We may be one step closer to an artificial intelligence (AI) tool capable of diagnosing Alzheimer's disease faster and more accurately, according to a new study conducted by Stevens Institute of Technology. "This is a real breakthrough. We're opening an exciting new field of research, and making it far easier to explain to patients why the A.I. came to the conclusion that it did, while diagnosing patients. This addresses the important question of trustability of A.I .systems in the medical field," said K.P. Subbalakshmi, the lead researcher. The new AI tool involves the analyzation of subtle linguistic patterns characteristic of people with a neurodegenerative disease.

An artificial intelligence-based analysis of epigenetic patterns in blood samples might be able to identify people with Alzheimer's disease, a new study has found. Alzheimer's disease affects nearly 47 million people around the world but can be difficult to diagnose, particularly in its early stages when therapeutic interventions might have the greatest effect. "Drugs used in the late stage of the disease do not seem make much difference, so there is a tremendous amount of interest in diagnosis in the early stages of the disease," Khaled Imam, director of geriatric medicine at Beaumont Health and a co-author of the new study, said in a statement. Imam added that "blood is thought to be a desirable way of approaching this. And it would be relatively cheap and minimally invasive as compared to an MRI or spinal tap."

Nanoparticles could make a reliable blood test for Alzheimer's disease a reality; image credit: National Cancer Institute, Daniel Sone Using nanoparticles with different surface properties, researchers are able to detect subtle changes in the composition of proteins in the plasma years before the presentation of clinical symptoms of Alzheimer's disease, which include memory loss, confusion, and cognitive difficulties. Owing to the unique properties of nanoparticles, different proteins in biological fluids selectively stick onto their surface forming a protein corona, which was found to change during disease. Researchers from the United States and Italy identify these subtle changes in plasma protein patterns to distinguish plasma samples from healthy individuals and those diagnosed with Alzheimer's disease. "Protein corona composition is both influenced by specific health conditions as well as the chemical and physical properties of the nanoparticles themselves," says Dr. Claudia Corbo of the University of Milano-Bicocca and lead author of the study published in Advanced Healthcare Materials. "Binding of proteins to the surface of particles is very precise and dependent on the chemistry and shape of the particles and the chemistry and structure of the proteins," says senior author Professor Omid Farokhzad of Brigham and Women's Hospital and Harvard Medical School.

Researchers have developed software that detects Alzheimer's using artificial intelligence (AI) at 95% accuracy. Stevens Institute of Technology researchers have developed software that detects subtle changes in Alzheimer's patients' languages. Also, it can explain the diagnosis and allows physicians to re-check the findings. "This is a real breakthrough," said Stevens Institute of Technology lead researcher K.P. Subbalakshmi adding that we are "opening an exciting new field of research." Subbalakshmi is the founding director of the Stevens Institute of Artificial Intelligence as well as an electrical and computer engineering professor at the Charles V. Schaeffer School of Engineering.

Jhansi: A team of five students led by a boy from Jhansi won one of the competitions in the Smart India Hackathon on Tuesday. The team was awarded with a cash prize of Rs one lakh. He and his team developed a mobile application which can help an Alzheimer's patient detect the severity of the disease through artificial intelligence. The reports produced by the application can be analysed by doctors. Alzheimer's disease is a type of dementia that affects one's memory, thinking and behaviour.

Similar to that at the RSNA, AI developed at the University of Toronto and the Center for Addiction and Mental Health trained their Al deep learning algorithm with data from the Alzheimer's Disease Neuroimaging Initiative through the National Institutes of Health's National Institute on Aging using data from over 800 geriatric patients ranging from healthy to mild cognitive impairment to Alzheimer's disease. Their algorithm was found to be able to accurately predict cognitive decline leading to AD in cohorts by analyzing brain scans, clinical data, and genetics by up to 5 years before symptoms appear; research was published in PLOS Computational Biology.

AI is showing so much promise in the medical field. It's an excellent example of how AI combines with human intelligence to create a "super brain" capable of predicting disease, uncovering patterns, and testing solutions for persistent problems. Precision Medicine is a medical nonprofit using this super combination of AI and human expertise to find solutions to one of the most devastating diseases we know, Alzheimer's. Ayin Vala, co-founder and someone who lost his own grandfather to this illness, outlines Precision Medicine's work. There are no treatments to prevent the disease and no cure.

Alzheimer's disease is a terminal neurodegenerative disease that has historically been diagnosed based on observing significant memory loss. There is currently no cure or disease-modifying therapy for this terminal illness, despite hundreds of clinical trials. It is thought these trials may have a high failure rate because the people enrolled are in the latest stages of the disease, likely already suffering a level of brain tissue loss that cannot easily be repaired. Thus, researchers have put their mind to how to detect this disease earlier, while a chance may still exist to slow its progression. Recent research has shown a biological marker associated with the disease, a peptide called amyloid-beta, changes decades before any memory-related issues are apparent.

Using a common type of brain scan, researchers programmed a machine-learning algorithm to diagnose early-stage Alzheimer's disease about six years before a clinical diagnosis is made – potentially giving doctors a chance to intervene with treatment. No cure exists for Alzheimer's disease, but promising drugs have emerged in recent years that can help stem the condition's progression. However, these treatments must be administered early in the course of the disease in order to do any good. This race against the clock has inspired scientists to search for ways to diagnose the condition earlier. "One of the difficulties with Alzheimer's disease is that by the time all the clinical symptoms manifest and we can make a definitive diagnosis, too many neurons have died, making it essentially irreversible," says Jae Ho Sohn, MD, MS, a resident in the Department of Radiology and Biomedical Imaging at UC San Francisco.

"One of the difficulties with Alzheimer's disease is that by the time all the clinical symptoms manifest and we can make a definitive diagnosis, too many neurons have died, making it essentially irreversible," said Jae Ho Sohn, a resident in the school's Department of Radiology and Biomedical Imaging and the study's lead researcher, in a statement. For the study, published in Radiology, Sohn and his team fed a common type of brain scans to a machine-learning algorithm, and it learned to diagnose early-stage Alzheimer's disease about six years before a clinical diagnosis could be made. The AI's diagnostic skills could give doctors a much-needed headstart on treating the degenerative disease. Sohn and his team focused on PET scans that monitored glucose levels across the brain, because glucose is the primary source of fuel for brain cells. Once the cells become diseased, they eventually stop using glucose, making it an important level to track.