One connector, four scholarly indexes, real DOIs and legal free PDFs. Works in Claude and ChatGPT.
Paste it into Claude or ChatGPT as a custom connector. 3-day free trial.
Karen A. Panetta, Rahul Rajendran, Aruna Ramesh, Shishir Paramathma Rao, Sos С. Agaian
The application of Artificial Intelligence in dental healthcare has a very promising role due to the abundance of imagery and non-imagery-based clinical data. Expert analysis of dental radiographs can provide crucial information for clinical diagnosis and treatment. In recent years, Convolutional Neural Networks have achieved the highest accuracy in various benchmarks, including analyzing dental X-ray images to improve clinical care quality. The Tufts Dental Database, a new X-ray panoramic radiography image dataset, has been presented in this paper. This dataset consists of 1000 panoramic dental radiography images with expert labeling of abnormalities and teeth. The classification of radiography images was performed based on five different levels: anatomical location, peripheral characteristics, radiodensity, effects on the surrounding structure, and the abnormality category. This first-of-its-kind multimodal dataset also includes the radiologist's expertise captured in the form of eye-tracking and think-aloud protocol. The contributions of this work are 1) publicly available dataset that can help researchers to incorporate human expertise into AI and achieve more robust and accurate abnormality detection; 2) a benchmark performance analysis for various state-of-the-art systems for dental radiograph image enhancement and image segmentation using deep learning; 3) an in-depth review of various panoramic dental image datasets, along with segmentation and detection systems. The release of this dataset aims to propel the development of AI-powered automated abnormality detection and classification in dental panoramic radiographs, enhance tooth segmentation algorithms, and the ability to distill the radiologist's expertise into AI.
Formatted from the source metadata, not typed by hand. For a whole reference list, use the citation generator or ask your assistant through Find Research.
Panetta, K. A., Rajendran, R., Ramesh, A., Rao, S. P., & Agaian, S. С. (2021). Tufts Dental Database: A Multimodal Panoramic X-Ray Dataset for Benchmarking Diagnostic Systems. IEEE Journal of Biomedical and Health Informatics, 26(4), 1650–1659. https://doi.org/10.1109/jbhi.2021.3117575
Panetta, Karen A., et al. "Tufts Dental Database: A Multimodal Panoramic X-Ray Dataset for Benchmarking Diagnostic Systems." IEEE Journal of Biomedical and Health Informatics, vol. 26, no. 4, 2021, pp. 1650–1659, https://doi.org/10.1109/jbhi.2021.3117575.
Panetta, Karen A., Rahul Rajendran, Aruna Ramesh, Shishir Paramathma Rao, and Sos С. Agaian. "Tufts Dental Database: A Multimodal Panoramic X-Ray Dataset for Benchmarking Diagnostic Systems." IEEE Journal of Biomedical and Health Informatics 26, no. 4 (2021): 1650–1659. https://doi.org/10.1109/jbhi.2021.3117575.
Panetta K. A., Rajendran R., Ramesh A. [и др.] Tufts Dental Database: A Multimodal Panoramic X-Ray Dataset for Benchmarking Diagnostic Systems // IEEE Journal of Biomedical and Health Informatics. — 2021. — Vol. 26, № 4. — P. 1650–1659. — URL: https://doi.org/10.1109/jbhi.2021.3117575
@article{panetta2021tufts,
title = {{Tufts Dental Database: A Multimodal Panoramic X-Ray Dataset for Benchmarking Diagnostic Systems}},
author = {Karen A. Panetta and Rahul Rajendran and Aruna Ramesh and Shishir Paramathma Rao and Sos С. Agaian},
journal = {IEEE Journal of Biomedical and Health Informatics},
year = {2021},
volume = {26},
number = {4},
pages = {1650--1659},
doi = {10.1109/jbhi.2021.3117575},
url = {https://doi.org/10.1109/jbhi.2021.3117575}
}TY - JOUR AU - Panetta, Karen A. AU - Rajendran, Rahul AU - Ramesh, Aruna AU - Rao, Shishir Paramathma AU - Agaian, Sos С. TI - Tufts Dental Database: A Multimodal Panoramic X-Ray Dataset for Benchmarking Diagnostic Systems JO - IEEE Journal of Biomedical and Health Informatics VL - 26 IS - 4 SP - 1650 EP - 1659 PY - 2021 DA - 2021/10/04 DO - 10.1109/jbhi.2021.3117575 UR - https://doi.org/10.1109/jbhi.2021.3117575 ER -
Add Find Research to Claude or ChatGPT and ask in plain language. The assistant reads this record, finds the papers around it and cites only what it actually found:
Setup takes about a minute: add the connector, then start the free trial.
Record from OpenAlex; open-access location from Unpaywall and the indexes. Find Research links only to legal copies.