Table 2 Fetal malpresentation estimation.

From: A mobile-optimized artificial intelligence system for gestational age and fetal malpresentation assessment

Subset

Number of participants

Number of malpresentations

AUC-ROC (95% CI)

Sensitivity (95% CI)

Specificity (95% CI)

All

613

65

0.977

(0.949, 1.0)

0.938

(0.848, 0.983)

0.973

(0.955, 0.985)

Low-cost device only

213

29

0.970

(0.944, 0.997)

0.931

(0.772, 0.992)

0.940

(0.896, 0.970)

Standard device only

598

65

0.980

(0.953, 1.000)

0.954

(0.871, 0.990)

0.977

(0.961, 0.988)

Novice only

189

21

0.992

(0.983, 1.000)

1.000

(0.839, 1.000)

0.952

(0.908, 0.979)

Sonographer only

424

43

0.972

(0.933, 989)

0.907

(0.779, 0.974)

0.987

(0.970, 0.996)

  1. The fetal malpresentation model was assessed by comparing predictions to the determination of a sonographer. In each subset of the data, we selected only the latest eligible visit from each patient. For sensitivity and specificity computations, model predictions were binarized according to a predefined threshold. Confidence intervals on the area under the receiver operating characteristic (AUC-ROC) were computed using the DeLong method. Confidence intervals on sensitivity and specificity were computed with the Clopper–Pearson method.
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