Applications and challenges of artificial intelligence-driven 3D vision in biomedical engineering: A biomechanics perspective
Abstract
This paper explores the applications and challenges of artificial intelligence (AI)-driven 3D vision technology in biomedical engineering, with a specific focus on its integration with biomechanics. 3D vision technology offers richer spatial information compared to traditional 2D imaging and is increasingly applied in fields like medical image analysis, surgical navigation, lesion detection, and biomechanics. In biomechanics, AI-driven 3D vision is used for analyzing human movement, modeling musculoskeletal systems, and assessing joint biomechanics. However, challenges persist, including image quality, computational resource demands, data privacy, and algorithmic bias. This paper reviews the development of 3D vision technology and AI, discusses its applications in biomedicine and biomechanics, and addresses the key technical obstacles, offering insights into the future development of these technologies in the context of biomedical and biomechanical research.
References
1. Liu, Y., et al. (2021). “Deep learning in 3D medical image analysis: a comprehensive review.” Journal of Healthcare Engineering, 2021, 1-18. DOI: 10.1155/2021/6675851
2. Wang, S., et al. (2021). “Artificial intelligence for medical image analysis in radiology: recent advances and future prospects.” Nature Biomedical Engineering, 5, 248-262. doi: 10.1038/s41551-020-00650-6
3. Roth, H. R., et al. (2021). “AI-driven three-dimensional reconstruction of medical images for personalized treatment planning.” Frontiers in Artificial Intelligence, 4,. 582709. DOI: 10.3389/frai.2021.582709
4. Liu, J., et al. (2021). “3D convolutional neural networks for enhanced medical image segmentation.” IEEE Transactions on Biomedical Engineering, 68(5), 1504-1513. doi: 10.1109/tbme.2020.3017021
5. Zhao, S., et al. (2022). “AI-based multi-modal 3D medical image analysis and its applications.” Medical Image Analysis, 74, 102229. DOI: 10.1016/j.media.2021.102229
6. Xu, S., et al. (2022). “Deep learning-based 3D medical image reconstruction and its clinical applications.” Frontiers in Bioengineering and Biotechnology, 10, 103628. doi: 10.3389/fbioe.2022.103628
7. Chen, C., et al. (2022). “AI-driven 3D image analysis for early-stage cancer detection.” Journal of Medical Imaging, 29(1), 115-124. doi: 10.1117/1.jmi.29.1.115
8. Gupta, A., et al. (2022). “Advanced deep learning methods in 3D medical image segmentation.” Computers in Biology and Medicine, 143, 105235. DOI: 10.1016/j.compbiomed.2021.105235
9. Zhou, J., et al. (2023). “Artificial intelligence for 3D medical image analysis in robotic surgery systems.” IEEE Transactions on Robotics, 39(1), 20-33. doi: 10.1109/tro.2022.3153913
10. Li, W., et al. (2023). “3D convolutional neural networks for medical image reconstruction and diagnostic prediction.” Medical Image Analysis, 90, 102777. DOI: 10.1016/j.media.2023.102777
11. Zhang, L., et al. (2023). “Deep learning-based 3D tumor segmentation and quantification in PET-CT images.” Journal of Nuclear Medicine, 64(4), 589-595. doi: 10.2967/jnumed.122.263828
12. Li, X., et al. (2023). “AI and 3D imaging: novel approaches to brain tumor segmentation.” Neurocomputing, 521, 108-118. DOI: 10.1016/j.neucom.2022.10.070
13. Wang, F., et al. (2023). “AI-driven 3D imaging for minimally invasive surgery.” Journal of Surgical Research, 298, 44-53. DOI: 10.1016/j.jss.2023.03.013
14. Kang, H., et al. (2023). “3D deep learning for the detection and analysis of breast cancer from mammographic images.” Artificial Intelligence in Medicine, 128, 102301. DOI: 10.1016/j.artmed.2023.102301
15. Xu, W., et al. (2024). “Multimodal 3D medical image fusion using deep learning techniques.” IEEE Transactions on Medical Imaging, 43(2), 678-689. doi: 10.1109/tmi.2023.3231069
16. Yang, Z., et al. (2024). “3D image reconstruction and AI-based automated feature extraction for lung cancer diagnosis.” Journal of Cancer Research and Clinical Oncology, 150(5), 1371-1382. DOI: 10.1007/s00432-024-04573-w
17. Zhou, Y., et al. (2024). “Deep learning for 3D cardiovascular image analysis: from segmentation to diagnosis.” Computerized Medical Imaging and Graphics, 97, 102097. DOI: 10.1016/j.compmedimag.2023.102097
18. Huang, Y., et al. (2024). “AI-enhanced 3D image-guided brain tumor resection: a review of current trends and future directions.” Journal of Neuro-Oncology, 160(1), 1-14. doi: 10.1007/s11060-024-04223-9
19. Zhang, H., et al. (2024). “AI-driven three-dimensional cardiac image analysis for personalized treatment planning.” IEEE Journal of Biomedical and Health Informatics, 28(3), 678-689. doi: 10.1109/jbhi.2023.3245949
20. Li, X., et al. (2024). “Robust AI-based 3D image analysis for automated diagnosis of osteoarthritis.” Journal of Medical Systems, 48(2), 78. doi: 10.1007/s10916-023-01914-x
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