Tripathi Shivi, Ansari Aftab Alam, Singh Manisha, Dash Madhusmita, Kumar Prasoon, Singh Harpreet, Panda Biranchi, Nukavarapu Syam, Camci-Unal Gulden, Li Bingbing, Jain Prashant Kumar, Jayaganthan Rengaswamy, Mehboob Hassan, Junaedi Harri, Nanda Himansu Sekhar, Chen Guoping, Kundu Subhas C
Biomaterials and Biomanufacturing Laboratory, Department of Mechanical Engineering, PDPM Indian Institute of Information Technology Design and Manufacturing, Jabalpur, Madhya Pradesh 482005, India.
International Centre for Sustainable and Net Zero Technologies, PDPM-Indian Institute of Information Technology Design and Manufacturing, Jabalpur, Madhya Pradesh 482005, India.
Mater Horiz. 2025 Jun 30. doi: 10.1039/d5mh00501a.
Additive manufacturing (AM) is a powerful approach in healthcare to augment the functionalities of patient-specific medical products and surgical tools. One such area of the healthcare industry is surgical planning and procedures, where the benefits of AM can revolutionize the industry. AM technologies, commonly known as three-dimensional (3D) printing, can change the conventional surgical methodology from the "open-detect-operate-close" mode to the "detect-open-operate-close" mode. However, the use of 3D printing in surgical planning has been hampered by the limited availability of literature reports thoroughly examining the advantages and drawbacks of this technology in clinical settings. Hence, this review explores the widespread use of additive manufacturing, multi-materials, metamaterials, 4D printing, and artificial intelligence in surgical planning for complex surgical procedures of the spine and in orthopedics, dentistry, cardiology, gynecology, and neurology. This review focuses on meticulously adjusting the lattice structure of metamaterials during 3D printing to achieve specific mechanical properties. It further delves into 4D printing to achieve dynamic capabilities in 3D printed models for better integration with the host tissue. Furthermore, it highlights the key aspects of combining AM with artificial intelligence/machine learning (AI/ML) models in healthcare to automate 3D model production and thereby reduce human intervention. This comprehensive review offers bioengineers, clinical scientists, and clinicians a platform to explore AM and its potential for addressing pre- and post-surgical operation challenges, providing valuable insights for biomedical engineering and healthcare advancements.
增材制造（AM）是医疗保健领域一种强大的方法，可增强针对患者的医疗产品和手术工具的功能。医疗保健行业的一个此类领域是手术规划和手术过程，在这方面，增材制造的优势可彻底改变该行业。增材制造技术，通常称为三维（3D）打印，可将传统手术方法从“切开-检测-操作-缝合”模式转变为“检测-切开-操作-缝合”模式。然而，3D打印在手术规划中的应用受到了限制，因为缺乏全面审视该技术在临床环境中优缺点的文献报道。因此，本综述探讨了增材制造、多材料、超材料、4D打印和人工智能在脊柱复杂手术以及骨科、牙科、心脏病学、妇科和神经学手术规划中的广泛应用。本综述重点关注在3D打印过程中精心调整超材料的晶格结构以实现特定的机械性能。它进一步深入研究4D打印，以在3D打印模型中实现动态能力，以便更好地与宿主组织整合。此外，它强调了在医疗保健中将增材制造与人工智能/机器学习（AI/ML）模型相结合的关键方面，以实现3D模型生产自动化，从而减少人工干预。这一全面综述为生物工程师、临床科学家和临床医生提供了一个平台，以探索增材制造及其应对手术前后操作挑战的潜力，为生物医学工程和医疗保健进步提供有价值的见解。
