Key-point estimation of knee X-ray images using a parallel fusion decoding network

Wu, Zhichao; Zhang, Ruijie; Bai, Haohao; Ma, Jianxiong; Ma, Xinlong; Zhu, Xinjun

doi:10.1016/j.knee.2022.11.026

Cited by 3 publications

(1 citation statement)

References 17 publications

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“…In another study CNNs, trained on 6,149 radiographs, reported a mean absolute error of 0.8 • on femoratibial angle prediction [20]. In another study on 1,842 knees X-ray images, the error obtained by CNNs on hinge point, surgical point, and Fujisawa point was, respectively 2.06±1.16 mm, 2.71±1.45 mm, and 2.01±1.30 mm [30]. In this work, we presented Holoknee, the first application combining AI and MR for preoperative planning in knee osteotomy.…”

Section: Discussionmentioning

confidence: 97%

Mixed Reality and Artificial Intelligence: A Holistic Approach to Multimodal Visualization and Extended Interaction in Knee Osteotomy

Moglia,

Marsilio,

Rossi

et al. 2024

IEEE J. Transl. Eng. Health Med.

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Objective: Recent advancements in augmented reality led to planning and navigation systems for orthopedic surgery. However little is known about mixed reality (MR) in orthopedics. Furthermore, artificial intelligence (AI) has the potential to boost the capabilities of MR by enabling automation and personalization. The purpose of this work is to assess Holoknee prototype, based on AI and MR for multimodal data visualization and surgical planning in knee osteotomy, developed to run on the HoloLens 2 headset. Methods: Two preclinical test sessions were performed with 11 participants (eight surgeons, two residents, and one medical student) executing three times six tasks, corresponding to a number of holographic data interactions and preoperative planning steps. At the end of each session, participants answered a questionnaire on user perception and usability. Results: During the second trial, the participants were faster in all tasks than in the first one, while in the third one, the time of execution decreased only for two tasks (“Patient selection” and “Scrolling through radiograph”) with respect to the second attempt, but without statistically significant difference (respectively \documentclass[12pt]{minimal} \usepackage{amsmath} \usepackage{wasysym} \usepackage{amsfonts} \usepackage{amssymb} \usepackage{amsbsy} \usepackage{upgreek} \usepackage{mathrsfs} \setlength{\oddsidemargin}{-69pt} \begin{document} $p$\end{document} = 0.14 and \documentclass[12pt]{minimal} \usepackage{amsmath} \usepackage{wasysym} \usepackage{amsfonts} \usepackage{amssymb} \usepackage{amsbsy} \usepackage{upgreek} \usepackage{mathrsfs} \setlength{\oddsidemargin}{-69pt} \begin{document} $p$\end{document} = 0.13, \documentclass[12pt]{minimal} \usepackage{amsmath} \usepackage{wasysym} \usepackage{amsfonts} \usepackage{amssymb} \usepackage{amsbsy} \usepackage{upgreek} \usepackage{mathrsfs} \setlength{\oddsidemargin}{-69pt} \begin{document} $p < 0.05$\end{document} ). All subjects strongly agreed that MR can be used effectively for surgical training, whereas 10 (90.9%) strongly agreed that it can be used effectively for preoperative planning. Six (54.5%) agreed and two of them (18.2%) strongly agreed that it can be used effectively for intraoperative guidance. Discussion/Conclusion: In this work, we presented Holoknee, the first holistic application of AI and MR for surgical planning for knee osteotomy. It reported promising results on its potential translation to surgical training, preoperative planning, and surgical guidance. Clinical and Translational Impact Statement - Holoknee can be helpful to support surgeons in the preoperative planning of knee osteotomy. It has the potential to impact positively the training of the future generation of residents and aid surgeons in the intraoperative stage.

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Section: Discussionmentioning

confidence: 97%

Mixed Reality and Artificial Intelligence: A Holistic Approach to Multimodal Visualization and Extended Interaction in Knee Osteotomy

Moglia,

Marsilio,

Rossi

et al. 2024

IEEE J. Transl. Eng. Health Med.

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Automated diagnosis of adenoid hypertrophy with lateral cephalogram in children based on multi-scale local attention

Rao,

Zhang,

Wang

et al. 2024

Sci Rep

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Adenoid hypertrophy can lead to adenoidal mouth breathing, which can result in “adenoid face” and, in severe cases, can even lead to respiratory tract obstruction. The Fujioka ratio method, which calculates the ratio of adenoid (A) to nasopharyngeal (N) space in an adenoidal-cephalogram (A/N), is a well-recognized and effective technique for detecting adenoid hypertrophy. However, this process is time-consuming and relies on personal experience, so a fully automated and standardized method needs to be designed. Most of the current deep learning-based methods for automatic diagnosis of adenoids are CNN-based methods, which are more sensitive to features similar to adenoids in lateral views and can affect the final localization results. In this study, we designed a local attention-based method for automatic diagnosis of adenoids, which takes AdeBlock as the basic module, fuses the spatial and channel information of adenoids through two-branch local attention computation, and combines the downsampling method without losing spatial information. Our method achieved mean squared error (MSE) 0.0023, mean radial error (MRE) 1.91, and SD (standard deviation) 7.64 on the three hospital datasets, outperforming other comparative methods.

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A Comparison of Optimizer Algorithms in YOLOv8 for Automatic Detection of Knee Landmarks

Supatman,

Yuniarno,

Purnomo

2023

2023 8th International Conference on Information Technology and Digital Applications (ICITDA)

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Key-point estimation of knee X-ray images using a parallel fusion decoding network

Cited by 3 publications

References 17 publications

Mixed Reality and Artificial Intelligence: A Holistic Approach to Multimodal Visualization and Extended Interaction in Knee Osteotomy

Mixed Reality and Artificial Intelligence: A Holistic Approach to Multimodal Visualization and Extended Interaction in Knee Osteotomy

Automated diagnosis of adenoid hypertrophy with lateral cephalogram in children based on multi-scale local attention

A Comparison of Optimizer Algorithms in YOLOv8 for Automatic Detection of Knee Landmarks

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