Table 3.
Specificity and sensitivity to evaluate accuracy of different methods.
| Detected faces | Facial features | Frontalized faces | |||||||
|---|---|---|---|---|---|---|---|---|---|
| Normal | NPV | Specificity | F1-score | NPV | Specificity | F1-score | NPV | Specificity | F1-score |
| Acromegaly | PPV | Sensitivity | F1-score | PPV | Sensitivity | F1-score | PPV | Sensitivity | F1-score |
| LM | 0.80 | 0.80 | 0.80 | 0.86 | 0.50 | 0.63 | 0.84 | 0.80 | 0.82 |
| 0.75 | 0.75 | 0.75 | 0.61 | 0.90 | 0.73 | 0.85 | 0.88 | 0.86 | |
| KNN | 0.87 | 0.80 | 0.83 | 0.91 | 0.88 | 0.89 | 0.93 | 0.93 | 0.93 |
| 0.77 | 0.85 | 0.81 | 0.86 | 0.90 | 0.88 | 0.89 | 0.89 | 0.89 | |
| SVM | 0.84 | 0.84 | 0.84 | 0.88 | 0.96 | 0.92 | 0.75 | 0.90 | 0.82 |
| 0.80 | 0.80 | 0.80 | 0.95 | 0.86 | 0.90 | 0.90 | 0.77 | 0.83 | |
| RT | 0.87 | 0.80 | 0.83 | 0.86 | 0.79 | 0.82 | 0.88 | 0.87 | 0.87 |
| 0.77 | 0.85 | 0.81 | 0.78 | 0.86 | 0.82 | 0.89 | 0.90 | 0.89 | |
| CNN | 0.87 | 0.87 | 0.87 | – | – | – | 0.92 | 0.96 | 0.94 |
| 0.88 | 0.91 | 0.89 | – | – | – | 0.96 | 0.91 | 0.93 | |
| Ensemble MLs | – | – | – | – | – | – | 0.95 | 0.96 | 0.95 |
| – | – | – | – | – | – | 0.96 | 0.96 | 0.96 | |
| Specialists in pituitary disease (average)a | – | – | – | – | – | – | 0.77 | 0.92 | 0.84 |
| – | – | – | – | – | – | 0.90 | 0.73 | 0.87 | |
| Primary care doctors (average)a | – | – | – | – | – | – | 0.70 | 0.85 | 0.77 |
| – | – | – | – | – | – | 0.83 | 0.68 | 0.75 | |
Abbreviations: Sensitivity refers to the classifier's ability to correctly detect acromegaly patients who do have the condition; Specificity relates to the test's ability to correctly reject healthy patients without acromegaly condition. NVP = negative predictive value; PPV = positive predictive value; LM = linear model; KNN = k-nearest neighbors; SVM = support vector machine; RT = forests of randomized tree; CNN = convolutional neural network; ML = machine learning.
a
The values for doctors are calculated based on doctors' diagnosis records on original facial images.