**5. Assistive algorithms**

Regarding the heterogeneity of genetically related HL, the precise evaluation of its cause is a challenging task. Apart from some, rather rare, unquestionable phenotypic manifestations, many of the cases of possibly genetically related HL (syndromic and nonsyndromic) are hard to distinguish. It should be noted that there is a group of useful, assistive algorithms, which are helpful for both: establishing the diagnosis and targeting a diagnostic approach.

The Face2Gene (http://suite.face2gene.com/) is a collection of phenotyping applications, which enable accurate and comprehensive assessment of a patient based on characteristic dysmorphic facial features. As the variations in the face and skull are very common and are present in about 40% of genetic disorders, they are very useful in identification of the disorder. Briefly, the picture of patient face is uploaded to an application, than the quantification of the similarity is calculated with the usage of the algorithm database. The result is the list of syndromes with analogous morphology. The algorithm Face2Gene is accessible free of charge to all healthcare specialists.

The AudioGene (http://audiogene.eng.uiowa.edu/) predicts the genes underling autosomal dominant nonsyndromic hearing loss (ADNSHL) based on the audiometric data. This algorithm also takes into account the age of the patient, which is essential in the ADNSHL study due to characteristic deterioration of hearing during patient's life. The algorithm is based on the computational analysis of specific audiometric data deposited in the database. Furthermore, the machine-learning method is applied to select and prioritize possibly causative genes for pathogenic variant screening [36, 37].

The Pictures of Standard Syndromes and Undiagnosed Malformations (POSSUM) (https:// www.possum.net.au/) is a comprehensive dysmorphology database designed for the diagnosis of multiple malformations, metabolic, teratogenic, chromosomal, and skeletal syndromes as well as their images. This database consists of information on over 4000 different syndromes and searching is based on the selected features and results in a specific list of possible diagnoses [38].
