The Application of Deep Learning to Quantify SAT/VAT in Human Abdominal Area

Zgallai, Walid A.; Brown, Teye; Murtada, Asma; Ali, Sara; Haji, Amina; Khalil, Khawla; Omran, Maryam; Dalah, Entesar Zawam; Faris, MoezAlIslam; Obaideen, Abdulmunhem

doi:10.1109/icaset.2019.8714313

Cited by 3 publications

(1 citation statement)

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“…Zgallai et al (58) focused on providing a quantification of SAT and VAT from MRI images of obese patient before and after fasting using DL and CNN. They utilized three hundred thirty images segmented manually by expertise and semi-automatically by using professional software.…”

Section: Ai and Radiomics State-of-the-art Overviewmentioning

confidence: 99%

Artificial intelligence, radiomics and other horizons in body composition assessment

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et al. 2020

Quant Imaging Med Surg

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This paper offers a brief overview of common non-invasive techniques for body composition assessment methods, and of the way images extracted by these methods can be processed with artificial intelligence (AI) and radiomic analysis. These new techniques are becoming more and more appealing in the field of health care, thanks to their ability to treat and process a huge amount of data, suggest new correlations between extracted imaging biomarkers and traits of several diseases as well as lead to the possibility to realise an increasingly personalized medicine. The idea is to suggest the use of AI applications and radiomic analysis to search for features that may be extracted from medical images [computed tomography (CT) and magnetic resonance imaging (MRI)], and that may turn out to be good predictors of metabolic disorder diseases and cancer. This could lead to patient-specific treatments and management of several diseases linked with excessive body fat.

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Section: Ai and Radiomics State-of-the-art Overviewmentioning

confidence: 99%