**Author details**

Kaya Kuru1\* and Yusuf Tunca2

\*Address all correspondence to: kkuru@gata.edu.tr

1 IT Department, Gulhane Military Medical Academy (GATA), Ankara, Turkey

2 Department of Medical Genetics, Gulhane Military Medical Academy (GATA), Ankara, Turkey

#### **References**

**•** FaceGP DDSS methodology can provide genetic screening which is a preliminary process of applying standard analysis to large populations to pick up underlying symptoms of ge‐ netic disorders. Genetic screening is not a diagnosis, but can produce a differential diag‐ nosis which would lead to a definitive diagnosis and hence to early intervention and

The hope is that the FaceGP DDSS methodology will be widely adopted by the scientific community, fostering a new era of cooperation and collaboration and facilitating crossstudy. Based on user feedback, we expect to continue to update the functionality of the methodology. As the data gathering for age groups and ethnic groups becomes more stand‐ ardized and evolved internationally in the sense of dysmorphology, general implementa‐

Further improvement in diagnosing/recognition seems to be possible by integrating a back‐ ground cropping mask algorithm that simply provides a face shaped region and minimize

The dysmorphic faces from main databases as well as from individual databases referring to the names of the diseases should be both categorized and trained by the application for further better diagnostic decision support. This is a huge time consuming and challenging process needed to be done in the near future for the easy acceptance of the methodology by the scientific community. We intend to extend this work to a wider environment by including domain experts from academic and government institutions by deploying the methodology at sever‐ al sites including as possible as many syndromes. Furthermore, 3D image processing and fetus image analysis in dysmorphology is going to be the subject of our future study.

The authors are very grateful to TÜBİTAK (The Scientific and Technological Research Coun‐

2 Department of Medical Genetics, Gulhane Military Medical Academy (GATA), Ankara,

1 IT Department, Gulhane Military Medical Academy (GATA), Ankara, Turkey

treatment.

86 Decision Support System

**5.1. Future work**

tions valid for everybody will be more possible.

the effect of background change [15].

**Acknowledgements**

**Author details**

Turkey

Kaya Kuru1\* and Yusuf Tunca2

cil of Turkey) for sponsoring the study.

\*Address all correspondence to: kkuru@gata.edu.tr


[16] Tripathi, B. K., & Kalra, P. K. (2010). High dimensional neural networks and applica‐ tions. In: Prathiar DK., Jain LJ. (ed) Intelligent autonomous systems: foundations and applications. Springer , 215-233.

**Section 2**

**Business Applications**


**Section 2**
