RETRACTED ARTICLE: Machine learning-based segmentation of images to diagnose the orthopedic diseases and to guide the orthopedic surgeries

Ling, Cai-Jin; Zeng, Ting; Dhaliwal, Vikramjit S.

doi:10.1007/s00500-023-08503-3

Cited by 2 publications

(1 citation statement)

References 23 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…In recent years, deep learning has achieved great success in object detection and image segmentation in the field of computer vision [4,5]. For example, U-Net [6], which is popular in medical image semantic segmentation, its U-shaped structure and jump connection have inspired a large number of researchers to think about the way the network is integrated [7].…”

Section: Introductionmentioning

confidence: 99%

An Evaluation Method of Dental Treatment Quality Combined with Deep Learning and Multi-index Decomposition

Peng,

Liu,

Yan

et al. 2023

Preprint

View full text Add to dashboard Cite

Dentists judge the quality of dental treatment for each patient is very time-consuming, and inefficient, lack of quantitative evaluation criteria, easy to cause errors. At the same time, the traditional method of extracting tooth and root canal image features based on experience is difficult to accurately extract the tooth area and root canal filling area, resulting in low accuracy of tooth and root canal segmentation, which in turn affects the accuracy of tooth treatment quality evaluation. In this paper, the deep learning convolutional neural network is used to segment the root canal filling area, tooth boundary and the boundary between tooth and soft tissue for the real patient ’s root canal treatment and filling image. Finally, the segmented image is quantitatively evaluated according to the multi-evaluation index of professional doctors. The experimental results show that the intelligent evaluation method of dental treatment quality combined with deep learning and multi-index decomposition proposed in this paper not only unifies the evaluation criteria of dental treatment quality, but also the therapeutic effect of quantitative scoring can effectively improve the work efficiency of doctors, which has reference significance for the application of artificial intelligence in the medical field.

show abstract

Section: Introductionmentioning

confidence: 99%