DiSPAH is an AI tool that uses data from patient follow-up studies to estimate the speed of disease progression and identify patterns of muscle decline.
Image Credit: Kano Okada, Nagoya University
Scientific Frontline: Extended "At a Glance" Summary: AI Prediction of ALS Progression (DiSPAH)
The Core Concept: DiSPAH is a machine learning tool developed by researchers at Nagoya University that analyzes patient data to estimate the speed of Amyotrophic Lateral Sclerosis (ALS) progression and identify specific patterns of muscle decline.
Key Distinction/Mechanism: Unlike previous predictive models, DiSPAH simultaneously and independently measures two variables in limb-onset ALS patients: how fast the disease advances and the exact sequence in which physical functions become impaired.
Major Frameworks/Components:
- Pattern Recognition: Identifies six distinct patterns of disease progression based on initial functional assessments.
- Independent Variable Tracking: Separates the speed of decline from the pathway of decline, revealing that severe functional pathways can progress slowly, while milder pathways can progress quickly.
- Genetic Integration: Incorporates genetic markers, such as the C9orf72 gene mutation, which is linked to cellular stress, protein mismanagement, and faster disease progression.
Branch of Science: Artificial Intelligence (Machine Learning), Neurology (Neurodegenerative Diseases), and Genomics.
Future Application: Predictions generated from a patient's first medical visit will allow doctors to personalize care plans, group patients accurately for highly targeted clinical trials, and eventually adapt the predictive framework for other neurodegenerative conditions like Alzheimer's and Parkinson's disease.
Why It Matters: ALS is a fatal condition with no known cure and a highly unpredictable trajectory. Accurate early forecasting is essential for developing effective new therapies, understanding the underlying cellular biology of the disease, and adequately preparing patients and their families.
Nagoya University Scientists Develop a Machine Learning Tool to Uncover How ALS Progresses in Patients and Offer Clues to Understand Why Some Patients Decline Faster Than Others
Amyotrophic lateral sclerosis (ALS) is a fatal neurodegenerative disease that gradually affects a person’s ability to move, speak, and breathe. It advances differently in every patient. Now, researchers at Nagoya University have developed an artificial intelligence (AI) tool that uses data from patient follow-up studies to estimate the speed of disease progression and identify patterns of muscle decline. The study was published in npj Digital Medicine.
Two Questions, One Tool
ALS patients differ in two main ways: how fast their disease advances and the order in which functions become impaired. Until now, existing AI-based research tools generally did not clearly separate these two differences in individuals. The research team developed DiSPAH, a machine learning system that addresses both at once by analyzing patient data collected during routine medical visits.
The researchers used two datasets of patients with limb-onset ALS, a form of the disease in which symptoms begin in the arms or legs rather than in the muscles controlling speech and swallowing (bulbar-onset ALS). The first provided data from 264 ALS patients and was used to train the model. The second, larger dataset of 2,565 patients was used to validate the results.
What the AI Found
The system identified six distinct patterns of disease progression among the patients. Some patients showed a slow decline in motor function, with little effect on speech or breathing, while others experienced rapid deterioration.
“Subtle differences between patients also emerged. For example, in some patients, gross motor functions such as walking declined before fine motor skills such as writing or buttoning a shirt, while in others, the opposite was true,” said Yuichiro Yada, coauthor and associate professor at Nagoya University’s Graduate School of Medicine. “These six patterns were identified in one patient dataset and largely reproduced in a second, larger dataset, suggesting that they capture common progression patterns in limb-onset ALS.”
Importantly, the speed and pattern of decline were found to be independent of each other. A patient could follow a severe pattern at a slow speed or a milder pattern at a fast speed. Previous tools could not measure both dimensions at once.
Prediction From Day One
One of the most important findings was that DiSPAH could, to some extent, predict a patient’s progression speed and broad progression pattern from information available at the first medical visit. This information consisted of basic functional assessments and the presence of certain gene mutations.
These early predictions have significant potential for patient care. Doctors could use them to plan treatments, prepare patients and their families, and design better clinical trials by grouping participants according to how their disease advances.
A Genetic Clue
The researchers also found that patients with a mutation in a gene called C9orf72 had faster disease progression. When they analyzed data from motor neurons grown in the laboratory from the patients’ own stem cells, the results showed that faster ALS progression may be linked to problems in how cells produce and manage proteins, as well as to signs of cellular stress.
This finding points to a possible biological explanation for why some patients decline faster than others and gives scientists a new target for future research into ALS treatments.
Better Tools for Patients
ALS currently has no cure. While a few drugs exist, they offer modest benefits. Better tools for prediction and monitoring are essential for the development of new therapies.
Yada noted that DiSPAH is a prototype that needs further validation and refinement: “It’s a promising first step and better than anything that existed before for this specific purpose, but it’s not reliable enough yet to use to make decisions about individual patients.”
The researchers aim to extend the tool to all ALS patient types, improve its reliability, and ultimately apply it to other chronic diseases such as Alzheimer’s and Parkinson’s diseases.
Funding: This work was partly supported by a JSPS Grant-in-Aid for Early-Career Scientists (JP23K16994), the Japan Agency for Medical Research and Development (AMED) Multidisciplinary Frontier Brain and Neuroscience Discoveries (Brain/MINDS 2.0) (JP24wm0625416 and JP25wm0625322), the JST Moonshot R&D–MILLENNIA Program (JPMJMS2024), and JST CREST (JPMJCR25Q2).
Published in journal: npj Digital Medicine
Title: Decomposing heterogeneity in disease progression speeds and pathways
Authors: Yuichiro Yada, and Honda Naoki
Source/Credit: Nagoya University
Edited by: Scientific Frontline
Reference Number: ai061526_01