We use cookies to understand how you use our site and to improve your experience. This includes personalizing content and advertising. To learn more, click here. By continuing to use our site, you accept our use of cookies. Cookie Policy.

Features Partner Sites Information LinkXpress
Sign In
Advertise with Us

Download Mobile App




Events

ATTENTION: Due to the COVID-19 PANDEMIC, many events are being rescheduled for a later date, converted into virtual venues, or altogether cancelled. Please check with the event organizer or website prior to planning for any forthcoming event.
30 Jan 2023 - 02 Feb 2023

AI Detects Early Knee Osteoarthritis from X-Ray Images

By MedImaging International staff writers
Posted on 16 Dec 2022
Print article
Image: AI tries to detect whether there is spiking on the tibial tubercles in the knee joint or not (Photo courtesy of University of Jyväskylä)
Image: AI tries to detect whether there is spiking on the tibial tubercles in the knee joint or not (Photo courtesy of University of Jyväskylä)

Osteoarthritis is the most common joint-related ailment globally and causes millions of medical visits every year in addition to burdening the economy. An early diagnosis can save the patient from unnecessary examinations, treatments and even knee joint replacement surgery. X-rays are the primary diagnostic method for early knee osteoarthritis. Now, researchers have developed an Artificial Intelligence (AI)-based neural network to detect early knee osteoarthritis from X-ray images.

The new AI-based method developed by researchers from the University of Jyväskylä (Jyväskylä, Finland) was trained to detect a radiological feature predictive of osteoarthritis from X-rays. The finding is not at the moment included in the diagnostic criteria, but orthopedic specialists consider it as an early sign of osteoarthritis. In practice, the AI tries to detect whether there is spiking on the tibial tubercles in the knee joint or not. Tibial spiking can be a sign of osteoarthritis. In an evaluation of the reliability of the method, the AI-based method was able to match a doctors’ diagnosis in 87% of cases. The development of AI models for diagnosing early osteoarthritis is active globally. The goal is that in the future, an AI would be able to detect early signs of knee osteoarthritis from X-rays, making it possible for the initial diagnosis to be made more often by general practitioners.

“The aim of the project was to train the AI to recognize an early feature of osteoarthritis from an X-ray. Something that experienced doctors can visually distinguish from the image, but cannot be done automatically,” explained Anri Patron, the researcher responsible for the development of the method. “Around 700 X-ray images were used in developing the AI model, after which the model was validated with around 200 X-ray images. The model managed to make an estimate of the spiking that was congruent with a doctors’ estimate in 87% of the cases, which is a promising result.”

“Several AI models have previously been developed to detect knee osteoarthritis. These models can detect severe cases that would be easily detected by any specialists,” said Sami Äyrämö, Head of the Digital Health Intelligence Laboratory at the University of Jyväskylä. “However the previously developed methods are not accurate enough to detect the early-stage manifestations. The method now being developed aims for, in particular, early detection from X-rays, for which there is a great need.”

“If we can make the diagnosis in the early stages, we can avoid uncertainty and expensive examinations such as MRI scanning,” added Juha Paloneva, CEO for Central Finland Health Care district and professor of surgery. “In addition, the patient can be motivated to take the measures to slow down or even stop the progression of the symptomatic osteoarthritis. In the best possible scenario, the patient might even avoid joint replacement surgery.”

Related Links:
University of Jyväskylä

Gold Supplier
SBRT Phantom with Removable Spine
E2E SBRT Phantom with Removable Spine Model 036S-CVXX-xx
New
Medical Software
Bladder Scanner Graphics Workstation Software
New
DR Retrofit Kit
Ultisys
New
Ultrasound Probe Covers
Intuit

Print article
CIRS -  MIRION

Channels

Ultrasound

view channel
Image: Dr. Derek Cool demonstrating the new robotic 3D ultrasound system (Photo courtesy of Lawson Health)

Robotic 3D Ultrasound System Improves Accuracy of Liver Cancer Treatment

Liver cancer is the fourth-leading cause of cancer death in the world. Surgery is one treatment option for liver cancer, although thermal ablation which uses heat to destroy the cancerous tumor has less... Read more

Nuclear Medicine

view channel
Image: Tracking radiation treatment in real time promises safer, more effective cancer therapy (Photo courtesy of Pexels)

Real-Time 3D Imaging Provides First-of-Its-Kind View of X-Rays Hitting Inside Body During Radiation Therapy

Radiation is used in treatment for hundreds of thousands of cancer patients each year, bombarding an area of the body with high energy waves and particles, usually X-rays. The radiation can kill cancer... Read more

General/Advanced Imaging

view channel
Image: The HIAS-29000 brain PET scanner with motion correction (Photo courtesy of Hamamatsu Photonics)

New Brain PET Scanner Corrects Blurring in Images Caused by Body Motion

Ordinary brain PET (positron emission tomography) scanners are unable to accurately measure the distribution of radiopharmaceuticals in the brain if the patients move their head during the examination process.... Read more

Imaging IT

view channel
Image: The new Medical Imaging Suite makes healthcare imaging data more accessible, interoperable and useful (Photo courtesy of Google Cloud)

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
Copyright © 2000-2023 Globetech Media. All rights reserved.