Background: With the emergence of advanced technology, such as robotics, three-dimensional (3D) imaging is necessary to execute preoperative surgical plans accurately. However, 3D imaging adds cost and potential risk to patients. This study determined the measurement accuracy, reliability, and repeatability of a novel artificial intelligence (AI) algorithm which converts two-dimensional (2D) radiographs to 3D bone models.
Methods: An AI algorithm was developed to convert 2D radiographs to 3D bone model reconstructions. The accuracy of the AI algorithm was evaluated by comparing mean absolute error in measurements performed on 3D bone reconstructions, 3D computed tomography (CT) scans, and manual measurements on five cadaveric knees. Reliability and repeatability of the AI algorithm were evaluated by assessing the inter-observer and intra-observer agreement between measurements performed on 3D bone reconstructions, respectively.
Results: Accuracy of the AI algorithm was considered excellent with mean absolute errors <2mm in 9 of 12 anatomical parameters compared with measurements performed on CTs and manual calipers. All inter-observer and intra-observer correlation coefficients were greater than 0.90 representing a high level of measurement reliability and repeatability by independent observers and the same observers.
Conclusion: This particular AI algorithm demonstrated a high degree of accuracy, reliability, and repeatability for converting 2D radiographs to 3D bone reconstructions similar to a CT-scan. Study results suggest this AI algorithm has the potential for use in preoperative surgical planning due to its efficiencies related to cost and time and reduced radiation exposure without the use of 3D imaging.
Keywords: 3D reconstruction; artificial intelligence; biplanar radiographs; knee surgery; total knee arthroplasty.
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