Tracking muscle mass in children with AI
Leveraging artificial intelligence and the largest pediatric brain MRI dataset, we have developed a growth chart for tracking muscle mass in growing children. The new study, led by investigators from AIM lab, found that their artificial intelligence-based tool is the first to offer a standardized, accurate, and reliable way to assess and track indicators of muscle mass on routine MRI. Their results were published in Nature Communications.
Lean muscle mass in humans has been linked to quality of life and daily functional status and is an indicator of overall health and longevity. Individuals with conditions such as sarcopenia or low lean muscle mass are at risk of dying earlier or otherwise being prone to various diseases that can affect their quality of life. Many young people, such as those with cancers or neurologic diseases, struggle with malnutrition and low muscle mass, but there has been no direct, practical way to measure this. Historically, body mass index (BMI) is used as a default measurement form. The weakness in using BMI is that while it considers weight, it needs to indicate how much of that weight is muscle. For decades, scientists and clinicians have known that the temporalis muscle, which lies outside the skull, is a marker of lean muscle mass in the body. However, temporalis muscle thickness (TMT) has been challenging to measure in real-time in the clinic, and it has remained unknown what constitutes “normal” versus “abnormal” thickness thresholds.
To address this, the research team applied their deep learning pipeline to MRI scans of patients with pediatric brain tumors treated at Dana-Farber/Boston Children's Cancer and Blood Disorders Center in collaboration with Boston Children’s Radiology Department. The team analyzed 23,852 normal healthy brain MRIs from individuals aged 4 through 35 to calculate temporalis muscle thickness (iTMT) and develop normal-reference growth charts for the muscle. MRI results were aggregated to create sex-specific iTMT normal growth charts with percentiles and ranges. They found that iTMT is accurate for a wide range of patients and comparable to trained human experts' analysis.
The new method could be used to assess patients who are already receiving routine brain MRIs that track medical conditions such as pediatric cancers and neurodegenerative diseases. The team hopes that the ability to monitor the temporalis muscle instantly and quantitatively will enable clinicians to intervene for patients who demonstrate signs of muscle loss quickly and thus prevent the adverse effects of sarcopenia and low muscle mass.
AIM Investigators
