A groundbreaking AI tool is set to revolutionize the care of patients suffering from Motor Neurone Disease (MND) by accurately predicting the need for a feeding tube. This advance could significantly enhance the quality of life for those affected by this debilitating condition.
Developed by a team at the University of Sheffield, this innovative tool aims to deliver essential information to both doctors and patients, enabling them to strategically time life-extending interventions.
MND, also referred to as Amyotrophic Lateral Sclerosis (ALS), is a progressive and fatal illness that impacts the nerve cells responsible for muscle control. As the disease progresses, many patients experience difficulties with swallowing, which can lead to severe weight loss and malnutrition. A gastrostomy—a procedure that involves placing a feeding tube directly into the stomach—is crucial for maintaining nutritional support, improving quality of life, and, in some cases, prolonging survival.
However, the timing of this procedure is critical. Performing it too early can negatively affect quality of life, while delaying it can increase risks and decrease effectiveness, especially as patients may enter a stage of severe malnutrition. In some cases, weakened respiratory muscles may also render the procedure impossible.
To address the unpredictable progression of MND, researchers from across Europe, led by Professor Johnathan Cooper-Knock at the University of Sheffield’s Institute for Translational Neuroscience (SITraN), developed a sophisticated machine-learning model. This AI-based tool utilizes standard measurements taken during diagnosis to evaluate how quickly the disease is likely to advance in individual patients, making it easier for clinicians to determine the optimal time for the necessary intervention.
“One of the greatest challenges in living with MND is the uncertainty it brings; it is a cruel and devastating disease,” remarked Professor Cooper-Knock.
“Previously, clinicians have struggled to predict when a patient with MND might require a feeding tube—this could happen anywhere from eight months to 20 years after diagnosis.
“With this model, we can identify the optimal timeframe for a gastrostomy within a three-month window, allowing both doctors and patients to plan more effectively for surgery. This will help maximize quality of life and potentially extend survival,” he explained.
The research team utilized data from over 20,000 MND patients to create the AI model, which predicts when significant weight loss—a key indicator for the need for a feeding tube—is likely to occur. Impressively, the tool can estimate this critical timeframe with a median deviation of just 3.7 months at diagnosis. For patients reassessed six months post-diagnosis, the model’s accuracy improved, achieving a median error of only 2.6 months.
Professor Cooper-Knock emphasized, “This innovation goes beyond mere surgical intervention; it is about preserving a patient’s dignity and ensuring safe nutritional intake. For healthcare providers, recognizing this vital timeframe shifts our approach from reactive to proactive, allowing us to provide optimal care and avoid the distress of rushing a fragile patient into surgery.”
“In the end, this tool guarantees that patients receive the right care at the right time, enhancing the quality of each day,” he added.
The encouraging findings of this study, published in the journal eBioMedicine, pave the way for a prospective clinical trial aimed at formally validating the tool before it is integrated into standard MND patient care.
