When people think about health, they usually go straight to things like cholesterol levels, blood pressure, or BMI. Which is fair enough as those are very useful numbers. But there’s one thing we often skip over that’s just as telling, and that’s muscle strength.
We’re not just talking about how much you can lift in the gym. We’re talking about strength as a genuine marker of health and longevity. Research shows that lower muscle strength is associated with a higher risk of falls, fractures, poor mobility, metabolic issues, and even all-cause mortality. Weak leg strength in particular has been linked to slower walking speeds, difficulty rising from a chair, and a much higher risk of losing independence as we age.
Poor muscle strength can lead to a downward spiral of less activity, more fat gain, worse balance, poorer metabolic health, and an increased risk of everything from type 2 diabetes to cardiovascular disease. It’s not just about frailty in older adults either. Even younger people with below-average muscle strength may be on a slower road to health problems if it’s not addressed.
So how do we assess muscle strength in a meaningful way?
How We Measure Muscle Strength
Muscle strength can be measured in several different ways, and each method has its pros and cons.
The most basic option is manual muscle testing, where a physio or PT asks someone to push or resist a movement, and then gives a score based on how much force they can apply. It’s quick and easy, but it’s also pretty subjective. It depends a lot on the tester’s judgement and strength.
Next up is handgrip strength, often measured using a hand-held dynamometer. This method is simple, affordable, and actually quite predictive of overall strength and even health outcomes. But it only tells us about grip strength, which doesn’t always reflect lower limb or full-body power.
Isokinetic dynamometers are the gold standard for detailed muscle strength testing. They measure force during joint movement at a set speed and are often used in research or rehab settings. They’re brilliant for precision, but also big, expensive, and not exactly portable.
Other functional tests include sit-to-stand assessments, timed up-and-go tests, or walking speed tests. These are great at showing real-world ability and are particularly helpful with older adults or clinical populations. But they can be affected by other factors like balance, joint pain, or motivation, which means they’re not a direct measure of muscle force.
What all these methods tend to miss is what’s actually going on inside the muscle. How much muscle there is? How much fat is within the muscle? And how those things relate to strength? That’s where this study we’re going to talk about takes things to the next level.
A New Way to Look Inside Muscle: The CNN & MRI Approach
In a 2025 study published in the Journal of Cachexia, Sarcopenia and Muscle, researchers developed a clever way to use computer vision (specifically a convolutional neural network, or CNN) to analyse fat-water MRI scans of the legs.
What they wanted to do was automate the process of assessing leg muscle volume and intramuscular fat (IMF). These are two markers of muscle health that usually take ages to analyse by hand.
The idea was to train a CNN to recognise and segment key muscle groups in the legs from MRI images. They used data from 95 adults, teaching the CNN to identify five specific muscle groups in both legs, including things like the soleus, gastrocnemius, and lateral compartment muscles, and then compare this to manual segmentations done by human experts.
They also looked at how those muscle measures related to people’s age, BMI, sex, and their maximum plantarflexion force, which is basically how hard they could push their foot down against resistance; a solid measure of leg strength.
What They Found
The CNN model performed really well. It could reliably measure muscle volume and IMF with accuracy that matched, and in some cases, outperformed human raters.
Here’s where it gets interesting. Muscle volume was positively linked with BMI and sex, as males tended to have larger volumes, but not age. Meanwhile, intramuscular fat increased with both age and BMI and was higher in females in certain muscle groups.
And when it came to strength? Muscle volume in the soleus, gastrocnemius, and lateral compartments correlated positively with plantarflexion force. In other words, bigger muscles in those areas generally meant stronger legs. But IMF didn’t show the same relationship. Higher fat inside the muscle wasn’t directly linked to lower force in this relatively young, healthy group.
The takeaway? The CNN model can reliably measure internal muscle health markers that tell us something real about strength, and it can do it quickly, at scale, and without the need for hours of manual analysis.
Real-World Applications: Where This Could Be Useful
This kind of tech has some massive potential.
Imagine a rehab setting where a physiotherapist can scan a client’s leg post-injury and instantly get a clear picture of how much muscle they’ve lost and how much fat has crept in. That could shape the rehab plan and help track whether muscle is being rebuilt or not.
In sports science, coaches could use this to assess muscle condition after periods of inactivity, say, an off-season or post-injury layoff, and plan strength work accordingly. It could even highlight muscle asymmetries that put athletes at risk of injury.
For older adults, these scans could act as an early warning system, picking up on muscle quality decline before it becomes visible or starts affecting mobility. That means interventions can start sooner, when they’re most effective.
Hospitals and researchers could also use it to track disease progression in conditions like cancer cachexia or sarcopenia, where muscle loss plays a huge role in outcomes.
Let’s Imagine a Few Scenarios
To get a better sense of how this kind of scanning could be useful in practice, let’s walk through a few real-world scenarios where having access to data on muscle volume and intramuscular fat (IMF) could genuinely change the game.
Take a 55-year-old client who’s been training regularly with a personal trainer. They’re consistent, motivated, and putting the work in, but the strength gains just aren’t showing up the way they should. Rather than guessing or blaming poor genetics or technique, a quick CNN scan reveals the client has high levels of IMF in their leg muscles. This fat inside the muscle tissue could be interfering with function and holding back progress. Now, the trainer has a clear direction: shift the focus toward targeted resistance work and introduce nutrition strategies aimed at improving muscle quality. That kind of personalised insight could make the difference between a plateau and a breakthrough.
Now picture a runner coming off the back of Achilles tendon surgery. They’ve been following the usual rehab protocol and are gradually getting back on their feet. But a CNN MRI scan shows that their calf muscles, the soleus and gastrocnemius, have significantly shrunk and started to accumulate fat. It’s a quiet shift that wouldn’t necessarily be obvious from the outside. But now the rehab can be fine-tuned, not just to address tendon healing, but also to rebuild lost muscle in the specific areas that are lagging behind. That means a stronger return to sport and less chance of re-injury.
In a different setting, a clinician is working with a patient who has type 2 diabetes. Blood sugar management has been tricky despite medication and lifestyle advice. A scan of the leg muscles shows elevated IMF, which is a red flag. High IMF is known to interfere with glucose metabolism, contributing to insulin resistance. With this visual evidence, the clinician can explain exactly why building muscle and reducing fat inside the muscle matters. The patient sees the scan, understands the ‘why’, and is more motivated to commit to a structured resistance training programme that’s about much more than just aesthetics or weight loss.
And in a care home setting, where falls and mobility issues are common, this tech could play a preventative role. Imagine every resident undergoing a quick MRI scan with CNN analysis. Those showing signs of muscle atrophy and higher IMF can be flagged for tailored physiotherapy programmes. It’s no longer about reacting after someone has fallen, it’s about proactively identifying those at greater risk and giving them the support they need to stay stronger for longer.
These are just a handful of ways this technology could be applied, and we’re genuinely only scratching the surface. Whether it’s in sport, rehab, chronic disease management, or ageing populations, the ability to measure what’s happening inside the muscle quickly and accurately, opens the door to smarter, more individualised care.
Wrapping It Up: Food for Thought
Muscle strength isn’t just about how much you can lift, it’s about how well your body functions. And thanks to tools like CNN-based imaging, we’re getting better at actually seeing what’s happening beneath the surface.
So here’s something to chew on:
- If you’re a strength and conditioning coach, are you factoring in muscle quality as well as performance when designing programmes?
- Could access to tools like this change the way we assess progress or identify at-risk clients?
- And how might our coaching shift if we could see in real-time the effects of inactivity, diet, or rehab on muscle composition?
If this piques your curiosity, you might want to explore areas like the relationship between IMF and insulin sensitivity, how neural factors affect force production, or whether certain training styles are better at preserving muscle quality with age.
Because when it comes to muscle, there’s so much more than meets the eye.
References
Li R, Xia J, Zhang XI, Gathirua-Mwangi WG, Guo J, Li Y, McKenzie S, Song Y. Associations of Muscle Mass and Strength with All-Cause Mortality among US Older Adults. Med Sci Sports Exerc. 2018 Mar;50(3):458-467. Click here to review the full research article.
Smith, A.C., Muñoz Laguna, J., Wesselink, E.O. et al. (2025). Leg Muscle Volume, Intramuscular Fat and Force Generation: Insights From a Computer‐Vision Model and Fat‐Water MRI. Journal of Cachexia, Sarcopenia and Muscle, 16:e13735. Click here to review the full research article.
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