Mentions
Extended reach
Total likes
Video views
AI synopsis
The discussions emphasize the technical strengths of the system, mentioning elements like annotated radiographs and performance metrics, with phrases like '97% mAP' and 'automated segmentation' indicating a focus on precision and efficiency. Words such as 'enhanced' and 'improving' reveal a positive tone about technological advancement, while references to practical applications suggest recognition of its potential impact.
Most discussions recognize the accuracy and significance of the deep learning method, with references to high performance metrics like 97% mAP, indicating general support for the research findings.
Posts show high interest by highlighting technical details such as annotation processes, training data, and performance metrics, suggesting engagement with the methodology.
Discussions include mentions of specific techniques like Mask R-CNN and implications for dental diagnostics, reflecting active engagement and consideration of the tool’s applications.
Users suggest that this system could improve diagnostic speed and accuracy, implying it could have a meaningful impact on dental practice, though some see it as an incremental improvement rather than transformative.
A total of 1,200 bitewing X-rays were annotated by oral radiologists and divided into 1,000 for training and 200 for testing. 97% mAP in FDI-based tooth segmentation on bitewing X-rays using Mask R-CNN. Watch on YT: https://t.co/3X3GbBnKK4
An enhanced tooth segmentation and numbering according to FDI notation in bitewing radiographs https://t.co/U7tVEFDtJK
This study develops a deep learning system using Mask R-CNN to automatically segment and number teeth in bitewing radiographs based on FDI notation. The system achieves high accuracy, supporting dental diagnostics by…
This 2022 study from Turkish universities introduces a Mask R-CNN based system for automatic tooth segmentation and numbering on bitewing radiographs using FDI notation. It achieved high accuracy, supporting efficient…
RT @Dentalai_pt: A total of 1,200 bitewing X-rays were annotated by oral radiologists and divided into 1,000 for training and 200 for testi…
A total of 1,200 bitewing X-rays were annotated by oral radiologists and divided into 1,000 for training and 200 for testing. 97% mAP in FDI-based tooth segmentation on bitewing X-rays using Mask R-CNN. Watch on YT: https://t.co/3X3GbBnKK4
An enhanced tooth segmentation and numbering according to FDI notation in bitewing radiographs https://t.co/U7tVEFDtJK
TL