A study finds that artificial intelligence for dementia diagnosis is as accurate as medical professionals with expertise in treating neurologic illnesses. More individuals are surviving into old age globally thanks to improvements in public health over the last several decades. Dementia, notably Alzheimer's disease, and other conditions that are often linked to aging are as a result seeing a major rise. This might impede the ability to provide prompt treatment to individuals in need, especially in light of a predicted physician shortage in the next decades. According to a recent study by researchers at the Boston University School of Medicine (BUSM), computational techniques (artificial intelligence/AI) may be able to help alleviate some of the challenges associated with delivering dementia care to an aging population.

To solve the conundrum of how to get timely medical care to people with memory loss or other impaired cognitive functioning, a new study suggests that artificial intelligence may be as accurate as clinicians in taking the first step: diagnosis. Findings from the study, which was conducted by researchers at Boston University School of Medicine, were published online Monday in the journal Nature Communications. "We're trying to leverage AI to create frameworks to mimic neurology experts," for dementia diagnosis, Vijaya B. Kolachalama, the study's principal investigator and assistant professor of medicine and computer science at Boston University, told UPI. He said his lab aims to use computer models to assist clinical practice. Kolachalama stressed that the aim of his team's work is to help reduce the workload of the busy neurology practice, not replace the expert clinician.

Chronic kidney disease (CKD) is caused by diabetes and hypertension. In 2017, the global prevalence of CKD was 9.1 percent, which is approximately 700 million cases. Chronic kidney damage is assessed by scoring the amount of interstitial fibrosis and tubular atrophy (IFTA) in a renal biopsy sample. Although image digitization and morphometric (measuring external shapes and dimensions) techniques can better quantify the extent of histologic damage, a more widely applicable way to stratify kidney disease severity is needed. Now, researchers from Boston University School of Medicine (BUSM) have developed a novel Artificial Intelligence (AI) tool to predict the grade of IFTA, a known structural correlate of progressive and chronic kidney disease.

For media inquiries, please contact Gina DiGravio: 617-224-8962, ginad@bu.edu Warning signs for Alzheimer's disease (AD) can begin in the brain years before the first symptoms appear. Spotting these clues may allow for lifestyle changes that could possibly delay the disease's destruction of the brain. "Improving the diagnostic accuracy of Alzheimer's disease is an important clinical goal. If we are able to increase the diagnostic accuracy of the models in ways that can leverage existing data such as MRI scans, then that can be hugely beneficial," explained corresponding author Vijaya B. Kolachalama, PhD, assistant professor of medicine at Boston University School of Medicine (BUSM).

Spotting these clues may allow for lifestyle changes that could possibly delay the disease's destruction of the brain. "Improving the diagnostic accuracy of Alzheimer's disease is an important clinical goal. If we are able to increase the diagnostic accuracy of the models in ways that can leverage existing data such as MRI scans, then that can be hugely beneficial," explained corresponding author Vijaya B. Kolachalama, PhD, assistant professor of medicine at Boston University School of Medicine (BUSM). Using an advanced AI (artificial intelligence) framework based on game theory (known as generative adversarial network or GAN), Kolachalama and his team processed brain images (some low and high quality) to generate a model that was able to classify Alzheimer's disease with improved accuracy. Quality of an MRI scan is dependent on the scanner instrument that is used.

"The general public has become quite aware of AI and the impact it can have on health care outcomes such as providing clinicians with improved diagnostics. However, if medical education does not begin to teach medical students about AI and how to apply it into patient care then the advancement of technology will be limited in use and its impact on patient care," explained corresponding author Vijaya B. Kolachalama, PhD, assistant professor of medicine at Boston University School of Medicine (BUSM). Using a PubMed search with'machine learning' as the medical subject heading term, the researchers found that the number of papers published in the area of ML has increased since the beginning of this decade. In contrast, the number of publications related to undergraduate and graduate medical education have remained relatively unchanged since 2010. Realizing the need for educating the students and trainees within the Boston University Medical Campus about ML, Kolachalama designed and taught an introductory course at BUSM.

Boston University School of Medicine has developed computer models based on artificial intelligence that significantly improve the analysis of routine kidney biopsy images. BU researchers, who conducted a proof-of-principle study on kidney biopsy sections, contend that their AI-based models have both diagnostic and prognostic applications and could lead to the development of software for diagnosing kidney disease as well as predicting kidney survival. In the study, images processed from renal biopsy samples were collected on 171 patients treated at the Boston Medical Center and were analyzed by convolutional neural networks (CNN) models and nephropathologists, who specialize in the analysis of kidney biopsy images. "With respect to kidney disease, biopsy is one of the gold standard procedures," says Vijaya Kolachalama, lead author and assistant professor of medicine at Boston University School of Medicine. "Most of the clinical decisions today are made based on information that nephropathologists can see from the biopsy."