Table 6.

AUC, sensitivity and specificity of algorithms.

Angle‐closure mechanisms	Prediction algorithm	Overall accuracy (%)	AUC	95% CI of AUC	sensitivity	95% CI of sensitivity	specificity	95% CI of specificity
Pupillary Block	LR	80.9	0.920	0.890, 0.950	86.87%	84.81%, 88.93%	86.57%	83.14%, 89.99%
	NBC	77.7	0.918	0.889, 0.947	87.88%	85.89%, 89.87%	83.96%	80.27%, 87.64%
	NN	82.1	0.917	0.887, 0.946	88.89%	86.97%, 90.81%	84.33%	80.67%, 87.98%
Plateau Iris Configuration	LR	66.0	0.715	0.659, 0.771	77.67%	75.13%, 80.21%	54.85%	49.95%, 59.75%
	NBC	68.6	0.708	0.651, 0.764	68.93%	66.11%, 71.76%	62.69%	57.92%, 67.45%
	NN	74.7	0.707	0.650, 0.764	68.93%	66.11%, 71.76%	60.45%	55.63%, 65.27%
Thick Peripheral Iris Roll	LR	79.6	0.867	0.823, 0.912	78.41%	75.90%, 80.92%	82.84%	78.82%, 86.86%
	NBC	73.7	0.833	0.784, 0.882	86.36%	84.27%, 88.46%	67.54%	62.55%, 72.53%
	NN	86.8	0.886	0.849, 0.922	87.50%	85.48%, 89.52%	77.24%	72.77%, 81.71%

AUC = area under the receiver operator characteristic curve, CI = confidence interval, LR = logistic regression, NBC = Naïve Bayes’ classification, NN = neural network.