Sentence examples for quantification classification from inspiring English sources

Exact(2)

On that basis, we propose quantification classification criteria for thoracic pedicles that could help surgeons predict whether a screw could be inserted into a thoracic pedicle during PVCR, thus guiding the instrumentation of thoracic pedicles with the free-hand technique.

We proposed a quantification classification method of thoracic pedicles based on determining the inner cortical width of pedicles using CT: Type I thoracic pedicle has no channel and an inner cortical width of the thoracic pedicle of 0 1 mm.

Similar(58)

If all these individual SBRs and their associated normal limits are treated as individual tests, the final semi-quantification classification is likely to be overly sensitive (increasing the risk of type I error) and may give a pessimistic view on this form of analysis.

For the selected ensemble of microstructures, quantification and classification were carried out using a recently developed data-driven (objective) approach based on principal component analyses of 2-point correlations.

Although the scientific literature contains ample descriptions of peculiar patterns of repolarization linked to arrhythmic risk, the objective quantification and classification of these patterns continues to be a challenge that impacts their widespread adoption in clinical practice.

This could be extended towards an evaluation of the impact of a small-voxel reconstruction on adrenal gland quantification and classification with FDG-PET/CT.

If the right quantification and classification algorithms are used, the TF representation may successfully be employed for automatic pattern recognition applications.

Furthermore, this approach allowed for the detection, quantification, and classification of fungi, bacteria, and viruses in a single analytical pass [18] [19].

To further validate quantification and classification of key transcripts, a panel of 11 meiosis-associated genes including Am1 was analyzed by qRT (quantitative RT-PCR).

Powerful algorithms have been developed for learning-based segmentation (Sommer et al., 2011) and quantification and classification of cell morphologies (Boland and Murphy, 2001; Carpenter et al., 2006; Eliceiri et al., 2012; Held et al., 2010; Walter et al., 2010).

Tumor specimens were classified as low or high methylation (see Materials and methods for details on quantification and classification of promoter methylation), divided into two groups and compared with the mRNA expression levels of FBXW7/hCDC4-β.

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