M
Motoki Fukuda
Researcher at Aichi Gakuin University
Publications - 30
Citations - 841
Motoki Fukuda is an academic researcher from Aichi Gakuin University. The author has contributed to research in topics: Medicine & Computer science. The author has an hindex of 10, co-authored 20 publications receiving 372 citations.
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Journal ArticleDOI
A deep-learning artificial intelligence system for assessment of root morphology of the mandibular first molar on panoramic radiography
Teruhiko Hiraiwa,Yoshiko Ariji,Motoki Fukuda,Yoshitaka Kise,Kazuhiko Nakata,Akitoshi Katsumata,Hiroshi Fujita,Eiichiro Ariji +7 more
TL;DR: The deep learning system showed high accuracy in the differential diagnosis of a single or extra root in the distal roots of mandibular first molars.
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Deep-learning classification using convolutional neural network for evaluation of maxillary sinusitis on panoramic radiography
Makoto Murata,Yoshiko Ariji,Yasufumi Ohashi,Taisuke Kawai,Motoki Fukuda,Takuma Funakoshi,Yoshitaka Kise,Michihito Nozawa,Akitoshi Katsumata,Hiroshi Fujita,Eiichiro Ariji +10 more
TL;DR: The diagnostic performance of the deep-learning system for maxillary sinusitis on panoramic radiographs was sufficiently high and is expected to provide diagnostic support for inexperienced dentists.
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Evaluation of an artificial intelligence system for detecting vertical root fracture on panoramic radiography
Motoki Fukuda,Kyoko Inamoto,Naoki Shibata,Yoshiko Ariji,Yudai Yanashita,Shota Kutsuna,Kazuhiko Nakata,Akitoshi Katsumata,Hiroshi Fujita,Eiichiro Ariji +9 more
TL;DR: The CNN learning model has shown promise as a tool to detect VRFs on panoramic images and to function as a CAD tool.
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Contrast-enhanced computed tomography image assessment of cervical lymph node metastasis in patients with oral cancer by using a deep learning system of artificial intelligence.
Yoshiko Ariji,Motoki Fukuda,Yoshitaka Kise,Michihito Nozawa,Yudai Yanashita,Hiroshi Fujita,Akitoshi Katsumata,Eiichiro Ariji +7 more
TL;DR: The deep learning image classification system yielded diagnostic results similar to those of the radiologists, which suggests that this system may be valuable for diagnostic support.
Journal ArticleDOI
Automatic detection and classification of radiolucent lesions in the mandible on panoramic radiographs using a deep learning object detection technique.
Yoshiko Ariji,Yudai Yanashita,Syota Kutsuna,Chisako Muramatsu,Motoki Fukuda,Yoshitaka Kise,Michihito Nozawa,Chiaki Kuwada,Hiroshi Fujita,Akitoshi Katsumata,Eiichiro Ariji +10 more
TL;DR: Radiolucent lesions of the mandible can be detected with high sensitivity using deep learning and the best combination of detection and classification sensitivity occurred with dentigerous cysts.