The role of body composition assessment in obesity and eating disorders. Issue 131 (October 2020) - Dalili, Danoob; Bazzocchi, Alberto; Dalili, Daniel E.; Guglielmi, Giuseppe; Isaac, Amanda
Highlights: Body mass index "BMI" is limited and open to inaccurate interpretation or omission of critical data. Advances in imaging allow better diagnosis of Obesity and eating disorders with qualitative and quantitative data and morphological correlation. Can be used as a baseline and for monitoring the effect of various interventions / therapies. Most of these conditions are often chronic but reversible. Artificial intelligence and state-of-the-art imaging improve accuracy of diagnosis and monitoring, improving patients' outcomes. Abstract: Lack of a balanced diet can have a significant impact on most organs of the body. Traditionally, evaluation of these conditions relied heavily upon body mass index "BMI" measurements, which are limited and open to inaccurate interpretation or omission of critical data. Advances in imaging allow better recognition of these conditions using accurate qualitative and quantitative data and correlation with any morphological changes in organs. Body composition evaluations include the assessment of the bone mineral density (BMD), visceral fat, subcutaneous fat, liver fat and iron overload and muscle fat (including the lean muscle ratio), with differential evaluation of specific muscle groups when required. Such measurements are important as a baseline and for monitoring the effect of therapies and various interventions. In addition, they may predict and help alleviate any potential complications, allowing counselling of patients in a relatable manner. This positively influences patient compliance and outcomes during early counselling, monitoring and modulation of therapy. This encourages patients suffering from obesity and eating disorders to better understand their often chronic but reversible condition. We present a review of current literature with reflection on our own practices. We discuss the importance of monitoring the reversibility of certain parameters in specific cohorts of patients. We consider the role of artificial intelligence and deep learning in developing software algorithms that can help the reading radiologist evaluate large volumes of data and present the results in a format that is easier to interpret, thereby reducing interobserver and intraobserver variabilities.