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ArticlePeer ReviewedOpen AccessPlagiarism Passed
Paper ID:10.55041/IJSREM29953

Bone Advisor: AI-guided Bone Regeneration Prediction and Recommendations

Stream/Area:

-

Subject:

-

Paper ID:

10.55041/IJSREM29953

Volume:

8 |

4 |

Apr 2024

Pages:

Publication Frequency:

Monthly (12 issues per year)

DOI
Digital Object Identifier
10.55041/IJSREM29953

License:International Journal of Scientific Research in Engineering and Management follows the Creative Commons Attribution 4.0 (CC BY 4.0) License, allowing free use and distribution with proper attribution.

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Authors

PS

P.Neela Sundari

P

Patibandla.KrishnaSri

K

Korada.Vennela

K

Kavala.Bhavana

M

Mandadapu.Srilakshmi

KH

Kollipara.Sri Harshitha

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Abstract

The bone is a major component of the human body. Bone provides the ability to move the body. Bone fractures are common in the human body. Although external fixation is being widely used by orthopedic surgeons in treatment of bone fractures, the surgical procedure and recovery includes the possible complications which can significantly decrease the treatment’s efficiency and effectiveness. In this project we propose the improvement of a fixation device design by introducing the machine learning and deep learning technology. The Bone Regeneration Prediction and Recommendation System project represents a paradigm shift in orthopedic care. Through the integration of predictive modeling and machine learning, the system forecasts bone regeneration outcomes with maximal accuracy. Beyond predictions, the system generates personalized recommendations, to the individuals based on the patient data given to the model.

KEYWORDS

Machine Learning Deep Learning Recommendation system prediction system orthopedic

Cite This Article

P.Neela Sundari, Patibandla.KrishnaSri, Korada.Vennela, Kavala.Bhavana, Mandadapu.Srilakshmi, Kollipara.Sri Harshitha, "Bone Advisor: AI-guided Bone Regeneration Prediction and Recommendations," International Journal of Scientific Research in Engineering and Management, vol. 8, no. 4, Apr 2024. https://doi.org/10.55041/IJSREM29953

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Publication Date

12/31/45382

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Double-Blind Peer Review

Review Rounds: 2

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