id	author	title	date	pages	extension	mime	words	sentence	flesch	summary	cache	txt
bam-8892	Recenti, Marco; Ricciardi, Carlo; Edmunds, Kyle; Gislason, Magnus K.; Gargiulo, Paolo	Machine learning predictive system based upon radiodensitometric distributions from mid-thigh CT images	2020	4	.pdf	application/pdf	2748	142	47	The utility of these parameters in quantifying differences in fat, lean muscle, and loose connective tissue was first explored in comparing young, aging, and pathological subjects.11-13 Results from this work illustrated the sensitivity of NTRA parameters to changes in soft tissue and suggested the employment of this method in the context of a larger CT image database. The AGES-Reykjavík dataset thereby presents a unique opportunity for the employment of big data analytics methods such as ML modelling.14 As ML algorithms Machine learning on radiodensitometric distributions from CT Eur J TranslMyol 30 (1): 121-124, 2020 - 2 - have illustrated strong predictive value in the regression of body mass index (BMI)15 and isometric leg strength (ISO), the present study sought to demonstrate their prediction using NTRA parameters obtained from CT mid-femur cross-sections in the AGES-Reykjavík dataset.	cache/bam-8892.pdf	txt/bam-8892.txt