Applying Machine Learning to Automatically Extract Spinal Parameters from Radiographs and Ultrasonographs
Creators: Wong, Jason
Publisher: name: University of Alberta Library
Year: 2024

<p>Adolescent idiopathic scoliosis is a three-dimensional spinal disorder, where the spine is characterized by lateral curvature in the coronal and sagittal planes and vertebral rotation in the axial plane. Typically, radiography is employed to assess the severity of scoliosis, requiring a clinician to manually measure the parameters that describe the deformity. Due to the sheer number of patients that clinicians see, measuring the parameters is time consuming for clinicians. Automating this measurement process would minimize errors from human judgment and significantly reduce time spent on this task, freeing up clinicians’ time to see other patients. Additionally, taking radiographs exposes patients to ionizing radiation, which may increase the risk of cancer. Consequently, the feasibility of applying ultrasound as a diagnostic imaging method for scoliosis is currently being investigated and is comparable to radiographic measurements in terms of measurement accuracy. However, ultrasound is difficult to interpret and measure, and so the required experience to measure and time consumed from measuring are significant barriers to entry for adopting ultrasound in scoliosis clinics.<br>
In this thesis, machine learning models, specifically convolutional neural networks, and image processing techniques were employed to develop fully automatic measurement algorithms for the primary parameters that are assessed in each plane of the body: the Cobb angle (coronal), the vertebral rotation (axial), and the kyphotic and lordotic angles (sagittal). Automatic algorithms were developed for each of these parameters on both spinal radiographs and ultrasonographs. To assess the measurement accuracy and reliability, validation studies for each automatic algorithm were conducted where the automatically performed measurements were compared with manual ones. The goal of knowledge translation was emphasized throughout this research, meaning that the algorithms were designed with the intention of making them accurate, quick, and interpretable.<br>
The automatic measurement algorithms for radiographs particularly demonstrated high clinical feasibility. When compared with manual radiographic measurements, the Cobb angle algorithm measured with excellent reliability with an inter-method intraclass correlation coefficient (ICC2,1) of 0.92 and a standard error of measurement (SEM) of 0.8°. The axial vertebral rotation (AVR) algorithm achieved a moderate reliability of 0.56 and a SEM of 2.3°. The T1-T12 kyphotic angle (KA) and L1-L5 lordotic angle (LA) algorithms measured with excellent and good reliability with ICC2,1 values of 0.91 and 0.84, and SEM values of 0.8° and 2.0°, respectively. All radiographic algorithms achieved a sub-30 second average measurement time per image, which improves upon the estimated time it takes experienced raters to manually measure the same parameters. As for the automatic measurement algorithms for ultrasonographs, reliability was overall lower than the radiographic counterparts due to the increased difficulty in analyzing 3D ultrasound images. When compared with manual ultrasound measurements, the Cobb angle achieved good reliability with 0.81 ICC2,1 and 1.4° SEM. The AVR algorithm measured with greater reliability, yielding 0.86 ICC2,1 and 1.0° SEM. The T1-T12 KA and L1-L5 LA algorithms achieved good and poor and reliability with ICC2,1 values of 0.79 and 0.37, and SEM values of 2.7° and 9.4°, respectively. Measuring all parameters on 3D ultrasonographs was accomplished within 36 seconds on average, which is quicker than the average estimated measurement time for an experienced rater to manually measure. For both imaging modalities, the automatic measurement algorithms output illustrations demonstrating how each measurement is obtained, allowing operators to interpret the results and intervene if they notice any errors.<br>
Overall, this research provides fully automatic measurement algorithms that can be deployed in both clinical and research settings to streamline workflow and in some cases, improve measurement accuracy and reliability, particularly for less experienced raters. Certain measurement algorithms, such as the kyphotic and lordotic angles on ultrasonographs, require more care and attention when evaluating the automatic measurements due to the lower accuracy and reliability attained in the validation studies. However, the interpretability that each algorithm offers – namely, images with the relevant spinal features labelled being output – facilitates the ability to be attentive to possible mismeasurements from the algorithms. Additionally, every automatic algorithm achieved an average measurement time that was lower than what an experienced rater would manually measure. With all these aspects in consideration, the automatic algorithms demonstrate clinical feasibility and further work would involve integrating them into a polished graphical user interface to begin distribution to scoliosis clinics and research centres.</p>