s AI technology to segment and quantify muscle atrophy (sarcopenia) in body images, as well as detect and quantify brain lesions in neuroradiology imaging exams.
Latest Imaging IT News
- Interactive AI Tool Supports Explainable Lung Nodule Assessment
- Breast Imaging Software Enhances Visualization and Tissue Characterization in Challenging Cases
- New Google Cloud Medical Imaging Suite Makes Imaging Healthcare Data More Accessible
- Global AI in Medical Diagnostics Market to Be Driven by Demand for Image Recognition in Radiology
- AI-Based Mammography Triage Software Helps Dramatically Improve Interpretation Process
- Artificial Intelligence (AI) Program Accurately Predicts Lung Cancer Risk from CT Images
- Image Management Platform Streamlines Treatment Plans
- AI-Based Technology for Ultrasound Image Analysis Receives FDA Approval
- AI Technology for Detecting Breast Cancer Receives CE Mark Approval
- Digital Pathology Software Improves Workflow Efficiency
Channels
Radiography
view channel
Rapid X-Ray Test Quantifies Pulmonary Regurgitation After Tetralogy of Fallot Repair
Tetralogy of Fallot is the most common cyanotic congenital heart defect and can leave patients with pulmonary valve regurgitation, a backward flow of blood into the right ventricle after repair.... Read more
AI Tool Flags Osteoporosis Risk from Routine Chest X-Rays
Osteoporosis is a progressive loss of bone density that is often silent until a fracture occurs. Current screening frameworks concentrate on older women and select high-risk groups. Many men, younger adults,... Read moreMRI
view channel
AI Approach Could Shorten Advanced Brain MRI Scans by Up to 90%
Long acquisition times for advanced brain magnetic resonance imaging (MRI) can limit access, extend waiting lists, and disrupt clinical workflows. Reducing data requirements without sacrificing image fidelity... Read more
Cardiac MRI Measure Improves Risk Prediction in Tricuspid Regurgitation
Tricuspid regurgitation, in which blood flows back from the right ventricle into the right atrium, can lead to progressive right-sided heart failure. Clinicians need reliable ways to gauge severity and... Read moreUltrasound
view channelAI Robotic Ultrasound System Automates Echocardiography and Improves Consistency
Echocardiography, an ultrasound examination of the heart, is central to diagnosing and managing cardiovascular disease. Many services struggle with limited availability of skilled sonographers, variable... Read more
Whole Cross-Section Ultrasound System Enables Operator-Independent Imaging
Conventional ultrasound is central to bedside imaging but is limited by a narrow field of view and operator variability. Comprehensive cross-sectional assessment typically requires computed tomography... Read moreNuclear Medicine
view channel
Targeted PET Platform Guides Osteosarcoma Resection and Margin Verification
Osteosarcoma, an aggressive primary bone cancer that mainly affects children and adolescents, demands wide excision to prevent local recurrence. Surgeons must achieve negative margins while preserving... Read more
Portable PET System Enables Real-Time Bedside Guidance for Biopsies and Ablations
Interventional radiology procedures typically rely on ultrasound, X-ray fluoroscopy, or computed tomography for image guidance. These modalities visualize anatomy but offer limited molecular information,... Read moreGeneral/Advanced Imaging
view channelNew SPECT/CT Method Differentiates Inflammation from Fibrosis in Interstitial Lung Disease
Interstitial lung disease (ILD) encompasses more than 200 disorders that inflame or scar the lung interstitium and can lead to progressive respiratory failure. Determining whether active inflammation is... Read more
Whole-Body PET/CT Tracks Metabolic Changes After Bariatric Surgery
Obesity surgery improves weight and comorbidity profiles, yet clinicians lack tools to monitor organ-level metabolic recovery after the procedure. A clear view of systemic changes could refine follow-up... Read moreImaging IT
view channel
Interactive AI Tool Supports Explainable Lung Nodule Assessment
Lung cancer is a leading cause of cancer mortality, and timely characterization of pulmonary nodules on chest computed tomography (CT) is essential for directing care. Interpreting nodule morphology demands... Read more
Breast Imaging Software Enhances Visualization and Tissue Characterization in Challenging Cases
Breast imaging can be particularly challenging in cases involving small breasts or implants, where image reconstruction and tissue characterization may be limited. Clinicians also need reproducible analysis... Read more
New Google Cloud Medical Imaging Suite Makes Imaging Healthcare Data More Accessible
Medical imaging is a critical tool used to diagnose patients, and there are billions of medical images scanned globally each year. Imaging data accounts for about 90% of all healthcare data1 and, until... Read more







