**4.7 Spine**

From the genesis of AI applications in surgery spine has been a site of significant innovation in ML and DL models, generating opportunities for applications in scoliosis quantification, vertebral fracture detection, and vertebral body segmentation.

The Cobb measuring method is the gold standard for quantification of the scoliotic curve [123]. With the digitalization of computerized radiography, most surgeons opt to use built-in computer software such as the Picture Archiving and Communications System (PACS); despite the proven efficiency of the software relative to the traditional "manual" method of Cobb angle measurement, systems like PACS use software (e.g., Surgimap) which requires users to manually select the upper and lower ends of vertebral bodies inherently introduces human error [123–127]. Hence, Cobb angle measurement has been an area of significant AI exploration.

Caesarendra et al. utilized a deep CNN to measure the Cobb angle of patients diagnosed with adolescent idiopathic scoliosis, producing accuracies up to 93.6% which demonstrates a high reliability compared to neurosurgeons' measurement (intraclass correlation coefficient > 0.95) [123]. Sun et al. assessed DL models based upon CNNs designed to segment each vertebra and locate the vertebral corners, finding a very high intraclass correlation coefficient (ICC) of 0.994, with a Pearson correlation coefficient and mean absolute error between the model and orthopedic annotation of 0.990 and 2.2° ± 2.0° [128]. These results are especially promising in cases where the Cobb angle does not exceed 90°.
