TURAN YURTSEVER, NURŞENBUĞDAYCI, ONUR2022-12-262022-12-262021-05-01ESEN B., TURAN M., TURAN YURTSEVER N., BUĞDAYCI O., "Current Status and Open Problems in Bone Age Estimation", ARTIFICIAL INTELLIGENCE THEORY and APPLICATIONS, cilt.1, sa.2, ss.308-318, 20212757-9778https://aita.bakircay.edu.tr/Yuklenenler/AITA/A21_02_002.pdfhttps://hdl.handle.net/11424/284084Bone age is an effective indicator for diagnosing various diseases and to determine bone ages of livings. The earliest well-known studies belong to the Greulich-Pyle and Tanner-Whitehouse, as a result bone age development atlases were published using hand and wrist radiography. Atlases works well for the younger ages between 0-18, while they have deviations at elder ages. Kazuro Anhara and Takao Suzuki emphasized the importance of changes in pubic symphysis of pubic bones belonging to 20 to 40 years old cases who were not alive for further ages. All this researches focuses on the hand intensive works. However, automation of bone age detection using artificial intelligence techniques such as image processing of radiological images is important in order to prevent human side-effects on the evaluation, they are called automated methods. Some examples are automatic bone age estimation fully automatic with carpal bone segmentation using fuzzy classification, fuzzybased radius model for bone age estimation including image preprocessing, and neural network applications mostly seen on the literature. It is obvious, artificial intelligence promises faster bone age estimation and to minimize different evaluations between experts. However, new studies are needed for applying new techniques (deep learning) efficiently and discovering new bones to estimate elder ages accurately in the field of forensic informatics especially.enginfo:eu-repo/semantics/openAccessBone age estimation Artificial neural network Image processing Forensic informaticsarticle12308318