Supporting data for "Incorporating Artificial Intelligence and Clinical Informatics for Curve Progression Risk Evaluation in Adolescent Idiopathic Scoliosis to Facilitate Population Screening"

Adolescent idiopathic scoliosis (AIS) is a complex three-dimensional spinal deformity affecting 2-5% of the general population. AIS is diagnosed when Cobb angles exceed 10°, and deterioration of curve magnitude during puberty occurs in two-thirds of patients. Whilst curves &lt;25° are considered mild, those of 25°-45° are of moderate severity, and those &gt;45° are severe and indicated for surgical correction. A student screening system for AIS has been adopted in Hong Kong since the 1990's. Patients are commonly diagnosed via such screening in their early teens when the curvature is mild, yet it remains uncertain which curves will continue to deteriorate upon the remaining period of growth. Prediction of curve progression risk in AIS remains elusive. Prior studies have revealed the potential for three-dimensional (3D) morphological parameters to prognosticate progression, but these require specialized biplanar imaging equipment and labor-intensive software reconstruction. In addition, patient demographics, vertebral morphology, skeletal maturity, and bone quality represent individual risk factors for progression but have yet to be integrated towards accurate prognostication. The objective of this study was to integrate composite clinical information into deep learning model to accurately predict AIS curves at-risk of progression.The dataset contains 710 AIS patients receiving regular clinical follow-up in 3-6-month intervals at the Duchess of Kent Children's Hospital (DKCH), enabling labelling of major curve trajectories from first clinic presentation until skeletal maturity. Additional inclusion criteria were (1) diagnosis of AIS, (2) Cobb angle between 11° and 30° upon standing posteroanterior X-rays at first visit, (3) DRU grading ≤ R9U8 to demonstrate growth potential, and (4) regular follow-up concluding at skeletal maturity (R11U9) or upon receiving surgery. This cohort was utilised to develop the spinal X-ray radiomics modules and identified from amongst scoliosis clinic attendees between January 2016 and September 2021, of which more than 90% were referrals from the two-tiered Hong Kong school-aged screening program. Only the major curve with largest Cobb angle was considered for patients with more than one curvature. Curve progression was defined by an increase ≥ 6° between first visit and skeletal maturity, as well as a Cobb angle ≥ 25° at skeletal maturity. Non-progression (NP) was defined by &lt; 6° of curvature increase, or a Cobb angle &lt; 25° at skeletal maturity. Patients with non-progressive curves according to these definitions but received brace treatment were also excluded. In preparation for automated hand X-ray analysis, an experienced orthopedic researcher used online labeling tool Roboflow to label regions of interest (ROIs) corresponding to (1) the 2nd to 4th metacarpals, (2) distal radial physis, and (3) distal ulnar physis. Pixel-level segmentation labels of the second metacarpus and the corresponding intramedullary mid diaphysis were subsequently labelled. Skeletal maturity indices (DRU and Sanders staging) from both the hand X-ray cohort as well as curve progression cohort were labelled by two experienced orthopedic researchers.Features contained within the major curve apex of PA spinal radiographs predict curve progression due to their capacity to convey rotation and torsion. On the other hand, whole spine X-rays facilitate assessment of global spinal imbalance as a risk factor for curve progression. Thus, we extracted a regional spinal X-ray ROI (300 × 200-pixel fixed window) centered upon the apical vertebrae/disc of the major curve, together with at least two adjacent vertebras above and below with lateral rib articulations. We also extracted a global spinal X-ray ROI (300 × 300-pixel fixed window) covering T1 to the sacrum together with clavicles, ribs, and pelvis. All ROI images were saved as single channel grayscale image in JPG formatting.青少年特发性脊柱侧凸（Adolescent Idiopathic Scoliosis, AIS）是一种复杂的三维脊柱畸形，影响普通人群的2%-5%。当Cobb角（Cobb angle）超过10°时即可诊断为AIS，且青春期期间脊柱侧凸角度进展的患者占三分之二。通常将侧凸角度<25°视为轻度，25°-45°为中度，>45°则为重度，需手术矫正。香港自1990年代起便推行了青少年特发性脊柱侧凸学生筛查系统，患者多在青少年早期通过该筛查被确诊，此时侧凸程度尚轻，但仍无法确定哪些侧凸会在剩余生长周期中持续进展。精准预测青少年特发性脊柱侧凸的进展风险仍是临床难题。既往研究表明，三维（3D）形态学参数具备预测侧凸进展的潜力，但这类方法需要专用的双平面成像设备以及耗时费力的软件重建流程。此外，患者人口统计学特征、椎体形态、骨骼成熟度与骨质量均为侧凸进展的独立危险因素，但目前尚未将这些因素整合以实现精准预后预测。本研究的目标是将多维度临床信息整合至深度学习模型中，以精准预测存在进展风险的青少年特发性脊柱侧凸的侧凸进展。本数据集纳入了710名在肯特公爵儿童医院（Duchess of Kent Children's Hospital, DKCH）接受3-6个月规律临床随访的青少年特发性脊柱侧凸患者，可对患者从首次就诊至骨骼成熟期间的主侧凸进展轨迹进行标注。入组标准包括：（1）确诊为青少年特发性脊柱侧凸；（2）首次就诊时站立位后前位X线片的Cobb角介于11°-30°之间；（3）DRU分级（DRU grading）≤R9U8，以体现生长潜力；（4）规律随访至骨骼成熟（R11U9）或接受手术治疗。该队列用于开发脊柱X线放射组学模块，其招募自2016年1月至2021年9月期间的脊柱侧凸门诊患者，其中超过90%来自香港两级学龄儿童筛查系统的转诊。对于存在多个侧凸的患者，仅纳入Cobb角最大的主侧凸进行分析。侧凸进展定义为：首次就诊至骨骼成熟期间侧凸角度增加≥6°，且骨骼成熟时Cobb角≥25°。非进展（NP）则定义为：侧凸角度增加<6°，或骨骼成熟时Cobb角<25°。符合上述非进展定义但接受了支具治疗的患者也被排除。为实现手部X线片的自动化分析，一名资深骨科研究员使用在线标注工具Roboflow对以下感兴趣区域（Regions of Interest, ROIs）进行标注：（1）第2-4掌骨；（2）桡骨远端骨骺；（3）尺骨远端骨骺。后续还完成了第2掌骨及其髓腔内骨干中段的像素级分割标注。来自手部X线片队列与侧凸进展队列的骨骼成熟度指标（DRU分级与Sanders分期）均由两名资深骨科研究员完成标注。后前位脊柱X线片的主侧凸顶点区域具备预测侧凸进展的能力，因其可反映脊柱的旋转与扭转状态。另一方面，全脊柱X线片可用于评估整体脊柱失衡情况，而这也是侧凸进展的危险因素。因此，我们分别提取了两类脊柱X线片的感兴趣区域：一类是以主侧凸的顶椎/椎间盘为中心的局部脊柱X线ROI（固定窗口尺寸300×200像素），且包含上下至少两个相邻的带有外侧肋骨关节的椎体；另一类是覆盖T1至骶骨，同时包含锁骨、肋骨与骨盆的全脊柱X线ROI（固定窗口尺寸300×300像素）。所有ROI图像均保存为单通道灰度JPG格式。