**2. Related research in the field**

Nowadays, the research of the new predictive diagnostic method of OSAS based on the 2D or 3D craniofacial image of the patient uses the advantage of machine learning, artificial intelligence, or statistical analysis. This research is based on the fact that OSAS occurrence is correlated with many diseases and syndromes (obesity, Down syndrome, adenotonsillar hypertrophy … ) manifesting on the head and face. Many modern diagnostic approaches for OSAS use automated detection of selected points on the head and face (e.g. eye corners, lips, earlobes, chin … ) and measure distance between selected points (**Figure 1**). Description of head and face with given distances serves as basic for classification process to compare with normal (physiological) model. Craniofacial points and their distances correspond with metrics obtained from paper sleep questionnaires dedicated to computing the score of OSAS risk.

Frontal and profile 2D facial photographic images of the control and experimental group are used in Ref. [16], and the features and landmarks were identified. The features were processed by support vector machine (SVM) classifier and get the resulting accuracy of 80% correct OSAS detections. In Ref [17], the authors use the training set of 3D images of 400 patients. All of them were identified with PSG, AHI index and were divided into 4 groups. The landmarks were identified manually and were expressed as the Euclidian and geodetic distance between them. The distances were considered as the features for further OSAS classification. In Ref [18], for OSAS distinction geometrical morphometry is used, also via 3D photography; in Ref [14]

**Figure 1.** *Selected examples of craniofacial measurement.*

convolutional neural network is a tool for OSAS prediction. Although the network was not trained on the depth data, the pretrained network achieved the 67% accuracy. Taking into account the information from previous research in this area, we can say that 3D model of the face and neck of the patient contains sufficient shape and structural features to determine the OSAS prediction. In most cases, the studies work with limited datasets, small groups of patients, or use only the frontal 3D scan of patients. As an alternative, we offer a standardized and well-described 3D model acquisition scanning system, applied in the clinical environment.
