AI Analyses Neuron Changes to Detect whether Drugs are Effective for Neurodegenerative Disease Patients

A research group from Nagoya University in Japan has developed an artificial intelligence (AI) for analyzing cell images that uses machine learning to predict the therapeutic effect of drugs. Called in silico FOCUS, this new technology may aid in the discovery of therapeutic agents for neurodegenerative disorders such as Kennedy disease.

Current treatments for neurodegenerative diseases often have harsh side effects, including sexual dysfunction and blocking muscle tissue formation. However, researchers searching for new, less harmful treatments have been hindered by the lack of effective screening technologies to discern whether a drug is effective. One promising concept is the 'anomaly discrimination concept', meaning neurons that respond to treatment have slight differences in shape compared to those that do not. However, these subtle differences are difficult to discern with the naked eye. Current computer technologies are also too slow to perform the analysis.

A group of Nagoya University professors, led by Associate Professor Ryuji Kato and Assistant Professor Kei Kanie of the Graduate School of Pharmaceutical Sciences, and Professor Masahisa Katsuno and Assistant Professor Madoka Iida of the Graduate School of Medicine, has developed a new artificial intelligence technology called in silico FOCUS. It analyzes the cell shape of model neurons and uses that information to assess whether they respond to therapeutic drugs. They published their results in the journal Scientific Reports.

The researchers tested the AI on a model of cells being treated for Kennedy disease, a neurodegenerative disorder that leads to motor neuron death. in silico FOCUS constructed a robust image-based classification model that had 100% accuracy in identifying the state of recovery of the model cells.

"This technology enables a highly sensitive and stable evaluation of the effects of therapeutic agents through the analysis of changes in the shape of diseased model cells to those of healthy cells, which we could not normally distinguish," Professor Kato explains. "This is an ultra-efficient screening technology that can predict drug efficacy by simply capturing images, thus reducing the time required for drug efficacy analysis and evaluation from several hours with several hundred thousand cells to only a few minutes. It allows for a highly accurate prediction of therapeutic effects, without complicated and invasive experiments."

Kato concludes: "These results suggest the possibility of accelerating the development of new drugs and we expect them to be widely applied to the discovery of therapeutic drugs for diseases that have been difficult to explore."

This research was supported by the FY2019 Nagoya University NU Cross-Departmental Innovation Creation Project.

Imai Y, Iida M, Kanie K, Katsuno M, Kato R.
Label-free morphological sub-population cytometry for sensitive phenotypic screening of heterogenous neural disease model cells.
Sci Rep. 2022 Jun 16;12(1):9296. doi: 10.1038/s41598-022-12250-0

Most Popular Now

Giving Doctors an AI-Powered Head Start …

Detection of melanoma and a range of other skin diseases will be faster and more accurate with a new artificial intelligence (AI) powered tool that analyses multiple imaging types simultaneously...

AI Agents for Oncology

Clinical decision-making in oncology is challenging and requires the analysis of various data types - from medical imaging and genetic information to patient records and treatment guidelines. To effectively support...

AI Medical Receptionist Modernizing Doct…

A virtual medical receptionist named "Cassie," developed through research at Texas A&M University, is transforming the way patients interact with health care providers. Cassie is a digital-human assistant created by Humanate...

Using Data and AI to Create Better Healt…

Academic medical centers could transform patient care by adopting principles from learning health systems principles, according to researchers from Weill Cornell Medicine and the University of California, San Diego. In...

AI Tool Set to Transform Characterisatio…

A multinational team of researchers, co-led by the Garvan Institute of Medical Research, has developed and tested a new AI tool to better characterise the diversity of individual cells within...

AI Detects Hidden Heart Disease Using Ex…

Mass General Brigham researchers have developed a new AI tool in collaboration with the United States Department of Veterans Affairs (VA) to probe through previously collected CT scans and identify...

Human-AI Collectives Make the Most Accur…

Diagnostic errors are among the most serious problems in everyday medical practice. AI systems - especially large language models (LLMs) like ChatGPT-4, Gemini, or Claude 3 - offer new ways...

Northern Ireland Completes Nationwide Ro…

Go-lives at Western and Southern health and social care trusts mean every pathology service is using the same laboratory information management system; improving efficiency and quality. An ambitious technology project to...

Highland Marketing Announced as Official…

Highland Marketing has been named, for the second year running, the official communications partner for HETT Show 2025, the UK's leading digital health conference and exhibition. Taking place 7-8 October...

MHP-Net: A Revolutionary AI Model for Ac…

Liver cancer is the sixth most common cancer globally and a leading cause of cancer-related deaths. Accurate segmentation of liver tumors is a crucial step for the management of the...

Groundbreaking TACIT Algorithm Offers Ne…

Researchers at VCU Massey Comprehensive Cancer Center have developed a novel algorithm that could provide a revolutionary tool for determining the best options for patients - both in the treatment...

The Many Ways that AI Enters Rheumatolog…

High-resolution computed tomography (HRCT) is the standard to diagnose and assess progression in interstitial lung disease (ILD), a key feature in systemic sclerosis (SSc). But AI-assisted interpretation has the potential...