UTILIZING ARTIFICIAL INTELLIGENCE FOR AUTOMATED COBB ANGLE MEASUREMENT IN SCOLIOSIS DIAGNOSIS AND ASSESSMENT.

Background: Scoliosis is defined as a curvature of the spine in the coronal plane, with its idiopathic form being the most frequently encountered. Diagnosis is based on spinal X-Ray imaging, performing a Cobb angle measurement, which represents the angle calculated between two perpendicular lines traced at the superior margin of the uppermost involved vertebra, and the inferior margin of the lowest affected vertebra. A Cobb angle greater than 10 degrees is considered as positive evidence of spinal deformity and confirms the diagnosis. The traditional Cobb angle measurements are time-consuming and prone to human errors; therefore, an automated measuring algorithm could reduce the diagnostic interval, and hypothetically improve measurement accuracy. Objective: The aim of this study is to investigate and test the application of artificial intelligence (AI), and its existing algorithms, for automated Cobb angle measurement and scoliosis assessment. Material and methods: Radiographic images of 197 patients have been obtained from the database of a private clinic from Targu-Mures, spanning the period between 30/11/2023 and 20/02/2024. The acquired images underwent processing using the AI Open-Source Software "Cobb Angle Calculator". Additionally, a manual calculation was performed using the "RadiAnt DICOM Viewer" software to ensure a comparison between automated and manual methods. Statistical analysis was carried out utilizing the GraphPad InStat software to statistically assess differences between the automated and the manually calculated measurements. Results: Out of the 197 analyzed X-Ray images, the manual measurement of the Cobb angle led to a diagnosis of scoliosis in 170 patients. The measurements were conducted three times, in order to minimize errors. Among these, 52 patients were diagnosed with thoracic scoliosis, 51 with lumbar deformities, and 67 with combined thoraco-lumbar curvatures. The roentgenograms were then processed with the AI software "Cobb Angle Calculator", and the outcomes compared with the ones calculated manually. Statistical analysis demonstrated that the difference in accuracy of the two methods is extremely significant (p value=0.0002), thus rejecting the null hypothesis. Subsequently, each X-ray image underwent a contrast-enhancing process to improve visualization of the skeletal structures, and the results were compared a second time. The obtained p value of 0.3019, considered not statistically significant, confirmed the null hypothesis, therefore proving the valuable aid of AI in Cobb angle quantification as an alternative to manual evaluation. Conclusions: Automated Cobb angle measurement, being at the beginning of its era, lacks sufficient accuracy when employed without a specific image contrast-enhancing procedure, hence displaying high probability of scoliosis misdiagnosis. Integrating an image contrast-enhancing process is a pivotal step forward in measurement automation, and consequently offers a crucial alternative, and possibly future substitution, to the available standard techniques. [ABSTRACT FROM AUTHOR]