Table 3.

Classifier metrics using nanoparticle trajectories to predict particle type (PS-COOH, PS-COOH in serum, PS-PEG, and PS-PEG in serum) in 0.4% agarose gel model of the brain. From left to right: (1) sample size of training and test datasets after spatial checkerboard split, (2) log median predictor using diffusion coefficients at τ = 100 ms, (3) log median predictor using mean diffusion coefficients at τ = 100 ms averaged over 9 μm × 9 μm windows, (4) neural network predictor trained with principal components from trajectory feature analysis using both individual and locally averaged geometric features

	Sample size		Median predictora		Neural networka
	Training	Test	Training	Test	Training	Test
PS-COOH	134 717	135 518	0.329	0.324	1.000	0.902
PS-COOH in serum	191 947	191654	0.357	0.374	1.000	0.916
PS-PEG	52 038	51 917	0.166	0.191	0.998	0.741
PS-PEG in serum	351 935	352 061	0.828	0.821	1.000	0.981
Avg/tot	730 637	731 150	0.420	0.427	0.998	0.885

^aPredictions performed using locally averaged features.