Medical Imaging Informatics and Artificial Intelligence at UCSF is headed by Dr. Dugyu Tosun-Torgut and brings together world-class researchers from multiple disciplines in order to find new, innovative ways to use artificial intelligence and imaging for medical diagnosis. By uniting neurologists, engineers, and data scientists Medical Imaging Informatics and Artificial Intelligence will be extremely impactful in increasing the scope of our current imaging systems when it comes to the brain. The Medical Imaging Informatics and Artificial Intelligence Lab at UCSF aims to foster a truly collaborative environment. All team members are expected to contribute and participate in meaningful ways as we seek to discover novel new ways to utilize technology to better diagnose and treat patients. We value long term partnerships and create a trusting environment for all to succeed.

This thematic area relates to the'medicine of the future', principally the customisation of healthcare, with medical decisions, treatments, practises, or products being tailored to the individual patients, instead of a one‐drug‐ fits‐all model. Preventive or therapeutic interventions can then be targeted at those who will benefit, sparing expense and side effects for those who will not. Data analytics, including data mining and machine learning, is an integral part of the precision medicine model, e.g., in the discovery of new predictive or prognostic biomarkers or subgroups of patients. The number of papers reporting advances in this field are on almost an exponential rise since 2010 with Aaron Ciechanover, a Nobel Prize winner in Chemistry 2004, branding personalised medicine the "third revolution" of drug research. Neurodegenerative diseases, including Alzheimer's dementia (AD) and Parkinson's disease (PD), are caused by the progressive loss of structure or function of neurons.

As the global population ages, the incidence of Alzheimer's disease and related dementia (ADRD) is rising at an alarming rate. The burden of caring for ADRD patients falls mainly on families, and the cost of care is rapidly escalating. However, early detection of ADRD can lead to more proactive care and better outcomes for patients. That's where DigiCARE Realized comes in. This AI-enabled platform uses machine learning to comb through electronic health records to detect early-stage ADRD with high accuracy.

Researchers from John Hopkins University and Cortical Labs suggest that it's time to create a new type of computer that uses biological components. They believe that biological computers could outperform electronic computers in certain applications and use significantly less electricity. The future of computing includes biology says an international team of scientists. The time has come to create a new kind of computer, say researchers from John Hopkins University together with Dr. Brett Kagan, chief scientist at Cortical Labs in Melbourne, who recently led development of the DishBrain project, in which human cells in a petri dish learned to play Pong. In an article published on February 27 in the journal Frontiers in Science, the team outlines how biological computers could surpass today's electronic computers for certain applications while using a small fraction of the electricity required by today's computers and server farms.

Alzheimer’s disease (AD) is a neurodegenerative condition that progresses over decades. Early detection of individuals at high risk of future progression toward AD is likely to be of critical significance for the successful treatment and/or prevention of this devastating disease. In this paper, we present an empirical study to characterize how predictable an individual subjects’ future AD trajectory is, several years in advance, based on rich multi-modal data, and using modern deep learning methods. Crucially, the machine learning strategy we propose can handle different future time horizons and can be trained with heterogeneous data that exhibit missingness and non-uniform follow-up visit times. Our experiments demonstrate that our strategy yields predictions that are more accurate than a model trained on a single time horizon (e.g. 3 years), which is common practice in prior literature. We also provide a comparison between linear and nonlinear models, verifying the well-established insight that the latter can offer a boost in performance. Our results also confirm that predicting future decline for cognitively normal (CN) individuals is more challenging than for individuals with mild cognitive impairment (MCI). Intriguingly, however, we discover that prediction accuracy decreases with increasing time horizon for CN subjects, but the trend is in the opposite direction for MCI subjects. Additionally, we quantify the contribution of different data types in prediction, which yields novel insights into the utility of different biomarkers. We find that molecular biomarkers are not as helpful for CN individuals as they are for MCI individuals, whereas magnetic resonance imaging biomarkers (hippocampus volume, specifically) offer a significant boost in prediction accuracy for CN individuals. Finally, we show how our model’s prediction reveals the evolution of individual-level progression risk over a five-year time horizon. Our code is available at https://github.com/batuhankmkaraman/mlbasedad.

Alzheimer's disease is a neurological disease that attacks the healthy cells of the brain, leading to long-term memory loss, impaired thinking, disorientation and behavioural abnormalities in affected individuals. Early detection of Alzheimer's disease is critical to initiate treatment and management that can slow its progression. Deep Learning, a branch of Machine Learning, has shown promise for early detection of Alzheimer's disease. The use of Deep Learning algorithms can be used to analyse medical imaging data such as the magnetic resonance imaging (MRI) scans to detect the characteristic changes in the brain associated with Alzheimer's disease. This approach provides a non-invasive and efficient way to diagnose this complex disease. With the aim of improving the early detection of Alzheimer's disease, this article focuses on the use of advanced algorithms, such as Deep Learning, to analyse large amounts of image data. To achieve this goal, the article proposes using a Convolutional Neural Network (CNN) model, which is a type of Deep Learning algorithm that excels in image analysis.

New experiences are absorbed into neural representations over time, symbolized here by a hyperboloid hourglass. Researchers at Salk Institute discovered that the neural networks responsible for spatial perception change in a non-linear fashion and could have implications for neurodegenerative conditions such as Alzheimer's disease. Young kids often harbor the misconception that the moon is chasing them or that they can touch it with their hands, as it seems much closer than its actual distance. During our daily movements, we tend to think that we navigate space in a linear way. However, scientists at Salk Institute have found that spending time exploring an environment can cause neural connections to develop in unexpected ways.

The Roman physician Galen was among the first people to realize that the brain controlled motor responses, cognitive function, and memory. Ever since Galen, this question has propelled the field of neuroscience. Beginning with Paul Broca's work in the 1800s, brain function has been described in terms of modular separation: each region in the brain controls a unique set of behaviors, actions, and capacities. This determination was made through observation of patients suffering neurological symptoms and connecting them to localized brain injuries. For example, Broca's area (a brain region in the inferior frontal gyrus) was found to be responsible for speech fluency (Acharya and Wroten 2022), and was discovered by studying two subjects, both of whom exhibited reduced speech capacity and suffered from lesions in the same area of the brain.

How old is your brain, really? Just like people who look older than they really are, scientists say a person's brain can age faster than the rest of their body. With that in mind, researchers at USC have created an artificial intelligence program which can accurately tell how old someone's brain is -- while also pointing out warning signs for Alzheimer's disease. The AI program analyzes MRI brain scans, looking for signs of cognitive decline which have a link to neurodegenerative diseases, like Alzheimer's. Brain aging is one of the most reliable markers for neurodegenerative disease risk.

Summary: Deep learning technology can accurately reflect a person's risk of cognitive decline and Alzheimer's disease based on brain age. The human brain holds many clues about a person's long-term health -- in fact, research shows that a person's brain age is a more useful and accurate predictor of health risks and future disease than their birthdate. Now, a new artificial intelligence (AI) model that analyzes magnetic resonance imaging (MRI) brain scans developed by USC researchers could be used to accurately capture cognitive decline linked to neurodegenerative diseases like Alzheimer's much earlier than previous methods. Brain aging is considered a reliable biomarker for neurodegenerative disease risk. Such risk increases when a person's brain exhibits features that appear "older" than expected for someone of that person's age.