Predicting the Performance and Adaptation of Artificial Elbow Due to Effective Forces using Deep Learning

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Abstract

Measuring power transmission in organs poses a significant challenge for researchers in the field, with various methods being explored, including the use of artificial intelligence algorithms. This study focused on developing a new neural network model to predict force transmission and performance in an artificial elbow. Rather than evaluating natural joints, the study simulated a prosthetic model using medical software. Empirical data was collected using MIMICS software to estimate power properties and transmission methods, which were then used to train a neural network in MATLAB. The neural network demonstrated strong performance, particularly with the use of CNN architecture. The model's accuracy was validated by comparing results with experimental data from Anatomy and Physiology Comparison software, showing that the neural network provided precise results.

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The 2021 WHO Classification of Tumors of the Central Nervous System: a summary

David N Louis, Arie Perry, Pieter Wesseling … (2021)

The fifth edition of the WHO Classification of Tumors of the Central Nervous System (CNS), published in 2021, is the sixth version of the international standard for the classification of brain and spinal cord tumors. Building on the 2016 updated fourth edition and the work of the Consortium to Inform Molecular and Practical Approaches to CNS Tumor Taxonomy, the 2021 fifth edition introduces major changes that advance the role of molecular diagnostics in CNS tumor classification. At the same time, it remains wedded to other established approaches to tumor diagnosis such as histology and immunohistochemistry. In doing so, the fifth edition establishes some different approaches to both CNS tumor nomenclature and grading and it emphasizes the importance of integrated diagnoses and layered reports. New tumor types and subtypes are introduced, some based on novel diagnostic technologies such as DNA methylome profiling. The present review summarizes the major general changes in the 2021 fifth edition classification and the specific changes in each taxonomic category. It is hoped that this summary provides an overview to facilitate more in-depth exploration of the entire fifth edition of the WHO Classification of Tumors of the Central Nervous System.

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Using Convolutional Neural Network to Design and Predict the Forces and Kinematic Performance and External Rotation Moment of the Hip Joint in the Pelvis

Seyed Masoud Ghoreishi Mokri, Newsha Valadbeygi, Irina G. Stelnikova (2024)

In order to improve the dynamic and kinematic adaptability of the hip joint, this paper presented a control attitude and kinematics and torque of the hip joint with power based neural network control. The CNN neural network uses input data only from the limb designed by the medical software, and is trained by different natural and artificially altered step patterns of healthy individuals. This type of network has been used for deep learning to realize adaptive speed control, dynamic and motion attitude, as well as prediction of force and torque performance. Detailed movement and torque tests were performed using MIMICS and ANATOMY AND PHYSIOLOGY software, and the obtained data were checked and varied by a healthy person, and finally, the test results showed that the neural network control system was able to control the selection. It has a variable and high speed with proper adaptation in various conditions. Finally, MATLAB software was used to design and predict the data of the problem, and favorable results were obtained.

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A multi-scale convolutional neural network for autonomous anomaly detection and classification in a laser powder bed fusion additive manufacturing process

Luke Scime, Jack Beuth (2018)

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Author and article information

Journal

Title: International Journal of Innovative Science and Research Technology (IJISRT)

Abbreviated Title: International Journal of Innovative Science and Research Technology (IJISRT)

Publisher: International Journal of Innovative Science and Research Technology

ISSN (Electronic): 2456-2165

Publication date (Electronic): March 18 2024

Pages: 651-657

Article

DOI: 10.38124/ijisrt/IJISRT24MAR754

SO-VID: 9b1ee325-5557-4689-b8d3-155112246dc0

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