# BUU-LSPINE: A Thai Open Lumbar Spine Dataset for Spondylolisthesis Detection
###### Abstract  
(1) Background: Spondylolisthesis, a common disease among older individuals, involves the displacement of vertebrae. The condition may gradually manifest with age, allowing for potential prevention by the research of predictive algorithms. However, one key issue that hinders research in spondylolisthesis prediction algorithms is the need for publicly available spondylolisthesis datasets. (2) Purpose: This paper introduces BUU-LSPINE, a new dataset for the lumbar spine. It includes 3600 patients' plain film images annotated with vertebral position, spondylolisthesis diagnosis, and lumbosacral transitional vertebrae (LSTV) ground truth. (4) Methods: We established an annotation pipeline to create the BUU-SPINE dataset and evaluated it in three experiments as follows: (1) lumbar vertebrae detection, (2) vertebral corner points extraction, and (3) spondylolisthesis prediction. (5) Results: Lumbar vertebrae detection achieved the highest precision rates of 81.93% on the AP view and 83.45% on the LA view using YOLOv5; vertebral corner point extraction achieved the lowest average error distance of 4.63 mm on the AP view using ResNet152V2 and 4.91 mm on the LA view using DenseNet201. Spondylolisthesis prediction reached the highest accuracy of 95.14% on the AP view and 92.26% on the LA view of a testing set using Support Vector Machine (SVM). (6) Discussions: The results of the three experiments highlight the potential of BUU-LSPINE in developing and evaluating algorithms for lumbar vertebrae detection and spondylolisthesis prediction. These steps are crucial in advancing the creation of a clinical decision support system (CDSS). Additionally, the findings demonstrate the impact of Lumbosacral transitional vertebrae (LSTV) conditions on lumbar detection algorithms.  
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annotations of the verteb position and spondylolisthesis diagnosis. Our dataset is valuable because it has many high-quality X-ray images with diverse demographics and types of spondylolistheses. Additionally, our dataset includes crucial clinical information such as patient age and gender, the presence of lumbosacral transitional vertebrae (LSTVs) [10], and pixel spacing [11], making it a unique resource for developing and evaluating automated spondylolisthesis detection algorithms.  
LSTV, also called Bertolotti's syndrome [12], is a congenital disability that affects the last lumbar vertebra [13; 14; 15]. Generally, the human lumbar spine consists of five vertebrae (L1 to L5), but patients with LSTV have a different number of lumbar vertebrae [16]; patients with four lumbar are denoted as having a sacralization of L5, and patients with six lumbar are denoted as having a lumbarization of S1. Misclassification of LSTV can affect the diagnosis and medical treatment [17] and affect the results of algorithms that use lumbar vertebrae. For example, [PERSON] et al. [18] indicated some failed prediction results from spinal disease due to lumbarization and sacralization.  
Pixel spacing in medical images specifies the distance, in millimeters (mm), between the centers of adjacent pixels along the x and y axes of the picture. This attribute is critical in converting distance in pixels on an image to physical space [19].