Zobrazeno 1 - 4
of 4
pro vyhledávání: '"Erfan Mahmoudinia"'
Autor:
Hossein Mohammad-Rahimi, Shankeeth Vinayahalingam, Erfan Mahmoudinia, Parisa Soltani, Stefaan J. Bergé, Joachim Krois, Falk Schwendicke
Publikováno v:
Diagnostics, Vol 13, Iss 5, p 996 (2023)
Using super-resolution (SR) algorithms, an image with a low resolution can be converted into a high-quality image. Our objective was to compare deep learning-based SR models to a conventional approach for improving the resolution of dental panoramic
Externí odkaz:
https://doaj.org/article/9bf1639c11604bfbb98a88c19b0f1878
Autor:
Hossein Mohammad‐Rahimi, Saeed Reza Motamedian, Zeynab Pirayesh, Anahita Haiat, Samira Zahedrozegar, Erfan Mahmoudinia, Mohammad Hossein Rohban, Joachim Krois, Jae‐Hong Lee, Falk Schwendicke
Publikováno v:
Journal of Periodontal Research. 57:942-951
Deep learning (DL) has been employed for a wide range of tasks in dentistry. We aimed to systematically review studies employing DL for periodontal and implantological purposes. A systematic electronic search was conducted on four databases (Medline
Externí odkaz:
https://explore.openaire.eu/search/publication?articleId=doi_dedup___::62d108c60a793f5b3999c9cac63c087a
https://doi.org/10.1111/jre.13037
https://doi.org/10.1111/jre.13037
Autor:
Hossein Mohammad-Rahimi, Saeed Reza Motamadian, Mohadeseh Nadimi, Sahel Hassanzadeh-Samani, Mohammad A. S. Minabi, Erfan Mahmoudinia, Victor Y. Lee, Mohammad Hossein Rohban
Publikováno v:
Korean Journal of Orthodontics. 52:112-122
This study aimed to present and evaluate a new deep learning model for determining cervical vertebral maturation (CVM) degree and growth spurts by analyzing lateral cephalometric radiographs.The study sample included 890 cephalograms. The images were
Externí odkaz:
https://explore.openaire.eu/search/publication?articleId=doi_dedup___::6c8690302d4c630c7a4653822d99fa3c
https://doi.org/10.4041/kjod.2022.52.2.112
https://doi.org/10.4041/kjod.2022.52.2.112
Autor:
Hossein Mohammad-Rahimi, Saeed Reza Motamedian, Mohammad Hossein Rohban, Joachim Krois, Sergio E. Uribe, Erfan Mahmoudinia, Rata Rokhshad, Mohadeseh Nadimi, Falk Schwendicke
Publikováno v:
Journal of dentistry. 122
Objectives Detecting caries lesions is challenging for dentists, and deep learning models may help practitioners to increase accuracy and reliability. We aimed to systematically review deep learning studies on caries detection. Data We selected diagn
Externí odkaz:
https://explore.openaire.eu/search/publication?articleId=doi_dedup___::400f693c5365f6205fbb3e5841fb9e09
https://pubmed.ncbi.nlm.nih.gov/35367318
https://pubmed.ncbi.nlm.nih.gov/35367318