6 use cases for CT body composition with AI

The physical state of a patient is relevant to many different areas of healthcare. A fast, commonly used metric for this is the body mass index (BMI, weight divided by height in meters squared). However, BMI only provides an indication of how the weight of a patient relates to their height and does not give any information about muscle mass or fat distribution. Therefore, it is a poor estimate of a patient's physical state.

A better and more precise measure of a person's physical state is his or her body composition: the body's layout in terms of muscles, fat and organs. This much more informative measure can be obtained using information in medical imaging studies acquired for other purposes. For example CT scans are routinely acquired tens of thousands of times per year in any hospital. However, deriving the body compositional information from these scans would require manually delineating the tissues depicted in the scan; a very time-consuming task. While researchers have long known that this method could work, it is not feasible to perform in clinical practice. Fully automatic segmentation of these CT scans could therefore be of great help in providing accurate measurements of body composition based on imaging data that is readily available.

201209 -  Body comp - Figure 1Figure 1: Body composition analysis can offer valuable additional value to BMI calculation alone.

A large range of different use cases are relevant in the context of body composition analysis. Below are six examples where such automatic measurements could be of significant clinical value, including cardiovascular risk analysis, oncology and COVID-19 prognostics.

AI body composition for cardiovascular risk analysis

Many studies found that body composition is an important risk factor in cardiovascular disease.1 Automatic body composition analysis could therefore provide additional biomarkers for cardiovascular risk assessment.

People with a similar BMI can have a very different cardiometabolic risk profile. Especially people with excess visceral fat are at a high risk, which is not captured by solely measuring their BMI. Increased amounts of visceral fat are associated with e.g. type 2 diabetes, atherosclerosis and cardiovascular disease. Furthermore, excess visceral fat is often also associated with micro- or macroscopic fat inside of other tissues (ectopic fat), such as in or around skeletal muscles, in the liver and around the heart, which poses additional health risks – and is predictive of outcome in multiple clinical settings.1. CT is an excellent modality to allow accurate measurements of fat distribution in the body and therefore a highly suited modality for body compositional measurements which can be easily obtained using automated AI-powered tools. 

AI body composition for oncology

A large number of studies have shown the predictive power of body composition on outcome in cancer patients.2 Accurate analysis of body composition has been shown to improve prognostication and early identification of patients at risk: for example, decreased muscle mass (sarcopenia) predicts adverse outcomes in gastric cancer, hepatocellular carcinoma and metastatic renal cell carcinoma3.

Moreover, treatment response to chemotherapy or immunotherapy is highly influenced by body composition.4,5 Skeletal muscle depletion has been recognized as an independent predictor of toxicity. Accurate AI-based body composition measurements could therefore also help in better estimating the required dose for each individual patient. Such personalized dose estimation could then prevent toxicity, improve efficacy and reduce costs of cancer treatments.

AI body composition for surgery

Another promising application of automatic body composition analysis is in preoperative risk assessment. For example in cardiovascular surgery, the predictive nature of body compositional features has been shown in patients undergoing transcatheter aortic valve implantation, where psoas muscle area analyzed on preoperative CT scans has been found to directly predict postoperative mortality.6 In surgical oncology, sarcopenia, skeletal muscle fat infiltration and visceral obesity measures derived from body composition analysis, predicted postoperative complications in patients undergoing gastrectomy for gastric cancer.7 Hence, deploying AI to support surgery preparations holds great potential for improved patient outcomes.

AI body composition for contrast dose estimation

Furthermore, body composition analysis could improve the estimation of required contrast dose in CT, MRI and PET scans. It has been shown that estimation of lean body mass can help to reduce iodine dose in CT, while maintaining the required level of attenuation.8 Commonly, lean body mass is estimated based only on total body weight and height. In multiphase CT scans, the required contrast dose could therefore be accurately estimated based on a fast AI-powered body composition analysis of the precontrast image.

AI body composition for chronic kidney disease

A related application is chronic kidney disease, where automatic body composition analysis can both help predict renal function, which derives from total muscle mass9, as well as accurately quantify muscle wasting, a frequent finding in kidney disease10. Muscle wasting is associated with physical disability, worse quality of life and increased morbidity. Accurate analysis of body composition can help in making personalized treatment decisions for these patients. Hence, automating and therefore standardizing these analyses using AI may advance treatment trajectories considerably.

AI body composition for COVID-19

Finally, in light of the recent COVID-19 pandemic, body composition has also been recognized as a prognostic factor for patients with COVID-1911. In this study, access to CT body composition features allowed doctors to quantify the ratio between long spine muscle circumference and waist circumference. This measurement was found to predict which patients would end up in the intensive care unit, making CT body composition a cost-effective method to estimate required clinical resources, especially when analysis can be automated using AI.

In conclusion

Body composition measurements are a very promising tool in a wide range of use cases – a tool that is now available to any physician or researcher through fully automatic AI-based analysis of any CT scan.

Interested to try out AI-powered body composition measurements on your own CT data? Get in touch!


  1. Neeland, I. J. et al. Visceral and ectopic fat, atherosclerosis, and cardiometabolic disease: a position statement. Lancet Diabetes Endocrinol. 7, 715–725 (2019).
  2. Brown, J. C., Cespedes Feliciano, E. M. & Caan, B. J. The evolution of body composition in oncology—epidemiology, clinical trials, and the future of patient care: facts and numbers. J. Cachexia. Sarcopenia Muscle 1200–1208 (2019) doi:10.1002/jcsm.12379.
  3. Shachar, S. S., Williams, G. R., Muss, H. B. & Nishijima, T. F. Prognostic value of sarcopenia in adults with solid tumours: A meta-analysis and systematic review. Eur. J. Cancer 57, 58–67 (2016).
  4. Prado, C. M. M. Body composition in chemotherapy: The promising role of CT scans. Curr. Opin. Clin. Nutr. Metab. Care 16, 525–533 (2013).
  5. Daly, L. E. et al. The impact of body composition parameters on ipilimumab toxicity and survival in patients with metastatic melanoma. Br. J. Cancer 116, 310–317 (2017).
  6. van Mourik, M. S. et al. CT determined psoas muscle area predicts mortality in women undergoing transcatheter aortic valve implantation. Catheter. Cardiovasc. Interv. 93, E248–E254 (2019).
  7. Zhang, Y. et al. Computed tomography–quantified body composition predicts short-term outcomes after gastrectomy in gastric cancer. Curr. Oncol. 25, 411–422 (2018).
  8. Matsumoto, Y. et al. Contrast Material Injection Protocol With the Dose Determined According to Lean Body Weight at Hepatic Dynamic Computed Tomography: Comparison Among Patients With Different Body Mass Indices. J. Comput. Assist. Tomogr. 43, 736–740 (2019).
  9. Donadio, C. Body composition analysis allows the prediction of urinary creatinine excretion and of renal function in chronic kidney disease patients. Nutrients 9, (2017).
  10. Sabatino, A. et al. Muscle mass assessment in renal disease: The role of imaging techniques. Quant. Imaging Med. Surg. 10, 1672–1686 (2020).
  11. Kottlors, J. et al. Body composition on low dose chest CT is a significant predictor of poor clinical outcome in COVID-19 disease - A multicenter feasibility study. Eur. J. Radiol. 132, 109274 (2020).