Table 4.

Classifier metrics using nanoparticle trajectories to predict particle type (PS-COOH, PS-COOH in serum, PS-PEG, and PS-PEG in serum) in organotypic rat brain slice model. 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 averaged over 9 μm × 9 μm windows, (3) neural network predictor trained with principal components from trajectory feature analysis using both individual and locally averaged geometric features, (4) neural network trained with principal components from trajectory feature analysis limited to PS-COOH nanoparticles, (5) neural network trained with principal components from trajectory feature analysis limited to PS-PEG nanoparticles, (6) neural network trained with principal components from trajectory feature analysis limited to PS-COOH nanoparticles using leave-one-out cross-validation (average recall values reported)

	Sample size		Median predictora		Neural networka		Neural networka,b		Neural networka,b,c		Neural networka,b,d
	Training	Test	Training	Test	Training	Test	Training	Test	Training	Test	Training	Test
PS-COOH	88 001	86 429	0.083	0.075	0.773	0.593	0.807	0.473	0.886	0.536	—	—
PS-COOH in serum	138 913	142 066	0.176	0.172	0.842	0.687	0.878	0.548	0.936	0.640	—	—
PS-PEG	35 969	36 864	0.142	0.171	0.438	0.283	0.517	0.065	—	—	0.658	0.258
PS-PEG in serum	95 111	93 907	0.029	0.023	0.879	0.800	0.878	0.641	—	—	0.967	0.908
Avg/tot	357 994	359 266	0.107	0.110	0.838	0.591	0.770	0.432	0.911	0.588	0.812	0.583

^aPredictions performed using locally averaged features.

^bReported recall values are averaged from 4 separate 3–1 train-test split predictors.

^cPredictor limited to PS-COOH nanoparticles to predict serum status.

^dPredictor limited to PS-PEG nanoparticles to predict serum status.