1 Paper: Developing computer-aided osteoporosis diagnosis system using fuzzy neural network Agus Zainal Arifin 1,2 , Akira Asano 3 , Akira Taguchi 4 , Takashi Nakamoto 5 , Masahiko Ohtsuka 5 , Mikio Tsuda 6 , Yoshiki Kudo 6 , Keiji Tanimoto 5 1 Department of Informatics, Faculty of Information Technology, Institut Teknologi Sepuluh Nopember (ITS), Surabaya, Indonesia. 2 Department of Information Engineering, Graduate School of Engineering, Hiroshima University, Higashi-Hiroshima, Japan. 3 Division of Mathematical and Information Sciences, Faculty of Integrated Arts and Sciences, Hiroshima University, Higashi-Hiroshima, Japan. 3 Department of Oral and Maxillofacial Radiology, Hiroshima University Hospital, Hiroshima, Japan. 5 Department of Oral and Maxillofacial Radiology, Division of Medical Intelligence and Informatics, Graduate School of Biomedical Sciences, Hiroshima University, Hiroshima, Japan. 6 Department of Obstetrics and Gynecology, Division of Clinical Medical Science, Graduate School of Biomedical Sciences, Hiroshima University, Hiroshima, Japan. E-mail: agus.za@its-sby.edu, asano@mis.hiroshima-u.ac.jp, {akiro, tnk, otsuka, mtsuda, yoshkudo, kg}@hiroshima-u.ac.jp A thin or eroded cortex of the mandible detected on dental panoramic radiographs is independently associated with low skeletal bone mineral density (BMD) or osteoporosis in postmenopausal women. The purposes of this study were to develop new computer-aided diagnosis system that combines these two panoramic measures by using fuzzy neural networks (FNN) for identifying postmenopausal women with osteoporosis. Dental panoramic radiographs of 100 postmenopausal women who visited our clinic and had BMD assessments at the lumbar spine and the femoral neck were used in this study. Mandibular cortical width and shape were measured by computer-aided systems and used as the inputs. This system partitioned the input space into a set of subspaces using a novel fuzzy thresholding and constructed the fuzzy inference system incorporated with multi layer perceptron neural network. Our results show that the combination of cortical width and shape by using FNN can be used for the identification of postmenopausal women with osteoporosis in dental clinic. Dentists may identify postmenopausal women accurately by using the new FNN based system and refer them to medical professional for BMD testing. Keywords: computer-aided; fuzzy neural network; osteoporosis; panoramic radiograph