Medical Design and Outsourcing

  • Home
  • Medical Device Business
    • Mergers & Acquisitions
    • Financial
    • Regulatory
  • Applications
    • Cardiovascular
    • Devices
    • Imaging
    • Implantables
    • Medical Equipment
    • Orthopedic
    • Surgical
  • Technologies
    • Contract Manufacturing
    • Components
    • Electronics
    • Extrusions
    • Materials
    • Motion Control
    • Prototyping
    • Pumps
    • Tubing
  • Med Tech Resources
    • DeviceTalks Tuesdays
    • Digital Editions
    • eBooks
    • Manufacturer Search
    • Medical Device Handbook
    • MedTech 100 Index
    • Podcasts
    • Print Subscription
    • The Big 100
    • Webinars / Digital Events
    • Whitepapers
    • Video
  • 2022 Leadership in MedTech
    • 2022 Leadership Voting!
    • 2021 Winners
    • 2020 Winners
  • Women in Medtech

A Method Based on Artificial Intelligence Allows to Diagnose Alzheimer’s or Parkinson’s

February 17, 2017 By University of Granada

Alzheimer’s disease, which currently affects more than 40 million people, is the most common neurodegenerative disease in elder people. (Credit: UGRdivulga)

Alzheimer’s disease, which currently affects more than 40 million people, is the most common neurodegenerative disease in elder people.

Early diagnosis is crucial both to treat the disease and to help the development of new medicines, as it hasn’t been possible to find a cure so far. The development of Alzheimer’s has been proven to be closely linked to structural changes -related to the gray matter, responsible for processing information- and functional ones -related to the white matter, which connects the different regions of the brain through fibers- in the brain connectivity network, since a significant loss of fibers also causes functional alternations, such as memory loss.

However, diagnosis remains a challenge in spite of the scientific advances made, and to date it hasn’t been possible to determine how functional cerebral activity deteriorates the structural one and vice versa, which is a key element to better understand the development of this type of diseases.

In this regard, computer aided diagnosis (CAD) is an important tool since it helps physicians to understand multimedia content obtained in tests carried out in patients, which allows a simpler and more effective application of the treatment. One such procedure is medical imaging, which provides high resolution “live” information on the subject matter and allows the use of information related to the disease contained in the image.

The BioSip research team, belonging to the University of Malaga, in collaboration with a group of researchers from the University of Granada, has been studying biomedical images and signals for years.

Researchers Andrés Ortiz, Jorge Munilla, Juan Górriz and Javier Ramírez (from the universities of Málaga and Granada) have recently published, in the renowned International Journal Of Neural Systems, a similar article called Ensembles of deep learning architectures for the early diagnosis of the Alzheimer’s disease. Said study presents a method for the diagnosis of Alzheimer’s by the fusion of functional and structural images based on the use of the deep learning technique.

This Artificial Intelligence (AI) technique aims to model high-level data abstractions in order to enable computers to differentiate the brain of a healthy person from that of an ill person, by automatically extracting the affected regions of interest.

As the researchers explain, “the study uses deep learning techniques to calculate brain function predictors and magnetic resonance imaging to prevent Alzheimer’s disease. To do this, we have used different neural networks with which to model each region of the brain to combine them afterwards”.

The study explores the construction of classification methods based on the Deep Learning architectures applied to brain regions defined by the Automated Anatomical Labeling (AAL), a digital atlas of the human brain. To this end, images of the gray matter of each area of the brain have been divided according to the regions separated in different sectors by the AAL, which have been used to train deep learning neural networks specialized in the different regions of the brain. The knowledge acquired by said networks is subsequently combined by different fusion techniques presented in this paper.

Classification architecture

The result of this work is a powerful classification architecture that combines supervised and unsupervised learning to automatically extract the most relevant features of a set of images. The proposed method has been evaluated using a large database from the Alzheimer’s Disease Neuroimaging Initiative (ADNI).

The results of this work, which has included patients with other cognitive deficits that can develop Alzheimer’s within two years, show the potential of AI techniques to reveal patterns associated with the disease. The accuracy rates obtained for the diagnosis allow to take a great step in the knowledge of the neurodegenerative process involved in the development of the disease, besides being useful as a starting point for the development of more effective medical treatments.

On the other hand, the techniques developed may serve as a starting point for the improvement of accuracy in the diagnosis of other dementias such as Parkinson’s disease.

Related Articles Read More >

DeepWell Digital Therapeutics Mike Wilson Ryan Douglas
How DeepWell is developing video games as tools for treating medical conditions
A woman with a small, handheld device in her lap with tubes that look like earphones plugged into her ears.
Ear-puffing device for migraine treatment wins FDA breakthrough designation
Abbott
Abbott launches upgraded digital health app for neurostimulation
Catheter delivery could enable better brain implants: Synchron’s neuroscience chief explains how

DeviceTalks Weekly.

May 20, 2022
DeviceTalks Boston Post-Game – Editors’ Top Moments, Insulet’s Eric Benjamin on future of Omnipod 5
See More >

MDO Digital Edition

Digital Edition

Subscribe to Medical Design & Outsourcing. Bookmark, share and interact with the leading medical design engineering magazine today.

MEDTECH 100 INDEX

Medtech 100 logo
Market Summary > Current Price
The MedTech 100 is a financial index calculated using the BIG100 companies covered in Medical Design and Outsourcing.
DeviceTalks

DeviceTalks is a conversation among medical technology leaders. It's events, podcasts, webinars and one-on-one exchanges of ideas & insights.

DeviceTalks

New MedTech Resource

Medical Tubing

Enewsletter Subscriptions

Enewsletter Subscriptions

MassDevice

Mass Device

The Medical Device Business Journal. MassDevice is the leading medical device news business journal telling the stories of the devices that save lives.

Visit Website
MDO ad
Medical Design and Outsourcing
  • MassDevice
  • DeviceTalks
  • MedTech 100 Index
  • Medical Tubing + Extrusion
  • Drug Delivery Business News
  • Drug Discovery & Development
  • Pharmaceutical Processing World
  • R&D World
  • About Us/Contact
  • Advertise With Us
  • Subscribe to Print Magazine
  • Subscribe to E-newsletter
  • Attend our Monthly Webinars
  • Listen to our Weekly Podcasts
  • Join our DeviceTalks Tuesdays Discussion

Copyright © 2022 WTWH Media, LLC. All Rights Reserved. Site Map | Privacy Policy | RSS

Search Medical Design & Outsourcing

  • Home
  • Medical Device Business
    • Mergers & Acquisitions
    • Financial
    • Regulatory
  • Applications
    • Cardiovascular
    • Devices
    • Imaging
    • Implantables
    • Medical Equipment
    • Orthopedic
    • Surgical
  • Technologies
    • Contract Manufacturing
    • Components
    • Electronics
    • Extrusions
    • Materials
    • Motion Control
    • Prototyping
    • Pumps
    • Tubing
  • Med Tech Resources
    • DeviceTalks Tuesdays
    • Digital Editions
    • eBooks
    • Manufacturer Search
    • Medical Device Handbook
    • MedTech 100 Index
    • Podcasts
    • Print Subscription
    • The Big 100
    • Webinars / Digital Events
    • Whitepapers
    • Video
  • 2022 Leadership in MedTech
    • 2022 Leadership Voting!
    • 2021 Winners
    • 2020 Winners
  • Women in Medtech