ArticleMedicine2026
Artificial intelligence-driven body surface area (BSA) estimation using computed tomography: Comparative evaluation with existing formulae.
Article in Medicine, 2026. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Not yet cited in PubMed.
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Abstract
Total body surface area (BSA) is thought to be a better predictor of metabolic mass than body weight because it is less affected by abnormal adipose mass, and as a result, it is often used for drug dosing in clinical medicine. There are several formulae used for estimating BSA, however, most are based on 2-dimensional and not 3-dimensional BSA measurements or used naïve statistical techniques to create the estimating formulae. This study analyzed 3-dimensional whole-body positron emission tomography/computed tomography studies from 698 patients. A limitation of this dataset was the imbalance in racial distribution. BSA was obtained from the computed tomography component of these studies using TotalSegmentator software. There was a positive correlation between BSA and height (R = 0.49, P < .001), and between BSA and weight (R = 0.83, P < .001). Multivariable ridge regression with 5-fold cross-validation was used to create new formulae to estimate BSA by adjusting for sex, race, height, and weight. The optimal formula to estimate BSA in m2, is , where H is height in meters and W is weight in kilograms. Our formulae were significantly more accurate than all other formulae and had a lower mean absolute error and mean squared error than the Dubois (P < .001, P < .001), Mosteller (P < .001, P < .001), Haycock (P < .001, P < .001), Gehan (P < .001, P < .001), Boyd (P < .001, P < .001), Fujimoto (P < .001, P < .001), Takahira (P < .001, P < .001), Shuter and Aslani (P < .001, P < .001), Lipscombe (P < .001, P < .001), and Schlich (P < .001, P < .001) BSA formulae respectively.
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