Computerized image understanding system for reliable estimation of spinal curvature in idiopathic scoliosis

Abstract

Analysis of scoliosis requires thorough radiographic evaluation by spinal curvature estimation to completely assess the spinal deformity. Spinal curvature estimation gives orthopaedic surgeons an idea of severity of spinal deformity for therapeutic purposes. Manual intervention has always been an issue to ensure accuracy and repeatability. Computer assisted systems are semi-automatic and is still influenced by surgeon’s expertise. Spinal curvature estimation completely relies on accurate identification of required end vertebrae like superior end-vertebra, inferior end-vertebra and apical vertebra. In the present work, automatic extraction of spinal information central sacral line and medial axis by computerized image understanding system has been proposed. The inter-observer variability in the anatomical landmark identification is quantified using Kappa statistic. The resultant Kappa value computed between proposed algorithm and observer lies in the range 0.7 and 0.9, which shows good accuracy. Identification of the required end vertebra is automated by the extracted spinal information. Difference in inter and intra-observer variability for the state of the art computer assisted and proposed system are quantified in terms of mean absolute difference for the various types (Type-I, Type-II, Type-III, Type-IV, and Type-V) of scoliosis.

The use of zebrafish to assess water quality and remediation efforts.

“…In contrast, at concentrations above and equal to 24 %, wastewater SWFE and JB induced morphological changes such as craniofacial abnormalities, pericardial and yolk sac oedema, notochord and somite defects as well as scoliosis. In short, scoliosis refers to the by sideways curvature of the spine/tail (Thalengala et al,2021). These findings are concordant with previously published work where zebrafish exposed to wastewater effluent samples collected from WWTPs in Brazil and hospitals, resulted in malformation of the head, yolk sac, tail, pericardial oedema, spinal cord curvature and pigmentation changes (Ribeiro et al,2020;Wittlerova et al,2020b).…”

Evaluation of Mis-Selection of Boundary Vertebrae and Its Effect on Measuring Cobb Angle and Curve Length in Adolescent Idiopathic Scoliosis

“…For example, if the applicable treatment for a scoliotic curve is instrumented arthrodesis, longer than necessary fusion means sacrificing too many segments of motion and therefore function [34][35][36][37], increased risk of neurological complications and infection, and increased cost. There is consensus that the incorrect selection of the boundary vertebrae is a significant source of intrinsic error in measuring the Cobb angle on AIS X-rays [22,23,27]. However, in studies of Cobb angle inaccuracy where the error is measured using preselected boundary vertebrae, the erratum is not significantly improved, with intra-observer MBE error in such studies ranging from 3° to 9.04° [25,26,28,29,38].…”

“…In this surgical procedure, selecting the appropriate vertebral boundaries determines the curve length to be fused, affecting the biomechanical stability achieved and reducing the incidence of post-surgical complications [18,19]. Conversely, there is a consensus in the literature that selecting the boundary vertebrae in the Cobb angle measurement method in AIS X-ray is a significant source of intrinsic error [20][21][22][23] or the primary source of intrinsic error [24][25][26][27]. The literature on the accuracy and precision of the Cobb method in AIS X-ray indicates that potential sources of intrinsic error in traditional manual measurement include erroneous choice of vertebral endplates, inaccurate drawing of lines along the vertebral endplates, imprecise drawing of perpendicular lines, and inaccurate angle measurement itself [22,[25][26][27][28][29].…”

A Review of the Application of Artificial Intelligence in Orthopedic Diseases

“…In addition, the images used in the study were two-dimensional, and the image dimensionality enhanced to improve the model's accuracy is an area that could be improved in the future. Ananthakrishna et al [75] proposed an accurate computer-based automated measurement method that estimates spinal curvature based on identified anatomical landmarks and categorizes the scoliosis into four grades, which helps to minimize subjective errors and shows good accuracy during testing.…”