Cell Phones Could Be Used to Increase Access to Healthcare Imaging
By MedImaging staff writers Posted on 27 May 2008 |
A new technique has recently been developed that uses cell phones for medical imaging purposes. According to the creators of the technology, this new advance could improve the accessibility of medical imaging to billions of people worldwide.
The World Health Organization (WHO) has reported that about 75% of the world's population is without access to ultrasounds, X-rays, magnetic resonance images, and other medical imaging technologies that can detect tumors, diagnose tuberculosis infections, and monitor pregnant women. Researcher Dr. Boris Rubinsky, a University of California (UC), Berkeley professor of bioengineering and mechanical engineering and leader of the development team, said that, "Medical imaging is something we take for granted in industrialized countries. Imaging is considered one of the most important achievements in modern medicine. Diagnosis and treatment of an estimated 20% of diseases would benefit from medical imaging, yet this advancement has been out of reach for millions of people in the world because the equipment is too costly to maintain. Our system would make imaging technology inexpensive and accessible for these underserved populations.”
There are three main components in medical imaging devices: data acquisition hardware (connected to the patient), image processing software, and a display device. When these three component pieces are combined into a single unit, the cost of the machine is substantially increased. This fact led Dr. Rubinsky and his team to physically separate the components, keeping the processing software required to convert the raw data into an image at an offsite but central location possessing the resources to operate and maintain it. The central location could act as a control center that serves several remote sites where simpler machines can gather data from the patients.
Cell phones, after collecting data from an acquisition device, can be used to upload the raw data to the control center that will be process it into an image. The cell phone would then act as a display device when the server returns the image. "This design significantly lowers the cost of medical imaging because the apparatus at the patient site is greatly simplified, and there is no need for personnel highly trained in imaging processing,” said Dr. Antoni Ivorra, a post-doctoral researcher and co-author of the study. "The data acquisition device can be made with off-the-shelf parts that somebody with basic technical training can operate. As for cell phones, you could be out in the middle of a remote village and still have cell phone access. They're so prevalent because so little infrastructure is required to maintain wireless networks.”
To demonstrate the use of cell phones as remote medical imaging devices, the researchers provided an example with electrical impedance tomography (EIT)--a medical imaging technique based on the idea that electrical signals are transmitted differently in diseased tissue than in healthy tissue. In EIT, the difference in resistance to electrical currents is converted into an image or map of the particular test tissue.
The researchers created a simple data acquisition device constructed from commercially available parts that contained 32 stainless steel electrodes. Half of the electrodes were electrical current input sensors and the other half measured the voltage. For the demonstration, the device was connected to a gel-filled container, similar to breast tissue containing a tumor. The device transmitted 225 voltage measurements to a cell phone via a universal serial bus (USB) cable, and the data were then sent via dial-up to a central computer that could process it. After an image was produced, it was returned to the cell phone for analysis.
Dr. Rubinsky noted that, "This could open up whole new avenues of healthcare for the developing world. Health professionals in rural clinics could affordably get the tools they need to properly diagnose and treat their patients.”
The study was published in April 30, 2008, in the open-access journal PLoS One
Related Links:
University of California, Berkeley
The World Health Organization (WHO) has reported that about 75% of the world's population is without access to ultrasounds, X-rays, magnetic resonance images, and other medical imaging technologies that can detect tumors, diagnose tuberculosis infections, and monitor pregnant women. Researcher Dr. Boris Rubinsky, a University of California (UC), Berkeley professor of bioengineering and mechanical engineering and leader of the development team, said that, "Medical imaging is something we take for granted in industrialized countries. Imaging is considered one of the most important achievements in modern medicine. Diagnosis and treatment of an estimated 20% of diseases would benefit from medical imaging, yet this advancement has been out of reach for millions of people in the world because the equipment is too costly to maintain. Our system would make imaging technology inexpensive and accessible for these underserved populations.”
There are three main components in medical imaging devices: data acquisition hardware (connected to the patient), image processing software, and a display device. When these three component pieces are combined into a single unit, the cost of the machine is substantially increased. This fact led Dr. Rubinsky and his team to physically separate the components, keeping the processing software required to convert the raw data into an image at an offsite but central location possessing the resources to operate and maintain it. The central location could act as a control center that serves several remote sites where simpler machines can gather data from the patients.
Cell phones, after collecting data from an acquisition device, can be used to upload the raw data to the control center that will be process it into an image. The cell phone would then act as a display device when the server returns the image. "This design significantly lowers the cost of medical imaging because the apparatus at the patient site is greatly simplified, and there is no need for personnel highly trained in imaging processing,” said Dr. Antoni Ivorra, a post-doctoral researcher and co-author of the study. "The data acquisition device can be made with off-the-shelf parts that somebody with basic technical training can operate. As for cell phones, you could be out in the middle of a remote village and still have cell phone access. They're so prevalent because so little infrastructure is required to maintain wireless networks.”
To demonstrate the use of cell phones as remote medical imaging devices, the researchers provided an example with electrical impedance tomography (EIT)--a medical imaging technique based on the idea that electrical signals are transmitted differently in diseased tissue than in healthy tissue. In EIT, the difference in resistance to electrical currents is converted into an image or map of the particular test tissue.
The researchers created a simple data acquisition device constructed from commercially available parts that contained 32 stainless steel electrodes. Half of the electrodes were electrical current input sensors and the other half measured the voltage. For the demonstration, the device was connected to a gel-filled container, similar to breast tissue containing a tumor. The device transmitted 225 voltage measurements to a cell phone via a universal serial bus (USB) cable, and the data were then sent via dial-up to a central computer that could process it. After an image was produced, it was returned to the cell phone for analysis.
Dr. Rubinsky noted that, "This could open up whole new avenues of healthcare for the developing world. Health professionals in rural clinics could affordably get the tools they need to properly diagnose and treat their patients.”
The study was published in April 30, 2008, in the open-access journal PLoS One
Related Links:
University of California, Berkeley
Latest Imaging IT News
- 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
- Patient-Centric Portal Facilitates Direct Imaging Access
- New Workstation Supports Customer-Driven Imaging Workflow
Channels
Radiography
view channel
World's Largest Class Single Crystal Diamond Radiation Detector Opens New Possibilities for Diagnostic Imaging
Diamonds possess ideal physical properties for radiation detection, such as exceptional thermal and chemical stability along with a quick response time. Made of carbon with an atomic number of six, diamonds... Read more
AI-Powered Imaging Technique Shows Promise in Evaluating Patients for PCI
Percutaneous coronary intervention (PCI), also known as coronary angioplasty, is a minimally invasive procedure where small metal tubes called stents are inserted into partially blocked coronary arteries... Read moreMRI
view channel
AI Tool Tracks Effectiveness of Multiple Sclerosis Treatments Using Brain MRI Scans
Multiple sclerosis (MS) is a condition in which the immune system attacks the brain and spinal cord, leading to impairments in movement, sensation, and cognition. Magnetic Resonance Imaging (MRI) markers... Read more
Ultra-Powerful MRI Scans Enable Life-Changing Surgery in Treatment-Resistant Epileptic Patients
Approximately 360,000 individuals in the UK suffer from focal epilepsy, a condition in which seizures spread from one part of the brain. Around a third of these patients experience persistent seizures... Read more
AI-Powered MRI Technology Improves Parkinson’s Diagnoses
Current research shows that the accuracy of diagnosing Parkinson’s disease typically ranges from 55% to 78% within the first five years of assessment. This is partly due to the similarities shared by Parkinson’s... Read more
Biparametric MRI Combined with AI Enhances Detection of Clinically Significant Prostate Cancer
Artificial intelligence (AI) technologies are transforming the way medical images are analyzed, offering unprecedented capabilities in quantitatively extracting features that go beyond traditional visual... Read moreUltrasound
view channel.jpeg)
AI-Powered Lung Ultrasound Outperforms Human Experts in Tuberculosis Diagnosis
Despite global declines in tuberculosis (TB) rates in previous years, the incidence of TB rose by 4.6% from 2020 to 2023. Early screening and rapid diagnosis are essential elements of the World Health... Read more
AI Identifies Heart Valve Disease from Common Imaging Test
Tricuspid regurgitation is a condition where the heart's tricuspid valve does not close completely during contraction, leading to backward blood flow, which can result in heart failure. A new artificial... Read moreNuclear Medicine
view channel
Novel Radiolabeled Antibody Improves Diagnosis and Treatment of Solid Tumors
Interleukin-13 receptor α-2 (IL13Rα2) is a cell surface receptor commonly found in solid tumors such as glioblastoma, melanoma, and breast cancer. It is minimally expressed in normal tissues, making it... Read more
Novel PET Imaging Approach Offers Never-Before-Seen View of Neuroinflammation
COX-2, an enzyme that plays a key role in brain inflammation, can be significantly upregulated by inflammatory stimuli and neuroexcitation. Researchers suggest that COX-2 density in the brain could serve... Read moreGeneral/Advanced Imaging
view channel
AI-Powered Imaging System Improves Lung Cancer Diagnosis
Given the need to detect lung cancer at earlier stages, there is an increasing need for a definitive diagnostic pathway for patients with suspicious pulmonary nodules. However, obtaining tissue samples... Read more
AI Model Significantly Enhances Low-Dose CT Capabilities
Lung cancer remains one of the most challenging diseases, making early diagnosis vital for effective treatment. Fortunately, advancements in artificial intelligence (AI) are revolutionizing lung cancer... Read moreIndustry News
view channel
GE HealthCare and NVIDIA Collaboration to Reimagine Diagnostic Imaging
GE HealthCare (Chicago, IL, USA) has entered into a collaboration with NVIDIA (Santa Clara, CA, USA), expanding the existing relationship between the two companies to focus on pioneering innovation in... Read more
Patient-Specific 3D-Printed Phantoms Transform CT Imaging
New research has highlighted how anatomically precise, patient-specific 3D-printed phantoms are proving to be scalable, cost-effective, and efficient tools in the development of new CT scan algorithms... Read more
Siemens and Sectra Collaborate on Enhancing Radiology Workflows
Siemens Healthineers (Forchheim, Germany) and Sectra (Linköping, Sweden) have entered into a collaboration aimed at enhancing radiologists' diagnostic capabilities and, in turn, improving patient care... Read more