MRI-associated classification to evaluate the outcome after autologous reconstruction of the medial patellofemoral ligament: a preliminary study

Best, Raymond; Hingelbaum, Swen; Huth, Jochen; Mauch, Frieder

doi:10.1007/s00402-015-2349-9

Cited by 4 publications

(1 citation statement)

References 21 publications

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“…To BLAST (Basic Local Alignment Search Tool) as the representative of analysis software has gradually become the backbone of gene sequence analysis. Today, analysis tools such as BLAST help scientists around the world analyze more than 190 billion nucleotides a day, greatly increasing the efficiency of gene sequencing [ 18 ]. Convolutional neural network (CNN) is mainly used in the field of image recognition.…”

Section: Proposed Methodsmentioning

confidence: 99%

Classification and Reconstruction of Biomedical Signals Based on Convolutional Neural Network

Zhu

Chen

Xie

et al. 2022

Computational Intelligence and Neuroscience

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The efficient biological signal processing method can effectively improve the efficiency of researchers to explore the work of life mechanism, so as to better reveal the relationship between physiological structure and function, thus promoting the generation of major biological discoveries; high-precision medical signal analysis strategy can, to a certain extent, share the pressure of doctors’ clinical diagnosis and assist them to formulate more favorable plans for disease prevention and treatment, so as to alleviate patients’ physical and mental pain and improve the overall health level of the society. This article in biomedical signal is very representative of the two types of signals: mammary gland molybdenum target X-ray image (mammography) and the EEG signal as the research object, combined with the deep learning field of CNN; the most representative model is two kinds of biomedical signal classification, and reconstruction methods conducted a series of research: (1) a new classification method of breast masses based on multi-layer CNN is proposed. The method includes a CNN feature representation network for breast masses and a feature decision mechanism that simulates the physician’s diagnosis process. By comparing with the objective classification accuracy of other methods for the identification of benign and malignant breast masses, the method achieved the highest classification accuracy of 97.0% under different values of c and gamma, which further verified the effectiveness of the proposed method in the identification of breast masses based on molybdenum target X-ray images. (2) An EEG signal classification method based on spatiotemporal fusion CNN is proposed. This method includes a multi-channel input classification network focusing on spatial information of EEG signals, a single-channel input classification network focusing on temporal information of EEG signals, and a spatial-temporal fusion strategy. Through comparative experiments on EEG signal classification tasks, the effectiveness of the proposed method was verified from the aspects of objective classification accuracy, number of model parameters, and subjective evaluation of CNN feature representation validity. It can be seen that the method proposed in this paper not only has high accuracy, but also can be well applied to the classification and reconstruction of biomedical signals.

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Section: Proposed Methodsmentioning

confidence: 99%

Classification and Reconstruction of Biomedical Signals Based on Convolutional Neural Network

Zhu

Chen

Xie

et al. 2022

Computational Intelligence and Neuroscience

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MRI following medial patellofemoral ligament reconstruction: assessment of imaging features found with post-operative pain, arthritis, and graft failure

et al. 2020

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Femoral tunnel widening is associated with tunnel malposition but not with clinical failure after medial patellofemoral ligament reconstruction with a free gracilis tendon graft

Schüttler

Hoeger

Heyse

et al. 2018

Arch Orthop Trauma Surg

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MRI-associated classification to evaluate the outcome after autologous reconstruction of the medial patellofemoral ligament: a preliminary study

Abstract: We were able to establish and validate an MRI-associated classification to graduate the postoperative outcome after MPFL reconstructions. Foresighted, the presented classification might support further decision making in case of unsatisfying postoperative results.

Cited by 4 publications

References 21 publications

Classification and Reconstruction of Biomedical Signals Based on Convolutional Neural Network

Classification and Reconstruction of Biomedical Signals Based on Convolutional Neural Network

MRI following medial patellofemoral ligament reconstruction: assessment of imaging features found with post-operative pain, arthritis, and graft failure

Femoral tunnel widening is associated with tunnel malposition but not with clinical failure after medial patellofemoral ligament reconstruction with a free gracilis tendon graft

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