Deep Learning for Caries Detection: A Systematic Review: DL for Caries Detection.
Hossein Mohammad-Rahimi,Saeed Reza Motamedian,Mohammad Hossein Rohban,Joachim Krois,Sergio Uribe,Erfan Mahmoudi Nia,Rata Rokhshad,Mohadeseh Nadimi,Falk Schwendicke +8 more
TLDR
In this article , a systematic review of diagnostic accuracy studies that used deep learning models on dental imagery (including radiographs, photographs, optical coherence tomography images, near-infrared light transillumination images).About:
This article is published in Journal of Dentistry.The article was published on 2022-03-01 and is currently open access. It has received 33 citations till now. The article focuses on the topics: Medicine & Transillumination.read more
Citations
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Journal ArticleDOI
Where Is the Artificial Intelligence Applied in Dentistry? Systematic Review and Literature Analysis
A. Thurzo,Wanda Urbanova,Bohuslav Novák,L. Czakó,Tomáš Siebert,Peter Stano,Simona Mareková,G Fountoulaki,H. Kosnáčová,Ivan Varga +9 more
TL;DR: The review confirms that the current use of artificial intelligence in dentistry is concentrated mainly around the evaluation of digital diagnostic methods, especially radiology; however, its implementation is expected to gradually penetrate all parts of the profession.
Journal ArticleDOI
Deep learning in periodontology and oral implantology: A scoping review.
Hossein Mohammad-Rahimi,Saeed Reza Motamedian,Zeynab Pirayesh,Anahita Haiat,Samira Zahedrozegar,Erfan Mahmoudinia,Mohammad Hossein Rohban,Joachim Krois,Jae-Hong Lee,Falk Schwendicke +9 more
TL;DR: A growing number of studies evaluated DL for periodontal or implantological objectives but heterogeneity in study design, poor reporting and a high risk of bias severely limit the comparability of studies and the robustness of the overall evidence.
Journal ArticleDOI
ChatGPT for Shaping the Future of Dentistry: The Potential of Multi-Modal Large Language Model
Hanyao Huang,Ou Zheng,Dongdong Wang,Jiayi Yin,Zijin Wang,Shengxuan Ding,Heng Yin,R. Yang,Qiang Zeng,Bi Shi +9 more
TL;DR: In this article , the authors introduce two primary large language models (LLMs) deployment methods in dentistry, including automated dental diagnosis and cross-modal dental diagnosis, and examine their potential applications.
Journal ArticleDOI
Deep Learning for Detection of Periapical Radiolucent Lesions: A Systematic Review and Meta-analysis of Diagnostic Test Accuracy.
Soroush Sadr,Hossein Mohammad-Rahimi,Saeed Reza Motamedian,Samira Zahedrozegar,Parisa Tafazoli Motie,Shankeeth Vinayahalingam,Omid Dianat,Ali Nosrat +7 more
TL;DR: In this article , the overall accuracy of deep learning models in detecting periapical (PA) radiolucent lesions in dental radiographs, when compared to expert clinicians, was investigated.
Journal ArticleDOI
Improving performance of deep learning models using 3.5D U-Net via majority voting for tooth segmentation on cone beam computed tomography
K Hsu,Dayo Yuh,Shao-Chieh Lin,Pin-Sian Lyu,Guan-Xin Pan,Yi-Chun Zhuang,Chia Ching Chang,Hsu Hsia Peng,Tung-Yang Lee,Cheng-Hsuan Juan,Cheng-En Juan,Yi-Jui Liu,Chun-Jung Juan +12 more
TL;DR: In this paper , a 3.5D U-Net was proposed to improve the performance of the U-net in segmenting teeth on CBCT, which achieved the best segmentation performance among all U-Nets.
References
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QUADAS-2: A Revised Tool for the Quality Assessment of Diagnostic Accuracy Studies
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