AI-Driven Precision: Transforming Below-Knee Amputation Care in Modern Healthcare

Sarah Duraid AlQaissi; Ahmed A.A.  AlDuroobi; Abdulkader Ali. A. Kadaw

doi:10.29194/NJES.27030366

Authors

Sarah Duraid AlQaissi Department of Prosthetics and Orthotics, College of Engineering, Al-Nahrain University, Baghdad, Iraq
Ahmed A.A. AlDuroobi Department of Prosthetics and Orthotics, College of Engineering, Al-Nahrain University, Baghdad, Iraq
Abdulkader Ali. A. Kadaw TU Bergakademie Freiberg, Institute for Machine Elements, Engineering Design and Manufacturing, 09599, Freiberg, Germany.

DOI:

https://doi.org/10.29194/NJES.27030366

Keywords:

Artificial Intelligence, Deep Learning, Image Processing, Magnetic Resonance Imaging (MRI)

Abstract

Recently, three-dimensional models 3DM in the prosthetics field gained popularity, especially in the context of residual limb shape creation resulting from collecting medical images in Digital Imaging and Communications in Medicine DICOM format from a magnetic resonance imaging MRI after image processing accurately. In this study, a three-dimensional model of the residual limb for a patient with transtibial amputation was realized with the integration of artificial intelligence and a computer vision approach demonstrating the benefits of AI segmentation tools and artificial algorithms to generate higher accuracy three-dimensional model before prosthetic socket design or in case of comparison the 3D model generated from MRI with another 3D model generated from another technique, where a residual limb of a 23 years old male patient with amputation in the left leg wearing a prosthetic socket liner, and having 62 kg weight, 168 cm height, with high activity level. The patient was scanned using GE Medical Systems, 1,5 Tesla Signa Excite. MRI images in DICOM format were read to retrieve essential metadata such as pixel spacing and slice thickness. These images were processed to obtain a model that reflects the real shape of the residual limb using a specific algorithm, and the 3D model was extracted using AI segmentation tools. The obtained 3D model result with high resolution proves the potential of the artificial intelligence approach with deep learning to reconstruct 3D models concluding that AI has an instrumental role in medical image analysis, particularly in the areas of organ and tissue classification and segmentation., thus generating automatic and repetitive a 3D model.

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