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Yoshitaka Kise
Researcher at Aichi Gakuin University
Publications - 40
Citations - 976
Yoshitaka Kise is an academic researcher from Aichi Gakuin University. The author has contributed to research in topics: Computer science & Medicine. The author has an hindex of 14, co-authored 34 publications receiving 557 citations. Previous affiliations of Yoshitaka Kise include Kyushu University.
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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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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.
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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.
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Effectiveness of imaging modalities for screening IgG4-related dacryoadenitis and sialadenitis (Mikulicz's disease) and for differentiating it from Sjögren's syndrome (SS), with an emphasis on sonography.
Mayumi Shimizu,Kazutoshi Okamura,Yoshitaka Kise,Yoshitaka Kise,Yohei Takeshita,Hiroko Furuhashi,Warangkana Weerawanich,Masafumi Moriyama,Yukiko Ohyama,Sachiko Furukawa,Seiji Nakamura,Kazunori Yoshiura +11 more
TL;DR: Changes in the submandibular glands affected by IgG4-DS could be easily detected using sonography (characteristic bilateral nodal/reticular change) and FDG-PET/CT (abnormal 18F-FDG accumulation) and even inexperienced observers could detect these findings.