Efficiency of Artificial Intelligence in Three-Dimensional Reconstruction of Medical Imaging

Abstract

Three-dimensional (3D) reconstruction is necessary for visualizing complex anatomy and supporting clinical decision-making in radiology. However, traditional techniques often struggle with limitations in scalability, speed, and reproducibility. The recent emergence of artificial intelligence (AI) has enabled a new generation of reconstruction tools that offer greater automation and new clinical capabilities. Motivated by the demand for efficient imaging, researchers have applied deep learning to overcome longstanding barriers, with impacts spanning diagnosis, surgical planning, and disease monitoring. This review included peer-reviewed studies published within the last 10 years, focusing exclusively on adult human imaging published in the English language, as anatomical development, imaging protocols, and clinical decision pathways differ significantly in pediatric populations. While this improved applicability to adult radiology, it limited insight into emerging pediatric and preclinical research. Searches focused on AI-driven 3D reconstruction across different radiologic modalities. Articles were selected based on the following predefined inclusion criteria: adult human participants, AI-based 3D reconstruction, clinical validation, and relevance to radiologic practice. Studies including pediatric or animal subjects, preclinical-only experimentation, non-English-language text, or without applied clinical evaluation were excluded. Our results showed that AI has improved the accuracy, speed, and clinical utility of 3D reconstruction in multiple specialties. Deep learning models such as U-Net, V-Net, DenseVNet, and generative adversarial networks have achieved high segmentation accuracy, often reporting Dice scores >0.90. These models have also been used for tumor detection, surgical planning, and reducing radiation exposure. However, challenges such as high computational requirements, lack of standardized datasets, limited real-world validation, and ethical concerns remain. In conclusion, 3D reconstruction is transforming radiology with more accurate patient-specific images for improved clinical decision-making. While it is rapidly being integrated into practice, these technologies still have limitations that need to be addressed. Nonetheless, with improved and ongoing innovation, AI has the potential to become a catalyst for precise imaging and patient care.

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