Výsledky vyhledávání - "Takuma Funakoshi"

Akademický článek

Detection and classification of unilateral cleft alveolus with and without cleft palate on panoramic radiographs using a deep learning system

Autor: Chiaki Kuwada, Yoshiko Ariji, Yoshitaka Kise, Takuma Funakoshi, Motoki Fukuda, Tsutomu Kuwada, Kenichi Gotoh, Eiichiro Ariji

Publikováno v: Scientific Reports, Vol 11, Iss 1, Pp 1-10 (2021)

Abstract Although panoramic radiography has a role in the examination of patients with cleft alveolus (CA), its appearances is sometimes difficult to interpret. The aims of this study were to develop a computer-aided diagnosis system for diagnosing t

Externí odkaz: https://doaj.org/article/a493db1ced3f4929b493e7338b127a10

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Performance of deep learning technology for evaluation of positioning quality in periapical radiography of the maxillary canine

Autor: Kiyomi Kohinata, Tomoya Kitano, Mizuho Mori, Yoshiko Ariji, Yukihiro Iida, Motoki Fukuda, Akitoshi Katsumata, Takuma Funakoshi, Eiichiro Ariji, Wataru Nishiyama

Publikováno v: Oral Radiology

Objectives The aim of the present study was to create and test an automatic system for assessing the technical quality of positioning in periapical radiography of the maxillary canines using deep learning classification and segmentation techniques. M

Externí odkaz: https://explore.openaire.eu/search/publication?articleId=doi_dedup___::23397382fd8016d0c074721aa848d82a
https://doi.org/10.1007/s11282-021-00538-2

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Deep-learning systems for diagnosing cleft palate on panoramic radiographs in patients with cleft alveolus

Autor: Chiaki Kuwada, Yoshiko Ariji, Yoshitaka Kise, Motoki Fukuda, Masako Nishiyama, Takuma Funakoshi, Rihoko Takeuchi, Airi Sana, Norinaga Kojima, Eiichiro Ariji

Publikováno v: Oral radiology.

Objectives The aim of the present study was to create effective deep learning-based models for diagnosing the presence or absence of cleft palate (CP) in patients with unilateral or bilateral cleft alveolus (CA) on panoramic radiographs. Methods The

Externí odkaz: https://explore.openaire.eu/search/publication?articleId=doi_dedup___::1c387de625db1ffd1c6b079c453ec758
https://pubmed.ncbi.nlm.nih.gov/35984588

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Detection and classification of unilateral cleft alveolus with and without cleft palate on panoramic radiographs using a deep learning system

Autor: Eiichiro Ariji, Kenichi Gotoh, Motoki Fukuda, Yoshiko Ariji, Chiaki Kuwada, Yoshitaka Kise, Tsutomu Kuwada, Takuma Funakoshi

Publikováno v: Scientific Reports
Scientific Reports, Vol 11, Iss 1, Pp 1-10 (2021)

Although panoramic radiography has a role in the examination of patients with cleft alveolus (CA), its appearances is sometimes difficult to interpret. The aims of this study were to develop a computer-aided diagnosis system for diagnosing the CA sta

Externí odkaz: https://explore.openaire.eu/search/publication?articleId=doi_dedup___::6335f34dc07c02a5eab3e7a409a31dbb
https://doi.org/10.1038/s41598-021-95653-9

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Application of super-resolution convolutional neural network technique to improve the quality of soft-tissue window cone-beam CT images

Autor: Munetaka Nitoh, Masako Nishiyama, Motoki Fukuda, Chiaki Kuwada, Eiichiro Ariji, Hiroshi Fujita, Akitoshi Katsumata, Takuma Funakoshi, Yoshiko Ariji, Michihito Nozawa

Objectives To assess the feasibility of using a super-resolution convolutional neural network to improve the quality of cone-beam computed tomography (CBCT) images for visualizing soft-tissue structures. Methods Multidetector computed tomography (CT)

Externí odkaz: https://explore.openaire.eu/search/publication?articleId=doi_________::4d5e9208aa954617dc9442fb557acb3a
https://doi.org/10.21203/rs.3.rs-242807/v1

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Effects of 1 year training on the performance of ultrasonographic image interpretation: A preliminary evaluation using images of Sjogren syndrome patients

Autor: Masako Nishiyama, Eiichiro Ariji, Kazunori Yoshiura, Mayumi Shimizu, Takuma Funakoshi, Tore Bjørnland, Anne Møystad, Chiaki Kuwada, Yoshitaka Kise, Yoshiko Ariji

Publikováno v: Imaging Science in Dentistry

Purpose This study investigated the effects of 1 year of training on imaging diagnosis, using static ultrasonography (US) salivary gland images of Sjogren syndrome patients. Materials and methods This study involved 3 inexperienced radiologists with

Externí odkaz: https://explore.openaire.eu/search/publication?articleId=doi_dedup___::a34ab0bbffbf9894d1af09c5a7683b97
http://hdl.handle.net/10852/92104

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Comparison of 3 deep learning neural networks for classifying the relationship between the mandibular third molar and the mandibular canal on panoramic radiographs

Autor: Hiroshi Fujita, Akitoshi Katsumata, Yoshitaka Kise, Eiichiro Ariji, Motoki Fukuda, Takuma Funakoshi, Chiaki Kuwada, Yoshiko Ariji, Chisako Muramatsu, Michihito Nozawa

Publikováno v: Oral surgery, oral medicine, oral pathology and oral radiology. 130(3)

Objective The aim of this study was to compare time and storage space requirements, diagnostic performance, and consistency among 3 image recognition convolutional neural networks (CNNs) in the evaluation of the relationships between the mandibular t

Externí odkaz: https://explore.openaire.eu/search/publication?articleId=doi_dedup___::3fc0b57eeb21c53900a63cee482ff494
https://pubmed.ncbi.nlm.nih.gov/32444332

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Usefulness of a deep learning system for diagnosing Sjögren’s syndrome using ultrasonography images

Autor: Akitoshi Katsumata, Yoshitaka Kise, Eiichiro Ariji, Takeshi Fujii, Masako Nishiyama, Yoshiko Ariji, Chiaki Kuwada, Hiroshi Fujita, Mayumi Shimizu, Haruka Ikeda, Kazunori Yoshiura, Takuma Funakoshi

Publikováno v: Dentomaxillofac Radiol

Objectives: We evaluated the diagnostic performance of a deep learning system for the detection of Sjögren’s syndrome (SjS) in ultrasonography (US) images, and compared it with the performance of inexperienced radiologists. Methods: 100 patients w

Externí odkaz: https://explore.openaire.eu/search/publication?articleId=doi_dedup___::8bf94a36967528d46ca8a66f236aff85
https://doi.org/10.1259/dmfr.20190348

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Deep-learning classification using convolutional neural network for evaluation of maxillary sinusitis on panoramic radiography

Autor: Makoto Murata, Taisuke Kawai, Eiichiro Ariji, Yoshitaka Kise, Takuma Funakoshi, Yoshiko Ariji, Yasufumi Ohashi, Hiroshi Fujita, Michihito Nozawa, Akitoshi Katsumata, Motoki Fukuda

Publikováno v: Oral radiology. 35(3)

To apply a deep-learning system for diagnosis of maxillary sinusitis on panoramic radiography, and to clarify its diagnostic performance. Training data for 400 healthy and 400 inflamed maxillary sinuses were enhanced to 6000 samples in each category

Externí odkaz: https://explore.openaire.eu/search/publication?articleId=doi_dedup___::d7712e615ae7128ab02b279dab201605
https://pubmed.ncbi.nlm.nih.gov/30539342

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