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Abedi, Rasoul, Fatouraee, Nasser, Bostanshirin, Mahdi, Arjmand, Navid, Ghandhari, Hasan. (1403). Prediction of Fusion Rod Curvature Angles in Posterior Scoliosis Correction Using Artificial Intelligence. , 12(7), 494-505. doi: 10.22038/abjs.2024.76701.3545
Rasoul Abedi; Nasser Fatouraee; Mahdi Bostanshirin; Navid Arjmand; Hasan Ghandhari. "Prediction of Fusion Rod Curvature Angles in Posterior Scoliosis Correction Using Artificial Intelligence". , 12, 7, 1403, 494-505. doi: 10.22038/abjs.2024.76701.3545
Abedi, Rasoul, Fatouraee, Nasser, Bostanshirin, Mahdi, Arjmand, Navid, Ghandhari, Hasan. (1403). 'Prediction of Fusion Rod Curvature Angles in Posterior Scoliosis Correction Using Artificial Intelligence', , 12(7), pp. 494-505. doi: 10.22038/abjs.2024.76701.3545
Abedi, Rasoul, Fatouraee, Nasser, Bostanshirin, Mahdi, Arjmand, Navid, Ghandhari, Hasan. Prediction of Fusion Rod Curvature Angles in Posterior Scoliosis Correction Using Artificial Intelligence. , 1403; 12(7): 494-505. doi: 10.22038/abjs.2024.76701.3545

Prediction of Fusion Rod Curvature Angles in Posterior Scoliosis Correction Using Artificial Intelligence

The Archives of Bone and Joint Surgery
مقاله 6، دوره 12، شماره 7، مهر 2024، صفحه 494-505    اصل مقاله (1.37 M)
نوع مقاله: RESEARCH PAPER
شناسه دیجیتال (DOI): 10.22038/abjs.2024.76701.3545
نویسندگان
Rasoul Abedi1؛ Nasser Fatouraee* 1؛ Mahdi Bostanshirin1؛ Navid Arjmand2؛ Hasan Ghandhari3
1Department of Biomedical Engineering, Amirkabir University of Technology, Tehran, Iran
2Department of Mechanical Engineering, Sharif University of Technology, Tehran, Iran
3Bone and Joint Reconstruction Research Center, Shafa Orthopedic Hospital, Iran University of Medical Sciences, Tehran, Iran
چکیده
Objectives: This study aimed to estimate post-operative rod angles in both concave and convex sides 
of scoliosis curvature in patients who had undergone posterior surgery, using neural networks and 
support vector machine (SVM) algorithms.
Methods: Radiographs of 72 scoliotic individuals were obtained to predict post-operative rod angles at all fusion 
levels (all spinal joints fused by rods). Pre-operative radiographical indices and pre-operatively resolved net joint 
moments of the apical vertebrae were employed as inputs for neural networks and SVM with biomechanical 
modeling using inverse dynamics analysis. Various group combinations were considered as inputs, based on the 
number of pre-operative angles and moments. Rod angles on both the concave and convex sides of the Cobb angle 
were considered as outputs. To assess the outcomes, root mean square errors (RMSEs) were evaluated between 
actual and predicted rod angles.
Results: Among eight groups with various combinations of radiographical and biomechanical parameters (such as 
Cobb, kyphosis, and lordosis, as well as joint moments), RMSEs of groups 4 (with seven radiographical angles in 
each case, which is greater in quantity) and 5 (with four radiographical angles and one biomechanical moment in 
each case, which is the least possible number of inputs with both radiographical and biomechanical parameters) 
were minimum, particularly in prediction of the concave rod kyphosis angle (errors were 5.5° and 6.3° for groups 4 
and 5, respectively). Rod lordosis angles had larger estimation errors than rod kyphosis ones.
Conclusion: Neural networks and SVM can be effective techniques for the post-operative estimation of rod angles 
at all fusion levels to assist surgeons with rod bending procedures before actual surgery. However, since rod lordosis 
fusion levels vary widely across scoliosis cases, it is simpler to predict rod kyphosis angles, which is more essential 
for surgeons.
 Level of evidence: IV
کلیدواژه‌ها
Biomechanical modeling؛ Cobb angle؛ Neural networks؛ Posterior surgery؛ Rod kyphosis
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